Connected And Autonomous Vehicles Research Articles

A Nonlinear Control Design for Cooperative Adaptive Cruise Control with Time-Varying Communication Delay

Cooperative adaptive cruise control (CACC) is one of the main features of connected and autonomous vehicles (CAVs), which uses connectivity to improve the efficiency of adaptive cruise control (ACC). The addition of reliable communication systems to ACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, the performance, stability, and safety of CACC could be affected by the transmission of outdated data caused by communication delays. This paper proposes a Lyapunov-based nonlinear controller to mitigate the impact of time-varying delays in the communication channel of CACC. This paper uses Lyapunov–Krasovskii functionals in the stability analysis to ensure semi-global uniformly ultimately bounded tracking. The efficaciousness of the proposed CACC algorithm is demonstrated in simulation and through experimental implementation.

Potential Impacts of Connected and Autonomous Vehicles on Controlling Criteria for Road Geometric Design: A Review

The development of connected and autonomous vehicles (CAVs) has progressed rapidly with advancements in intelligent transportation systems, automated driving technologies, and regulatory reforms worldwide. As the efficiency and safety of surface transportation heavily rely on highway geometric design, understanding the impact of CAVs on design control elements is essential to fully capitalize the benefits CAVs may offer. The objective of this study is to conduct a comprehensive review of existing literature to synthesize how CAVs can influence the controlling criteria for highway geometric design. Over 30 articles were thoroughly reviewed to gain insights into the impacts of CAVs on highway design and potential explanations for them. As CAVs could potentially reduce the minimum required stopping sight distance, they create opportunities to decrease other geometric dimensions, such as horizontal curve radius, length of vertical curves, horizontal sight offset, extra widening, and so on. However, this optimization is more feasible in an environment with fewer human-driven vehicles. Moreover, further related research needs to be done in mixed traffic streams and complex driving conditions. The critical discussions of this study can offer valuable insights, shedding light on the potential benefits and implications that an automated transportation sector may bring to future road geometry design.

A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network

Numerous contemporary studies have posited that connected and autonomous vehicles (CAVs) hold the potential to enhance traffic safety and augment efficiency substantially. One widely discussed approach to optimize CAV operations within urban traffic networks involves the implementation of dedicated lanes (DLs). This study aims to assist system planners in optimally deploying DLs within heterogeneous urban traffic networks of CAVs and Human-Driven Vehicles (HDVs). In pursuit of this objective, we have introduced a multi-class dynamic traffic assignment framework that enhances network performance and offers insights into traffic dynamics. Additionally, our methodology considers dynamic routing behaviour while devising DLs, formulating and approximating the problem as a mixed-integer linear program (MILP). The resulting strategy delineates the temporal and spatial aspects of the deployment of DLs for CAVs, specifying the quantity and locations of these lanes. Subsequently, we assessed our framework using test-bed networks of varying sizes and demand profiles, evaluating the solution’s quality and the model’s adaptability to diverse traffic conditions. Our findings indicate that implementing DLs for CAVs can bolster vehicular throughput across the network while neglecting dynamic capacity variation in mixed traffic may yield misleading outcomes.

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

Mix-Zones as an Effective Privacy Enhancing Technique in Mobile and Vehicular Ad-hoc Networks

Intelligent Transportation Systems (ITS) promise significant increases in throughput and reductions in trip delay. ITS makes extensive use of Connected and Autonomous Vehicles (CAV) frequently broadcasting location, speed, and intention information. However, with such extensive communication comes the risk to privacy. Preserving privacy while still exchanging vehicle state information has been recognized as an important problem. Mix zones have emerged as a potentially effective way of protecting user privacy in ITS. CAVs are assigned pseudonyms to mask their identity; a mix zone is an area where CAVs can change their pseudonyms to resist being tracked. In order to be effective, mix zone placement must take account of traffic flows. Also, since a mix zone can degrade throughput, mix zones must be used sparingly. Determining the number and placement of mix zones is a difficult dynamic optimization problem. This paper outlines the various approaches recently taken by researchers to deal with this problem.

