Research on game-based human-machine shared driving (HMSD) has traditionally been constrained by stringent game theory assumptions, high requirements for automated steering controllers, and the need to acquire the human driver’s target trajectory. To address these issues, this paper proposes a bargaining game-based HMSD control authority allocation strategy for a steer-by-wire vehicle. First, two types of lateral path tracking controllers are developed to simulate the human driver and the automatic steering system. Then, a bargaining game-based HMSD control authority allocation strategy is created using the Nash bargaining solution. Finally, a Simulink/CARSIM co-simulation platform and a driver-in-the-loop experimental bench are utilized to verify the proposed strategy. The results show that the proposed bargaining game-based HMSD control authority allocation strategy has potential advantages over existing game-based HMSD, including decoupling of human-machine interaction, lower requirements for automated steering controllers, and feasible game modeling ability. Moreover, the proposed strategy demonstrates excellent performance in terms of shared driving, which extends the applicability of HMSD in various operating maneuvers. Both the human driver and the automatic steering system benefit from the proposed HMSD system, which could increase the acceptance and trustworthiness of the human driver with the HMSD system and reduce the human driving load to make the driver-vehicle interaction more reliable and smoother.

As one of the research focuses in the field of intelligent driving, improving the performance of path tracking has become a goal for many scholars. Among many path tracking control algorithms, model predictive control (MPC) controllers are widely used due to their excellent control performance. However, the traditional MPC control has shortcomings because it does not consider the particularity of the driving car with preview driving characteristics, i.e., it is only directly controlling from the vehicle state. To fully retain the advantages of the MPC controller and simultaneously exert the preview characteristic of the intelligent vehicle to improve the path tracking performance, this work proposes a model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The algorithm mainly consists of two parts: The MPC controller with adaptive preview characteristic is proposed based on the lateral error and target curvature; The longitudinal vehicle speed-assisted constraint algorithm based on the largest ideal lateral acceleration becomes a supplementary part of the algorithm as a supplementary constraint of the MPC controller. Series of simulations based on the Simulink/CARSIM software individually verified the model predictive control with adaptive preview characteristics and an algorithm of longitudinal vehicle speed-assisted constraint for the path tracking algorithm. The proposed adaptive preview strategy is suitable for the path tracking algorithm controlled by the MPC controller and improves the path tracking performance.

Game theory has been extensively studied in recent years for its potential benefits on solving interactions between autonomous vehicles in platoon control. In this paper, we propose two behavioral decision-making approach based on non-cooperative game theory with both the complete information and incomplete information for cooperative vehicle platoon systems. The non-cooperative game payoff function takes the platooning performances of economy, comfort, safety, and further achieving self-driving functions into consideration. For the driving situations with incomplete information, a belief pool is constructed to represent the action probability for different behavioral types of vehicles, which will be updated by combining the driving intention identification with a Bayesian probability formula. With this, the stable strategies can be obtained for the two potentially conflicting parties, ensuring that neither of them has a motivation to change their driving behavior. Finally, the simulation results demonstrate that with the proposed behavioral decision-making approaches with complete information, the cooperative platoon performance can be markedly improved; with incomplete information, the platoon can determine the behavioral types of conflicting vehicles and complete collaborative decision-making approaches for solving not only the simple road-rights conflicting problems, but also for the extended vehicle platoon driving scenarios in a more complex transportation system.

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.