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An automatic vehicle classification (AVC) system designed from either still images or videos has the potential to bring significant benefits to the development of a traffic control system. On AVC, numerous articles have been published in the literature. Over the years, researchers in this domain have created and used a variety of datasets, but most often, these datasets may not reflect the exact scenarios of the Indian subcontinent due to specific peculiarities of the road conditions, road congestion nature, and vehicle types usually seen in Indian subcontinent. The primary goal of this paper is to create a new still image dataset, called JUIVCDv1, which contains 12 different local vehicle classes that are collected using mobile cameras in a different way for developing an automated vehicle management system. We have also discussed the characteristics of the current datasets, and various other factors taken into account while creating the dataset for the Indian scenario. Apart from this, we have benchmarked the results on the developed dataset using eight state-of-the-art pre-trained convolutional neural network (CNN) models, namely Xception, InceptionV3, DenseNet121, MobileNetV2, and VGG16, NasNetMobile, ResNet50 and ResNet152. Among these, the Xception, InceptionV3 and DenseNet121 models produce the best classification accuracy scores of 0.94, 0.93 and 0.92 respectively. These models are further utilized to make an ensemble model to enhance the performance of the overall categorization model. Majority voting-based ensemble, Weighted average-based ensemble, and Sum rule-based ensemble approaches are used as ensemble models that give accuracy scores of 0.95, 0.94, and 0.94, respectively. [ABSTRACT FROM AUTHOR]

Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) are being developed and designed to operate on existing roads. Their safe and efficient operation during roadworks, where traffic management measures are often introduced, is crucial. Two alternative measures are commonly applied during roadworks on motorways: (i) closing one or multiple lanes (ii) narrowing one or all lanes. The former can cause delays and increased emissions, while the latter can pose safety risks. This study uses a VISSIM‐based traffic microsimulation to compare the effectiveness of these two strategies on traffic efficiency and safety, considering various market penetration rates (MPR) of CAVs. The model was calibrated and validated with the data collected from M1 motorway in the United Kingdom. Results show that average delays per vehicle‐kilometre‐travelled decreased from 102.7 to 2.5 s (with lane closure) and 23.6 to 0.6 s (with narrow lanes) with 0% and 100% CAV MPR, respectively. Moreover, safety in narrow lanes improved by 4.8 times compared to 1.5 times improvement in lane closure with a 100% CAV MPR; indicating that narrow lanes would result in better safety performance. These findings could assist transport authorities in designing temporary traffic management measure that results in better CAV performance when navigating through roadworks. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving technology utilizing machine learning, AI, and sensors, research on autonomous driving control has become more active, and a large number of innovative studies are underway. In the near future, all autonomous vehicle fleets will be able to communicate with each other for sharing information and overall optimal traffic control will be achieved. One of the vehicle control systems that are based on the premise of such a fully automated society is the "signal-less intersection." There is an intersection traffic control method that achieves safe and rational route selection by using virtual walls (VWs), which are virtual obstacles, but there are issues in terms of total route length and reduction of computation time. To address the issues, we propose a method that (1) prunes unneeded paths and (2) arranges VWs in a stepwise manner. The effectiveness of the proposed method was evaluated by simulation, and the results showed that the total route length and execution time were reduced. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively. [ABSTRACT FROM AUTHOR]

In this work, the connected vehicle's messages are used to create an enhanced adaptive traffic signal control (ATSC) system for improved traffic flow. Few existing studies use connected and automated vehicles (CAVs) to develop traffic signal control algorithms under hybrid connected and autonomous conditions. The proposed approach focuses on a four‐phase traffic intersection with both CAVs and human‐driven vehicles (HVs). CAVs share real‐time state information, and a model called Roads Dynamic Segmentation estimates queuing procedures and vehicle fleet numbers on dynamic road sections. This information is used in the Store and Forward Model (SFM) to predict intersection queuing length. The ATSC system, based on model predictive control (MPC), aims to minimize intersection queue length while considering traffic constraints (undersaturated, saturated, and oversaturated) and avoiding free‐flow problems due to queue overflow. To reduce computational complexity, a linear‐quadratic‐regulator (LQR) is used. Real‐world vehicle trajectories and the SUMO tool are used for experimental purposes. Results show that the proposed method reduces average delay by 38.50% and 33.42% compared to fixed timing and traditional MPC in cases of oversaturated traffic flow with 100% CAV penetration. Even with a penetration rate of only 20%, average delay decreases by 13.65% and 6.50%, respectively. This study showcases not only the potential benefits of CAV in enhancing traffic, but also enables the optimal utilization of green duration in signalized intersection control systems. This helps prevent traffic congestion and ensures the efficient and smooth movement of traffic flow. [ABSTRACT FROM AUTHOR]

Self-driving car companies aim to offer hands-off journeys with Automated Vehicle (AV) technology, but public skepticism persists on safety aspects of AVs. A proposed solution involves creating dedicated AV corridors with traffic control and interruptions. Realistically, full penetration of AVs is unlikely to occur in the near future while mixed traffic of AVs with human driven vehicles is more likely. This study applies this concept to a hypothetical scenario in NYC, proposing an AV corridor from the Staten Island Expressway to the Brooklyn Battery Tunnel. The goal is to assess the network-wide impacts of a limited AV deployment from a multi-modal perspective. The NYC transportation system is modeled using MATSim, an open-source agent-based modeling platform. Although each traveler is treated as an agent making multi-modal trip making decisions, it does not model microscopic behavior of cars. Thus, the modeling approach for AVs has to be a macroscopic one, where average headways are reduced on the AV (with mixed traffic) corridor portion of the overall network based on a link capacity relationship adopted from a study on Advanced Cruise-Assist Highway Systems [1]. Two reduced headway scenarios are simulated in this MATSim NYC model, 1.1 and.85 second headways. Results indicate that Scenario B (1.1-second headway) is preferable over Scenario A (0.85 second headway), mitigating potential congestion on connectors, especially on Victory Boulevard in Staten Island. This study suggests implementing Scenario B for a smoother corridor-wide introduction of AVs to avoid congestion on connectors of the AV corridor to the rest of the NYC network. [ABSTRACT FROM AUTHOR]

