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Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback–Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent speed adaption system for vehicles on conventional roads. The fuzzy logic based expert system outputs a recommended speed to ensure both safety and passenger comfort. This intelligent system includes geometrical features of the road, as well as subjective perceptions of the drivers. It has been developed and checked with real data that were measured with an instrumental system incorporated in a vehicle, on several two‐lane roads located in the Madrid Region, Spain. Along with the road geometrical characteristics, other input variables to the system are external factors, such as weather conditions, distance to the preceding vehicle, tire pressure, and other subjective criteria, such as the desired comfort level, selected by the driver. The expert system output is the most suitable speed for the specific road type, considering real factors that may modify the category of the road and thus, the appropriate speed. This information could be added to the adaptive cruise control of the vehicle. The recommended speed can be a very useful input for both, drivers and the autonomous vehicles, to improve safety on the road system. [ABSTRACT FROM AUTHOR]

This paper presents a novel model predictive adaptive cruise control strategy of intelligent electric vehicles based on deep reinforcement learning algorithm for driver characteristics. Firstly, the influence mechanism of factors such as inter-vehicle distance, relative speed and time headway (THW) on the driver's behavior in the process of car following is analyzed by the correlation coefficient method. Then, the driver behavior in the process of car following is learned from the natural driving data, the car following model is established by the deep deterministic policy gradient (DDPG) algorithm, and the output acceleration of the DDPG model is used as the reference trajectory of the ego vehicle's acceleration. Next, in order to reflect the driver behavior and achieve multi performance objective optimization of adaptive cruise control of intelligent electric vehicles, the model predictive controller (MPC) is designed and used for tracking the desired acceleration produced by the car following DDPG model. Finally, the performance of the proposed adaptive cruise control strategy is evaluated by the experimental tests, and the results demonstrate the effectiveness of proposed control strategy. [ABSTRACT FROM AUTHOR]

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle's longitudinal motion commands, thus improving the system's response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques. [ABSTRACT FROM AUTHOR]

Considering the perception of a driver, driving safety, and riding comfort, an adaptive cruise control (ACC) method based on a multi-objective (MO) real-time optimization control algorithm is proposed. The ACC system can be divided into various working modes according to the driver's perception, as well as the relative state of the host vehicle and the preceding vehicle. To distinguish the driver's intention in the rapid acceleration, strong deceleration, steady, and larger clearance modes, a fuzzy tool is proposed to solve the problem. To maintain the inter-clearance, velocity, and jerking action in a reasonable region, an MO control algorithm based on model predictive control was used to obtain the optimal control vectors. Moreover, the control vector increment is restricted to suppressing the fluctuation caused by switching among various modes. Simulations were conducted for various scenarios to verify the effectiveness of the ACC method. The simulation results indicated that the driver's comfort and fuel economy improved by 35.3% and 16.6%, respectively, compared with the linear quadratic algorithm in a complicated driving environment. Various simulation results demonstrated that the MO-ACC controller can reduce fuel consumption while barely sacrificing riding comfort or tracking performance, as compared to the linear quadratic controller. [ABSTRACT FROM AUTHOR]

• Basic (owner's manuals) and comprehensive (+responsibilities) ACC training was provided. • Older and younger adults differed in their ACC usage after training. • Older adults reported less dynamic situational trust and higher workload than younger drivers. • However, comprehensive training reduced the difference between younger and older adults. • Training on driver support system is connected to the acceptance of FAVs. With the development of driver support systems (SAE Levels 1 – 2), drivers must take on new monitoring and supervision tasks in additional to manual driving. Training is necessary to clarify drivers' new roles and promote safe usage and trust in these systems. Providing training for lower-levels of automation may also benefit drivers' acceptance of future Fully Automated Vehicles (FAVs, SAE Level 5). However, younger and older drivers differ in training preferences (e.g., owner's manual vs on-road trial and error) and hold different attitudes towards automation. This study investigates the effects of additional training on drivers' roles and responsibilities when using Adaptive Cruise Control (ACC, SAE Level 1) for younger and older drivers. Thirty-nine adults (20 younger + 19 older) were trained on one of two ACC training protocols: basic (system functionality, operational procedures, and limitations) and comprehensive (basic training + ACC background and roles of responsibilities). Participants' situational trust and ACC usage was evaluated before, during, and after experiencing an emergency event while using ACC in a driving simulator study. Results showed that the comprehensive training promoted drivers' situational trust in ACC, ACC usage, and the acceptance of FAvs Compared to younger drivers, older drivers used ACC less, reported less dynamic situational trust, higher levels of workload, and lower acceptance. Overall, comprehensive training resulted in older drivers behaving similarly to younger drivers. The comprehensive training also promoted the acceptance of FAVs for both younger and older drivers. In conclusion, training of drivers' roles and responsibilities has an impact on drivers' usage of ACC and may be particularly useful for older drivers. [ABSTRACT FROM AUTHOR]

Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

The paper deals with the design of nonlinear adaptive cruise controllers for vehicular platoons operating on an open road or a ring road. The constructed feedback controllers are nonlinear functions of the distance between successive vehicles and their speeds. It is shown that the proposed novel controllers guarantee safety (collision avoidance), speed positivity, bounded acceleration, speed limits by explicitly characterising the set of allowable inputs and by leveraging barrier function techniques. Moreover, we guarantee global asymptotic stability of the platoon to the desired configuration, as well as string stability. Certain macroscopic properties are also investigated. The main challenges stem from the fact that all previous objectives must be satisfied simultaneously and the fact that the control systems studied do not evolve in a finite-dimensional linear space but rather on specific sets. The efficiency of the nonlinear adaptive cruise controllers is demonstrated by means of numerical examples. [ABSTRACT FROM AUTHOR]

