Search

Vehicle platooning improves energy savings via vehicle-to-vehicle (V2V) communication. Ecological cooperative adaptive cruise control (Eco-CACC) is implemented in platoons for merging task by using regrouped platoon models. The merging positions are selected in the middle and tail of an original platoon with a two-vehicle sub-platoon. The distributed nonlinear model predictive controller based on signal temporal logic (DNMPC-STL) approach is developed to model the Eco-CACC merging strategy. The performance of the Eco-CACC merging strategy is modeled by objective control for a predecessor-leader following (PLF) topology. The results demonstrate that merging positions located in the tail exhibit superior performance and can be used to improve stability, tracking performance, energy consumption efficiency and SOC of battery. [ABSTRACT FROM AUTHOR]

In the context of an increasing interest towards the safe introduction of technologies that can help human drivers while performing their driving tasks in the ordinary traffic, the proposed work provides a contribution, investigating safety-related aspects of assisted vehicles in a simulated environment, by considering other driving behavioral parameters. In particular, the influence of Adaptive Cruise Control (ACC), combined with driver distraction and route familiarity, on the driving behavior was investigated using a driving simulator. A sample of 37 drivers, aged between 21 and 34 years performed the driving tests in the simulator environment, with different scenarios applied to one road section. The interactions between a lead vehicle and a follower were investigated, collecting the kinematic parameters of the vehicle (speed, acceleration) and its position in the road (lateral position and distance from the lead vehicle) either with or without active ACC conditions. Driving simulation scenarios differed among each other not only for the ACC turned on or off, but also for the secondary tasks presented to the drivers in specific time frames of the test. Moreover, tests were repeated to induce a route familiarity effect and to study its influence on driving behavior, thus on safety. Results highlighted that the active ACC was correlated with more cautious behaviors, in terms of speed, deceleration/acceleration and distance from the lead vehicle. Women drivers were associated to a greater variability in the driving behavior. The visual distraction induced by secondary tasks appeared to have negative effects on safety-related driving performances, especially when the secondary task was highly demanding. On the other hand, when drivers became familiar with the route, they performed more aggressive driving patterns. [ABSTRACT FROM AUTHOR]

This article studies cooperative adaptive cruise control (CACC) for vehicle platoons with consideration of the unknown nonlinear vehicle dynamics that are normally ignored in the literature. A unified data‐driven CACC design is proposed for platoons of pure automated vehicles (AVs) or of mixed AVs and human‐driven vehicles (HVs). The CACC leverages online‐collected sufficient data samples of vehicle accelerations, spacing, and relative velocities. The data‐driven control design is formulated as a semidefinite program that can be solved efficiently using off‐the‐shelf solvers. Efficacy of the proposed CACC are demonstrated on a platoon of pure AVs and mixed platoons with different penetration rates of HVs using a representative aggressive driving profile. Advantage of the proposed design is also shown through a comparison with the classic adaptive cruise control (ACC) method. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work investigates the fuel efficiency potential of Adaptive Cruise Control (ACC) systems, focusing on two optimization-based control approaches for internal combustion engine (ICE) vehicles. In particular, this study compares two model predictive control (MPC) designs. In the first approach, a strictly quadratic cost is adopted, and fuel consumption is indirectly minimized by adjusting the weights assigned to state tracking and control effort. In the second approach, a fuel consumption map is explicitly included in the MPC cost function, aiming to directly minimize it. Both approaches are compared to a globally optimal benchmark obtained with dynamic programming. Although these methods have been discussed in the literature, no systematic comparison of their relative performance has been conducted, which is the primary contribution of this article. The results demonstrate that, with proper tuning, the simpler quadratic approach can achieve comparable fuel savings to the approach with explicit fuel consumption minimization, with a maximum variation of 0.5%. These results imply that the first alternative is more suitable for online implementation, due to the more favorable characteristics of the associated optimization problem. [ABSTRACT FROM AUTHOR]

Safety and comfort are among the most crucial factors considered in the automotive industry. Anti-slip control and adaptive cruise control are therefore respectively of interest of the study of automotive technology. These two topics have, however, been studied independently; at the same time, in many promising applications, e.g., autonomous driving, there is a vital need for a system capable of controlling vehicle speed on slippery roads. The objective of this paper is therefore to propose a unified anti-slip cruise control strategy for electric vehicles. The proposed scheme is validated through typical standard driving cycles by software simulation and experiments with signal hardware-in-the-loop system based on the model of a commercial car. The results verify the advantages of the novel control strategy such as the ability to automatically operate the vehicle on a snowy condition road for 1.3 km with low slip ratio range from - 0.04 (deceleration) to 0.06 (acceleration). [ABSTRACT FROM AUTHOR]

