Connected Autonomous Vehicles Research Articles

A Formally Integrated Adaptive Speed Management for Proactive Traffic Safety

The emergence of connected autonomous vehicles (CAVs) represents a key development in the quest to enhance traffic safety. CAVs hold significant promise for improving traffic safety and have great potential to contribute to transportation sustainability. However, their safety depends on the accuracy of the programmed rules and algorithms that guide their decision-making process. This paper introduces an adaptive traffic management system that integrates a formal traffic safety rule defined by pre-established bounds for Traffic Conflict Techniques. This system enables dynamic speed adjustments for vehicles violating the traffic safety rule in order to prevent potential collisions. To evaluate the effectiveness of our approach, we study a traffic flow on the SR528 highway in Orlando, Florida, and analyze the behavior of each vehicle in traffic based on extracted traffic safety indicators such as time-to-collision and space headway. This analysis is performed by the traffic safety rule to identify violating vehicles, and the speed update is achieved through the integration of the computer algebra system Mathematica and a micro-simulation tool called SUMO. Our study aims to improve the safety and efficiency of the traffic flow by combining simulation, TCTs analysis, and semi-formal tools like Mathematica to aid in the decision-making process of vehicles during traffic events, particularly shockwaves. Preliminary results demonstrate the efficacy of our approach in mitigating the impact of shockwaves. After applying the speed update, we observe an increase in time-to-collision and space headway. This indicates improved safety and reduced likelihood of collisions. Our findings highlight the potential of adaptive traffic management systems to enhance transportation safety and efficiency.

Multi-Vehicle Cooperative Decision-Making in Merging Area Based on Deep Multi-Agent Reinforcement Learning

In recent years, reinforcement learning (RL) methods have shown powerful learning capabilities in single-vehicle autonomous driving. However, few studies have focused on multi-vehicle cooperative driving based on RL, particularly in the dynamically changing traffic environments of highway ramp merge zones. In this paper, a multi-agent deep reinforcement learning (MARL) framework for multi-vehicle cooperative decision-making is proposed based on actor–critic, which categorizes vehicles into two groups according to their origins in the merging area. At the same time, the complexity of the network is reduced and the training process of the model is accelerated by utilizing mechanisms such as partial parameter sharing and experience playback. Additionally, a combination of global and individual rewards is adopted to promote cooperation in connected autonomous vehicles (CAVs) and balance individual and group interests. The training performance of the model is compared under three traffic densities, and our method is also compared with state-of-the-art benchmark methods. The simulation results show that the proposed MARL framework can have stronger policy learning capability and stability under various traffic flow conditions. Moreover, it can also effectively improve the speed of vehicles in the merging zone and reduce traffic conflicts.

Adaptive backstepping control for nonlinear CAVs platoon with input delays and disturbances

In recent years, the platoon control of connected autonomous vehicles (CAVs) has attracted a lot of attention in intelligent transport research. The third-order nonlinear vehicle dynamics model, due to its practicality, has been adopted in many research projects. In platoon control, controller input delays and disturbances are the significant issues that cannot be ignored. To this end, this paper investigates the cooperative control problem of third-order nonlinear CAVs platoon with controller input delays and unknown external disturbances. First, the neural network (NN) is used to approximate external disturbances with unknown bounds. Then, with the help of Pade approximation, a new variable is introduced to deal with the input delays. Subsequently, a corresponding compensation system is constructed based on the introduced variable. At last by using backstepping approach, a distributed adaptive control strategy based on vehicle-to-vehicle (V2V) communication is proposed in this paper. Additionally, Lyapunov functions and the uniformly ultimately bounded stability theorem are used to demonstrate the stability of the closed-loop systems, and the effectiveness of the control strategy is demonstrated by a simulation example.

Mixed traffic group-based pinning control strategy and stability analysis: a cyber-physical perspective

In mixed traffic scenarios, vehicle interactions are more complex than in homogeneous traffic. To improve traffic efficiency, a group-based pinning strategy for mixed traffic is proposed. Initially, vehicles are divided into subgroups, and a longitudinal control algorithm for connected autonomous vehicles (CAVs) is implemented. Strategies for group-based control, including the splitting and formation of groups, are introduced. The string stability of subgroups with vehicle dynamics is analyzed, and two sufficient conditions for ensuring stability are derived. The proposed method is validated, demonstrating significant advantages in reducing travel time.