Nowadays, due to traffic jam and many cars on traffic, it is very necessary to control the distance between cars and obstacle. Many car producers have been designed and manufacture Cruise Control Systems for cars. Reinforcement learning, one of the popular artificial intelligence techniques, is a method used to train autonomous systems in many different fields. In this simulation study, the adaptive cruise control (ACC) of a ring bus serving in the campus area is controlled with Deep Deterministic Policy Gradient, which is one of the reinforcement learning methods. This simulation study is carried out considering the speed limit in the campus area and the acceleration values required for a comfortable journey of the passengers. Acceleration, velocity and distance values are given with graphs. Consequently; the proposal neural predictor has superior performance to adapt and predict the distance, velocity and acceleration of ego vehicle (bus). [ABSTRACT FROM AUTHOR]

In this paper, we extend the nonlinear adaptive time gap car-following model to enhance the local and string stability for adaptive cruise control systems considering a time-lag in the lower level vehicle dynamics and a sensor time-delay. Both over-damped local and string stability analyses are performed mathematically and examined by simulation. The over-damped string stability criterion fulfills all the Lp stability norms, where p ∈ [ 1 , ∞ ]. Here we consider a time-lag operating in the lower level of the longitudinal control system's architecture, a sensor time-delay, and heterogeneity in the vehicle dynamics of the platoon. The adaptive time gap model without these attributes is intrinsically stable. However, it turns out that the introduction of a lag, a delay, or heterogeneity in the lower vehicular level reduces the performance in terms of stability, yielding unsafe damped oscillating collective behaviors. Henceforth we extend the model to enhance the stability by transforming the model to a homogeneous structure, without changing the fundamental dynamics. The results show that the extended model satisfies over-damped criteria for both local and string stability, considering actuator time-lag, sensor time-delay, and heterogeneity in the lower level vehicle dynamics. Such features are expected for automated driving systems. [ABSTRACT FROM AUTHOR]

A recent empirical study (Shi and Li, 2021) showed that commercial automated vehicles (AVs) became more unstable as the headway was set to a smaller value, implying possible intrinsic tradeoffs between safety, mobility, and stability aspects in AV following control design. This study aims to analytically explain the underlying vehicle control mechanism that dictates these tradeoffs. To this end, a robust optimization model is formulated based upon a parsimonious linear AV following model to capture the first-order tradeoffs between safety, mobility, and stability. The robust optimization model aims to maintain a sufficient safety buffer to avoid collisions against all possible realistic preceding vehicle trajectories. As opposed to a numerical solution, we managed to solve this model to an analytical solution that captures relationships between the key parameters determining safety, mobility, and stability. The analytical solution reveals that improving AV mobility (or reducing AV following headway) would require overcoming more safety challenges (e.g., enhancing vehicle control to maintain a short safety buffer) while causing more string-instability. The theoretical findings are consistent with the empirical observations in previous studies. Further, they provide a possible explanation for the observed string instability of commercial AV following control (e.g., adaptive cruise control) as a tradeoff for a smaller headway. Overall, this study lays a new methodology foundation for incorporating safety in traffic flow analysis that traditionally focused on only mobility and stability. Further, the findings yield a set of managerial insights into reasonable AV following design and its implications to emerging AV traffic management. • Build a robust optimization model relating safety, mobility, and stability. • Capture the tradeoffs between safety, mobility, and stability for commercial AVs. • Solve an analytical formulation for these tradeoffs. • Reveal that improving AV mobility would require overcoming more safety challenges while causing more string instability. • Show the consistency between theoretical findings and empirical observations. [ABSTRACT FROM AUTHOR]

In this article, we propose an ecological predictive cruise control method for connected and automated vehicles (CAVs) using data-driven predicted leading vehicle speed in a car-following scenario. Many existing studies assume that the leading vehicle's behavior is known when planning the trajectory of the ego vehicle. Unfortunately, predicting the future behavior of adjacent vehicles is extremely uncertain and inaccurate for use in control. To overcome this, we adopt the vector autoregressive model (VAR) that is well suited for generating simultaneous forecasts of the response variables when predicting the short-term behavior of the vehicle ahead. As many human drivers behave similarly in a car-following situation, vehicle connectivity is specifically used to drop hints of the behaviors of the cars following the connected car. Once the leading vehicle's future trajectory is predicted, the ego vehicle is controlled in a way that minimizes energy consumption by optimizing its speed trajectory while remaining safe. Through simulation case studies, we demonstrate that our approach can achieve improved energy efficiency considerably than the conventional strategy that cannot predict the speed of vehicles ahead. Given the recent market penetration of vehicle connectivity technologies and advanced driving assistance systems (ADAS), the proposed method is expected to have a high commercialization potential. [ABSTRACT FROM AUTHOR]

• To the best of our knowledge, this is the first publicly available study based on field operational test data that examined the use of Level 1 and 2 driving automation systems by road geometric characteristics on interstates and freeways. • Drivers were less likely to drive with the systems on as horizontal curves became sharper. • Even within the operational design domain, there are roadway features such as curves where drivers are less comfortable using the technology or that conditions may have been such that the systems disengaged. Little is known about how the actual use of Level 1 and 2 driving automation systems may be affected by geometric road characteristics in naturalistic driving environments. This study examined the use of these systems on horizontal curves on interstates and freeways. It used travel data collected in a field operational test conducted with two 2016 Land Rover Range Rover Evoque vehicles equipped with adaptive cruise control (ACC) and two 2017 Volvo S90 vehicles equipped with ACC and Pilot Assist (PA). Logistic regression models estimated changes in the likelihood of ACC use associated with horizontal curvature in the Evoque vehicles, and of PA and ACC use in the S90 vehicles, while accounting for traffic conditions. Drivers were less likely to drive with ACC or PA on as horizontal curves became sharper. In the Evoque vehicles, the likelihood of using ACC was 71.6% lower on the sharpest category of horizontal curves (those with a degree of curvature>2.5 degrees per 100 feet of arc or a radius smaller than 2,292 feet), compared with straight road segments or the flattest horizontal curve category (those with a degree of curvature <= 1.5 degrees per 100 feet of arc or a radius no <3,820 feet). In the S90 vehicles, the likelihood of using PA and ACC declined 74.6 and 66.3%, respectively, on the sharpest curves. Many driving automation systems face challenges on horizontal curves, even within their operational design domain. Future implementations that improve functionality may enhance driver experience and boost drivers' confidence in these systems, which should increase their use and maximize the safety benefits these systems might offer. [ABSTRACT FROM AUTHOR]