To analyze the impact of electrical system thermal management on cruise performance and the selection of performance parameters under different operating conditions for the tilt-wing eVTOL, a tilt-wing eVTOL cruise simulation model is established by using the aircraft cruising speed as the control variable, the cruising time and electrical energy consumption as the objective functions, based on the 2D dynamics, drag, energy consumption and electrical system thermal management calculation model of the aircraft. Set different weights according to actual flight conditions,and optimize the solution based on the specific characteristics of the model using an improved particle swarm optimization algorithm. Taking the typical tilt-wing eVTOL aircraft Vahana as an example, performance parameters are obtained and compared. The simulation results show that the model has good optimization effects on reducing aircraft cruising time and reducing electrical energy consumption under various weight combinations. Compared to the most energy-efficient flight conditions, the fastest arrival can save about 50% of time but also consume 120% more electricity. Compared to the compromise of considering both time and energy consumption, the fastest arrival saves 20% of time while consuming 28% more electricity. [ABSTRACT FROM AUTHOR]

The development of autonomous driving vehicles has increased the global demand for robust and efficient automotive radar systems. This study proposes an automotive radar-based ego-vehicle speed detection network (AVSD Net) model using convolutional neural networks for estimating the speed of the ego vehicle. The preprocessing and postprocessing methods used for vehicle speed correction are presented in detail. The AVSD Net model exhibits characteristics that are independent of the angular performance of the radar system and its mounting angle on the vehicle, thereby reducing the loss of the maximum detection range without requiring a downward or wide beam for the elevation angle. The ego-vehicle speed is effectively estimated when the range–velocity spectrum data are input into the model. Moreover, preprocessing and postprocessing facilitate an accurate correction of the ego-vehicle speed while reducing the complexity of the model, enabling its application to embedded systems. The proposed ego-vehicle speed correction method can improve safety in various applications, such as autonomous emergency braking systems, forward collision avoidance assist, adaptive cruise control, rear cross-traffic alert, and blind spot detection systems. [ABSTRACT FROM AUTHOR]

This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of this document describes the sensory detectors used in ADASs, including radars, cameras, LiDAR, and ultrasonics. The subsequent chapter presents the most popular driver assistance systems, including adaptive cruise control (ACC), blind spot detection (BSD), lane keeping systems (LDW/LKS), intelligent headlamp control (IHC), and emergency brake assist (EBA). A key element of this work is the evaluation of the effectiveness of these systems in terms of safety and driving comfort, employing a survey conducted among drivers. Data analysis illustrates how these systems are perceived and identified areas requiring improvements. Overall, the paper shows drivers' positive reception of ADASs, with most respondents confirming that these technologies increase their sense of safety and driving comfort. These systems prove to be particularly helpful in avoiding accidents and hazardous situations. However, there is a need for their further development, especially in terms of increasing their precision, reducing false alarms, and improving the user interface. ADASs significantly contribute to enhancing safety and driving comfort. Yet, they are still in development and require continuous optimization and driver education to fully harness their potential. Technological advancements are expected to make these systems even more effective and user-friendly. [ABSTRACT FROM AUTHOR]

As autonomous vehicles (AVs) and human-driven vehicles (HVs) are expected to share the road for the foreseeable future, understanding how to improve the stability of mixed-autonomy platoons is crucial. This paper introduces a novel adaptive control strategy tailored for a specific platoon configuration termed as " 1 + n + 1 ", consisting of a leading AV, n intervening vehicles, and a trailing AV. Utilizing vehicle-to-vehicle communication, the trailing AV adapts to real-time traffic states, thereby promoting overall platoon stability. Our analysis, grounded in Lyapunov theory, demonstrates that stabilizing only the trailing vehicle is sufficient to ensure the entire system reaches a stable state. To mitigate the negative effects of sensor and actuator uncertainties, we also introduce a corrective signal framework capable of nullifying adverse inputs. Numerical experiments validate the effectiveness of the proposed strategy in platoon control, which can also be adapted to other platoon configurations. Additionally, the performance of this strategy on macroscopic traffic flow is explored, suggesting substantial potential throughput increases compared with the benchmark strategy of Cooperative Adaptive Cruise Control (CACC). [ABSTRACT FROM AUTHOR]

