Eco-Driving Framework for Autonomous Vehicles at Signalized Intersection in Mixed-Traffic Environment

The use of connected autonomous vehicles (CAVs) can help reduce the gaps between vehicles. However, smaller gaps may cause challenges for on-ramp vehicles to merge on the freeway. This study introduces several strategies that allow on-ramp vehicles to merge safely and efficiently with the mainline by providing adequate merging gaps or resolving merging conflicts in a mixed traffic environment. Eight methods are proposed to facilitate merging. These strategies are simulated in a mixed traffic environment with different traffic characteristics, including different proportions of CAVs. The results indicate that the operational performance is affected by the CAV penetration rate and traffic management strategy. For a relatively low freeway traffic volume or 100% CAV penetration rate, traffic operations under the do-nothing option perform almost as well as the best traffic management strategy. However, at lower CAV penetration rates and high freeway traffic volumes, the proposed strategies are more viable than the do-nothing option.

Connected Autonomous Vehicles (CAV) utilize vehicular communication to collect information about the surrounding environment to make informed decisions about speed and maneuvering. This enables safe driving and decreases the number of accidents and thereby the associated fatalities. However, vehicular communication may suffer from high latency and low reliability, especially in dense vehicle environments, which may negatively affect the safety of CAVs. Therefore, it is crucial to study the impact of these metrics on the safety application performance while taking into account realistic CAV kinematics and dynamics. In this paper, we address this problem by comparing the performance of the Short Range Communication (DSRC) to that of the Fifth-Generation New Radio (5G-NR) and their impacts on the safety applications in the CAV environment under different settings. We develop a full-fledged simulation framework that can realistically model both vehicular mobility and communication and can capture the impact of communication on safety applications. Within this framework, we implement an important CAV's safety application, namely, the forward collision avoidance system, in which following vehicles use vehicular communications to gather information from leading vehicles to compute the safe speed and avoid collisions. We then use this framework to study and compare the performance safety of the forward collision avoidance system using both DSRC and 5G-NR communications. The results show that the packet delays and drops in communication networks can adversely affect CAV safety. The results also demonstrate that 5G is more capable of supporting the safety requirements under higher packet traffic loads and vehicle densities.

This paper combines millimeter-wave radar (MMW) data with connected autonomous vehicle (CAV) trajectory data to estimate queue length on a second-by-second basis. Firstly, queued vehicles on multiple lanes with the same traffic movement are mapped to a virtual lane. Then, the presence of CAV or human-driven vehicle (HDV) for any given queueing index is determined. A Bayesian joint probability model is subsequently established for queue length expectation, considering the existence of the queued vehicle type as a condition. The average time headway and dissipation speed distribution are derived from departure timestamps, which allows for the calculation of the prior probability ratio of queue length. Lastly, the maximum likelihood estimation (EM) algorithm is employed for iteratively estimating the CAV penetration rate. Simulation results demonstrate the method offers a compelling trade-off between precision and second-based real-time performance, while field test results further confirm the wide applicability under various traffic conditions.

This study proposes an optimal control method for connected autonomous vehicles (CAVs) through signalized intersections to reduce the energy consumption of mixed human-driven vehicles (HDVs) and CAV traffic. A real-time optimal control model was developed to optimize the trajectory of each CAV by minimizing energy consumption during the control period while ensuring traffic efficiency and safety. The control conditions of the CAVs were analyzed under different driving scenarios considering the impact of signal phase timing and preceding vehicles. Additionally, a method is proposed for CAVs to guide other vehicles directly and reduce the energy consumption of the entire signalized intersection. Simulation experiments using MATLAB and SUMO were conducted to evaluate the performance of the proposed method under various traffic conditions, such as different levels of saturation, market penetration rates (MPRs), and the green ratio. The performance was measured using average energy consumption and an average time delay. The results show that the proposed method can effectively reduce vehicle energy consumption without compromising traffic efficiency under various conditions. Moreover, under traffic saturation, the proposed method performs better at a high MPR and green ratio, especially at 40–60% MPR.

As mixed traffic of human‐driven vehicles (HDVs) and connected autonomous vehicles (CAVs) increases, incorporating HDV unpredictability and heterogeneity into cooperative control becomes a major challenge. This paper proposes a cooperative control method for uncontrolled intersections using a leader‐follower decision‐making process. Vehicles' roles are assigned through a directed graph and the weighted maximum clique algorithm breaks loops while considering HDVs' influence. After loop breaking, CAVs plan trajectories using dynamic programming and quadratic planning, with results transmitted via V2X communication to a central controller. Unlike traditional graph‐based methods, our model allows lower‐priority vehicles to find passage windows between higher‐priority vehicles, optimising interaction space. Through simulation analysis, we discussed the effects of traffic flow density and CAV penetration rate on the model's performance and compared it with the traffic light control and first‐in‐first‐out scheduling algorithms, demonstrating that our model has a better traffic flow characteristic.