Three-colour four-way traffic light completed 100 years in 2020. Even though the traffic light in the form of Semaphore arms has been in use in London since 1868, electric traffic lights came into existence in 1912 and the standard three-colour four-way light in 1920. Research is continuously being carried out to develop better algorithms to improve safety, reduce travel delays, and optimize road capacity. Hence a review of the evolution of traffic lights is warranted. This paper presents an all-round review using a six-prong approach. Timeline of the evolution of the literature in the last 100 years, the evolution of hardware, algorithms, traffic control schemes, standards and the pedestrian lights and count down timer are the six areas in which the review is carried out. A timeline of the different keywords related to the various algorithms in use is presented. This article delves into the thinking and meticulous approach of early researchers and practitioners of the field while dwelling on the past. They laid the rock-solid foundation of today's research. Also, future research areas like connected vehicles and automated vehicles are pointed out, and a summary of the findings is presented at the end. [ABSTRACT FROM AUTHOR]

With recent technological advancements, the commercialization of autonomous vehicles (AVs) is expected to be realized soon. However, it is anticipated that a mixed traffic of AVs and human-driven vehicles (HVs) will persist for a considerable period until the Market Penetration Rate reaches 100%. During this phase, AVs and HVs will interact and coexist on the roads. Such an environment can cause unpredictable and dynamic traffic conditions due to HVs, which results in traffic problems including traffic congestion. Therefore, the routes of AVs must be controlled in a mixed traffic environment. This study proposes a multi-objective vehicle routing control method using a deep Q-network to control the driving direction at intersections in a mixed traffic environment. The objective is to distribute the traffic flow and control the routes safely and efficiently to their destination. Simulation results showed that the proposed method outperformed existing methods in terms of the driving distance, time, and waiting time of AVs, particularly in more dynamic traffic environments. Consequently, the traffic became smooth as it moved along optimal routes. [ABSTRACT FROM AUTHOR]

This paper offers a novel view for managing and controlling the movement of driverless, i.e., autonomous, vehicles by converting this movement to a simulated train movement moving on a rail track. It expands on the "virtual track" theory and creates a model for virtual track autonomous vehicle management and control based on the ideas and methods of railway train operation. The developed model and adopted algorithm allow for large-scale autonomous driving vehicle control on the highway while considering the temporal-spatial distribution of vehicles, temporal-spatial trajectory diagram optimization, and the management and control model and algorithm for autonomous vehicles, as design goals. The ultimate objective is to increase the safety of the road traffic environment when autonomous vehicles are operating in it together with human-driven vehicles and achieve more integrated and precise organization and scheduling of these vehicles in such mixed traffic conditions. The developed model adopted a "particle swarm" optimization algorithm that is tested in a hypothetical network pending a full-scale test on a real highway. The paper concludes that the proposed management and control model and algorithm based on the "virtual track" theory is promising and demonstrates feasibility and effectiveness for further development and future application. [ABSTRACT FROM AUTHOR]

Automated Vehicles (AVs) are designed and programmed to follow traffic rules. However, there is no separate and comprehensive regulatory framework dedicated to AVs. The current Queensland traffic rules were designed for humans. These rules often contain open texture expressions, exceptions, and potential conflicts (conflict arises when exceptions cannot be handled in rules), which makes it hard for AVs to follow. This paper presents an automatic compliance checking framework to assess AVs behaviour against current traffic rules by addressing these issues. Specifically, it proposes a framework to determine which traffic rules and open texture expressions need some additional interpretation. Essentially this enables AVs to have a suitable and executable formalization of the traffic rules. Defeasible Deontic Logic (DDL) is used to formalize traffic rules and reasoning with AV information (behaviour and environment). The representation of rules in DDL helps effectively in handling and resolving exceptions, potential conflicts, and open textures in rules. 40 experiments were conducted on eight realistic traffic scenarios to evaluate the framework. The evaluation was undertaken both quantitatively and qualitatively. The evaluation result shows that the proposed framework is a promising system for checking Automated Vehicle interpretation and compliance with current traffic rules. [ABSTRACT FROM AUTHOR]

With the increasing number of vehicles equipped with conditional driving automation entering the market, it has become important to address the topic of improving driver takeover performance. This study focuses on designing the request to intervene (RtI) under conditional driving automation. We investigated takeover guidance and surrounding traffic information in the RtI influent takeover performance by conducting a driving simulator experiment. 32 participants were divided into four different groups based on the RtI design. The results show that adding surrounding traffic information to the RtI improves the safety of takeover performance. However, the combination of surrounding traffic information and takeover guidance impairs the takeover performance quality. Although all types of additional content (takeover guidance, surrounding traffic information, combination of takeover guidance, and surrounding traffic information) did not increase the participants’ workload during takeover, the participants gave a higher rating to the usability of the surrounding traffic information than the combination. This study demonstrates the importance of providing surrounding traffic information in RtI and the effectiveness of the system usability scale (SUS) in evaluating RtI designs. [ABSTRACT FROM AUTHOR]