This paper studies a safe model predictive control (MPC)-based disturbance rejection control for a broad range of uncertain nonlinear systems subject to complex state safety constraints. The system under study is composed of a nominal model and an uncertain term that encapsulates modeling uncertainty, control mismatch, and external disturbances. In order to estimate the system state and total uncertainty, an extended state observer (ESO) is first designed. Utilizing the output of the ESO, the control compensates for the total uncertainty in real time and concurrently implements a control barrier function (CBF)-based MPC for the compensated system. The proposed control framework guarantees both safety and disturbance rejection. Compared to the baseline algorithm CBF-MPC, the proposed method significantly enhances system stability with a smaller root mean square (RMS) error of the system state from the equilibrium point. Rigorous theoretical analysis and simulation experiments are provided to validate the effectiveness of the proposed scheme. • A safe control scheme for uncertain nonlinear systems subject to complex state safety constraints. • Extended state observer (ESO) for estimating the system state and disturbance. • Control barrier function (CBF)-based model predictive control (MPC) for guaranteeing the safety constraints. • Sufficient conditions are established for maintaining stability, feasibility and safety of the closed-loop system. • The scheme manifested high performance in the application of adaptive cruise control. [ABSTRACT FROM AUTHOR]

Objective: Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or "takeover" control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance. Method: A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]). Results: Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration). Conclusions: These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving. [ABSTRACT FROM AUTHOR]

The problem of maximizing the range of a propeller-driven aircraft in a level flight cruise is analyzed within the framework of optimal control. The specific fuel consumption and propeller efficiency of its propulsive system are characterized by functions of the velocity and engine power (full model), in contrast to previous works, where they were considered to be constant. To conduct the study, a notional Piper Cherokee PA-28 is selected as representative of light aircraft, defining both the airplane and mission features. Two simplified models are also derived: the Von Mises model, with constant specific fuel consumption and propeller efficiency, and the Parget and Ardema model, defined by constant specific fuel consumption and propeller efficiency depending on the velocity. The problem is solved numerically by means of a direct transcription method. Since the optimal problems of the Von Mises and Parget and Ardema models are singular, it is necessary to incorporate a regularization term. Such a numerical algorithm is validated against the analytical solution given by the Breguet formulation. In this context, the velocity and mass (state variables), the power throttle (control), and the best range are determined. The full model provides a maximum range of 1492 km. The differences between the Von Mises and Parget and Ardema models are about 24 km and 1 km, respectively. A non-optimal steady cruise is also analyzed, providing a significant reduction in the flight time, with a decrease of about 2% of the range. The evolution of the state variables and control in the steady cruise, however, separates from the full model. On the other hand, the Parget and Ardema model almost reproduces the full model results, leading to a clear image of the physics involved: the best range comes from maximizing the product of the propeller and aerodynamic efficiencies with respect to the velocity, which determines the optimal arc. [ABSTRACT FROM AUTHOR]

Autonomous vehicles frequently encounter performance degradation during high-speed cornering due to excessive speed and lateral acceleration, potentially leading to collisions or rollovers. This paper proposes a novel curve-passing control approach that first estimates the curve radius and then controls the steer and speed for smooth and comfortable handling. In particular, the curve radius is innovatively estimated using a combination of a camera-based lane detection model and a steering wheel dynamic model. The curve-passing control approach is validated on high-speed ramps and curves, demonstrating its robustness and intelligence to adapt to dynamic changes in curve curvature. The model effectively prevents vehicles from entering curves at dangerously high speeds from straight roads and mitigates sudden accelerations or decelerations when entering curves. Experimental results indicate that the vehicle speed is reduced to around 50 km/h and the corresponding acceleration is −0.6 m/s2 upon entering curves with a minimum radius of 150 m. This demonstrates that the proposed control model can ensure a comfortable and safe driving experience by autonomously decelerating the vehicle before entering various types of curves. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories, ensuring safe and efficient navigation. Simulated onboard sensors, including vehicle cameras and millimeter-wave radar, are used to detect and respond to dynamic changes in the surrounding environment, enabling real-time decision-making for LCM and ACC. The simulation results highlight the superiority of the MPC-based approach in maintaining safe distances, executing controlled lane changes, and optimizing fuel efficiency. Specifically, the MPC controller effectively manages collision avoidance, reduces travel time, and contributes to smoother traffic flow compared to traditional path planning methods. These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios. Future research will focus on validating these results through real-world testing, addressing computational challenges for real-time implementation, and exploring the adaptability of MPC under various environmental conditions. This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation, paving the way for broader adoption of MPC in AV systems. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle (UAV) in the presence of parameter uncertainties, unmodeled uncertainties and wind disturbances. First, a fixed-time disturbance observer (FXDO) based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model. Then, a fixed-time integral sliding mode control (FXISMC) is combined with the FXDO to achieve strong robustness and chattering reduction. Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability. Finally, numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances. In addition, the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results. [ABSTRACT FROM AUTHOR]

