With Petri nets, this paper aims to design a fast response and recovery strategy for abnormal situations where some of right-of-way (ROW) resources are not available due to accidents, thereby enhancing the resilience of unsignalized autonomous intersections. First, based on the analysis of vehicle trajectory characteristics at intersections, we extend the semantic types of places in classical Petri nets and propose the ROW resource Petri net (RRPN) for modeling fundamental autonomous intersections. Then, we introduce lane change transitions and establish a failure and recovery model for a failed ROW. With these two models, the RRPN with resource failure and recovery (RRPN-FR) model for autonomous intersections is built. Subsequently, based on the RRPN-FR model, we introduce maximal perfect resource transition circuit (MPC) and resource recovery circuit (RRC), and analyze the deadlocks caused by these two structures. Then, we introduce the notion of enhanced transition cover and design a resilient intersection control strategy based on the enhanced transition cover. It formally shows that this control strategy can enhance the resilience of a controlled intersection, effectively responding to unexpected situations and abnormal traffic conditions, which in turn prevents traffic congestion. Simulation experiments and comparative analysis further validate the effectiveness of the proposed control strategy in the failure and recovery process and overall performance. Model is available at:https://github.com/yaxwei/Supplement/blob/TITS/SupplementaryMaterials.pdf

Recent advancements in computing and autonomous driving technologies have led to the integration of new functionalities into intelligent automotive, such as environmental perception, path planning, assisted driving, and entertainment services. This integration requires the rapid processing of critical tasks, including collision detection, emergency braking, lane keeping, and Vehicle-to-Everything (V2X) communication, exceeding past functional demands. Consequently, high-performance multi-core processors have emerged as the preferred hardware solution due to their superior processing speeds, energy efficiency, and parallel task execution capabilities. This paper explores various applications of multi core processors in intelligent automotive systems, systematically reviewing recent advancements in multi-core scheduling methods. It categorizes and analyzes approaches to task loading, task migration, efficiency improvement, safety assurance, communication, and resource contention in both general and automotive contexts. To objectively assess these methods, the paper establishes reference standards for evaluating scheduling methods and system architectures and provides analytical methods aligned with these standards. Finally, the paper discusses future challenges, considers trends in intelligent automotive systems and multi-core processors, and offers recommendations for future research in this field.

Human–machine co-driving is expected to be a long-term driving mode. However, existing cooperative steering control strategies often struggle to effectively handle human–machine interaction conflicts and dynamically allocate driving authority, making it difficult to balance safety and driver’s comfort. To address these issues, this article establishes a Stackelberg game-based model predictive control (MPC) framework and derives a human–machine optimal control strategy under the equilibrium conditions. Furthermore, a two-layer adaptive authority allocation model is developed using the deep deterministic policy gradient (DDPG) method, which comprehensively considers environmental risks, human–machine conflict, and driver’s states. This model prevents a vehicle from entering an unstable state by dynamically allocating the driving authority to both the human driver and the autonomous driving system. Results from driver-in-the-loop experiments indicate that, in obstacle avoidance scenarios, the proposed control strategy enhances vehicle driving stability by 31.49%, improves control stability by 48.11%, and reduces the driver’s burden by 42.87%, demonstrating that the proposed strategy can assist the driver in completing obstacle avoidance tasks and ensure driving safety in high-risk scenarios. In low-risk scenarios, it maintains the driver’s freedom, prevents excessive intervention from the autonomous driving system that could lead to significant human–machine conflicts, and alleviates the driver’s operational burden.

Coordination at unsignalized intersections has attracted increasing attention in recent years, which aims at improving the efficiency of intersection operations, while eliminating conflicts and deadlocks for Connected and Automated Vehicles (CAVs). This paper addresses the challenging issue of systematically optimizing CAV trajectories, tackling the high computational cost for finding a good solution, especially as the number of lanes and CAVs increases. To do so, a novel systematic optimal trajectory planning model is designed to efficiently guide CAVs through intersections without conflicts and deadlocks. To tackle the computational hurdles and enable real-time applications, based on the model, we develop a Petri net-aided Iterative Trajectory Optimization (P-ITO) solution algorithm. Leveraging the unique characteristics of the problem, this algorithm first designs a Petri net-based controller for conflict and deadlock avoidance so that initial feasible solutions are generated. Then, refinement is made on the initial feasible solutions to obtain an optimal or near optimal solution by designing an iterative process. This algorithm effectively ensures solution feasibility and enhances computing efficiency by searching for an optimal solution in the feasible region. Numerical experiments for intersections with bidirectional six-lane configurations illustrate the efficacy of our model in facilitating the safe and efficient passage of all CAVs while mitigating the risk of deadlocks. The P-ITO algorithm significantly outperforms the commercial solvers in both solution quality and computational time. Furthermore, our method is versatile, applicable to diverse intersection scenarios, and capable of maintaining high computational efficiency.

This paper mainly addresses the issue of designing safety Petri net-based controllers to prevent vehicle flow deadlocks at intersections. The designed controller can monitor the flow of vehicles at an intersection and guide them to pass an intersection safely without causing deadlocks. This study begins by investigating the intersection deadlock scenarios, analyzing the physical size of the “right-of-way” cells with the discretization of the interior area of an intersection, and developing the intersection initial Petri net (PN) model based on simple sequential processes with resources (S3PR). Then, based on the initial PN model, we propose a deadlock prevention strategy for controller design to control the vehicle flows to ensure that the vehicle flows in different directions do not result in deadlocks. Finally, the effectiveness of the proposed strategy is illustrated by examples and theoretical proof. This study contributes to the advancement of the state-of-the-art in the design of safety controllers for self-driving vehicles passing through intersections.