With the development of autonomous vehicles, a mixed traffic flow scenario of Connected Autonomous Vehicles (CAV) and Human-Driven Vehicles (HVs) would be popular in the near future. The traditional traffic light control systems (TLCSs) for HVs do not make full use of traffic information collected via VANET; meanwhile emerging traffic light systems for CAV assume complicated and quick reactions, which human drivers may not be able to handle. Therefore, they are not suitable for the mixed scenario. This paper proposed a novel TLCS, named PALM, for tackling the challenge for handling mixed traffic scenarios. PALM considers the traffic flow at each intersection and adjacent ones and adjusts the traffic lights schedule for the next few phases accordingly. It also optimizes the signal timing and phases to better serve the platoons formed by CAV. The simulation results show that our approach achieves up to 75.34% and 33.02% drop in the average waiting time compared to the static and actuated TLCS, respectively.

Enhancing accessibility in transportation systems is an escalating interest among researchers, fueled by the rising issues with traffic congestion and technological innovations. Accessibility significantly contributes to improving the quality of life, thereby necessitating an examination of how route guidance systems impact it. This study explores the influence of system optimum (SO) and user equilibrium (UE) route guidance systems on accessibility, particularly in the context of connected autonomous vehicles (CAVs) embedded in the network. We employ a hybrid assignment model that enables the concurrent allocation of SO and UE assignments, along with the Gravity model to compute accessibility. The Sioux Falls network, previously leveraged in prior research, was chosen for numerical simulations to permit insightful comparisons. Our study scrutinizes three scenarios: the first scenario examines the repercussions of route guidance systems on human-driven vehicles, while the second and third assess their effects on CAVs. The difference between the second and third scenario is in the way of increasing the capacity in the assignment. Our findings reveal that at lower penetration rates of route guidance systems, accessibility initially dips, and then ascends. When all human-driven users adopt route guidance systems, accessibility increases by 12.75% compared to the initial state (without any intervention). Remarkably, should all users employ autonomous vehicles, accessibility would surge by 220% compared to the initial state (without any intervention). These insights highlight the importance of integrating CAVs and route guidance systems into transportation planning to enhance accessibility and improve the quality of life.

Connected Autonomous Vehicles (CAVs) are going to share the road environment with human drivers until their full market deployment achievement. In this context, this paper tackles and solves the problem of decentralized crossing at unsignalized intersections for mixed traffic flows, composed of both connected human-driven vehicles (CHVs) and CAVs. To this aim, a novel cooperative fully-distributed control protocol for CAVs is proposed, augmenting the Adaptive Cruise Control (ACC) action based on proximity sensors measurements, with an additional networked protocol exploiting Vehicle-to-Vehicle (V2V) information for the cooperative evaluation of the Time-to-Intersection of all incoming vehicles within the communication range. This further collaborative action automatically adapts the CAVs motion at intersection avoiding both collisions with other vehicles and intersection deadlocks, while improving the intersection throughput. Safety and mobility performances are evaluated via the purposely designed Virtual Testing platform - Mixed Traffic Simulator (MiTraS)- combining the Matlab/Simulink environment with the SUMO traffic simulator. The analysis is carried out for an exemplary two-lane four-way unsignalized intersection considering both different traffic demands and different CAVs penetration rates. The unavoidable presence of variable delays in information delivery due to the wireless communication network has been also explicitly accounted for in both control design and validation. The extensive simulation analysis confirms how the inclusion of CAVs, equipped with the proposed control algorithm, within the mixed traffic flow strongly improves both the safety and mobility performances of the intersection. Lastly, we disclose how the benefits of the proposed controller increase with CAV penetration.

Providing real-time en-route suggestions to CAVs for congestion mitigation: A two-way deep reinforcement learning approach

Connected autonomous vehicles (CAVs) will be deployed over the next decade with autonomous functionalities supported by new sensing and communication capabilities. Such functionality exposes CAVs to new attacks that current vehicles will not face. To ensure the safety and security of CAVs, it is important to be able to identify the ways in which the system could be attacked and to build defences against these attacks. One possible approach is to use reference architectures to perform an attack surface analysis. Existing research has developed a variety of reference architectures but none for the specific purpose of attack surface analysis. Existing approaches are either too simple for sufficiently detailed modelling or require too many details to be specified to easily analyse a CAV’s attack surface. Therefore, we propose a reference architecture using a hybrid Functional-Communication viewpoint for attack surface analysis of CAVs, including the Devices, Edge and Cloud systems CAVs interact with. Using two case studies, we demonstrate how attack trees can be used to understand the attack surface of CAV systems.

In the past few decades, Intelligent Transport System (ITS) has gained the popularity in smart society. Global automotive stakeholders such as Google, Uber, and Toshiba are the driving force in that and also looking for the interconnection of autonomous vehicles (AVs) for road traffic management, that is, connected AVs (CAV). CAVs are vulnerable to various safety and security issues, for instance, denial‐of‐service, global positioning system (GPS) spoofing, man‐in‐the‐middle, and timing attacks, which raise serious concerns for their development in application specific areas. Although many solutions exist in the literature, most of them are focused on centralized systems, which is having a single‐point‐failure. In light of the above facts, we analyze and classify the security and privacy issues in CAV and proposed a solution taxonomy in this article. We emphasized the defense mechanism for these attacks on the CAV system along with their enactment on issues and discovered how emergent blockchain technology could handle it. Furthermore, we proposed a blockchain‐based secure and decentralized CAV architecture to mitigate security and privacy issues such as denial of service, man‐in‐the‐middle, hijacking, GPS spoofing, and replay attacks. Finally, we reconnoiter the open research challenges associated with the blockchain‐based CAV system. To validate the efficacy of the proposed system, a real‐time case study on smart city integration over edge computing using computation and communication costs is proposed in this article.

