To solve the problem of significant degradation in braking efficiency of medium and heavy-duty vehicles during prolonged downhill descents due to frequent engagement of the main brake system，a four salient poles liquid-cooled electromagnetic retarder structure is proposed in this paper. A vehicle downhill dynamics model is established to analyze the braking demand，using the magnetic equivalent circuit method to calculate its braking torque，and using the finite element method to numerically analyze its braking characteristics. The hierarchical variable domain fuzzy control strategy combined with a retarder is used for vehicle downhill braking control，and the controller and overall vehicle downhill braking model are established using MATLAB/Simulink for joint simulation. A 2 100 N·m prototype is designed. The braking characteristics of the prototype are tested through bench test and on-road vehicle tests. The results show that the actual measured value and the calculated value is basically consistent，with an average error within 5%，with the braking torque reaching 2 200 N·m when the speed is 1 250 r/min，which can meet the needs of medium and heavy duty vehicle braking.

Effectively predicting the future risk indicators of multiple traffic participants under the driver's field of vision is the key to providing risk warnings to human drivers and avoiding potential collision risk. Most existing research on risk only considers the pairwise interaction between a single individual and the vehicle in the scene， and conducts research from the perspective of evaluation rather than prediction， while ignoring the different interaction between heterogeneous traffic participants and future risk status. This paper proposes a heterogeneous multi-objective risk prediction method Risk-STGCN based on spatiotemporal graph convolutional neural network， using graph convolution and temporal convolution to learn single-frame scene graph information and timing information respectively， combined with multi-layer timing prediction network to predict the multi-objective risk indicator TTC. Training and verification are conducted on the open source data set BLVD and the real vehicle self-collected data set， which is then compared with commonly used sequence prediction models. The experimental results show that the average TTC error of the proposed model on different data sets is less than 0.95 s， with multiple experimental indicators better than other models mentioned in this paper. The proposed model has good robustness and improves the interpretability of risk prediction in complex traffic scenarios.

For the problems of path planning on curved roads， a path planning fusion algorithm based on global oriented artificial potential field method is proposed in this paper. Considering the curved road conditions， a grid map based on deformed grid is constructed. Considering the driving risk in the road environment， the heuristic function of A* algorithm is optimized based on the driving risk field theory. To improve the limitation and inherent defects of the traditional artificial potential field method， in view of the outline shapes of the subject vehicle， environment vehicles and obstacles， the artificial potential field method is improved as the local path planning method by introducing in the globally guided path. Taking the path planned by the improved A* algorithm as the global optimal guided path， the path planning fusion algorithm is designed based on the improved artificial potential field method. The simulation results show that the proposed fusion algorithm can generate effective and reasonable driving path， which is close to the real vehicle path extracted from the dataset. Moreover， the path planned in the environment with obstacles is safe and efficient， meeting the driving requirements of the vehicle.

The evaluation of scenario complexity is crucial for improving adaptability and flexibility of autonomous vehicles in coping with complex environments and enhancing the applicability of the algorithms. A graph-based algorithm for evaluating scenario complexity is developed in this paper， which fully considers interactive topology and categorizes traffic scenarios into three complexity levels. The reasonability and effectiveness are validated in ramp merging scenarios. To demonstrate its scalability， the evaluation algorithm is applied in the development of the trajectory prediction and decision-making algorithms of automated driving. The proposed algorithms are then tested using natural driving datasets and vehicle-in-the-loop experiments. The results indicate that scenario complexity evaluation enables early estimation of prediction uncertainty， enhances the real-time and optimality of decision-making algorithms. In data replay tests， the complexity assessment module can reduce the failure rate and collision rate during lane merging by approximately 38% and 92%， respectively， indicating promising application prospects.

A trajectory planning method based on graph search and optimization is proposed for intelligent vehicle trajectory planning in dynamic unstructured environments. Firstly， the graph search method is employed to search for motion primitives for intelligent vehicles to obtain initial trajectories that conform to kinematic characteristics. Then， based on nonlinear model predictive control methods， the trajectory is optimized to obtain smoother and safer trajectories. In order to achieve rapid and secure expansion of primitives in dynamic unstructured environments， a method for primitive collision detection is proposed. This method uses obstacle expansion and grid discrete motion elements to perform static collision detection on irregular obstacles， and introduces in the concept of velocity obstacles to perform dynamic collision detection on dynamic obstacles in velocity space. The proposed algorithm is compared by simulations in ROS/Gazebo environment， and is evaluated by field tests. The results show that compared to the TEB algorithm， the proposed trajectory planning method improves the average obstacle avoidance success rate by 18% while meeting the real-time computing requirements， demonstrating higher safety obstacle avoidance ability and feasibility.

