The modular chassis architecture of distributed drive electric vehicles (DDEVs) provides flexibility for integrating more electrical control units. To address the challenge of enhancing longitudinal dynamics while guaranteeing ride comfort, especially under frequent urban acceleration and deceleration conditions, this paper proposes a multi-agent system (MAS)-based framework to integrate the torque vectoring system (TVS) and the active suspension system (ASS), aiming to achieve better vehicle dynamics performance. First, a half-vehicle dynamics model is constructed to describe the coupling between longitudinal and vertical motions. The polytope technique is employed to address tire nonlinearity and time-varying system states. Then, cooperative control between the TVS and ASS is developed using the MAS system, where interaction behavior is modeled based on distributed model predictive control (DMPC) optimization results, and game theory is applied to find the optimal solution. This design effectively addresses the need for modularity and scalability in integrated chassis control systems. Furthermore, terminal constraints are introduced to ensure system stability performance. Finally, the experimental tests are performed to verify the performance of the proposed MAS framework. The results demonstrate the effectiveness in enhancing vehicle longitudinal driving performance while ensuring driving comfort.

Shared control provides a human-centered development direction for intelligent driving. However, existing shared control methodologies address the risk factors related to both the driver and the traffic environment inadequately. To this end, a shared steering control strategy is proposed based on the driver-vehicle-road (DVR) system risk assessment result. Firstly, the driver’s steering behavior is described through a two-point preview driver model. The key parameters are identified using real driving data. Meanwhile, the deep deterministic policy gradient (DDPG) algorithm is applied to train a reinforcement learning (RL) agent considering tracking accuracy, steering smoothness and vehicle stability as the autonomous driving controller. Afterwards, three time-varying risk factors are designed to evaluate the DVR system risk level, which represent driver risk, road risk and lane departure risk, respectively. Based on the system risk level, the control authority is initially calculated by a fuzzy inference method. Then, considering the smoothness of authority transition, a model prediction control (MPC) method is applied to optimize the initial authority level in real-time. Finally, simulation and the driver-in-the-loop (DIL) experiments are performed to validate the proposed strategy. The results demonstrate that the proposed shared control strategy could reduce driving burden and demonstrates distinct superiority in terms of human-machine collaboration, driving comfort and personalized support.

Trajectory prediction (TP) of surrounding vehicles (SVs) is crucial for autonomous vehicles (AVs) to understand traffic situations and achieve safe-efficient decision-making and motion planning. However, different drivers’ personalized driving preferences will bring uncertainties for long-term TP in the mixed traffic environment. To this end, this paper proposes a TP model with interaction awareness and driving style awareness for long-term TP of heterogeneous SVs. Firstly, the driving conditions in the highD dataset are distinguished, and three different driving styles of the vehicle in the car-following condition are obtained based on an unsupervised clustering algorithm. Then, an encoder-decoder architecture based on novel lane attention and multi-head attention mechanisms is proposed, where the encoder analyzes historical trajectory patterns and the decoder generates future trajectory sequences. The lane attention mechanism enhances the spatial perception capability of vehicles towards the target lane, and the multi-head attention mechanism extracts high-dimensional global interaction information about the heterogeneous vehicle group (HVG) surrounding the target vehicle (TV). Experimental results show that the proposed model outperforms state-of-the-art models in root-mean-square-error (RMSE) for long-term TP and exhibits excellent adaptability to diverse driving tasks. Moreover, this paper verifies that the driving style topology within the HVG has multiple impacts on the TP accuracy of the TV.

The paper concerns the contemporary approaches of Distributed Drive Electric Vehicles (DDEVs) to the development process and their problems. It starts with describing the complete independent control of a vehicle's throttling and braking forces through in-wheel motors. The study then goes further down the line and addresses vehicle reaction rate, eco-friendliness, and road-controlling precision. The paper uses the model of complete vehicle dynamics to develop a controller that would collect the specified characteristics of the given vehicle. It will make it more stable and safe on the road and explain the controller design and optimization procedure in this way. Furthermore, simulation methods are used to model the performance of different control strategies and algorithms and how they react to specific driving situations. The research findings, imply that DDEVs enable the exploitation of different kinds of torque management values, loss of unstopped energy, and stability enhancement, which are crucial in the effort toward shedding the negative emission detriments from our transportation systems.

This research proposes a coordination method for multi-connected and automated vehicles (CAVs) and traffic signal. It aims at reducing stop-and-go maneuvers of CAVs and enhancing traffic efficiency. The proposed method has the following highlights: i) Adaptive to actual CAV and humandriven vehicle (HV) mixed traffic; ii) Jointly optimization of both vehicle trajectory and signal timing via formulating in the spatial domain; iii) Parallel distributed computing. Simulation test results demonstrate that the proposed coordinated control significantly outperforms the benchmark method. The proposed method reduces the average travel delay by 29.56%, enhances fuel efficiency by 18.87%, and reduces stop count by 87.10%. The proposed parallel distributed computing algorithm ensures a computation time basically within 10 milliseconds. It indicates that the proposed method is ready for real-time large-scale implementation.

Fatigue and distraction are the most common long-term poor state and short-term abnormal behavior of drivers, significantly increasing the driving risk of vehicles equipped with the advanced driver assistance system (ADAS). To provide a more reliable decision-making basis for ADAS and improve driving safety, this paper proposes a driving risk assessment framework considering the driver’s long-term poor state and short-term abnormal behavior. Firstly, based on the self-built fatigue dataset and transfer learning method, an adaptive fatigue detection model with strong generalization capability is established to enable multi-view driver fatigue detection. Then, the idea of multi-clustering and adding offset parameters is introduced into the classical contrast loss function, and the D-InfoNCE loss function is designed to realize the accurate identification of the driver’s specific distraction behavior under open set detection. Subsequently, a driving risk assessment system is developed to quantify driving risk based on the vehicle driving risk factors when fatigued or distracted driving occurs. Finally, the proposed driving risk assessment system is validated by the datasets and driver-in-the-loop test bench. The results show that the proposed framework can accurately detect the driver’s fatigue state and distraction behavior and give ADAS the corresponding driving risk levels to enhance driving safety.

Fatigue driving has been regarded as one of the most important factors that cause traffic accidents. This paper proposes a robust human-machine shared control strategy to improve the vehicle performance for different driver fatigue states. Firstly, the time-varying driver steering model is proposed to address the model mismatch caused by fatigue driving. And the driver fatigue evaluation system is established based on facial features to quantify driver fatigue levels. Based on the quantified fatigue levels, a novel strategy for allocating authorities of the driver and controller is developed for building the driver-vehicle interaction system. Then, to weaken the influence of parameter perturbations caused by the time-varying driver states, we design a fatigue-based shared controller through state feedback. The actuator saturation and system constraints are considered in the controller design through the robust set-invariance property to improve vehicle safety and driving comfort. The driver-in-the-loop platform is conducted to validate the effectiveness of the proposed shared steering controller. The experimental results show that the proposed strategy can adaptively optimize the human-machine authorities according to fatigue states and comprehensively improve vehicle performance.