Risk-Aware Lane Change and Trajectory Planning for Connected Autonomous Vehicles Based on a Potential Field Model

To enhance the safety of lane changes for connected autonomous vehicles in an intelligent transportation environment, this study draws from potential field theory to analyze variations in the risks that vehicles face under different traffic conditions. The safe minimum vehicle distance is dynamically adjusted, and a comprehensive vehicle risk potential field model is developed. This model systematically quantifies the risks encountered by connected autonomous vehicles during the driving process, providing a more accurate assessment of safety conditions. Subsequently, vehicle motion is decoupled into lateral and longitudinal components within the Frenet coordinate system, with quintic polynomials employed to generate clusters of potential trajectories. To improve computational efficiency, trajectory evaluation metrics are developed based on vehicle dynamics, incorporating factors such as acceleration, jerk, and curvature. An initial filtering process is applied to these trajectories, yielding a refined set of candidates. These candidate trajectories are further assessed using a minimum safety distance model derived from potential field theory, with optimization focusing on safety, comfort, and efficiency. The algorithm is tested in a three-lane curved simulation environment that includes both constant-speed and variable-speed lane change scenarios. Results show that the collision risk between the target vehicle and surrounding vehicles remains below the minimum safety distance threshold throughout the lane change process, ensuring a high level of safety. Furthermore, across various driving conditions, the target vehicle’s acceleration, jerk, and trajectory curvature remained well within acceptable limits, demonstrating that the proposed lane change trajectory planning algorithm successfully balances safety, comfort, and smoothness, even in complex traffic environments.

MARP: A Cooperative Multiagent DRL System for Connected Autonomous Vehicle Platooning

MARP: A Cooperative Multiagent DRL System for Connected Autonomous Vehicle Platooning

Safeguarding Personal Identifiable Information (PII) after Smartphone Pairing with a Connected Vehicle

The integration of connected autonomous vehicles (CAVs) has significantly enhanced driving convenience, but it has also raised serious privacy concerns, particularly regarding the personal identifiable information (PII) stored on infotainment systems. Recent advances in connected and autonomous vehicle control, such as multi-agent system (MAS)-based hierarchical architectures and privacy-preserving strategies for mixed-autonomy platoon control, underscore the increasing complexity of privacy management within these environments. Rental cars with infotainment systems pose substantial challenges, as renters often fail to delete their data, leaving it accessible to subsequent renters. This study investigates the risks associated with PII in connected vehicles and emphasizes the necessity of automated solutions to ensure data privacy. We introduce the Vehicle Inactive Profile Remover (VIPR), an innovative automated solution designed to identify and delete PII left on infotainment systems. The efficacy of VIPR is evaluated through surveys, hands-on experiments with rental vehicles, and a controlled laboratory environment. VIPR achieved a 99.5% success rate in removing user profiles, with an average deletion time of 4.8 s or less, demonstrating its effectiveness in mitigating privacy risks. This solution highlights VIPR as a critical tool for enhancing privacy in connected vehicle environments, promoting a safer, more responsible use of connected vehicle technology in society.

Congested traffic patterns of mixed lattice hydrodynamic model combining the perceptual range differences with passing effect

With the aid of Vehicle-to-everything (V2X) technologies for network connectivity, connected autonomous vehicles (CAVs) can broaden the drivers' perceptual boundaries and receive a greater quantity of exogenous vehicle information, thereby governing the vehicle's acceleration information of the next moment. Nonetheless, constrained by contemporary communication networks and sophisticated vehicle control technology, the process of promoting CAVs is long-lasting, and throughout this stage of transition, both CAVs and human-driven vehicles (HDVs) will happen on the road. Moreover, passing behaviour is uncommon in the traffic flow model despite being a fundamental microscopic driving behaviour, particularly within mixed-traffic situations. To bridge that hole, we introduce the percentage ratios of CAVs into the lattice hydrodynamic model by integrating the perceptual range differences between two different types of vehicles with passing effects. Subsequently, the stability norm associated with the new model is ascertained by performing the linear stability analysis. When the above-mentioned stability condition is not achieved, we investigate the complex behaviour of the new model, and the associated existing conditions, as well as the modified Korteweg-de Vries (mKdV) equation, are determined simultaneously. When the passing ratio is inadequate, no jam and kink jam make up the whole phase region; while when the passing ratio surpasses the minimum, the initial unstable region can be segregated into two extra segments: the chaotic sub-region and the kink jam sub-region, the density wave progressively transitioned from being a kink-Bando traffic wave to a chaotic phase. Lastly, the findings of the numerical experiment identify with the theoretical derivation made above.