Reinforcement learning is commonly associated with training of reward‐maximizing (or cost‐minimizing) agents, in other words, controllers. It can be applied in model‐free or model‐based fashion, using a priori or online collected system data to train involved parametric architectures. In general, online reinforcement learning does not guarantee closed loop stability unless special measures are taken, for instance, through learning constraints or tailored training rules. Particularly promising are hybrids of reinforcement learning with classical control approaches. In this work, we suggest a method to guarantee practical stability of the system‐controller closed loop in a purely online learning setting, in other words, without offline training. Moreover, we assume only partial knowledge of the system model. To achieve the claimed results, we employ techniques of classical adaptive control. The implementation of the overall control scheme is provided explicitly in a digital, sampled setting. That is, the controller receives the state of the system and computes the control action at discrete, specifically, equidistant moments in time. The method is tested in adaptive traction control and cruise control where it proved to significantly reduce the cost. [ABSTRACT FROM AUTHOR]

To facilitate cooperation among connected automated vehicles (CAVs), such as cooperative platooning or cooperative lane changes in mixed traffic comprising CAVs, connected human-driven vehicles (CHVs), and conventional (unconnected) human-driven vehicles (HDVs), an ego CAV needs to discern which surrounding vehicles are connected (CAVs or CHVs) and which are unconnected (HDVs). Therefore, this paper introduces a surrounding vehicles identification system (SVIS) designed to enable CAVs to identify the connectivity status of surrounding vehicles. Unlike a previously developed preceding vehicle identification system that only identifies the connectivity of its immediately preceding vehicle, the proposed SVIS generalizes this identification to nearby surrounding vehicles. Furthermore, the proposed SVIS significantly reduces the error in the connected vehicle identification by integrating distance and the magnitude of velocity for matching instead of distance-only matching used by the previous system. The SVIS is a must to enable CAVs to form platoons via lane changes after a connected vehicle is detected in an adjacent lane. We compared a newly proposed distance-plus-speed-matching approach with a previously developed distance-only-matching approach using Next Generation Simulation (NGSIM) data. Through comparison, the efficacy of the proposed distance-plus-speed-matching SVIS is demonstrated to be considerably superior to that of the distance-only-matching SVIS. Given that a reliable and robust method for identifying surrounding connected vehicles is key to the successful formation of cooperative adaptive cruise control (CACC) platoons and cooperative lane changes, the proposed distance-plus-speed-matching SVIS would help quickly and efficiently form platoons in a mixed traffic environment. [ABSTRACT FROM AUTHOR]

Introduction: Traditional adaptive cruise control systems ignore the impact of the driver's intentions and driving behavior on system performance. Methods: In response to this issue, this study designs a new adaptive cruise control system by combining personalized driving style recognition, dynamic distance control, prospective energy management, and a model predictive control framework that integrates long short-term memory neural networks and ensemble learning. Results: It was verified that the accuracy of the algorithm was 96.2%. In addition, experts had average ratings of 95, 96, and 98 for the economy, safety, and comfort of the system, respectively. Discussion: This model is expected to achieve comprehensive performance optimization and improvement of EREV in complex driving environments, injecting new vitality and power into the intelligent development of electric vehicles. [ABSTRACT FROM AUTHOR]

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon. [ABSTRACT FROM AUTHOR]