With advanced technologies such as perception and automatic control, automated vehicles (AVs) are gradually replacing human-driven vehicles (HDVs). They will become a significant component of transportation systems. As the crucial longitudinal control model for AVs, the existing adaptive cruise control (ACC) model faces challenges such as poor linear stability and compromised safety. To solve the problem, this paper proposes an adaptive cruise control model to avoid collision risk. First, the delay-considerate collision risk control strategy (DCRCS) is developed to consider vehicles' perception and control delay. Then, a proposed adaptive cruise control (PACC) model is designed by incorporating DCRCS into the ACC model based on the control gain coefficient. Finally, the effectiveness of the PACC model is discussed and analyzed in terms of stability, safety, efficiency, energy consumption, and pollutant emissions through a combined approach of theoretical analysis and simulation experiments. The result shows that (1) compared to the classical ACC model, the PACC model demonstrates significant improvements in system stability, reduced safety risks, enhanced efficiency, and decreased energy consumption and pollutant emissions. These enhancements are observed when the control gain coefficient is set to a positive value. (2) The equilibrium speed minimally influences the effectiveness of the PACC model. Under varying speed conditions, the PACC model consistently maintains stable performance. (3) The effectiveness of the PACC model is closely related to the selection of the control gain coefficient. Considering the effects of the control gain coefficient on stability, safety, efficiency, energy consumption, and pollutant emissions of the PACC model, the optimal range for the control gain coefficient is in the range of [0.8, 1.0]. In summary, compared to the classical ACC model, the PACC model exhibits outstanding performance in various aspects. It provides theoretical support for the longitudinal control of AVs in the future. [ABSTRACT FROM AUTHOR]

This study analyzes the problem of adaptive cruise control of vehicles in different driving cycles and divides diverse weight coefficient intervals for the vehicles under the different driving cycles to improve the adaptability of the vehicles in various environments. This paper first describes the driving environment of the adaptive cruise vehicle, and a model prediction algorithm with fixed weight coefficients is established to control the vehicle state. Then, a neural network is established to identify the vehicle driving cycles, the weight intervals are divided in accordance with different driving cycles, and the weight value is dynamically adjusted through fuzzy control. Lastly, the variable weight coefficients of different driving cycles are combined with the model prediction controller. The software cosimulation shows that the method designed in this paper plays a positive role in the fuel economy of adaptive cruise. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) has become an exigent domain of Intelligent Transport system (ITS). Providing efficient communication among rapidly moving vehicles is a challenging task in highway environment. This paper analysis the connectivity of High Speed Mobility and Lane Changing (HSMLC) based on Discretionary Lane Changing (DLC) approach for V2V environment. The Recurrent Neural Network System (RNN) which models a driver's decision to perform a Discretionary Lane Changing (DLC) process on highways. The RNN system model the DLC decision making using Adaptive Cruise Control (ACC) computational model. The ACC mechanism defines and extends traditional cursive control based on the input metrics which are extracted from Highway Traffic Management System database (HTMS). The RNN was trained and tested with HTMS data collected from Tamilnadu highway of India with ACC properties. The result part reviews the proposed DLC trajectories by lane changing phases, connectivity probability during DLC and packet delivery rate. During 65Kmph with 100 vehicles, the DLC takes highly 4.3 to 5.2 s for lane changing process and during this moment the PDR and throughput of the networks are 62 to 75% and 31.7 to 39Kbps, respectively. The simulation work done by two different simulators such as SUMO—mobility simulator and NS2—network simulator. [ABSTRACT FROM AUTHOR]

Recently developed efficient driving schemes usually solve a predictive optimization problem or determining the vehicle control input, and at the expense of high computational cost, they improve the overall traffic flows and individual driving performances on urban roads. This paper presents a more practical technique for automated vehicles’ predictive driving by extending the existing adaptive cruise control (ACC) scheme with a look-ahead functionality. Such a look-ahead driving scheme (LDS) predicts the states of the preceding vehicle at an adaptive look-ahead time step and, with negligible computation costs, computes the vehicle control input more circumspectly for efficient driving in urban traffic. The proposed LDS is evaluated in typical urban traffic at the signalized intersections by observing the intersection utilization, flowing characteristics, and individual vehicles’ fuel efficiency. Furthermore, we also evaluate the influences of the LDS-vehicles’ penetration rates on overall traffic performances at various traffic volumes. [ABSTRACT FROM AUTHOR]

Adaptive Cruise Control (ACC) is a multi-objective control problem, where the balance of the ride comfort and safety performance of vehicles is a challenge. This paper proposes a multi-objective dynamic coordinated ACC based on variable weight model predictive control (MPC). The weight coefficients are regulated according to the inter distance and relative speed between the front vehicle and ego vehicle through a fuzzy controller. To improve the object detection accuracy, the decision-level sensors fusion scheme based on millimeter wave radar and camera is designed. The upper and lower layers of the hierarchical ACC controller are implemented. The proposed ACC control system is tested on a smart electric vehicle. The dynamic coordinated ACC can increase the tracking accuracy of the safe inter distance to enhance the vehicle safety in scenarios where there is possible risk of collision, such as when the inter distance is small and the relative speed is negative. In some safe conditions, the dynamic coordinated ACC can reduce the acceleration and jerk to improve the ride comfort. The performance of driving safety and ride comfort are dynamically coordinated and adapted to the changing driving conditions through the implementation of the dynamic ACC. The ACC has the ability to adapt to these changes and respond accordingly. • A dynamic coordination idea is proposed to regulate the weight factors of MPC. • The decision-level sensors fusion scheme is developed to improve detection accuracy. • The proposed MPC-based ACC system is tested on a real vehicle. • The experiment in this paper promotes the development of MPC in real vehicles. [ABSTRACT FROM AUTHOR]