Distributed Data-Driven Control for a Connected Autonomous Vehicle Platoon Subjected to False Data Injection Attacks

Distributed Data-Driven Control for a Connected Autonomous Vehicle Platoon Subjected to False Data Injection Attacks

Advanced Sensor Technologies in CAVs for Traditional and Smart Road Condition Monitoring: A Review

This paper explores new sensor technologies and their integration within Connected Autonomous Vehicles (CAVs) for real-time road condition monitoring. Sensors like accelerometers, gyroscopes, LiDAR, cameras, and radar that have been made available on CAVs are able to detect anomalies on roads, including potholes, surface cracks, or roughness. This paper also describes advanced data processing techniques of data detected with sensors, including machine learning algorithms, sensor fusion, and edge computing, which enhance accuracy and reliability in road condition assessment. Together, these technologies support instant road safety and long-term maintenance cost reduction with proactive maintenance strategies. Finally, this article provides a comprehensive review of the state-of-the-art future directions of condition monitoring systems for traditional and smart roads.

Cellular automaton model for the analysis of design and plan of bus station in the mixed traffic environment

With the quick development of Connected Autonomous Vehicles (CAVs), CAVs would gradually become an important part of urban traffic. Hence, the impacts of CAVs on urban traffic should be further explored. This study aims to analyze the mixed traffic comprising CAVs and human-driven vehicles (HDVs) in different urban scenarios in which different bus station types (i.e., roadside and bay bus stations) and capacities are considered. To do this analysis, this study proposes a cellular automaton model which simulates car following behaviours of vehicles using a two-state safe speed model, and designs specific modeling rules for lane-changing behaviours and vehicle behaviours near bus stations with consideration of the differences between CAVs and HDVs. The analysis results indicate that the impacts of various bus station types and capacities on the mixed traffic flow vary with traffic volumes, while increasing the CAV penetration rate can reduce the traffic congestion caused by bus stop-and-go. Furthermore, the study explores the optimal number of bus routes for different types of bus stations in different urban traffic scenarios, and several possible policy implications have been given according to the analysis results.

A Consortium Blockchain-Based Edge Task Offloading Method for Connected Autonomous Vehicles

In recent years, the proliferation of Connected Autonomous Vehicles(CAV) has revolutionizing the transportation industry. However, these vehicles often face limitations in terms of local computing resources, leading to the need for offloading interactive-intensive application tasks to servers for processing. Traditional paradigm has its limitations in meeting the demands of massive task processing. The combination of Web3.0 and edge computing offers users high-reliable, low-latency, and highly flexible services. Nevertheless, the new paradigm also presents its own challenges such as ensuring privacy data protection, and reducing the time and energy costs associated with task offloading. To tackle these challenges, a edge task offloading framework based on consortium blockchain for CAVs has been developed. Within this framework, a consortium blockchain-based interaction-intensive task offloading method, called CBIToMe, has been designed. CBIToMe specifically addresses the multi-stage nature of interactive-intensive CAV tasks and aims to minimize task completion time and offloading costs, particularly when the waiting time for interaction is uncertain. Additionally, CBIToMe effectively utilizes consortium blockchain technology to safeguard the CAV privacy data. Results from experiments conducted in various scenarios demonstrate that CBIToMe outperforms three representative methods, showcasing its superior performance.