In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance traffic capacity, they suffer from notable limitations regarding string stability and diminished safety margins at high velocities. In our previous work, we proposed applying CDG in specific scenarios, such as starting platoons at signalized intersections, where traffic throughput is critical and safety requirements can be met due to relatively low speeds. We demonstrated the substantial potential of CDG to increase the capacity of signalized intersections under oversaturated conditions. However, our study also revealed potential performance drops of CDG in dense traffic networks. To address these issues, we propose close-range coordination between vehicles to (1) limit platoon length, (2) create gaps for merging, and (3) avoid entering intersections when there is a high likelihood of stopping within the intersection area. In this paper, we extend our previous work by implementing these three measures. We successfully evaluate their positive impact on CDG's performance in entire traffic systems through large-scale traffic simulations involving several thousand vehicles, thereby affirming our earlier hypothesis [ABSTRACT FROM AUTHOR]

To alleviate the performance degeneration of the cooperative adaptive cruising control (CACC) of connected and automated vehicles (CAVs) with low market penetration rates (MPRs), it is necessary to resolve the performance degeneration of CAVs operating at weak mixed-platoon intensities for any-MPR CAVs. A car following model (PMCC) for CAVs is proposed, accounting for human reaction time, communication delay, traffic information memory, and vehicle-to-vehicle (V2V) communication. Linear stability analysis was conducted to validate the stability of mixed platoons at weak platoon intensities for different CAV following models. Numerical simulations were conducted to simulate weak-intensity mixed platoons for different CAV following models operating at high and low equilibrium velocities. The results suggest that PMCC exhibits wider stability equilibrium intervals under different theoretical time headway scenariosand performs well at dissipating the speed fluctuation propagation and minimizing the speed fluctuation amplitude. [ABSTRACT FROM AUTHOR]

The evolution of autonomous vehicles hinges significantly upon the advancements in driving assistance systems. Adaptive cruise control, a pivotal component of these systems, warrants continuous real-world examination to assess its operational efficiency. The study investigates these systems integrated into diverse commercial vehicles with a specific focus on the following distances they provide. The findings reveal that camera-based systems offer shorter following distances relative to ISO standards, while radar-based and combined camera and radar-based systems provide larger following distances. The study contributes to understand adaptive cruise control technology and its alignment with safety standards, thereby aiding in the on-going development of self-driving vehicles. [ABSTRACT FROM AUTHOR]

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero. [ABSTRACT FROM AUTHOR]

Summary: We construct a nonlinear predictor‐feedback cooperative adaptive cruise control (CACC) design for homogeneous vehicular platoons subject to actuators delays, which achieves: (i) positivity of vehicles' speed and spacing states, (ii) ℒ∞$$ {\mathcal{L}}_{\infty } $$ string stability of the platoon, (iii) stability of each individual vehicular system, and (iv) regulation to the desired reference speed (dictated by the leading vehicle) and spacing. The design relies on a nominal, nonlinear adaptive cruise control (ACC) law that we construct, which guarantees (i)–(iv) in the absence of actuator delay, and nonlinear predictor feedback. We consider a classical (for ACC/CACC design) third‐order, nonlinear model subject to input delay, for the vehicles' dynamics. The proofs of the theoretical guarantees (i)–(iv) rely on derivation of explicit estimates on solutions (both during open‐loop and closed‐loop operation), capitalizing on the ability of predictor feedback to guarantee complete delay compensation after the dead‐time interval has elapsed, and derivation of explicit conditions on initial conditions and parameters of the nominal control law. We also present consistent simulation results, considering a platoon of ten vehicles, which validate the design developed. [ABSTRACT FROM AUTHOR]

To investigate the chaotic characteristics of mixed traffic flow and discern the factors influencing the degree of chaos in mixed traffic platoons, the Cao method and the improved Cao method were employed based on the traditional traffic flow theory to determine the delay time and embedding dimension of the mixed traffic flow. The phase space of mixed traffic flow sequences was reconstructed to determine the chaotic characteristics by calculating the maximum Lyapunov exponent. The influential parameters of the proportion of intelligent connected vehicle (ICV) using cooperative adaptive cruise control (CACC) and the delay time in mixed traffic flow were analyzed. The results show that when the maximum Lyapunov exponent of the headway sequence during the car-following process is less than zero, chaos exists in the mixed traffic flow. Increasing the proportion of CACC vehicles can mitigate chaos in certain time intervals. The car-following system tends to be stable when the proportion of CACC vehicles reaches 0.6. The delay time of CACC vehicles significantly affects chaos, and maintaining low communication delays is essential for CACC vehicles to effectively suppress chaos. [ABSTRACT FROM AUTHOR]