Summary: As the adaptive cruise control system (ACCS) on vehicles is well developed today, vehicle manufacturers have increasingly employed this technology in new‐generation intelligent vehicles. Pulse‐and‐glide (PnG) strategy is an efficacious driving strategy to diminish fuel consumption in traditional oil‐fueled vehicles. However, current studies rarely focus on the verification of the energy‐saving effect of PnG on an electric vehicle (EV) and embedding PnG in ACCS. This study proposes a pulse‐and‐glide‐driven adaptive cruise control system (PGACCS) model, which leverages the PnG strategy as a parallel function with cruise control (CC) and verifies that PnG is an efficacious energy‐saving strategy on EVs by optimizing the energy cost of the PnG operation using Intelligent Genetic Algorithm and Particle Swarm Optimization (IGPSO). This article builds up a simulation model of an EV with regenerative braking and ACCS based on which the performance of the PGACCS and regenerative braking is evaluated; the PnG energy performance is optimized, and the effect of regenerative braking on PnG energy performance is evaluated. As a result of PnG optimization, the PnG operation in the PGACCS could cut down 28.3% energy cost of the EV compared with the CC operation in the traditional ACCS, which verifies that PnG is an effective energy‐saving strategy for EVs and the PGACCS is a promising option for EV. [ABSTRACT FROM AUTHOR]

Quantifying the effect of Cooperative Adaptive Cruise Control (CACC) on traffic mobility and vehicle fuel consumption has been a challenge because it requires a modeling framework that depicts the interactions among manually driven vehicles and CACC vehicles in the complex multilane traffic stream. This study adopted a state-of-the-art traffic flow modeling framework to explore the impacts of CACC on vehicle fuel efficiency in mixed traffic. The analyses at a freeway merge bottleneck indicated that the CACC string operation resulted in a maximum of 20% reduction in energy consumption compared to the human driver only case. At 100% market penetration, CACC equipped vehicles consume 50% less fuel than adaptive cruise control (ACC) vehicles without vehicle-to-vehicle (V2V) communication and cooperation. This implied the importance of incorporating the V2V cooperation component into the automated speed control system. In addition, the CACC string operation could substantially improve freeway capacity without degrading the vehicle fuel efficiency. At 100% CACC market penetration, the capacity increased by 49% while the vehicle fuel consumption rate per vehicle mile traveled remained the same as the rate observed in the human driver only case. At lower CACC market penetrations, the vehicle fuel efficiency could be improved via using the dedicated CACC lane or implementing wireless connectivity on the manually driven vehicles. In the 40% CACC case, those strategies brought about 15% to 19% capacity increase without decreasing vehicle fuel efficiency. Those results highlighted the necessity of deploying CACC-specific operation strategies under lower CACC market penetrations. [ABSTRACT FROM AUTHOR]

• Propose a methodology that combines both empirical experiments and theoretical models to construct the fundamental diagram for automated vehicle traffic. • It is found that the traditional triangular fundamental diagram structure remains applicable to automated vehicle traffic. • The shortest automated vehicle headway setting can significantly improve the road capacity, while the other headway settings may decrease the road capacity. • Propose a parsimonious formula to estimate the fundamental diagram for mixed traffic with measures from pure human-driven traffic and pure automated vehicle traffic. Increasingly, commercial vehicles are equipped with automated vehicle (AV) features such as adaptive cruise control systems. The AV feature can automatically control the headway between the current vehicle and the preceding vehicle in an adaptive manner. The automatic control may lead to significantly different car- following motions compared with those of human-driven vehicles, which challenges the applicability of classic traffic flow theory to emerging road traffic with AVs. To investigate the impacts of commercial AVs on traffic flow, this paper proposes a general methodology that combines both empirical experiments and theoretical models to construct a fundamental diagram (FD), i.e., the foundation for traffic flow theory for AV traffic. To demonstrate the empirical experiment settings, we collected high-resolution trajectory data with multiple commercial AVs following one another in a platoon with different headway settings. The field experiment results revealed that the traditional triangular FD structure remains applicable to describe the traffic flow characteristics of AV traffic. Further, by comparing the FDs between AVs and human-driven vehicles, it was found that although the shortest AV headway setting can significantly improve road capacity, other headway settings may decrease road capacity compared with existing human-driven-vehicle traffic. It was also found that headway settings may affect the stability of traffic flow, which has been revealed by theoretical studies but was first verified by empirical AV data. With these findings, mixed traffic flow FDs were derived by incorporating different headway settings and AV penetration rates. The method proposed in this paper, including experiment designs, data collection approaches, traffic flow characteristics analyses, and mixed traffic flow FD construction approaches, can serve as a methodological foundation for studying future mixed traffic flow features with uncertain and evolving AV technologies. [ABSTRACT FROM AUTHOR]

• This paper has characterized the empirical car-following (CF) behaviors of a commercial ACC system and explained the underlying mechanism(s). • The ACC behaviors (i.e., response time, oscillation growth, and overshoot) largely depend on the ACC headway, speed level, and leader stimulus. • In a platoon, the oscillation growth progressively changes from one ACC to another, implying oscillation escalation and potential safety hazards. Emerging automated vehicle (AV) technologies are increasingly being deployed around the world and it is only a matter of time before the transportation landscape changes dramatically. Unfortunately, those changes cannot be well predicted due to the lack of empirical data. But adaptive cruise control (ACC) vehicles are common in the market and can be used to fill this gap. In this paper, we aim to characterize the empirical car-following behaviors of a commercial ACC system and understand how ACC behaves in different conditions and the underlying impact mechanism. It is found that for a single ACC: (i) the ACC response time is comparable to human drivers but much larger than the ACC controller time gap and it exhibits small variance, (ii) the ACC response can amplify or dampen an oscillation, (iii) after the oscillation, the stabilization process can exhibit overshooting or undershooting, and (iv) these CF behaviors depend largely on the ACC headway setting, speed level, and leader stimulus, which produce the impacts directly and/or indirectly through the mediation of earlier ACC behaviors. For a three-vehicle platoon, our main finding is that the change from one ACC vehicle to the next is progressive for oscillation growth, and regressive for deceleration, acceleration, and overshooting. This implies that in long platoons, oscillation amplitude tends to exacerbate very quickly, which forces ACC vehicles further upstream to apply very strong braking followed by a strong acceleration. This can cause significant overshooting and safety hazards. [ABSTRACT FROM AUTHOR]