Cyber security analysis of connected vehicles

AbstractThe sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

BWM Integrated VIKOR Method Using Neutrosophic Fuzzy Sets for Cybersecurity Risk Assessment of Connected and Autonomous Vehicles

Connected and autonomous vehicles (CAVs) have gained significant importance in intelligent transportation systems. However, CAVs are associated with significant cyber-risks caused by different threat-agents, which require robust mitigation strategies for secure operation. Different categories of threat-agents come with varying resources, motivation, and skills that must be addressed effectively for the secure operation of CAVs. This study offers a comprehensive analysis of cybersecurity risks from different categories of threat-agents targeting CAVs. A novel hybrid multi-criteria decision making (MCDM) technique has been developed to prioritize the threat-agent categories as part of the cyber defense mechanism. In this study, the major threat-agent categories are identified from the existing literature and cybersecurity reports. Then expert opinion is collected on the likelihood and severity of cyber-attacks undertaken by different threat-agent categories. Single-valued neutrosophic fuzzy sets (SVNFS) are implemented to consolidate the subjectivity of the linguistic opinions given by the experts. Then Best-Worst Method (BWM) is used to determine the relative criteria weightage on the cyber-attack consequences. These weights are then supplied to the ViseKriterijum-ska Optimizacija I Kompromisno Resenj (VIKOR) method for ranking the categories of threat-agents based on the risk perceived by the experts. A case study is presented with five categories of threat-agents and five loss criteria targeting CAVs to validate the viability of the proposed model. The result shows that insider attackers pose maximum risk for cybersecurity of CAVs. The proposed technique will provide helpful insights to the decision-makers for formulating effective defence strategies against cyber-attackers.

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. This study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

Constraint-following adaptive robust control for multi-vehicle system formation with collision avoidance

For the formation on unstructured road with complex road conditions, the rationality of constraint design and uncertainty compensation seriously affect the connected and autonomous vehicle (CAV) system performance. The system constraints are studied for safe and efficient formation driving, and an adaptive robust formation control scheme is proposed. First, for the desired configuration and global collision avoidance, a series of equality and unilateral inequality constraints are designed for inter-vehicle spacing. Second, a constraint handling scheme is proposed based on the diffeomorphism method, adjustable transformation function, and bounded constraint. Third, (possibly fast) time-varying but bounded multi-source uncertainty is considered, and then a self-adjusting leakage-type adaptive law is proposed to estimate the integrated uncertainty bound online. Fourth, the performance measure β is defined to evaluate the constraint-following error, and an adaptive robust control is proposed. The effectiveness of the proposed control scheme is verified by simulation. The results show that the proposed control renders the error to be uniformly bounded and uniformly ultimately bounded. The CAV system approximately follows the constraints, regardless of the uncertainty.

Anomaly Detection in Connected and Autonomous Vehicle Trajectories Using LSTM Autoencoder and Gaussian Mixture Model

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field.

How can on-street parking regulations affect traffic, safety, and the environment in a cooperative, connected, and automated era?

On-street parking is a commonly used form of parking facility as part of transportation infrastructure. However, the emergence of connected and autonomous vehicles (CAVs) is expected to significantly impact parking in the future. This study aims to investigate the impacts of on-street parking regulations for CAVs on the environment, safety and mobility in mixed traffic fleets. To achieve this goal, a calibrated and validated network model of the city of Leicester, UK, was selected to test the implementation of CAVs under various deployment scenarios. The results revealed that replacing on-street parking with driving lanes, cycle lanes, and public spaces can lead to better traffic performance. Specifically, there could be a 27–30% reduction in travel time, a 43–47% reduction in delays, more than 90% in emission reduction, and a 94% reduction in traffic crashes compared to the other tested measures. Conversely, replacing on-street parking with pick-up/drop-off stations may have a less significant impact due to increased stop-and-go events when vehicles pick-up and drop-off passengers, resulting in more interruptions in the flow and increased delays. The paper provides examples of interventions that can be implemented for on-street parking during a CCAM era, along with their expected impacts in order for regional decision-makers and local authorities to draw relative policies. By replacing on-street parking with more efficient traffic measures, cities can significantly improve mobility, reduce emissions, and enhance safety.