In this paper, the problem of distributed state estimation of human-driven vehicles (HDVs) by connected autonomous vehicles (CAVs) is investigated in mixed traffic transportation systems. Toward this, a distributed observable state-space model is derived, which paves the way for estimation and observability analysis of HDVs in mixed traffic scenarios. In this direction, first, we obtain the condition on the network topology to satisfy the distributed observability, i.e., the condition such that each HDV state is observable to every CAV via information-exchange over the network. It is shown that strong connectivity of the network, along with the proper design of the observer gain, is sufficient for this. A distributed observer is then designed by locally sharing estimates/observations of each CAV with its neighborhood. Second, in case there exist faulty sensors or unreliable observation data, we derive the condition for redundant distributed observability as a q-node/link-connected network design. This redundancy is achieved by extra information-sharing over the network and implies that a certain number of faulty sensors and unreliable links can be isolated/removed without losing the observability. Simulation results are provided to illustrate the effectiveness of the proposed approach.

This study evaluates the effects of various market penetration rates of connected autonomous vehicles (CAV) on traffic operations at roundabouts. We have utilized a simulation-and an optimization-based approach for this purpose. The simulation-based approach included calibrated car following models with different driving behavior parameters for a mixed fleet of conventional vehicles and CAVs with various penetration rates ranging from 0% to 100%. We also used an optimization-based approach for the 100% CAV market penetration rate case to evaluate operations while trajectories of CAVs were optimized by a central controller. The simulation results showed that CAVs improved traffic operations in under-and semi-saturated flow conditions. Nevertheless, the optimization of CAV trajectories resulted in significant delay reductions and improvements in the roundabout performance. These results indicate that CAVs have great potentials for improving traffic operations once an effective control algorithm is available.

Variable speed limits (VSLs) have a potential to improve mobility and safety of motorway through harmonisation of traffic flow. However, the success of VSL is highly dependent on drivers’ compliance to the displayed speed limits. Emerging technologies in the field of connected autonomous vehicles (CAVs) is likely to revolutionise the way VSL will be operated in the near future. VSL can be integrated with CAVs where CAVs will automatically obey the displayed speed limits. However, 100% penetration rate of CAVs is a long‐term goal. At the initial stage of deployment, CAVs will coexist with manually driven vehicles on motorways. This study explores ways to implement VSL under a mixed traffic condition where CAVs and manually driven vehicles coexist in the traffic stream. A hybrid approach combining particle swarm optimisation with proportional‐integrated‐derivative is proposed to improve VSL implementation under a range of mixed traffic conditions. The simulation results showed that the proposed method successfully achieved the desired speed limits and rapidly stabilised the system at the initial and transient points at penetration rates of 50 and 75%, whereas the method encountered serious overshoots at lower penetration rates.

VENTURER was one of the first three UK government funded research and innovation projects on Connected Autonomous Vehicles (CAVs) and was conducted predominantly in the South West region of the country. A series of increasingly complex scenarios conducted in an urban setting were used to: (i) evaluate the technology created as a part of the project; (ii) systematically assess participant responses to CAVs and; (iii) inform the development of potential insurance models and legal frameworks. Developing this understanding contributed key steps towards facilitating the deployment of CAVs on UK roads. This paper aims to describe the VENTURER Project trials, their objectives and detail some of the key technologies used. Importantly we aim to introduce some informative challenges that were overcame and the subsequent project and technological lessons learned in a hope to help others plan and execute future CAV research. The project successfully integrated several technologies crucial to CAV development. These included, a Decision Making System using behaviour trees to make high level decisions; A pilot-control system to smoothly and comfortably turn plans into throttle and steering actuation; Sensing and perception systems to make sense of raw sensor data; Inter-CAV Wireless communication capable of demonstrating vehicle-to-vehicle communication of potential hazards. The closely coupled technology integration, testing and participant-focused trial schedule led to a greatly improved understanding of the engineering and societal barriers that CAV development faces. From a behavioural standpoint the importance of reliability and repeatability far outweighs a need for novel trajectories, while the sensor-to-perception capabilities are critical, the process of verification and validation is extremely time consuming. Additionally, the added capabilities that can be leveraged from inter-CAV communications shows the potential for improved road safety that could result. Importantly, to effectively conduct human factors experiments in the CAV sector under consistent and repeatable conditions, one needs to define a scripted and stable set of scenarios that uses reliable equipment and a controllable environmental setting. This requirement can often be at odds with making significant technology developments, and if both are part of a project’s goals then they may need to be separated from each other.