The congestion caused by ramp overflow has an essential impact on the energy consumption of autonomous vehicles. It is urgent to solve the energy waste of ramp overflow on connected and autonomous vehicles and improve the energy utilization rate. Aiming at the traffic congestion caused by ramp overflow, this paper obtains the data of autonomous vehicles and traffic flow based on vehicle road cooperative perception, analyzes the impact mechanism of vehicle energy consumption, and summarizes the energy consumption modes of autonomous vehicles into three categories. Second, an energy consumption evaluation framework is proposed based on the CSANS strategy. This strategy can make up for the deficiency of constructing neighborhoods within European distance, find important influencing variables on connected and autonomous vehicles' energy consumption, and accurately capture the manifold characteristics of energy consumption data. Finally, flow control is carried out from the macro perspective of traffic engineering to optimize ramp overflow's impact on autonomous vehicles' energy consumption. Through multiple groups of experiments, it has been found that it can effectively reduce the energy consumption of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Accurately predicting lane-changing behaviors (lane keeping, left lane change and right lane change) in real-time is essential for ensuring traffic safety, particularly in mixed-traffic environments with both autonomous and manual vehicles. This paper proposes a fused model that predicts vehicle lane-changing behaviors based on the road traffic environment and vehicle motion parameters. The model combines the ensemble learning XGBoost algorithm with the deep learning Bi-GRU neural network. The XGBoost algorithm first checks whether the present environment is safe for the lane change and then evaluates the likelihood that the target vehicle will make a lane change. Subsequently, the Bi-GRU neural network is used to accurately forecast the lane-changing behaviors of nearby vehicles using the feasibility of lane-changing and the vehicle's motion status as input features. The highD trajectory dataset was utilized for training and testing the model. The model achieved an accuracy of 98.82%, accurately predicting lane changes with an accuracy exceeding 87% within a 2-second timeframe. By comparing with other methods and conducting experimental validation, we have demonstrated the superiority of the proposed model, thus, the research achievement is of utmost significance for the practical application of autonomous driving technology. [ABSTRACT FROM AUTHOR]

Achieving fully autonomous driving requires seamless collaboration between advanced autonomous driving and road infrastructure technologies. As the proportion of autonomous vehicles (AVs) increases, challenges may arise from their insufficient knowledge of the behavior of traffic objects and inability to effectively drive short distances. Therefore, traffic control centers that can proactively control these issues in real time are essential. In this study, first, the terminology is defined and the types of AV-mixed Traffic Information that a traffic control center needs to efficiently collect, store, and analyze to accommodate the coexistence of AVs and conventional vehicles are identified. Second, a generic notation for an AV-mixed Traffic Information model is defined and the results of modeling each AV-mixed Traffic Information type are presented. Third, an AV-mixed Traffic Information services model that included the names, operations, input/output messages, and relationships of all services is suggested. Finally, the importance of the service functionalities is evaluated through a survey. This study will serve as an initial guideline for the design, construction, and operation of traffic control centers and will help proactively address issues that may arise from the interaction between AVs and conventional vehicles on the road. Moreover, it contributes to identifying the types of traffic information and services that traffic control centers must provide in the era of AV-mixed traffic and suggests future directions for analysis and utilization of traffic information. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

Connected and Automated Vehicles (CAVs) offer the potential to improve roadway capacity and safety. Thus, improving road infrastructure condition could be prioritized to eliminate further degradation of the transportation infrastructure. In order to foster the adoption of CAVs, incentives can be used; but there is a need to identify what type of incentive would be most effective. To identify effective incentive types, this study uses electric vehicles (EV) and hybrid vehicles as a surrogate to CAVs because of the similarities in obstacles faced for wider adoption. This study then provides some recommendations by examining incentives offered in 15 different countries and by reviewing the literature on the effectiveness of incentive types. [ABSTRACT FROM AUTHOR]

Shared autonomous vehicles (SAVs) aspire to change not only vehicles but also the way people and goods move in urban areas. However, the promotion of such services, that is, whether travelers are willing to share their trips with other service users, is still a challenge. This study aims to examine the contributory factors that influence the willingness of individuals to use shared autonomous vehicles by simultaneously identifying the differences in terms of preferences with conventional competitive transport modes, namely, private cars and public transport. A stated preference experiment combined with perception ratings was designed and conducted in Athens, Greece. Based on the collected responses, a multinomial logit model was estimated. The results show that the flexibility of SAVs and, particularly, the possibility of performing door-to-door trips has a serious added value that travelers are willing to pay. Compared with public transport, additional waiting time does not increase the disutility. Furthermore, people who belong to high-education and -income groups expressed a higher willingness to use SAVs and socialize while traveling. The familiarity of each potential user with technology is a necessary precondition. Lastly, it is confirmed that environmentally conscious people are more positive about using these new services. [ABSTRACT FROM AUTHOR]

Platooning is generally known as a control method for driving a group of connected and automated vehicles in motorway context. Nevertheless, platoon control might also work on urban roads. One possible strategy to increase overall road traffic performance and to reduce congestion in urban traffic networks is to combine platooning with traffic signal control at intersections. The traffic flow can be maximized with coordinated scheduling of traffic signals together with platooning activities, resulting in decreased travel times and fuel consumption. This paper investigates several aspects of this combined control, such as the procedures for coordination and communication between platooning vehicles and traffic signals. Efficient algorithms are suggested to optimize platoon formation and dissolution at junctions and to change traffic signal phases depending on platoon arrival and departure times. The proposed solutions have been tested and verified with SUMO, a high-fidelity microscopic traffic simulator. [ABSTRACT FROM AUTHOR]