In‐fleet structural health monitoring of roadway bridges using connected and autonomous vehicles’ data

AbstractDrive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors’ knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles’ vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges.

Employing a Model of Computation for Testing and Verifying the Security of Connected and Autonomous Vehicles

<div>Testing and verifying the security of connected and autonomous vehicles (CAVs) under cyber-physical attacks is a critical challenge for ensuring their safety and reliability. Proposed in this article is a novel testing framework based on a model of computation that generates scenarios and attacks in a closed-loop manner, while measuring the safety of the unit under testing (UUT), using a verification vector. The framework was applied for testing the performance of two cooperative adaptive cruise control (CACC) controllers under false data injection (FDI) attacks. Serving as the baseline controller is one of a traditional design, while the proposed controller uses a resilient design that combines a model and learning-based algorithm to detect and mitigate FDI attacks in real-time. The simulation results show that the resilient controller outperforms the traditional controller in terms of maintaining a safe distance, staying below the speed limit, and the accuracy of the FDI estimation.</div>

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

The Performance of Connected and Autonomous Vehicles with Trajectory Planning in a Fixed Signal Controlled Intersection

Connected and autonomous vehicles (CAVs) are recognised as a technology trend in the transportation engineering arena. As one of the most popular capabilities of CAVs, trajectory planning attracts extensive attention and interest from both academia and the industry. Segmented trajectory planning is gaining popularity for its simplicity and robustness in computation and deployment. Constructive recommendations and guidelines can be provided by exploring the effects of segmented trajectories in different settings of CAVs and intersections. This research proposes a control strategy for segmented trajectory planning in a fixed signal timing environment. To test the effects of this control strategy, this research designs simplified fixed signalised intersection scenarios and implements segmented trajectory planning features of CAVs with different traffic demand scenarios, distances and speed limits. The results show that the proposed control strategy has stable superior performances in different traffic scenarios especially when the traffic volume is near capacity.

A new highway cost allocation framework in the day of connected and autonomous vehicles

The objectives of the present study are to explore potential changes expected in highway infrastructure expenditures and revenues, and develop a robust framework that will facilitate highway cost allocation when connected and autonomous vehicles (CAVs) are in operation. The scope of this study covered both federal and state expenditures and revenue sources. The highway cost allocation elements covered in the paper include vehicle miles of travel, highway expenditures highway revenues, and equity analysis. In the day of CAVs, significant changes are expected in all these elements, and these changes are incorporated into the proposed highway cost allocation framework. Pavement-related expenditures would increase due to the addition of CAV-related infrastructure integrated into the pavement infrastructure to ensure it is an intelligent pavement. Bridge-related expenditures are expected to increase due to the infrastructure technologies on the bridges making them smart bridges, and the design loads of these bridges may have to be increased due to potential platooning on long span bridges. It is expected that many of the CAVs on the road will be electric vehicles. All these changes are incorporated into the proposed highway cost allocation framework.

Exploring Safety-Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts.

Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.