This study focuses on how to improve the merge control prior to lane reduction points due to either accidents or constructions. A Cooperative longitudinal Control for Merging maneuvers (CCM) strategy based on Automated Vehicles (AV) is proposed considering cooperation among vehicles, courtesy, and the coexistence of AV and Human-Driven Vehicles (HDV). CCM introduces a modified/generalized Cooperative Adaptive Cruise Control (CACC) for vehicle longitudinal control prior to lane reduction points. It also takes courtesy into account to ensure that AV behave responsibly and ethically. CCM is evaluated using microscopic traffic simulation and compared with no control and CACC merge strategies. The results show that CCM consistently generates the lowest delays and highest throughputs approaching the theoretical capacity. Its safety benefits are also found to be significant based on vehicle trajectories and density maps. CCM mainly requires vehicles to have automated longitudinal (such as Adaptive Cruise Control (ACC)) and lane-changing control, which are already commercially available on some vehicles. Also, it does not need 100% AV penetration, presenting itself as a promising solution for improving traffic operations in lane reduction transition areas such as highway work zones. [ABSTRACT FROM AUTHOR]

• Examined perceptions towards traditional and technology-based speeding interventions. • Low-level speeding was a common behaviour. • Police enforcement perceived as most effective for complying with speed limits. • ADAS perceived as effective for complying with speed limits in 100 km/hr urban area. • Recommendation to educate drivers about how ADAS can help with speed compliance. Introduction: Speeding behaviour contributes significantly to road crashes and subsequent injuries and fatalities. The purpose of this study was to examine which traditional countermeasures (i.e., police enforcement and on-road signs) and technology-based countermeasures (i.e., advanced driver assistance systems [ADAS] and in-vehicle speed audio alerts) drivers perceived as effective in assisting them to comply with posted speed limits. Methods: Participants (N = 680; M age = 49.34 years) who held a current driver's licence completed a 20-minute online survey. Participants in the experimental condition were randomly assigned to read one of four scenarios which differed based on location (urban or regional) and posted speed limit (60 km/hr or 100 km/hr) or to the control condition (no scenario), before answering questions about the perceived effectiveness of the traditional and technology-based interventions. For the experimental conditions, participants were instructed to respond to these questions based on how they would drive in the situation outlined in the scenario. Results: Low-level speeding behaviour was common, with 40.7 % reporting regularly driving 5 km over the posted speed limit in a 60 km/hr speed zone and 50.4 % reporting regularly driving 5 km over the posted speed limit in a 100 km/hr speed zone. A mixed ANOVA revealed that participants perceived police enforcement activities to be the most effective at assisting them to comply with posted speed limits when compared to other traditional and technology-based approaches. Further, ADAS was rated by participants as significantly more effective at assisting them to comply with the posted speed limit in the 100 km/hr urban condition compared to the 60 km/hr urban condition. Conclusions: Low-level speeding behaviour remains common practice and there is a need for continued roadside police presence to discourage this behaviour. Drivers with ADAS-equipped vehicles could also be encouraged to use systems, such as adaptative cruise control, to assist with speed management on high-speed roads. [ABSTRACT FROM AUTHOR]

The impacts of merging on mixed traffic flow in the highway on-ramp zone are numerically investigated in this paper. An on-ramp mixed traffic flow merging model is proposed. Our model focuses on the on-ramp merging zone and considers a mixed traffic flow comprising of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Firstly, we establish the Velocity-Modified Comfortable Driving (V-MCD) model for HDVs, which incorporates a velocity estimation module into the base Modified Comfortable Driving (MCD) model. The Cooperative Adaptive Cruise Control (CACC) model developed by PATH Labs is used for CAVs. A cooperative merging model has been developed to capture the interaction behavior of vehicles in various driving scenarios. Merging rules are established based on vehicle type and merging order. The merging rules for HDV-HDV are based on safety gap. Meanwhile, those for CAV-HDV are based on velocity difference, and for CAV-CAV are based on first-come-first-served. We conduct numerical simulation experiments to derive the fundamental diagram of the mixed flow and analyze the interaction between vehicles on the on-ramp and mainline. The simulation results indicate that the V-MCD model can effectively reflect on the hysteresis effect caused by on-ramp merging vehicles in real traffic scenarios. The increase in CAV penetration improves road traffic efficiency in terms of both traffic velocity and capacity. However, as the penetration of CAVs continues to rise, their positive impact will diminish. The sensitivity analysis of the hysteresis effect at both micro and macro levels can enhance our understanding of the impacts on traffic flow. As the hysteresis effect increases, merging has a greater impact on mainline upstream traffic, leading to reduce headway and more erratic velocity changes. [ABSTRACT FROM AUTHOR]