To alleviate the lane-changing conflicts between weaving vehicles and enhance the traffic efficiency in the weaving section of urban expressway under the connected autonomous vehicle (CAV) environment, a cooperative lane-changing strategy for CAVs is proposed. The strategy consists of an upper layer of decision making, which determines the lane-changing sequences of weaving vehicles based on their lane-changing advantages quantified by a set of utility functions, and a lower layer of control, which generates detailed instructions of speed adjustments and lane-changing manoeuvres for weaving vehicles. To verify the effectiveness of the proposed strategy under different traffic demand settings, a numerical simulation, including a base case and a control case, is conducted. Then, to further verify the effectiveness of the proposed strategy for the mixed traffic state and compare its performance with the existing CAV lane-changing method, benchmark and comparison tests with six different market penetration rates (MPRs) of CAVs are carried out under the congested demand setting. In addition, the delay improvement ratio, inverse time-to-collision, and ratio of large deceleration time are selected as performance indicators to investigate the effect of the proposed strategy on enhancing the operational efficiency, traffic safety, and passenger's comfort within the weaving section. According to the simulation results, the overall efficiency, safety and comfort in the weaving section under the CAV environment, are all improved, when the proposed strategy is applied to weaving vehicles. The proposed strategy is also superior to the existing CAV lane-changing method on maintaining traffic efficiency and safety. Therefore, the proposed cooperative lane-changing strategy, based on CAV technologies, shows good potential in solving the problem of lane-changing conflicts within the weaving section and facilitating the traffic management and traffic control of urban expressway. [ABSTRACT FROM AUTHOR]

Intelligent traffic control at nonsignalized intersections under a connected and automated vehicle (CAV) environment can reduce traffic congestion, vehicle emissions [e.g., carbon dioxide ðCO2Þ, carbon monoxide (CO), nitrogen oxide (NO)], but it is still not safe enough for an unpredictable emergency. For example, an accidental stop or slow down of a vehicle in the conflict area will cause secondary accidents of vehicles from other directions because they do not have enough distance to brake. Such risks have not been given sufficient attention in the literature. This paper proposed a safety guarantee mechanism of intersection control to avoid secondary accidents by adding a safe buffer area outside the conflict area of the intersection. To improve the traffic efficiency of intersections and reduce vehicle emissions under the mechanism, we proposed a heuristic vehicle trajectory planning control strategy based on vehicle kinematics. Three planning principles are applied to generate the microscopic trajectory for each vehicle: (1) the vehicle first decelerates and then accelerates before passing the intersection; (2) try not to change the running state of the vehicle; and (3) the vehicles do not collide with each other. Finally, to further improve efficiency, we proposed the concept of a dynamic safety area, which reduces the delay by self-adjusting the safety area to each vehicle’s trajectory. The paper selected three simulation scenarios of road intersections to validate our methodology. The simulation results show that the proposed control method with the safe buffer significantly reduces average vehicle delay and emissions compared to traditional signal control systems. Compared with the fixed buffer, in the dynamic safe buffer scenario, the average vehicle delay can be reduced by 66.94%, the throughput is increased by 18.75%, and CO2 emissions are reduced by 9.9%. The dynamic safe buffer can not only relax the safety concern of a nonsignalized intersection, which is not well addressed in the literature, but also has much better performance than the traditional intersection. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles (CAVs) with their evolving data gathering capabilities will play a significant role in road safety and efficiency applications supported by Intelligent Transport Systems (ITSs), such as Traffic Signal Control (TSC) for urban traffic congestion management. However, their involvement will expand the space of security vulnerabilities and create larger threat vectors. In this article, we perform the first detailed security analysis and implementation of a new cyber-physical attack category carried out by the network of CAVs against Adaptive Multi-Agent Traffic Signal Control (AMATSC), namely, coordinated Sybil attacks, where vehicles with forged or fake identities try to alter the data collected by the AMATSC algorithms to sabotage their decisions. Consequently, a novel, game-theoretic mitigation approach at the application layer is proposed to minimize the impact of such sophisticated data corruption attacks. The devised minimax game model enables the AMATSC algorithm to generate optimal decisions under a suspected attack, improving its resilience. Extensive experimentation is performed on a traffic dataset provided by the city of Montréal under real-world intersection settings to evaluate the attack impact. Our results improved time loss on attacked intersections by approximately 48.9%. Substantial benefits can be gained from the mitigation, yielding more robust adaptive control of traffic across networked intersections. [ABSTRACT FROM AUTHOR]

Copyright of Materiały Budowlane is the property of Wydawnictwo SIGMA-NOT 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.)

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios. [ABSTRACT FROM AUTHOR]

Focusing on a freeway lane running with mixed semi-autonomous vehicles (semi-AVs) and fully-autonomous vehicles (fully-AVs), this study aims to investigate the heterogeneous platooning effect of the two types of autonomous vehicles (AVs) on the travel efficiency of the mixed traffic. Such impact in the mixed semi-/fully-AV traffic, to the best of our knowledge, has not been well addressed in the literature as the existing mixed traffic studies on platooning effect typically focused on the traffic scenes with only human-driven vehicles (HVs) and fully-AVs. The existence of semi-AVs, which are optionally controlled by human drivers and semi-autonomous driving systems, and their significant difference in platooning behavior from the fully-AVs have received very limited attention. To fill the gap, we first characterize the semi- and fully-AVs' difference in connection ability and willingness to platoon in the vehicle platooning process, and derive the heterogeneous platooning probabilities of semi- and fully-AVs. To formulate the platooned traffic after vehicle platooning process, we proceed to develop a novel Markov chain model to determine the probability distributions of vehicular platoon size and headway type among the mixed semi-/fully-AV traffic analytically. Furthermore, with the probability distributions, we derive the fundamental diagram (FD) and the capacity of the freeway lane for the mixed semi-/fully-AV traffic, and are thus able to investigate impact of platooning on the travel efficiency numerically. The developed models and unveiled insights in this study will help researchers and practitioners better understand the role, potential and prospect of automated transport system in the transitional stage with semi-AVs, which will be useful for guiding the mixed AV traffic management and control in near future. • Heterogeneous platooning behavior of semi- and fully-AVs is modeled. • Closed-form solutions for platoon size and headway type are derived. • A general approach to the FD and capacity is proposed. • Managerial insights into platooning impact of semi-AVs are provided. [ABSTRACT FROM AUTHOR]