Cluster stability-driven optimization for enhanced routing in heterogeneous vehicular networks

The new era of the Internet of Things is promoting the evolution of self-driving vehicles into connected and autonomous vehicles (CAVs). The deployment of CAVs in smart cities is highly dependent on the performance of their underlying networks known as vehicular networks. Designing an effective clustering approach is of great importance in such dynamic networks as it can significantly improve the reliability and scalability of the routing protocols. In this paper, we consider a heterogeneous vehicular network architecture that supports vehicle-to-vehicle and vehicle-to-infrastructure communications based on IEEE 802.11p and cellular networks (LTE/5G) with direct communications, namely cellular vehicle-to-everything (C-V2X) technologies. We introduce a novel clustering scheme for real-time routing based on the proposed network architecture. We formulate the problem of optimal clustering for connected and autonomous vehicles (OCCAV) and show that the problem is NP-hard. We then develop a clusters' stability maximization algorithm (CSM), which utilizes the stability degree of vehicles over a prediction horizon to efficiently solve the optimization problem in real-time. The algorithm is used within a rolling horizon framework for continuously solving the problem, making the resulting clusters adaptive to future traffic dynamics. We propose a hybrid routing protocol based on our clustering scheme, aiming to improve the packet delivery ratio and reduce the average delivery delay. For evaluation purposes, we use extensive realistic simulations based on mobility scenarios validated using real vehicular trajectories. The results demonstrate that our clustering scheme improves the alternative algorithms in terms of the average cluster head duration, cluster head change rate, cluster member duration, and overall stability by 61%, 62%, 44%, and 52%, respectively. CSM also outperforms clustering overhead by 54%. Compared to the other cluster-based routing, our scheme also achieves a higher packet delivery ratio by up to 30%, and a lower average delay by up to 52%. To provide an in-depth analysis of the optimality of our scheme and its alternatives, we also use the Gurobi optimizer to find an optimal solution to the OCCAV problem. The results suggest that our scheme can achieve near-optimal cluster stability.

Comparing autonomous vehicle acceptance of German residents with and without visual impairments

Connected and autonomous vehicles (CAVs) will greatly impact the lives of individuals with visual impairments, but how they differ in expectations compared to sighted individuals is not clear. The present research reports results based on survey responses from 114 visually impaired participants and 117 panel recruited participants without visual impairments, from Germany. Their attitudes towards autonomous vehicles and their expectations for consequences of wide-spread adoption of CAVs are assessed. Results indicate significantly more positive CAV attitudes in participants with visual impairments compared to those without visual impairments. Mediation analyses indicate that visually impaired individuals’ more positive CAV attitudes (compared to sighted individuals’) are largely explained by higher hopes for independence, and more optimistic expectations regarding safety and sustainability. Policy makers should ensure accessibility without sacrificing goals for higher safety and lower ecological impact to make CAVs an acceptable inclusive mobility solution.

Cooperative control of dynamic CAV dedicated lanes and vehicle active lane changing in expressway bottleneck areas

Bottleneck areas on expressways plague the operational efficiency of entire road systems. In mixed traffic flow environments consisting of connected and autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), it is believed that road capacity can be improved to relieve traffic congestion in bottleneck areas by setting CAV dedicated lanes (CDLs) on expressways. Existing static CDL setup methods provide fixed exclusive right-of-way for CAVs but cannot accommodate dynamic changes in traffic demand. To address this issue, utilizing lane control signals and CAV active lane-changing technology, a cooperative method involving dynamic CAV dedicated lane control (CDLC) and active lane-changing control (LCC) in the bottleneck areas of expressways is proposed in this paper. First, a fundamental diagram of traffic flow with CDLs is established based on the microscopic car-following model of mixed traffic flow, which can be used to determine the conditional thresholds for setting the CDLs of multilane expressways. Second, an expressway traffic model with CDLs is established by improving the lane-level cell transmission model. Finally, cooperative control of dynamic CAV dedicated lanes and active lane changing (CDLLC) is proposed based on the model predictive control (MPC) framework for solving the congestion problem at expressway bottlenecks in a mixed driving environment. The simulation results show that the proposed CDLLC strategy can effectively reduce the total vehicle travel time and decrease the risk of congestion propagation at expressway bottlenecks when compared to the LCC-only strategy.