This study proposes a guidance strategy based on human–machine transition (HMT) for cooperative adaptive cruise control (CACC) truck platoon actuator failures. Existing research on the CACC platoon mainly focuses on upper-level planning and rarely considers platoon planning failures caused by actuator failures. This study proposes that the truck in the platoon creates sufficient space on the target lane through HMT when the actuator fails, thereby promoting lane changes for the entire team. The effectiveness of the proposed strategy is evaluated using the Simulation of Urban Mobility (SUMO) simulation. The results demonstrate that under conditions ensuring the normal operation of traffic flow, this guidance strategy enhances the platoon's lane-changing capability. In addition, this strategy exhibits stronger robustness and efficiency in different traffic densities. This guidance strategy provides valuable insights into improving the driving efficiency of CACC truck platoons in complex road environments. [ABSTRACT FROM AUTHOR]

In order to reduce pollution caused by ship emissions, the International Maritime Organization (IMO) implemented sulfur emission control areas (SECAs). In comparison to ordinary vessels, cruise ships with dual attributes of transportation and tourism generate a greater amount of marine pollution, which poses a significant threat to the marine environment in both berthing ports and the sailing area. In light of the fierce competition of the cruise tourism market, cruise lines are looking for strategies, such as designing more attractive cruise routes, to maintain their core competencies under the emission control policy. In order to achieve this goal, this paper presents a mixed-integer non-linear programming (MINP) model with two objectives and is derived from the traditional route optimization problem. The primary objective is to optimize the route and speed of a cruise liner, while simultaneously enhancing route competitiveness and minimizing carbon emissions both within and outside the SECAs. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was used to reach the objective, and simulations were carried out to verify the effectiveness of the model and method. The results show that speed and sailing route optimization can affect carbon emissions. This paper has a certain application value and guiding significance for cruise line decision makers that will be beneficial for the environment. [ABSTRACT FROM AUTHOR]

• Eco-driving practice is a path to safer commercial motor vehicle operations. • The study uses safety-related events from 2,637 long-haul class 8 drivers in Canada. • Real-time feedback coach is efficient in eco-driving techniques by using an appropriate amount of engine. • Fuel-efficient techniques reduce 7% of hard-braking events and 4% of collisions for each unit increase in the ISAAC score. • Driver age and cruise control usage affect safety outcomes. Introduction: This study investigates the effect among commercial motor vehicle (CMV) drivers of the adoption of fuel-efficient driving techniques (commonly known as eco-driving) on the odds of being involved in safety-related events. Method: For 2,637 long-haul class 8 drivers employed by four carriers in Canada, information on driving style, total distance driven, and safety-related events like collisions, hard-braking, hard-turning, and stability control events were collected for each trip. Three carriers provided driving style-related data from the ISAAC instrument, which provides a score on a 0 to 100 scale that measures the degree to which a driver is using an appropriate amount of engine power according to driving conditions. The fourth carrier provided data on driving style characteristics, including fuel consumption, use of cruise control, and use of top gear. Depending on the carrier, information on speeding, driver age, and years of experience driving a commercial vehicle was also collected. Logit statistical models were developed to estimate the change in odds of a driver experiencing a safety-related event dependent on the measures of driving style. Results: A one-unit increase in the ISAAC score was associated with a 7%, 8%, 8%, and 4% reduction in the odds of having a hard-braking event, hard left-turn event, hard right-turn event, and collision, respectively. For the carrier not employing the ISAAC system, an increase of 10% in the time spent driving in top gear with steady speed near 100 km per hour (km/h) was associated with a substantial 34% decrease in stability control events. In addition, a year increase in the driver's age, as well as a 1% increase in the amount of time spent driving using cruise control, reduced the number of hard-braking events by 9% and 3%, respectively. Conclusion/Practical Applications: The adoption of fuel-efficient driving techniques enhances the safety of CMV drivers. [ABSTRACT FROM AUTHOR]

