With the rapid development of intelligent connected vehicles, the domain-based electronic and electrical (E/E) architecture is providing the potential upgrade for the autonomous vehicle. As a representative, the domain-centralized E/E architecture can be installed in the autonomous vehicle which performs its powerful software updates, cabling reduction, and functional integration. For the efficient and stable motion control of autonomous vehicles equipped with domain-centralized E/E architecture, this paper proposes an improved deep reinforcement learning framework based on multi-hops loop delay and accelerated gradient optimization. Firstly, the domain-centralized E/E architecture and motion control problem of autonomous vehicle are modeled, respectively. Then, a multi-hops loop delay analysis is carried out for the E/E architecture to estimate the theoretical boundary value of heterogeneous topology loop delay. Subsequently, the deep reinforcement learning algorithm using modified heuristic experience replay is developed for the motion control of autonomous vehicle equipped with domain-centralized E/E architecture. In the implementation of deep reinforcement learning system, the estimated loop delay value is integrated into the motion controller optimization, and the Nesterov accelerated gradient is introduced and combined with the adaptive moment estimation to improve the optimization effect. Finally, the real-world scenarios and virtual driving environment simulation are applied to evaluate the performance of the improved deep reinforcement learning framework. The results show that the proposed motion control framework achieves better performance and guarantees the stability to the loop delay caused by domain-centralized E/E architecture.

Scenarios such as vehicle platooning urgently require high-precision object tracking algorithms with strong ID consistency. However, current state-of-the-art (SOTA) 3D tracking methods often suffer from ID switches. To address this limitation, this paper proposes a LiDAR-UWB Object Tracking (LUOT) algorithm designed to achieve centimeter-level (1σ) 3-D object tracking with zero ID switches. The method fuses high-precision LiDAR-based bounding boxes (BBox) with the ID consistency of ultra-wideband (UWB). Considering the high precision of 3D BBoxes at positions densely populated by point clouds, LUOT replaces the conventional association center point with coordinates transformed to the vehicle coordinate frame through the vehicle’s rear-end frame, where point clouds are denser, thereby minimizing errors arising from center point detection inaccuracies. To mitigate the destructive impact of occasional UWB outliers, this study pioneers the introduce of a Mahalanobis-distance-based Chi-square test, integrating multiple UWB ranges to comprehensively detect and eliminate outliers. Finally, real-world vehicle experiments demonstrate that LUOT achieves zero ID switches with a tracking accuracy of 0.0868 m (1σ), fully satisfying centimeter-level precision requirements for vehicle autonomous driving. These results establish LUOT as a state-of-the-art solution for vehicle-to-vehicle global object tracking and fill an important gap in LiDAR-UWB fusion methods. The source code is publicly available at: https://github.com/ly3106/LUOT.

With the increasing computational demands of intelligent connected vehicles (ICVs), traditional roadside mobile edge computing (MEC) faces resource and coverage limitations. As a mobile computing entity, a vehicle platoon enables coordinated resource scheduling and low-latency communication, serving as an effective supplement to edge computing. However, limited onboard resources make vehicle platoons insufficient for handling heavy task loads. This study focuses on the joint task offloading and resource allocation between vehicle platoons and MEC servers. A MEC-assisted vehicle platoon computing network (MVPCN) is constructed, where both platoon vehicles (PVs) and MEC servers serve as computing nodes. We formulate a joint optimization problem of task offloading and resource allocation in the vehicle platoon, aiming to minimize the long-term weighted cost of time and energy consumption. Given the complexity of the problem and the limitations of conventional solution methods, we design a Discrete Two-Stage Stackelberg Game (DTSG) to decompose the problem into two subproblems: leader (platoon leader) level and follower (computing nodes) level, and prove the existence of a Stackelberg equilibrium (SE). Based on the game model, we propose a Nested Platoon Task Offloading and Resource Allocation (NPTORA) algorithm. The leader employs the multi-agent proximal policy optimization (MAPPO) to generate offloading decisions for the platoon, while the followers allocate computational resources using a Dynamic Priority-based Hybrid Optimization (DPHO) algorithm based on the received offloading decisions. Simulation results show that NPTORA outperforms baseline methods in task success, cost efficiency, and adaptability, demonstrating strong performance and practical potential.

Time-sensitive networking (TSN) is increasingly applied in automotive and industrial Internet fields due to its low latency and deterministic communication. Gate control list (GCL) is foundational for deploying TSN. Currently, most scheduling research focuses on frame-to-window-based scheduling. This scheduling approach typically generates a specific window for each frame, leading to a proliferation of GCL in large networks, which increases the complexity of implementing TSN. To simplify deployment and enhance scheduling reliability, this article introduces a systematic flexible-window-based scheduling framework. Utilizing a gapless GCL design approach, it optimizes flow’s worst-case end-to-end (e2e) delays through window length design, with delays obtained through network calculus analysis. A generic solving framework based on metaheuristic algorithms is established to address this optimization problem. The scheduling framework also features a load-balanced turn prohibition routing strategy to balance link loads and avoid cyclic dependencies, alongside a K-means priority clustering method based on routing overlap to reduce the number of priorities. Simulation validation in a high-level autonomous driving vehicle’s in-vehicle network shows that the proposed method can decrease GCL numbers by nearly 90% against frame-to-window scheduling. In common industrial Internet scenario, it significantly reduces worst-case e2e delays and enhances scheduling success rates compared to the analogous scheduling method. Large-scale complex network scenario further demonstrates its scalability.