We consider a partial differential equation - ordinary differential equation system to describe the dynamics of traffic flow with autonomous vehicles. In the model, the bulk flow of human drivers is represented by a scalar conservation law, while each autonomous vehicle is described by an ordinary differential equation. The coupled PDE-ODE model is introduced, and existence of solutions for this model is shown, along with a proposed algorithm to construct approximate solutions. Next, we propose a control strategy for the speeds of the autonomous vehicles to minimize the average fuel consumption of the entire traffic flow. Existence of solutions for the optimal control problem is proved, and we numerically show that a reduction in average fuel consumption is possible with an AV acting as a moving bottleneck. [ABSTRACT FROM AUTHOR]

Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation's simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies. [ABSTRACT FROM AUTHOR]

Autonomous driving technology, in which cars recognise their surroundings and drive by way of mechanical control, has attracted much attention. However, before the introduction of autonomous driving technology, there will be a mixture of autonomous and non-autonomous vehicles, which will require a traffic operation management strategy to ensure the safety and operational efficiency of the mixed traffic flow. In response, an operation plan is proposed here to design and operate a managed lane to minimise friction between autonomous and non-autonomous vehicles in the commercialisation phase of autonomous vehicles (AVs). To evaluate this AV managed-lane operation strategy, effectiveness measurements relation to safety, operation efficiency and the environment were derived by conducting a simulation analysis using Vissim software. Then, a value function was developed that reflects the three measurements and their importance weighting values. Using this approach, the paper provides a strategy of optimal AV managed-lane operation that can increase operational efficiency and safety in expressway on/off-ramp areas. [ABSTRACT FROM AUTHOR]

Traffic at urban intersections constitute a series of complex conflicting vehicular movements and contribute greatly to the problems of traffic congestion and air pollution. The emerging of connected and automated vehicle (CAV) technologies inspires innovative ideas in traffic management at intersections; it not only enables new control paradigms, but also allows for possibilities to revolutionize the geometric layouts of intersections. This study proposes a novel intersection design called knotted intersection (KI), to resolve the complexity of conflicting relations at intersections in a full CAV environment. This new design is associated with a set of neat control rules for vehicles to realize the smooth traffic operation at these intersections. The design problem of intersection geometry and control strategy is optimized jointly and formulated as a mixed-integer linear program (MILP), and some basic theoretical properties of KI design are derived, including nearly throughput-maximizing property and bounded within-intersection delay of vehicles. Furthermore, the KI design is then extended to a road network composed of multiple intersections to improve the overall efficiency. The optimal design of multiple knotted intersections (MKI) is developed to coordinate the traffic organization methods among intersections and a two-level solution method is proposed to solve the optimization efficiently. Numerical and simulation experiments are conducted at both the intersection level and network level under various cases, and the results validate the superior performance of the proposed methods compared to classical traffic signal control (TSC) strategies. • Knotted intersection design for resolving the complexity of intersection conflicts • Joint design of intersection geometric layout and traffic control strategy • Theoretical properties for nearly-maximum throughput and bounded delay • Optimal design of multiple knotted intersections to enhance network efficiency [ABSTRACT FROM AUTHOR]

In this article, we study the problem of stabilizing the traffic flow on a ring road to a uniform steady-state using autonomous vehicles (AV). Traffic is represented at a microscopic level via a Bando-Follow-the-Leader model capable of reproducing phantom jams. For the single-lane case, a single AV can stabilize an arbitrary large ring road with an arbitrary large number of cars. Moreover, this stabilization is exponentially quick with a decay rate independent of the number of cars and a control gain also independent of the number of cars. On the other side, the stabilization domain and stabilization time depend on the number of cars. Two types of controller algorithms are proposed: a proportional control and a proportional-integral control. In both cases, the measurements used by the controller only depend on the local data around the AV, enabling an easy implementation. After numerical tests of the single-lane case, a multilane model is described using a safety-incentive mechanism for lane change. Numerical simulations for the multilane ring road suggest that the control strategy is also very efficient in such a setting, even with a single AV. [ABSTRACT FROM AUTHOR]

To better prepare and guide both intersection planning and operations under different market penetration rates (MPRs) of connected and autonomous vehicles (CAVs) and traffic demands, this research estimates both the lane-level and intersection-level capacity. On the lane-level, adjustment factors for saturation headway and saturation traffic flow rate for each lane under different MPRs of CAVs are investigated. On the intersection-level, the maximum throughput function for different MPRs of CAVs is calibrated. With 100% CAVs, the saturation headways for the exclusive through lane, exclusive left-turn lane, and shared-right-and-through lane decrease by 55.8%, 48.9%, and 42.4%, respectively. The maximum throughput of the intersection with 100% CAVs increases by 70% compared to the scenario with only human driving vehicles (HDVs). Moreover, the maximum throughput increases rapidly after 60% MPRs of CAVs. The research could provide a solid reference for traffic engineers and planners in calculating the intersection capacity under different MPRs of CAVs. [ABSTRACT FROM AUTHOR]

This manuscript presents an Adam optimization-based Deep Reinforcement Learning model for Mixed Traffic Flow control (ADRL-MTF), so as to guide Connected and Autonomous vehicle's (CAV) longitudinal trajectory on a typical urban roadway with signal-controlled intersections. Two improvements are made when compared with prior literatures. First, the common assumptions to simplify the problem solving, such as dividing a vehicle trajectory into several segments with constant acceleration/deceleration, are avoided, to improve the modeling realism. Second, built on the efficient Adam Optimization and Deep Q-Learning, the proposed model avoids the enumeration of states and actions, and is computational efficient and suitable for real time applications. The mixed traffic flow dynamic is firstly formulated as a finite Markov decision process (MDP) model. Due to the discretization of time, space and speed, this MDP model becomes high-dimensional in state, and is very challenging to solve. We then propose a temporal difference-based deep reinforcement learning approach, with ε -greedy for exploration-exploitation balance. Two neural networks are developed to replace the traditional Q function and generate the targets in the Q-learning update. These two neural networks are trained by the Adam optimization algorithm, which extends stochastic gradient descent and considers the second moments of the gradients, and is thus highly computational efficient and has lower memory requirements. The proposed model is shown to reduce fuel consumption by 7.8%, which outperforms a prior benchmark model based on Monte Carlo Tree Search. The model's runtime efficiency and stability are tested, and the sensitivity analysis is also performed. [ABSTRACT FROM AUTHOR]