Summary: This article studies robust adaptive control for a class of autonomous vehicle platoons. In particular, two innovative adaptive control laws are proposed to address both position and velocity tracking for a vehicle platoon. In addition, it is shown that robust asymptotic string stability can be delivered by the underlying adaptive control laws for the vehicle platoon, in the sense that these adaptive control laws are capable of mitigating parameter uncertainties involved in the nonlinear vehicular dynamics and achieving truly cooperative adaptive cruise control while ensuring the required safety spacing between each neighboring vehicle pair asymptotically. It is also shown that the control performance of the vehicle platoon can be further improved if the operating equilibrium points of all vehicles can be adaptively estimated, leading to two linear time‐invariant control laws for individual vehicles under both position and velocity controls and for the vehicle platoon. Simulation studies illustrate the effectiveness of the proposed control method, validating the results obtained for the class of vehicle platoons. [ABSTRACT FROM AUTHOR]

Among automated driving technologies, adaptive cruise control (ACC), which controls the distance between vehicles and their relative speed, is becoming popular. Cooperative ACC (CACC) uses vehicle-to-vehicle communication and sensors to control the distance between vehicles. Recently, cyber attacks against automated driving systems have been a focus, in which information on pedestrians or preceding vehicles obtained from LiDAR and sensors is disguised. Such cyber attacks enable attackers to obtain accurate information on preceding vehicles and pedestrians. This can result in accidents. The purpose of this study is to develop a filtering function to reduce the impact of cyber attacks using available information. The contribution lies in proposing a filtering function for situations in which sensor offsets and communication delays arise owing to cyber attacks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High‐level autonomous vehicles (AVs) have more possibilities for improving traffic efficiency. The improvement of traffic efficiency for mixed flow at near‐saturated short‐distance tandem signalized intersections (STSI) needs attention. Most of the existing studies design a generalized control rule for AVs, ignoring the heterogeneity among different AVs. Herein, a multivehicle trajectory planning framework based on a multiagent reinforcement learning (MRL) algorithm is designed to heuristically explore the optimal traffic efficiency of mixed flow at STSI. The core algorithm of this framework is improved from the classical MRL algorithm multi‐agent proximal policy optimization based on the idea of the virtual group instead of designing control rules. The trajectories planned by the framework show outstanding performance in improving throughputs and reducing emissions at the global system level, comparing natural driving, classic adaptive cruise control model and cooperative adaptive cruise control model. The framework can be used to explore optimal traffic efficiency for mixed flow and better heterogeneous rules for high‐level AVs. [ABSTRACT FROM AUTHOR]

Aiming at the networked cruise control scenario of CAV (connected autonomous vehicle) queue, we propose a new networked cruise control strategy for CAV by introducing the average information of ET (electronic throttle) opening of the downstream vehicle group as a feedback signal. By performing linear stability analysis on the new model, we derive its linear stability conditions. Further, we design exhaustive numerical simulation experiments aiming to systematically investigate the effect of the multi-vehicle ahead electronic throttle opening average feedback signal on CAV traffic stability, fuel consumption, and key emission factors, such as CO, HC, and NOx, during the cruise control process. The results show that the feedback signal can not only significantly improve the operational stability of the CAV traffic flow but also significantly improve its fuel consumption and the emission levels of CO, HC, and NOx. When the number of CAV vehicles in the feedback signal is set to three, the levels of CO, HC, and NOx emissions as well as fuel consumption in the road system can reach a stable and optimized state. [ABSTRACT FROM AUTHOR]

In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics of a 4WID high ground clearance self-propelled electric sprayer. By utilizing MATLAB/Simulink software, three subsystems, namely, the inverse longitudinal dynamics model, torque distribution model, and motor model, are established. The model takes into account the effects of longitudinal driving resistance, slope, and vehicle roll angle on the distribution of loads among the four wheels during slope driving. A seven-degrees-of-freedom dynamic model is developed. A hierarchical control structure is designed, incorporating an upper-level PID controller and a lower-level fuzzy PID controller, to control the overall system. The control algorithms are tailored to the specific characteristics of the sprayer's operation, and simulation experiments are conducted under the corresponding operating conditions. Building upon this, a sensor-equipped experimental platform is set up in the self-propelled sprayer manufactured by the team in the preliminary stage. Real vehicle tests are conducted in two scenarios: transition transportation and field operations, with the evaluation of the overall vehicle speed serving as the performance metric to validate the correctness of the model and the control theory. [ABSTRACT FROM AUTHOR]