As the Internet of Things proliferates, cloud-assisted mobile-edge computing (MEC) enables intelligent connected vehicles (ICVs) to offload their computationally intensive tasks to servers within the Internet of Vehicles, thereby reducing delay and energy consumption. However, most existing research on edge computing offloading overlooks the dependency relationships between subtasks. These dependencies significantly increase the complexity of task offloading, making it difficult to devise general solutions for scenarios of varying scales a challenging endeavor. To tackle this challenge, we present a heterogeneous graph attention network (HGAT) augmented deep reinforcement learning dependency-aware task offloading framework, aiming to achieve minimal task completion time and energy consumption. The dynamic system of vehicles and servers is modeled as an undirected graph, with nodes corresponding to servers/vehicles and edges capturing the intensity of task competition. Tasks are modeled as directed acyclic graphs, where nodes denote subtasks and directed edges define their dependencies. An HGAT-based encoder is then introduced to effectively capture the intricate relationships between subtasks and each servercores. Subtask selection and servercores assignment are formulated as a Markov decision process and solved using the proximal policy optimization method. Simulation results demonstrate that the proposed algorithm outperforms existing ones across various scenarios, showcasing superior adaptability and performance benefits.

High-level autonomous vehicles and domain-based electronic and electrical (E/E) architectures are important development directions of the intelligent automobile industry. The domain-centralized E/E architecture has become the potential upgrade to the autonomous vehicle benefitting from its powerful software updates, cabling reduction, and functional integration. Aiming at the efficient motion control of the autonomous vehicle equipped with domain-centralized E/E architecture, a novel control framework with algorithms improvement is proposed in this paper, which contains the multi-hops loop delay analysis to solve the control stability problem caused by the heterogeneous topology loop delay of domain-centralized E/E architecture. In this framework, the motion controller is generated through the combination of modified double reinforcement learning algorithm and multi-steps predictive control method, and the loop delay is integrated into the controller optimization. Through the virtual driving environment simulation and real world scenario, the results show that the proposed framework achieves better performance in terms of path tracking and obstacles avoidance, and the stability of control strategies to loop delay is also guaranteed.

Autonomous driving technology and platooning driving technology are important directions for the development of intelligent and connected vehicles. Aiming at the motion control problem of autonomous vehicle platoon, this article proposes a multilayer predictive control framework (MPCF) based on heuristic learning agent and improved distributed model. First, the leading autonomous vehicle and following heterogeneous vehicles are modeled, respectively, and the motion control problem of autonomous platoon is described. Then, the multilayer motion control framework is designed, which contains highly automated tracking control optimization for the leading vehicle (LV) and high-precision formation keeping optimization for the following vehicles (FVs). In the upper layer, the heuristic Dyna algorithm-based predictive control (HDY-PC) method is proposed to improve the path tracking performance of the LV. In the lower layer, the improved distributed model-based predictive control (IDM-PC) method is developed to guarantee the motion effectiveness and stability of the vehicle platoon. Besides, the multilayer control framework can handle various communication topologies and dynamic cut-in/cut-out maneuvers. The virtual environment simulation shows that the proposed motion control framework for heterogeneous autonomous vehicle platoon achieves better performance in path tracking and platoon keeping. The adaptability of the framework is also verified using another real-world scene.

In-vehicle time-sensitive networking (TSN) delivers highly secure, ultralow latency deterministic communication for intelligent connected vehicles (ICVs). To tackle the traffic scheduling problem of in-vehicle TSN, this study establishes in-vehicle network topologies and flow models, abstracts the traffic scheduling problem as a job-shop scheduling problem (JSSP), and formulates a priority-based optimization function capable of various end-to-end (E2E) delay requirements. A joint routing and scheduling optimization strategy based on improved grey wolf optimization (IGWO) is proposed, which incorporates acrlong LF, historical experience learning, and acrlong TS operators to significantly enhance search capabilities and optimization efficiency. This strategy can rapidly solve large-scale in-vehicle network scheduling and generate scheduling results with outstanding delay performance. Dynamic routing that combines load-balanced and shortest path effectively minimizes interference between flows, further reducing E2E delay. Simulation experiments grounded in realistic ICV scenarios demonstrate the effectiveness of the proposed strategy. Furthermore, the simulation results verify the impact of flow period parameters and network topologies on E2E delay, offering guidance for in-vehicle TSN engineering design.