With the rapid development of autonomous driving technology, future road traffic must be composed of autonomous vehicles and artificial vehicles. Although autonomous vehicles have greatly improved road capacity, few studies have involved capacity at signal-controlled intersections, and most of the studies are based on experimental simulation. As such, there is a need to more scientifically analyze the impact of autonomous vehicles on road and intersection capacity. Based on three theories of flow-density relationships, traffic flow equilibrium analysis, and the following model, this paper firstly deduces the flow-density relationship of different vehicle types in a single environment. Secondly, flow-density relationships under different proportions of self-driving vehicles are derived. Through the derivation of these two models, the basic road saturation flow rates under different permeabilities of self-driving vehicles, can be obtained. Based on these results, a revised calculation model for the capacity of signalized intersections with different proportions of autonomous vehicles is proposed, which is essentially to revise the basic saturation flow rate under different permeabilities of autonomous vehicles. By using SUMO 1.15.0 traffic simulation software, the theoretical models are individually tested. The results show that the error rate between the theoretical calculation results and the SUMO simulation results, is less than 16%. This study can provide a basis for the calculation of basic capacity of roads and intersections in a future man-machine hybrid driving environment, and provide theoretical guidance for traffic management and control. [ABSTRACT FROM AUTHOR]

To enhance the traffic operation efficiency at signalized intersections, a model for coupled control of traffic signals and connected autonomous vehicles at isolated signalized intersections is proposed. This model estimates the time that CAVs reach stop lines with real-time information about the speed and position of CAVs. The arrival time is leveraged to optimize traffic signal timing by rolling horizon, with the maximization of phase saturation as the optimization objective. Based on the optimized traffic signal timing, the speed profile of CAVs is optimized by a linear integer programming, with the maximization of speed at the moment of reaching the stop line as the optimization objective. Through the coupled control of travel speed and the traffic signal, CAVs can pass through the intersection safely, efficiently, and smoothly. NetLogo, a multiagent microscopic simulator, is developed to test this strategy, and an intersection in Weihai is taken for verification and analysis lastly. The simulation results demonstrate that, compared with the fixed traffic signal timing control and the model optimizing only speed profile of CAVs, the proposed model can reduce the average number of stops by 47.0% and the queuing time by 41.3%. In addition, the optimization is better during off-peak hours, about 10% higher than the peak hours. [ABSTRACT FROM AUTHOR]

Waiting in a long queue at traffic lights not only wastes valuable time but also pollutes the environment. With the advances in autonomous vehicles and 5G networks, the previous jamming scenarios at intersections may be turned into non-stop weaving traffic flows. Toward this vision, we propose a highly efficient traffic planning system, namely DASHX, which enables connected autonomous vehicles to cross multi-way intersections without a stop. Specifically, DASHX has a comprehensive model to represent intersections and vehicle status. It can constantly process large volumes of vehicle information, resolve scheduling conflicts, and generate optimal travel plans for all vehicles coming toward the intersection in real time. Unlike existing works that are limited to certain types of intersections and lack considerations of practicability, DASHX is universal for any type of 3D intersection and yields the near-maximum throughput while still ensuring riding comfort. To better evaluate the effectiveness of traffic scheduling systems in real-world scenarios, we developed a sophisticated open source 3D traffic simulation platform (DASHX-SIM) that can handle complicated 3D road layouts and simulate vehicles' networking and decision-making processes. We have conducted extensive experiments, and the experimental results demonstrate the practicality, effectiveness, and efficiency of the DASHX system and the simulator. [ABSTRACT FROM AUTHOR]

Connected and Automated Vehicles (CAVs) have the capability to relieve congestion by improving roadway capacity by enabling vehicles to safely follow each other with smaller headways than traditional vehicles. This study aims to provide a basis for determining when the implementation of a Dedicated CAV Lane (DCAVL) would be beneficial to typical three-lane freeways. Total vehicle delay was used as the performance metric for assessing when it is ideal to have a DCAVL. Factors that can influence this decision - such as Market Penetration Rate (MPR) of CAVs, CAV-CAV headway, and demand volume - were considered for analysis. A total of 198 scenarios were created in a traffic flow simulation software, each reflecting a unique combination of the input parameters. A base scenario with no DCAVL was used to serve as a basis for comparison. A scenario comparison analysis was utilized to ultimately determine when a DCAVL will cause total vehicle delay to improve beyond the base scenario. The results of this study indicate that a three-lane freeway with a DCAVL will consistently begin to outperform the same three-lane freeway without a DCAVL when the MPR is between 34% and 37%. This research effectively provides a macroscopic view of the effects CAVs and DCAVLs will have on roadway performance and serves as a foundation for policymakers, traffic engineers, and researchers to base their future work upon. [ABSTRACT FROM AUTHOR]

The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. These threats jeopardize the safety of vehicles, riders, and the entire system. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Nevertheless, wirelessly coupled cars on the network are in constant peril. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles' security both at the nodes and at the network level. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This enables enhanced security in vehicular networks. [ABSTRACT FROM AUTHOR]

As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context. [ABSTRACT FROM AUTHOR]