• Heterogeneity of drivers and vehicles makes platoons more unstable, on average. • L ∞ characterization of string stability is more appropriate than L 2 one. • Analytical sufficient condition for L ∞ string stability is derived. • L p norm variability due to platoon length, speed, and parameters pdfs is analysed. • Parameters pdfs shape and correlation sensibly affect string stability. This paper shows that the heterogeneity of drivers' and vehicles characteristics makes platoons, on average, more string-unstable. However, the string instability degree of unstable platoons is much higher in a homogeneous flow than in a heterogeneous one. These results are based on an L ∞ characterization of string stability, which is shown to be the most appropriate one from a traffic safety viewpoint. Mechanisms and conditions are discussed in which an L 2 characterization is not able to capture the amplification of a speed drop through a string of vehicles. An analytical sufficient condition for the L ∞ string stability of heterogeneous vehicles, which move according to a general class of car-following models, is derived. Above all, a thorough comparison of L ∞ and L 2 string stability characterizations between a homogeneous and a heterogenous flow, is performed. To this aim, the L p norms of heterogeneous platoons are calculated within a quasi-Monte Carlo framework. The variability of the L p norm values due to the platoon length, the equilibrium speed, and the probability distribution model of the uncertain vehicle parameters, is analysed. Overall, it is shown that the platoon stability behaviour sensibly changes with the shape and the correlation structure of vehicle model parameter distributions. Therefore, traffic heterogeneity needs to be modelled in order to correctly characterize the string stability of a mixed traffic flow. [ABSTRACT FROM AUTHOR]

The statistical models supporting the Highway Safety Manual quantify associations between aggregate traffic measures, such as average daily traffic volume or posted speed limit, and crash frequencies accumulated over several years. For some time though, it has been recognized that crash risk can vary as traffic conditions vary due to special events or within-day changes in traffic. Additionally, the Highway Safety Manual's predictive tools are essentially statistical summaries of conditions present during the recent past, and transferring this knowledge to environments containing automated vehicles is likely to be problematic. This paper illustrates how both issues can be addressed by supplementing standard statistical modeling with models describing crash mechanisms. In particular, Brill's random walk model of how traffic shockwaves generate rear-end crashes is combined with a traffic flow model based on a fundamental diagram in order to quantify the relation between traffic density and rear-end crash risk. Approximating Brill's random walk with a finite Markov chain leads to a computationally tractable model, and the model's predicted relationship is consistent with empirical findings. Transferring the model to a hypothetical environment with automated vehicles is then illustrated. [ABSTRACT FROM AUTHOR]

In order to be acceptable to drivers, the adaptive cruise control (ACC) systems need to be designed based on the analysis of human driver driving behavior. A new safe distance model is proposed in this paper based on the analysis of real-world driving test data. The goal of the control algorithm is to achieve naturalistic behavior of the vehicle that can comprehensively address the issue of driving safety, tracking performance, fuel economy and ride comfort. Firstly, a hierarchical control architecture is utilized which lower controller compensates for nonlinear vehicle dynamics and enables tracking of the desired acceleration. Then the acceleration rate of the following vehicle is introduced as state variables in the model of the car-following system for a more realistic and comprehensive description of dynamic evolution between the preceding car and the following car. Next, the control objectives above are formulated into a constrained quadratic programming problem under the framework of model predictive control (MPC). Finally, the simulation results show that the control strategy proposed in this paper can provide natural following performance that not only can satisfy driving safety, tracking performance but also can achieve fuel economy and ride comfort. [ABSTRACT FROM AUTHOR]

Self-driving safety is one of the major concerns raised with regard to pushing the use of automated vehicles on roads. Fully automated vehicles are forced to make appropriate decisions in an uncertain environment where driverless and human-driven vehicles share the road together. This study proposes a new model that can be used to enhance the autonomous driving behavior at a non-signalized intersection considering the traffic safety guarantee, delay time, and smooth driving. The proposed model is called the responsibility-sensitive safety-based partially observable Markov decision process model for the decision-making mechanism of automated vehicles. The model not only increases traffic safety guarantee and smooth driving, but also reduces the delay time. First, we generate some specific driving scenarios using the automated driving toolbox in MATLAB. Second, the driving strategy of automated vehicles is optimized by the partially observable Markov decision process framework using the adaptive cruise control system. Finally, the responsibility-sensitive safety algorithm is implemented under adaptive model predictive control. The proposed model performs better than the classical adaptive model predictive control. In the best case, the proposed model took a 31.60 % reduction in braking time and a 51.20 % improvement in smoothing speed control. [ABSTRACT FROM AUTHOR]

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections. [ABSTRACT FROM AUTHOR]

Numerous automotive control systems depend on the prediction of the velocity of the controlled vehicle or other road users in close proximity. A forecast is indispensable for the design and operation of model predictive control strategies which are utilized for example in order to optimize fuel economy in cruise control systems. In this article, a novel velocity time series prediction method, which depends exclusively on past velocity measurements, is presented and analyzed. It is based on conditional probabilities of acceleration and deceleration, which are estimated from real driving data. The proposed velocity prediction is used in a new model predictive control algorithm for an adaptive cruise control system for heavy duty vehicles, where the predicted future velocity of a preceding vehicle is needed, as well as its probability distribution. Simulation results show that an accurate velocity prediction is crucial for achieving energy-efficiency goals. [ABSTRACT FROM AUTHOR]

