A game-based cooperative steering control (GCSC) approach is introduced to facilitate effective collaboration between human drivers and automation, incorporating the neuromuscular delay inherent in human responses. In this framework, a dynamic coordination between driver and automation goals is achieved through the establishment of a game equilibrium in instances of driver and automation conflict. In response to the challenges posed by frequent modifications in driving weights and their subsequent burden on human drivers, this article proposes a strategy that integrates fixed initial weights with dynamic adjustments to driver–automation driving weights. Moreover, a comprehensive evaluation method including subjective and objective evaluation indexes is proposed. Different drivers are invited to perform virtual driving experiments, and the experimental results are analyzed by the proposed evaluation method. It is concluded that the driver’s driving weight should be kept at a high level during cooperative steering control when the driver’s intention cannot be perfectly obtained, and the determination of the driving weight should also consider the driver’s driving skills.

The embedding of high-level traffic semantics has elevated the precision of vehicle trajectory prediction tasks to a new level. However, owing to the absence of feature-level integration, the information from high-definition maps is underutilized. To this end, a map search-based vehicle trajectory prediction method conditioned on lane segments is proposed in this article. The map is discretized into a graph, where nodes represent lane centerline segments. On this basis, the agent-to-agent, agent-to-map, and map-to-map modules are designed to depict heterogeneous interaction patterns involving vehicles and pedestrians. In addition, a goal node querying mechanism is introduced, which integrates vehicle motion, interaction, and traffic flow states and serves as prior information for trajectory prediction. Finally, a feasible path selection strategy is proposed, generating traffic rule-related prediction trajectories point by point, fully utilizing map information. The experimental results on the nuScenes dataset indicate that the proposed method achieves state-of-the-art prediction accuracy compared with advanced methods.

For personalized human-machine cooperative (HMC) control, traditional model-driven approaches, which rely on predefined driver-vehicle-road (DVR) models, often struggle to adapt to individual driver differences. To address this, a data-learning shared control strategy based on game output regulation and adaptive dynamic programming (ADP) is presented. Firstly, considering the differences in driver’s characteristics, vehicle-road dynamics and human-machine interaction, an uncertain DVR system is established. Subsequently, robust output regulation (ROR) is utilized to handle road curvature perturbations and ensure closed-loop system stability. Subsequently, a dynamic game framework between the front-wheel steering system (AFS) and the active rear-wheel steering system (ARS) is further developed to ensure both vehicle stability and path-tracking accuracy in complex environments. Finally, the AFS-ARS optimal control strategies are iteratively learned and updated by ADP, using online DVR system data, without requiring prior knowledge of specific drivers or vehicles. Through driver-in-the-loop experiments, it is demonstrated that the presented method exhibits good adaptability to different drivers.

This paper introduces a novel nonlinear game-based driver-automation cooperative steering control method to mitigate collision caused by the driver’s limited experience on low adhesion road conditions. First, we utilize a model predictive control (MPC) driver model to capture the characteristics of driver experience deficit in low adhesion road conditions, considering the driver’s neuromuscular delay as the system time lag. Then, a dynamic driving weighting strategy is proposed to adjust the driving weights, taking into account both driver-automation handling conflicts and road risks. Next, in order to account for the nonlinear tire dynamics encountered on low adhesion road surfaces, the problem of driver-automation cooperative steering control is mathematically framed as a nonlinear game. The utilization of the piecewise affine(PWA) theory enables the linearization of the nonlinear game optimization problem, facilitating the derivation of an optimal control strategy for ensuring vehicle stability on low adhesion road conditions. Finally, the proposed method is rigorously validated through simulations and driver-in-the-loop tests, comparing its performance against an existing driver-automation cooperative steering control approach. The experimental results substantiate the effectiveness of the proposed method in mitigating the driver’s steering workload and leveraging tire forces optimally to enhance vehicle stability.

As a critical component of the Internet of Things, connected automated vehicles (CAVs) are progressively gaining attention for their benefits in terms of increased safety and reduced traffic congestion. In this article, a novel distributed data-driven model-predictive control (DDMPC) approach including feedforward for disturbance is proposed for cruise control of a hybrid platoon with a combination of human-operated and autonomous vehicles. By employing a predictor constructed from input/output data, predictive controllers are obtained without depending on the characteristic information of the system. A robustness analysis is performed with a combination of the input-to-state stability (ISS) theory with the sampled-data systems theory, and the$\mathcal {L}_{2}$-norm string stability is ensured by strict mathematical proof. In addition, we also discuss the asymptotic stability when the controller switches. CarSim simulation and bench experiment results verify that the DDMPC for connected vehicles can be robust to velocity disturbances and achieve satisfactory performance in ensuring string stability.

Connected automated vehicle (CAV) platoon control is becoming increasingly prevalent because of its unique advantages in reducing fuel consumption and improving traffic efficiency. A novel control framework for CAV platoon control is designed in this article. First, a model predictive control (MPC)-based method is proposed to obtain the optimal velocity of the whole platoon, in which both reducing fuel consumption and improving transport efficiency are taken into account in the optimization process. Then, a distributed adaptive triple-step nonlinear control strategy is investigated from the perspective of multiagent system control. The adaptive performance of the control strategy can guarantee the string stability of the CAV platoon under the premise of the existence of dynamic uncertainties. Various simulation conditions with heterogeneous dynamic disturbances are designed to validate the proposed control strategy, and the results show that the proposed control strategy can be robust to dynamic disturbances while ensuring the string stability of the CAV platoon.

The driving model switched between an intelligent vehicle and a human driver is a hot discussing issue for intelligent driving system, and it relates to the safety of the intelligent vehicle and traffic efficiency of transportation system. It presents a hazard-evaluation-based driver-automation switched shared steering control approach for intelligent vehicles in this manuscript. The switched operation between human driver and autopilot system is carried out when the hazard situation is tested by the autopilot controller. The driver's operation and the deviation from the road center line are employed to carry out the hazard evaluation. The autopilot controller is designed using the constrained model predictive control (MPC) approach to keep the intelligent vehicle run in the safe area that is between the road boundary. In order to verify the control performance of the proposed algorithm, simulation verification under hazard situation of the proposed approach are carried out and compared with the non-switching method. The results show that the intelligent vehicle can keep safe in the hazard situation.