The in-vehicle network (IVN) architecture significantly impacts the performance of high-level autonomous vehicles. This paper proposed an innovative optimization design framework for in-vehicle time-sensitive networking (TSN) architecture. This pioneering framework is specifically designed to optimize switch port assignment, load distribution, and end-to-end delay. To balance port allocation and load distribution, a multi-objective optimization problem is formulated. An adaptive non-dominated sorting genetic algorithm (NSGA-II), which in-corporates adaptive crossover and mutation probabilities, is employed to identify candidate topologies. The end-to-end delay is evaluated by an improved gray wolf optimizer (IGWO) based TSN scheduling algorithm. By integrating genetic and tabu search operators, the efficiency and scheduling effectiveness of the IGWO are significantly enhanced. The simulation verifies the superiority of the adaptive NSGA-II and IGWO on search capability. A design instance for a high-level autonomous vehicle is completed based on the proposed framework. The results demonstrate the effectiveness of the design framework, and some design ideas are summarized.

Time-sensitive networking (TSN) can offer deterministic low-latency communication, making it a critical solution for high-level autonomous vehicle's in-vehicle network. The deterministic transmission of TSN relies on TSN traffic scheduling. To ensure real-time transmission performance and vehicle functional safety, in-vehicle TSN scheduling aims to reduce end-to-end delay. Despite the promising potential of graph neural networks and deep reinforcement learning (DRL) in navigating complex TSN scheduling environments, its application has predominantly been limited to enhancing schedulability without a targeted focus on minimizing delays. This article introduces a DRL in-vehicle TSN scheduling framework based on the graph attention network (GAT). The scheduling problem is abstracted as a delay optimization problem and mapped to a Markov decision process (MDP), which is solved using the proximal policy optimization (PPO) algorithm. The GAT with attention mechanism is incorporated to extract critical information to enhance feature extraction and improve scheduling accuracy. This GAT-based PPO method can achieve high-precision offline scheduling through training, producing low-delay scheduling results. Simulation results demonstrate that the proposed method improves offline scheduling performance compared to other DRL-based scheduling methods. Leveraging the trained neural network, the proposed method can also deliver high robustness in online scheduling under link failure scenarios. It can produce a scheduling solution in just 3.8 s, and the scheduling results for all failure scenarios surpass those of rule-based benchmarking methods.

Path planning in unknown environments is extremely useful for some specific tasks, such as exploration of outer space planets, search and rescue in disaster areas, home sweeping services, etc. However, existing frontier-based path planners suffer from insufficient exploration, while reinforcement learning (RL)-based ones are confronted with problems in efficient training and effective searching. To overcome the above problems, this paper proposes a novel hierarchical path planner for unknown space exploration using RL-based intelligent frontier selection. Firstly, by decomposing the path planner into three-layered architecture (including the perception layer, planning layer, and control layer) and using edge detection to find potential frontiers to track, the path search space is shrunk from the whole map to a handful of points of interest, which significantly saves the computational resources in both training and execution processes. Secondly, one of the advanced RL algorithms, trust region policy optimization (TRPO), is used as a judge to select the best frontier for the robot to track, which ensures the optimality of the path planner with a shorter path length. The proposed method is validated through simulation and compared with both classic and state-of-the-art methods. Results show that the training process could be greatly accelerated compared with the traditional deep-Q network (DQN). Moreover, the proposed method has 4.2%–14.3% improvement in exploration region rate and achieves the highest exploration completeness.

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.

Cyber-physical systems (CPS) have become the cutting-edge technology for the next generation of industrial applications, and are rapidly developing and inspiring numerous application areas. This article presents an optimal forward-looking distributed CPS application for the safety-following driving control of connected and automated vehicles (CAV) in the intelligent transportation. The relevant components and required technologies of the CPS concept in intelligent transportation systems are introduced firstly. Under this framework, each CAV is considered as an independent CPS. In the safe driving of vehicles, historical data is used to build vehicle behavior prediction models and dynamic driving system models. At the same time, a new range strategy considering the probability of merging behavior is proposed and applied to the CAV’s safe cruise control. The results show that through the application framework of CPS, the proposed range strategy can improve the following safety of the vehicle.

This paper proposes a novel control strategy integrated path-following with yaw motion control for autonomous distributed drive electric vehicles with differential steering (DS) technology. First, the path-following and vehicle dynamics model, and DS system are introduced and analyzed. Then, the control framework is proposed, where the model predictive control (MPC) is adopted for path-following and yaw motion control. Given the optimized command by MPC, the quadratic programming (QP) algorithm is applied for in-wheel motors' torque allocation optimization. Series of simulation validations are carried out, proving that the proposed strategy can effectively achieve superior path-following effect, guarantee the vehicle yaw stability, and implement the steering control in DS system, simultaneously.