In the following process, in order to improve the driving safety and road utilization of the adaptive cruise control (ACC) system, a variable time headway spacing strategy was studied. In view of the fact that the variable spacing strategy cannot adapt to the complex and variable deceleration conditions, an improved variable time headway strategy is proposed, which changes with the deceleration time and deceleration of the preceding vehicle. Based on this, the upper controller of adaptive cruise control based on model predictive control is designed, and numerical simulation of the variable time headway spacing strategy is performed, which verifies the effectiveness of the improved variable time headway strategy. The results show that the spacing strategy proposed in this paper can more smoothly keep up with the preceding vehicle, and improve driving safety, comfort and road utilization. [ABSTRACT FROM AUTHOR]

High speed selection and road traffic crashes are related to behavioural perceptions. These are the main cause of deaths or serious injuries. There is evidence to suggest that adaptive cruise control (ACC) increases the driver's perception of having fun while driving, which affects behavioural factors. Thus, the study aims to examine the interaction effects of perceived enjoyment and behavioural factors towards the use of ACC on high speed and crash involvement. The study model was validated using a sample collected from 321 male and 211 female drivers of vehicles equipped with ACC on two occasions separated by 2 months interval. The results show that age and mileage were positively related to crash involvement, whereas gender was negatively related to high speed selection (HSS). The predictors of HSS were intention, Perceived behavioral control (PBC), attitude towards the use of ACC, and perception of enjoyment from the use of ACC. In addition, the positive predictors of the crash involvement were intention and attitude toward the use of ACC. Perception of enjoyment from the use of ACC negatively predicted crash involvement. Interaction relations between perceived enjoyment and intention towards the use of ACC predicted HSS. Finally, the interaction relation between enjoyment from the use of ACC and PBC predicted crash involvement. [ABSTRACT FROM AUTHOR]

In this work, a real‐time energy management problem for a parallel hybrid electric vehicle (HEV) is proposed. The considered powertrain is built from a commercial HEV model. First, a non‐linear optimal control problem under model predictive control scheme is formulated. The designed controller aims to generate the optimal power split and gear ratio schedule with respect to minimise the energy consumption of fuel and electricity. Moreover, the multiple shooting algorithm is introduced to decouple the dynamic constraints with the ability of avoiding the strong non‐linearity while solving the optimisation problem. After that the optimisation problem is solved using sequential quadratic programming solver. Then, to evaluate the performance of the proposed real‐time optimisation strategy on different traffic scenarios, the controller is applied to an adaptive cruise control (ACC) under connected environment. In this case, a solution of ACC with consideration of minimising energy consumption and maintaining string stability is provided. Finally, the proposed controller can be implemented in the traffic‐in‐the‐loop platform without the knowledge of the predefined driving route. Simulations reveal that the proposed real‐time control scheme shows great optimisation performance under the designed scenarios. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) systems assist drivers by automatically adjusting vehicle speed with respect to the driver selected time-gap to the lead vehicle and maximum desired speed. Although ACC systems have been around for 20 years, drivers are not always fully aware of their limitations. The main focus of this research was to investigate the limitations of the ACC system and the resulting effects on driver performance, taking into account variations of mental workload. Thirty participants drove two simulated scenarios, with and without the ACC system active, in a fixed-base driving simulator. The variability of the observed parameters between the drives with and without the ACC provided insights into individual driver performance, impact of age and gender, and behavioral trends resulting from changes to mental workload. Data on cognitive workload were measured using a detection response task (DRT) device to monitor availability of mental resources during the various tasks. The statistical analysis showed that with an active ACC system, participants reached lower maximum speeds, maintained longer headways, and reacted slower and more abruptly to sudden events. The data from the DRT showed a significantly lower cognitive load when participants were subject to secondary tasks during the ACC scenarios. [ABSTRACT FROM AUTHOR]

Objective: This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC).Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving.Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study.Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study.Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data.Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving. [ABSTRACT FROM AUTHOR]

• Novel control design method for fractional-order controllers for more demanding design requirements. • A more graphical approach to tune fractional-order controllers, easing the control system deployment on any type of vehicles. • Application of such design approach to real world problems and real vehicles platforms. • A method to guarantee iso-damping properties, which is essential for automated car-following. This work deals with the control design and development of an automated car-following strategy that further increases robustness to vehicle dynamics uncertainties. The control algorithm is applied on a hierarchical architecture where high and low level control layers are designed for gap-control and desired acceleration tracking, respectively. A fractional-order controller is proposed due to its flexible frequency shape, fulfilling more demanding design requirements. The iso-damping loop property is sought, which yields a desired closed-loop stability that results invariant despite changes on the controlled plant gain. In addition, the graphical nature of the proposed design approach demonstrates its portability and applicability to any type of vehicle dynamics without complex reconfiguration. The algorithm benefits are validated in frequency and time domains, as well as through experiments on a real vehicle platform performing adaptive cruise control. [ABSTRACT FROM AUTHOR]

This article deals with the robust optimal control and identification of uncertain adaptive cruise control systems. Both measurement delay and engine's lag are investigated in system modeling and control design. The control structure consists of two steps. In the first step, by using a new approach, the uncertain parameters of longitudinal dynamics of each vehicle is identified. Afterwards, at the second step, by using the relative position and velocity measurement with respect to ahead vehicle, a robust control against size changing of platoon is presented to assure the internal stability, string stability, and safety of heterogeneous vehicular platoons. A cost function involving important metrics internal stability, maximum overshoot, and settling time is introduced, and the particle swarm optimization algorithm is used to find the optimal values of control parameters minimizing the cost function. It will be shown that the proposed robust controller guarantees the internal stability, string stability, and safety of adaptive cruise control systems in the presence of measurement and parasitic delays. Several simulation results are provided to show the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Road traffic crashes are currently one of the leading causes of death. Many accidents occur when the attention of a driver is diverted owing to fatigue or external factors. Adaptive cruise control (ACC) is a driver assistance system for safe vehicle transportation by combining a safe following distance with speed regulation. However, existing ACC systems fail to control the braking system when the preceding vehicle applies full brakes that can cause a jerk or collision. In addition, current ACC systems fail to consider road elevation and spatiotemporal problems that cause tracking errors. The existing issues result in high fuel consumption, discomfort during travel, and high error rates. To overcome these issues, a "Combinatorial Repeated Local Search and Simulated Annealing-Based Hill Climbing Optimization" (CRLSSA-HCO) technique is introduced for ACC with a predictive controller. In this technique, two models are introduced, namely the repeated local search-based hill climbing optimization (RLS-HCO) model, and the simulated annealing-based hill climbing optimization (SA-HCO) model. The RLS-HCO model performs local search optimization to avoid vehicle jerks and collisions by maintaining a safe distance between vehicles on a horizontal road. The RLS-HCO model operates in a spacing control mode and a speed control mode to maintain the safe distance. The input and control variables are optimized by performing a repeated local search, thereby reducing the fuel consumption and error rate. The SA-HCO model performs a global search optimization for avoiding traffic and addresses the spatiotemporal problems on hilly roads. The SA-HCO model optimizes the engine speed, engine torque, and gear ratio by using simulated annealing for reducing the fuel consumption and error rate. An experimental evaluation of the CRLSSA-HCO technique is carried out using performance metrics such as fuel consumption, distance error, and speed error, which are compared to those of state-of-the-art studies. [ABSTRACT FROM AUTHOR]