Vehicle trajectory data play an important role in autonomous driving and intelligent traffic control. With the widespread deployment of roadside sensors, such as cameras and millimeter-wave radar, it is possible to obtain full-sample vehicle trajectories for a whole area. This paper proposes a general framework for reconstructing continuous vehicle trajectories using roadside visual sensing data. The framework includes three modules: single-region vehicle trajectory extraction, multi-camera cross-region vehicle trajectory splicing, and missing trajectory completion. Firstly, the vehicle trajectory is extracted from each video by YOLOv5 and DeepSORT multi-target tracking algorithms. The vehicle trajectories in different videos are then spliced by the vehicle re-identification algorithm fused with lane features. Finally, the bidirectional long-short-time memory model (LSTM) based on graph attention is applied to complete the missing trajectory to obtain the continuous vehicle trajectory. Measured data from Donghai Bridge in Shanghai are applied to verify the feasibility and effectiveness of the framework. The results indicate that the vehicle re-identification algorithm with the lane features outperforms the vehicle re-identification algorithm that only considers the visual feature by 1.5% in mAP (mean average precision). Additionally, the bidirectional LSTM based on graph attention performs better than the model that does not consider the interaction between vehicles. The experiment demonstrates that our framework can effectively reconstruct the continuous vehicle trajectories on the expressway. [ABSTRACT FROM AUTHOR]

More and more vehicles are connected today via emerging connected and automated vehicle (CAV) technologies. An intriguing application of CAVs is to cross intersections without stops through cooperative scheduling by traffic control infrastructure. Nonetheless, with the increase of CAVs' requests for green, two problems will surface: (I) accommodating too many CAVs' green requests will generate severe interruptions to general traffic; (II) simple scheduling policies like first-come-first serve is inappropriate due to heterogeneous importance of CAVs. To overcome these challenges, we present a mixed-integer linear programming (MILP) formulation for congestion-aware heterogeneous CAV scheduling problems at intersections in this paper. The objective is to ensure that intensive and heterogeneous green requests by CAVs can be scheduled at intersections while the mobility of background traffic is still maintained. The MILP formulation is developed in the context of discrete space-time and phase-time networks whose variables are space-time arc choice variables with respect to individual vehicles and phase-time arc choice variables. We also build an efficient search algorithm based on the "A-D curves" for real-time applications. Three experiments are conducted to validate the proposed MILP formulation and search algorithm. The simulation-based performance evaluation for the congestion-aware CAV scheduling reveal promising results for real-world applications in the future. [ABSTRACT FROM AUTHOR]

Traffic management methods aim to increase the infrastructure's capacity to lower congestion levels. Using vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X) connectivity technologies, connected autonomous vehicles (CAVs) have the potential to operate as actuators for traffic control. In this study, a CAV-based alternative approach for traffic management is proposed (SWSCAV), and its performance is compared to that of lane control signals (LCS) and variable speed limits (VSL), which are also traffic management systems. When a shockwave is detected due to an incident, the CAVs on the road slow until they reach the speed of the observed shockwave (SWS), according to this proposed procedure. Thus, the incoming traffic flow towards the incident is slowed, preventing the queue behind from extending. In a simulation of the urban mobility (SUMO) environment, the suggested method is evaluated for 4800 scenarios on a three-lane highway by varying the market penetration rate of CAVs in traffic flow, the control distances, the incident lane, and the duration. The proposed method reduces the incidence of density values of over 38 veh/km/lane and 28 veh/km/lane in the vicinity of the incident region by 12.68 and 8.15 percent, respectively. Even at low CAV market penetration rates, the suggested method reduces traffic density throughout the network and in the location of the incident site by twice as much as the LCS system application. • A method for real-time traffic management (SWSCAV) that uses CAVs is proposed. • SWSCAV is compared with the lane control signals (LCS) and variable speed limits (VSL) implementations. • SWSCAV is tested for 4800 scenarios, in a 3-lane highway, constructed in SUMO environment. • The results show that SWSCAV can decrease the traffic density even for low CAV percentages. [ABSTRACT FROM AUTHOR]

This survey offers a unified up-to-date presentation of macroscopic models of traffic flow, pointing out their main characteristics and possible drawbacks. The presentation is completed by several pictures illustrating the models' features. Some open problems and future research directions are also given to inspire the reader. [ABSTRACT FROM AUTHOR]

With the rapid progress of urbanization and continuous increasing of automobiles, expressway on‐ and off‐ramp area becomes the bottleneck, and recurrent congestion occurs frequently. In order to solve the problem of traffic jam caused around off‐ramps, various methods have been employed. Among all, mainline variable speed limit (VSL) control accounts for some proportion. In this study, mainline VSL adjustment of off‐ramp upstream is investigated with the reinforcement learning algorithm under the connected vehicles environment to alleviate the traffic congestion. First, the assumptions are made to be suitable for the traffic conditions of mainline VSL control on off‐ramp upstream, and then VSL algorithm based on reinforcement learning is presented, and Q‐learning is chosen as the main algorithm. Next, the state space, action space, and reward function required by Q‐learning are constructed orderly, and the related parameters are labelled. After that, according to the platform based on Python and VISSIM, three schemes, free control (Scheme 0), mainline VSL adjustment of off‐ramp upstream based on rule (Scheme 1), and mainline VSL adjustment of off‐ramp upstream based on Q‐learning algorithm (Scheme 2), are designed, and the three schemes are simulated and compared quantitatively to reflect the off‐ramp travel efficiency. The results indicate that mainline dynamic VSL adjustment of off‐ramp upstream based on Q‐learning algorithm performs the best in terms of general and specific indexes. The results provide potential insights for relieving the traffic congestion and traffic flow control under CAVs environment. [ABSTRACT FROM AUTHOR]

