Yixiao Chen

Autonomous driving in unstructured environments faces challenges such as missing road boundaries, terrain variations, random obstacle distributions, and complex vehicle–terrain interactions, making it difficult to achieve safe navigation by relying on lane-level priors from structured roads. To address the problems of the relative separation between traversability analysis and trajectory planning, the ineffective propagation of perception uncertainty, and the insufficient scene adaptability of coupling mechanisms, this paper takes traversability as the main thread and systematically reviews the research progress of perception–planning coupling mechanisms in unstructured environments. First, traversability analysis methods based on geometric terrain, semantic understanding, and physical dynamics are reviewed, and the representation and propagation mechanisms of uncertainty in the perception–planning chain are analyzed. Second, the role of traversability information in global path search, local trajectory optimization, and data-driven planning is discussed, and the applicable boundaries of different coupling architectures are summarized from the perspectives of representation level and system organization form. Finally, datasets, simulation platforms, and evaluation metric systems are summarized, and a risk-state-oriented adaptive perception–planning coupling framework is proposed to dynamically adjust coupling strength based on risk-state information, thereby improving the safety, interpretability, and environmental adaptability of autonomous driving in unstructured environments.

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control.

In the field of automated driving, scenario-based safety testing is essential for ensuring vehicle safety and promoting technological development. However, traditional mileage-based real-world testing methods are limited by the massive testing requirements and extremely long cycles, hardly meeting efficient validation demands. To address this, this paper proposes a Hybrid Optimal Acceleration Evaluation (HOAE) model based on virtual testing. First, a hybrid segmentation model based on fitting error is constructed to accurately characterize the distribution features of naturalistic driving data, and an acceleration model is designed with the number of tests as the optimization objective to solve for the optimal number of segments. Second, the Importance Sampling (IS) method is introduced to increase the occurrence probability of risk scenarios, and the optimal IS function is solved based on the above acceleration model. Finally, a simulation test platform is established, and comprehensive comparative experiments are conducted under various acceleration evaluation methods, indicators, and parameter distributions. The results demonstrate that the proposed HOAE method offers better universality and higher testing efficiency, and it can increase the test mileage to 10⁴ times of the Monte Carlo (MC) method, thereby advancing safety testing of automated vehicles toward greater efficiency and broader applicability.

With the rapid development of automated wheeled vehicle technology, complex vehicle functions require extensive safety testing for verification. Compared with real-vehicle testing, scenario-based virtual testing, which constructs virtual environments to simulate real scenarios and efficiently evaluates vehicle safety and risk decision-making capabilities, has become a core means for the safety evaluation of automated wheeled vehicles. This paper outlines the research progress of scenario-based virtual testing for automated wheeled vehicles (including highway autonomous vehicles and off-highway autonomous vehicles); classifies three key technologies in highway scenarios, hazard evaluation, hazardous scenario generation and generalization, and acceleration evaluation; and reveals the challenges faced when existing methods are migrated to agricultural vehicles, engineering vehicles, etc., such as low scenario adaptability, multi-dimensional coupling of risk targets, and weak data foundation. This study finds that current technologies have formed a symmetric framework in highway scenarios, but there are significant adaptability problems when migrating to off-highway scenarios due to scenario asymmetry. To this end, this paper proposes ideas for realizing off-highway scenario testing by adopting methods such as dynamic safety distance reconstruction, multi-physics simulation, and digital twin-driven approaches, providing theoretical support for building a unified safety assessment platform for automated wheeled vehicles.

Aiming at the characteristic of a variable and optimized steering ratio of the Steer-by-Wire System (SBW), this paper studies the design method of the steering ratio starting from the influence of the steering ratio on the vehicle steering maneuverability and the driver’s steering burden. Through the analysis of the influencing factors of the steering ratio and the analysis of the driver’s steering characteristics, a yaw rate gain control model is established. Combined with the evaluation index of handling stability, the yaw rate gain is optimized, and the optimal yaw rate gain corresponding to different scenarios and different drivers’ steering characteristics is determined, so as to design the characteristics of the variable steering ratio that meet the preferences of different drivers. In order to verify the control effect of the variable gain steering ratio, a comprehensive feedback control strategy for the front wheel angle is established, and vehicles with a fixed steering ratio and a constant gain steering ratio are selected as references. Comparative tests under typical working conditions are carried out in the “driver-vehicle-road” closed-loop simulation system. The results show that the variable gain steering ratio considering the driver’s steering characteristics can not only improve the handling stability of the vehicle at medium and high speeds, but also enhance the driver’s steering comfort, enabling the SBW to achieve the goal of “the vehicle adapting to the person”.

The lane-changing behavior exerts a profound influence on the dynamic attributes of traffic flow and road safety. Accurate analysis of lane-changing behavior not only facilitates the comprehension of traffic phenomena and the prevention of traffic accidents but also contributes to constructing a dynamic and realistic background traffic flow for autonomous driving tests. In this paper, we propose a two-dimensional lane-changing dynamics model based on force by abstracting the motion of the vehicle as the variation in acceleration under the influence of forces. Specifically, based on the similarity analysis of factors affecting acceleration and constitutive relationships, the three types of forces to which the vehicle is subjected are represented by a combination of basic visco-elastic elements, while the driver’s attention to the current and target lanes decreases and grows during the lane changing process is analyzed. This model not only comprehensively delineates the lane-changing process but also represents the influences of driver characteristics, neighboring vehicles, and road conditions. In order to verify the performance of the proposed model, we identified the parameters using the least squares method (LSM) based on the highD naturalistic driving dataset. Comparative results illustrate that the lane-changing dynamics model effectively capture complex lane-changing behaviors and their interactions between ego vehicle and surrounding vehicles through a simple and unified way.

Simultaneous localization and mapping (SLAM) is one of the core technologies to realize automated valet parking (AVP). Currently, advanced visual feature-based SLAM systems suffer from feature extraction failure and tracking loss due to the constraints of textureless scenes, unclear illumination, and dynamic conditions. To address these problems, this paper proposes a visual SLAM algorithm based on a semantic closed-loop detection algorithm using surround-view cameras and inertial measurement units (IMU) as sensors. The algorithm combines semantic features and the idea of inverse index to improve the traditional keyframes selection methods and the closed-loop detection algorithms, effectively avoiding the tedious and complicated feature point matching and improving the computational efficiency of the computer. Experiments show that the algorithm in this paper achieves better results in terms of precision and recall, absolute trajectory error (ATE), and relative pose error (RPE), and can meet the demand for SLAM and subsequent navigation in indoor parking lots.