In this paper, dynamic programming (DP) economical cruise control algorithms under discrete distance and discrete time strategies are designed to improve battery electric vehicle (BEV) energy consumption and riding comfort. In our experiment, energy consumption of constant-speed driving is reduced by up to 21.6% using the optimum velocity profiles from discrete distance DP. 'Variable step length and boundary conditions' are utilized to reduce discrete distance DP processing time by 90.8% with no effect on accuracy. Under discrete time DP, position is introduced as an additional variable, so discrete time DP allows more inputs including real-time preceding vehicle position and traffic information. The experiment results indicate that under traffic situation, discrete time DP further improves energy consumption and riding comfort by avoiding unnecessary stop at intersections compared with discrete distance DP. Further analysis indicates that under properly controlled velocity, the transfer between potential energy and kinetic energy is more efficient than that between potential energy and electric energy. These DP economical cruise control algorithms are effective for BEVs, hybrid electric vehicles and plug-in hybrid electric vehicles. 'Variable step length and boundary conditions' method in DP can also be applied in other domains where DP processing time is crucial. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) considering vehicle performances becomes a core technology for autonomous vehicle to adjust the vehicle speed while maintaining a safe distance from preceding vehicles. Improvement of the vehicle performances has limitations by only designing ACC's control parameters or its strategies. Furthermore, uncertainties of vehicle parameters and vehicle occupants can affect the vehicle performances considerably. Therefore, this paper studies the total design process of ACC system considering the control and the vehicle parameters simultaneously and reflecting the uncertainties. Three cases of deterministic design optimisation (DDO) are performed to show the effectiveness of considering the vehicle parameters. Additionally, one case of robust design optimisation (RDO) is performed to demonstrate the importance of considering the uncertainties. Conclusively, by performing the RDO, the vehicle performances are improved, and especially mean of fuel efficiency is increased and the standard deviation of the fuel efficiency is decreased. Moreover, enhancement and robustness of the product's performance are expected by implementing this design process to the development of control system with structural discipline at the concept design stage. [ABSTRACT FROM AUTHOR]

In this study we developed an autonomous braking algorithm to satisfy both safety and ride comfort of a vehicle. The proposed algorithm is composed of two-step braking strategies depending on the value of time-to-collision. The first step is a partial braking strategy to provide not only deceleration but also good ride comfort in a normal braking situation, and the second step is a full braking strategy to avoid forward collisions in an emergency braking situation. Further, the partial braking is divided into a recovery and a release zones. The former is to apply braking pressures for the safe deceleration, whereas the latter is to release the braking pressure smoothly for good ride comfort. To determine the partial braking pressures, the nonlinear characteristics of the tire friction is considered and the linear momentum of a preceding vehicle is calculated based on the extrapolation of its motion. Computer simulations using CarSim were executed combined with MATLAB/Simulink to implement the driving situations, and finally we obtained successful performances satisfying ride comfort as well as safety of the vehicle. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) system possesses more remarkable ability to suppress disturbance and enhance the traffic capacity than adaptive cruise control (ACC). However, CACC asks for strict requirement on wireless communication and precise equipment, which remains a big difficulty to implement. This paper extends a new ACC model by introducing the self-stabilizing control with historical data, aimed at achieving the close performance of CACC and make it practicable. Substituting real-time information with pre-stored data substantially reduces the technical demand and offers high reliability to withstand the network delay. Linear stability analysis for this model points out enhancing the value of the gain or time delay of self-stabilizing control benefits to stabilize the traffic. The theories are corroborated via the simulation and further numerical simulations explicate the impact on fuel consumption and emissions and traffic capacity. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7 – 20.5 % higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2 – 17.3 %). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

This paper proposes a modeling framework to characterize steady-state traffic flow relations for heterogeneous traffic composed of both standard (S) and Cooperative Adaptive Cruise Control (CACC, labeled C here) vehicles, capturing the impact of C market penetration and vehicle sequence within a lane. The resulting parameterized fundamental diagram is then integrated with a first-order macroscopic traffic model, allowing us to characterize the operational performance on a network for heterogeneous traffic with varying C market penetration rates. This approach is demonstrated through an illustrative case study which considers a small freeway section with time-varying demand, merging traffic from an entrance ramp, and C market penetration ranging from 0.0–1.0. The results indicate that maximum throughput does not change appreciably as C traffic is first introduced, but eventually increases significantly for mid-to-high C penetration rates. Additionally, it shows that increasing C market penetration and separating vehicle classes slows upstream congestion propagation. [ABSTRACT FROM AUTHOR]