Decentralized human-like control strategy of mixed-flow multi-vehicle interactions at uncontrolled intersections: A game-theoretic approach

A critical challenge that future autonomous driving systems face is improving the ability to cope with complex real-world interaction scenarios such as uncontrolled intersections. In the near future, a mixed traffic flow of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) will coexist in transport networks, which motivates us to explore the interaction between HDVs and CAVs to improve traffic efficiency and safety. To help CAVs better interact with HDVs and adapt to the mixed-flow environment, we propose a human-like decentralized control strategy for CAVs. First, a game-theoretic framework is proposed to model multi-vehicle interactions (including HDV-CAV, CAV-CAV interactions) in the mixed-flow environment. The existence of solutions is proven to ensure the feasibility of the proposed game-theoretic model. Next, a driving style recognition algorithm is embedded into the proposed model to help CAVs understand and predict human drivers’ actions. The proposed model is calibrated via a real-world dataset and used to simulate traffic in several testing scenarios. Real-world vehicle trajectories are used to verify the accuracy of generated vehicle trajectories in simulations. Experimental results indicate that 1) CAVs can take more reasonable actions to determine whether to yield while ensuring safety when competing for the right of way with HDVs using the proposed method compared with conservative driving strategies, 2) a higher penetration rate of CAVs can significantly enhance travel efficiency and lower collision risk at uncontrolled intersections.

Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

Secure Collaborative Learning for Self-Adaptive Systems on Connected Autonomous Vehicles

Capturing impacts of travel preference on connected autonomous vehicle adoption of risk-averse travellers in multi-modal transportation networks

ABSTRACT This paper proposes a reliability-based combined modal split and traffic assignment model for the multi-modal transportation network with uncertainties in which the travellers' travel preferences, vehicle ownership, and the characteristics of connected autonomous vehicles (CAVs) are considered. Travellers are classified into captive travellers and choice travellers according to their preference for travel modes and vehicle ownership. The choice travellers need to make a tradeoff among the out-of-pocket cost, comfort, and travel time reliability and make decisions between continuing to commute by subway or purchasing a human-driven vehicle (HV) or CAV. The dogit-nested-logit model is adopted to characterise the effect of travel preference on the adoption of CAVs of the choice travellers, and the multinomial logit model is adopted to capture the effect of the differences between HV and CAV on information quality and value of travel time reliability on the path choice behaviour of travellers. The problem is formulated as an equivalent variational inequality model based on the properties of the equilibrium state, and its equivalency and solution existence are discussed. A path-based algorithm is employed for solving the problem, which combines the Monte Carlo simulation-based method and the column generation method. Numerical experiments are presented to illustrate the properties of the proposed models and validate the algorithm.

Game-Based Flexible Merging Decision Method for Mixed Traffic of Connected Autonomous Vehicles and Manual Driving Vehicles on Urban Freeways

Connected Autonomous Vehicles (CAVs) have the potential to revolutionize traffic systems by autonomously handling complex maneuvers such as freeway ramp merging. However, the unpredictability of manual-driven vehicles (MDVs) poses a significant challenge. This study introduces a novel decision-making approach that incorporates the uncertainty of MDVs’ driving styles, aiming to enhance merging efficiency and safety. By framing the CAV-MDV interaction as an incomplete information static game, we categorize MDVs’ behaviors using a Gaussian Mixture Model–Support Vector Machine (GMM-SVM) method. The identified driving styles are then integrated into the flexible merging decision process, leveraging the concept of pure-strategy Nash equilibrium to determine optimal merging points and timing. A deep reinforcement learning algorithm is employed to refine CAVs’ control decisions, ensuring efficient right-of-way acquisition. Simulations at both micro and macro levels validate the method’s effectiveness, demonstrating improved merging success rates and overall traffic efficiency without compromising safety. The research contributes to the field by offering a sophisticated merging strategy that respects real-world driving behavior complexity, with potential for practical applications in urban traffic scenarios.

Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles

Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles

An integrated MCDM approach for enhancing efficiency in connected autonomous vehicles through augmented intelligence and IoT integration

The rapid advancement of Connected Autonomous Vehicles (CAVs) equipped with self-powered sensors is poised to revolutionize road safety, efficiency, and the quality of travel. However, the effective integration of these technologies within dynamic road environments poses significant challenges, highlighting the need for innovative multi-criteria decision-making (MCDM) approaches to optimize their deployment. This study tries to solve the integration problem by proposing an MCDM method that uses fuzzy sets to evaluate and rank different scenarios for better performance of augmented intelligence and the Internet of Things (IoT) in CAVs. This research uses two key techniques: the Fuzzy Logarithm of Incremental Weights (F-LMAW) method for criteria evaluation and the Fermatean Fuzzy Weighted Aggregated Sum Product Assessment (FF-WASPAS) method for scenario evaluation. To ensure the reliability and accuracy of the results, a sensitivity analysis was conducted. The analysis confirmed the effectiveness of the proposed approach. The study's results showed that the third scenario (the integration of augmented intelligence and IoT in urban areas via self-powered sensors) got the highest score, which shows how important it is compared to the other choices. The obtained results highlight the importance of integrating augmented intelligence and IoT technologies to enhance public transportation with autonomous vehicles equipped with self-powered sensors.

Method for utilizing the reserved lane capacity: Formation of the mixed traffic flow

As the surge in demand for the motor travel continues, the lane capacity is becoming increasingly ineffective in matching the operation of the traffic flow. A method that can utilize the reserved lane capacity and thereby effectively increase the lane capacity is urgently needed for this problem. It is considered in this paper that the formation of a mixed traffic flow which includes both the human-driven vehicles (HDVs) and the connected autonomous vehicles (CAVs) is one such method. Relying on the exploration of the following rules between the HDVs and the CAVs, a heterogeneous vehicle following model (HVFM) can be established as a guide for the formation of the mixed traffic flow. Under the guidance of the HVFM, the effects of different following mode types on the formation characteristics of the mixed traffic flow can be defined, determined and evaluated. At the same time, with the applicability of the three-phase traffic theory in mind, a three-phase traffic flow model under the mixed traffic flow condition (M-TTFM) is established for providing guidance on the operating mechanism of the mixed traffic flow, the determining method of the lane capacity and the utilizing method of the reserved lane capacity at the micro-theoretical level. The maximum lane capacity under the mixed traffic flow conditions can be up to 205.94 % of the lane capacity under HDV traffic flow conditions with the formation of the mixed traffic flow, which indicates that the reserved lane capacity can be greatly utilized by forming the mixed traffic flow. In particular, by mixing the CAV fleets platooned by the CAVs into the HDV traffic flow, the utilizing effect for the reserved lane capacity can be improved with a maximum level of 194.25 %, which is called the in-depth utilization of the reserved lane capacity. What is more, the fundamental diagrams expressing the relationship between the traffic flow rate and the density under the mixed traffic flow condition can be obtained and the recommended value of the lane capacity under the mixed traffic flow can be measured. In summary, the utilizing method for the reserved lane capacity in this paper is expected to further enhance the lane capacity and contribute to solving the problems caused by the lack of the lane capacity.

Application of dynamic desired headway based adaptive backstepping sliding mode control design to mixed traffic system

To optimize the mixed car-following behavior of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), an adaptive backstepping sliding mode control (ABSMC) strategy for longitudinal velocity and distance control model (namely, MCF-ABSMCM) is put forth. First, the variable desired headway model (VDHM) is designed based on the vehicle-to-vehicle and vehicle-to-infrastructure information. Also, an asymmetric stochastic car-following model (ASCM) is designed to realistically describe the stochastic factor as well as vehicle acceleration and braking behaviors in mixed car-following. Second, an adaptive cruise sliding mode control law with a backstepping approach is devised to precisely track the desired following distance based on the VDHM. After creating the basic diagram of the two-model traffic flow, the intelligent driver model (IDM) is used as a baseline to compare and analyze the traffic capacity. Lastly, the effectiveness of the MCF-ABSMCM approach is confirmed in terms of both the mixed traffic flow density wave evolution and the vehicle control strategy, and the MCF-ABSMCM is validated under various adversary inputs. The simulation results demonstrate that the mixed-flow queue can finish headway tracking and keep a safe distance when using the MCF-ABSMCM described. This control strategy has been validated in VISSIM to significantly improve the efficiency of traffic operations compared to IDM and ASCM.