Existing signalized intersection control methods allocate intersection resources to each phase from the time dimension based on traffic conflict. Solving the traffic control issues caused by the unbalanced traffic demands at intersections is always a challenge. Recently developed connected and automated vehicle (CAV) technologies render it possible to collect detailed real‐time traffic information from vehicles for the optimal control and management of time‐space resources at signalized intersections. In this paper, we propose a mixed integer quadratic programming to jointly optimize signal timings and variable guide lane settings for a typical four‐arm intersection in the CAV environment. From the time dimension, restrictions of the conventional concept of the signal cycle are overcome. Phase sequences, green start, and duration are freely assigned for each CAV platoon based on the movement‐based signal timing. From the space dimension, lane resources can be reasonably allocated in consideration of the dynamic traffic demand distribution. In this study, a demand‐response control is realized for the signalized intersection through time‐space resources cooperated optimization. Furthermore, vehicle trajectory control is integrated into the collaborative control framework to reduce or eliminate wasted green time. At last, we propose the dynamic control process using the collaborative control framework. Numerical simulation results show that the collaborative control method outperforms over fixed‐time and signal optimization control modes in terms of travel time with both under‐saturated and over‐saturated conditions. [ABSTRACT FROM AUTHOR]

This paper presents a methodology to control the trajectory of cooperative connected automated vehicles (CAVs) at roundabouts with a mixed fleet of CAVs and human‐driven vehicles (HVs). We formulate an optimization program in a two‐dimensional space for this purpose. A model predictive control‐based solution technique is developed to optimize the trajectories of CAVs at discretized time steps based on the estimated driving behavior of HVs, while the actual behavior of HVs is controlled by a microscopic traffic simulator. At each time step, the location and speed of vehicles are collected, and a decomposition‐based methodology optimizes CAV trajectories for a few time steps ahead of the system time. The optimization methodology has convexification, alternating direction method of multipliers, and cutting plane decomposition components to tackle the complexities of the problem. We tested the solution technique in a case study roundabout with different traffic demand flow rates and CAV market penetration rates. The results showed that increasing the CAV market penetration rate from 20% to 100% reduced total travel times by 2.8% to 35.8%. The analyses indicate that the presence of cooperative CAVs in roundabouts can lead to considerable improvements. [ABSTRACT FROM AUTHOR]

Connected and Autonomous Vehicles is a technology that will be disruptive for all layers of traffic control. The Lagrangian, in-the-flow nature of their operation offers untapped new potentials for sensing and actuation, but also presents new fundamental challenges. In order to use these vehicles for traffic state reconstruction and control, we need suitable traffic models, which should be computationally efficient and able to represent complex traffic phenomena. To this end, we propose the Front-tracking Transition System Model, a cell-free modelling approach that can incorporate Lagrangian measurements, and has a structure that yields itself to on-line model learning and control. The model is formulated as a transition system, and based on the front-tracking method for finding entropy solutions to the Lighthill–Whitham–Richards model. We characterize the solution of this model and show that it corresponds to the solution of the underlying PDE traffic model. Algorithms for traffic state reconstruction and model learning are proposed, exploiting the model structure. The model is then used to design a prediction-based control law for stop-and-go wave dissipation using randomly arriving Connected and Autonomous Vehicles. The proposed control framework is able to estimate the traffic state and model, adapt to changes in the traffic dynamics, and achieve a reduction in vehicles' Total Time Spent. • Connected and Automated Vehicles can be used as traffic sensors and actuators. • Front-tracking Transition System Model is based on front tracking numerical methods. • The model captures stop-and-go waves and moving bottlenecks. • Algorithms are proposed for traffic state reconstruction and model learning. • Prediction-based moving bottleneck control is used to dissipate stop-and-go waves. [ABSTRACT FROM AUTHOR]

The essential of developing an advanced driving assistance system is to learn human-like decisions to enhance driving safety. When controlling a vehicle, joining roundabouts smoothly and timely is a challenging task even for human drivers. In this paper, we propose a novel imitation learning based decision making framework to provide recommendations to join roundabouts. Our proposed approach takes observations from a monocular camera mounted on vehicle as input and use deep policy networks to provide decisions when is the best timing to enter a roundabout. The domain expert guided learning framework can not only improve the decision-making but also speed up the convergence of the deep policy networks. We evaluate the proposed framework by comparing with state-of-the-art supervised learning methods, including conventional supervised learning methods, such as SVM and kNN, and deep learning based methods. The experimental results demonstrate that the imitation learning-based decision making framework, which ourperforms supervised learning methods, can be applied in driving assistance system to facilitate better decision-making when approaching roundabouts. [ABSTRACT FROM AUTHOR]

Determining the trajectories of all vehicles on freeways is a challenging yet critical topic as trajectories reflect the characteristics of traffic flow and serve as a good basis for traffic management and control. With the advances of mobile sensing technology, connected and automated vehicles (CAVs) as a new source of probe car can provide high-resolution sampled trajectory data. Furthermore, as CAVs sense the surrounding traffic situation, they can offer information to understand the vehicle motions around them. Utilizing the data from CAVs thus supports the trajectory reconstruction of fully-sampled traffic flow and enables sophisticated evaluation of traffic states. This study develops a CAV detection data-based trajectory reconstruction method for freeway traffic. First, the intelligent driver model (IDM) is used to judge the motion of undetected human-driven vehicles (HV) between trajectories. The undetected vehicles will be inserted in traffic flow with the position and speed estimated by a modified IDM model. Subsequently, the complete trajectories of the inserted HVs will be reconstructed by IDM. Last, the validity of the method is verified by both simulation and empirical experiments. The results demonstrate the proposed method enables sufficient reconstruction of vehicle trajectories under different traffic densities and penetration rates of CAVs. [ABSTRACT FROM AUTHOR]

Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions. [ABSTRACT FROM AUTHOR]