• Acceptance of five Level 2 systems varied across test vehicles. • Adaptive cruise control was trusted more than lane centering. • Functional attributes predicted agreement that automation improved driving experience. • Uncomfortable experiences with automation occurred in common road scenarios. Driving automation systems are being introduced into mass-market vehicles, but little is known about whether drivers will trust driving automation systems and use the technology. In this study, volunteer drivers operated five vehicles equipped with automated longitudinal and lateral control and completed surveys about their experience. A subset of drivers also documented uncomfortable experiences as they used the automation while driving. Driver agreement that the automation improved the overall driving experience was significantly higher for Vehicle A than the systems implemented in the other four vehicles. Drivers reported significantly higher trust in adaptive cruise control than in lane centering in every vehicle but Vehicle B. Increased agreement that the automation consistently detected lane lines; detected moving vehicles ahead; and made smooth, gentle steering inputs was associated with significant increases in agreement that the automation improved the overall driving experience. Situations where drivers reported feeling uncomfortable with the automation during their drive were dominated by instances where lane centering struggled with common roadway features such as hills and intersections. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand. [ABSTRACT FROM AUTHOR]

This research conducted a comprehensive study to better estimate the impact of production Adaptive Cruise Control (ACC) on uninterrupted flow facilities using the highly cited ACC car-following models in the literature. Four different car-following models were investigated, including the Autonomous Adaptive Cruise Control (AACC) model, Intelligent Driver Model (IDM), California Partners for Advanced Transit and Highways (PATH) empirical ACC model, and the Technical University of Delft empirical ACC model. Each of the four models were recalibrated using data collected by a 2013 Cadillac SRX with production ACC engaged while following a human-driven 2013 Cadillac SRX in northern Virginia. Off-the-shelf commercial microscopic simulation software VISSIM was utilized to simulate the ACC traffic flow. Sensitivity analysis was conducted on ACC market penetration and following headway. Capacity and fundamental diagrams were used to quantify the impact of the ACC on traffic flow. Results indicate that freeway capacity highly depends on the market penetration of ACC vehicles. Small number of ACC vehicles (<25%) is beneficial to roadway capacity, while wider deployment of ACC vehicles (>75%) significantly harms capacity. The results strongly suggest the urgent need for vehicle connectivity. [ABSTRACT FROM AUTHOR]

In this paper, the stability criteria of a platoon of adaptive cruise control (ACC) vehicles with actuator lag and senor delay are derived, and their impact on ACC vehicles stability is analyzed and tested. The results show that compared to the ideal ACC model with zero delays, both sensor delay and actuator lag deteriorate the dynamic stability of vehicles and platoons; they, however, have negligible impact on the static performance. Comparatively, the negative effect from communication delay is larger than that from actuator lag. In order to minimize the impacts of the two types of delays, a new ACC model is proposed. Then, the optimal control parameters of the proposed ACC system are designed based on a proportional-derivative controller from the perspective of the homogeneous and heterogeneous string stability. The results provide a feasible design to the proportional-derivative controller of ACC. Finally, the sensitivity analysis of ACC system is conducted. [ABSTRACT FROM AUTHOR]

• Owners of advanced driver assistance systems often do not read the user's manual. • Trial-and-error on road experience is a frequent learning method. • System limitations might not be known to the users beforehand. • A timely education about system functions and limitations is advised. The present study aims to explore what kind of information users of two advanced driver assistance systems (ADAS) have about the systems, how they acquired it, and which methods they would prefer for learning about the systems' capabilities and limitations. Data was collected within the scope of a broader ADAS user experience study using in-depth interviews. Answers from 38 Czech owners of cars with Forward Collision Warning (FCW) and Adaptive Cruise Control (ACC) were analysed using qualitative content analysis. The results suggest that most owners do not read the user's manual and rely on their own trial-and-error on-road experience or information from the car dealership. Thereby, information about the limitations of the systems might not be known to them beforehand and could lead to surprises when these are experienced in real traffic. Therefore, prior education about the functions, control, and limitations of FCW and ACC is advised. [ABSTRACT FROM AUTHOR]

While checking functional correctness for automated driving is often achieved through vast amounts of automated and manual field testing, automating specification verification is essential for developing and releasing fully self-driving vehicles. This paper presents an automatic bounded model checking approach for functional specifications over longitudinal vehicle controller implementations. The proposed method checks the actual embedded program, rather than an extracted abstract model. The specification is converted into a monitor that is interleaved with the execution of the program over a finite time horizon in closed-loop simulation. A decomposition is proposed to tackle the verification complexity. This allows verifying the program for whole parameter sets using a software model checker as opposed to testing only individual samples. The approach is capable of identifying functional flaws in several exemplary longitudinal controllers with variable complexity. • Platform-independent verification of adaptive cruise controller implementations. • Verifying requirements for the closed-loop behavior using bounded model checking. • Utilizing specification decomposition to reduce verification complexity. [ABSTRACT FROM AUTHOR]

• We develop a data-driven framework to analyze disturbance amplification for automated vehicle car following. • The data-driven framework facilitates an analysis of stochastic time-invariant disturbance amplification. • The framework facilitates a disturbance amplification analysis for unknown or nonlinear car following laws. This paper presents a data-driven framework to quantitatively analyze the disturbance amplification behavior of automated vehicles in car-following (CF). The data-driven framework can be applied to unknown CF controllers based on the concept of empirical frequency response function (FRF). Specifically, a well-known signal processing method, Welch's method, together with a short time Fourier transformation is developed to extract the empirical transfer functions from vehicle trajectories. The method is first developed assuming a generic linear controller with time- invariant CF control features (e.g., control gains) and later extended to capture time- variant features. The proposed methods are evaluated for estimation consistencies via synthetic data-based simulations. The evaluation includes the performances of the linear approximation accuracy for a linear time-invariant controller, a nonlinear controller, and a linear time-variant controller. Results indicate that our framework can provide reasonably consistent results as theoretical ones in terms of disturbance amplification. Further it can perform better than a linear theoretical analysis of disturbance amplification, particularly when nonlinearity in CF behavior is present. The methods are applied to existing field data collected from vehicles with adaptive cruise control (ACC) on the market. Findings reveal that all tested vehicles tend to amplify disturbances, particularly in low frequency (< 0.5 Hz). Further, the results demonstrate that these ACC vehicles exhibit time-varying features in terms of disturbance amplification ratio depending on the leading vehicle trajectories. [ABSTRACT FROM AUTHOR]