Complex disturbances such as external interference， model uncertainty and parameter perturbation directly affect the accuracy and driving safety of intelligent vehicle path tracking control. Commercial vehicles are more susceptible to complex disturbances during driving because of their load characteristics. A hybrid path tracking control method is proposed in order to improve the accuracy and smoothness of commercial vehicle path tracking. Firstly， a robust sliding mode controller based on extended observer and an incremental LQR controller with stable changes are established. Particle swarm optimization algorithm is used to tune the parameters of the incremental LQR. Then， in order to improve robustness while weakening chattering， a fuzzy controller is used to adjust weight coefficient between them according to vehicle speed and lateral error. Finally， simulation analysis and vehicle experiments are conducted. The experimental data shows that SMC+LQR has good control performance to cope with complex external disturbances.

To address the problem of speed trajectory deviation of connected vehicles （CVs） caused by human driver error， a real-time eco-driving strategy for connected mixed platoons considering human driver error is proposed in this paper. Firstly， real vehicle tests are conducted to collect human driver error data of different drivers to establish the human driver error model based on Markov chain so as to predict the human driver error for a period of time in the future. Then， with the optimization objective of minimizing the fuel consumption of the entire platoon， the platoon speed trajectory optimization problem is formulated as an optimal control problem. Fast stochastic model predictive control （FSMPC） is employed to calculate the optimal speed trajectories of the connected vehicle in the mixed platoon. Both the simulation and intelligent and connected micro-car test results indicate that， compared to the traditional eco-driving strategy based on fast model predictive control （FMPC）， the proposed eco-driving strategy can effectively reduce the speed trajectory deviation and fuel consumption of the whole platoon as well as meet the real-time requirements.

Scenario-based simulation test method is an important means of automated driving vehicle safety verification； however， current test scenarios generation methods are mostly for independent scenarios. How to simulate the human real driving process to generate continuous interactive test scenario with challenges has become a problem that needs to be solved urgently in automated driving test evaluation. In this paper， an automated driving anthropomorphic continuous interactive test scenarios generation method is proposed. Firstly， the architecture for anthropomorphic continuous interactive test scenarios generation is established， and the vehicle motion behavior analysis is conducted based on the HighD dataset. On this basis， the current behavior of tested automated driving vehicle based on the trajectory similarity feature is analyzed， and the prediction of the future trajectory through the state transfer matrix is realized. Then， the type of the future behaviors of the traffic vehicles based on the trajectory interaction rules are determined， and the specific trajectory is generated by Transform network. Finally， the key performance indicators such as danger and anthropomorphism of the generated test scenarios are evaluated in simulation test environment， which proves the effectiveness of the method proposed in this paper.

In autonomous driving scene understanding task， accurate segmentation of drivable areas， dynamic and static objects is essential for subsequent local motion planning and motion control. However， the current general semantic segmentation method based on lidar point cloud cannot achieve real-time and robust prediction on vehicle-end edge computing devices， and cannot predict the motion state of objects at the current moment. In order to solve this problem， a multi-task segmentation network MultiSegNet for driving areas and dynamic and static objects is proposed in this paper. The network uses the depth map output by the lidar and the processed residual image as the representation of the encoded spatial features and motion features to input to the network for feature learning， so as to avoid directly processing disordered high-density point clouds. For the large difference in the number of target distributions in different directions of the depth map， a variable resolution grouping input strategy is proposed， which can reduce the amount of network computation and improve the segmentation accuracy of the network. In order to adapt to the size of the convolutional receptive field required for targets at different scales， a depth-value-guided hierarchical dilated convolution module is proposed. At the same time， in order to effectively correlate and fuse the spatial position and attitude information of objects in different time domains， a spatiotemporal motion feature enhancement network is proposed. The effectiveness of the proposed MultiSegNet is verified on the large-scale point cloud driving scene datasets SemanticKITTI and nuScenes. The results show that the segmentation IoU of driving area， static object and dynamic object reaches 98%， 97% and 70%， respectively， which is better than that of mainstream networks， with real-time inference realized on edge computing devices.

With the rapid development of automated driving technology， ride comfort has become a key factor affecting user acceptance and overall experience with automated vehicles. In this paper， a comprehensive review of the current state of research concerning the evaluation of riding comfort in automated vehicles is presented. Firstly， the concept of comfort is thoroughly articulated， followed by an analysis of key factors influencing ride comfort. Subsequently， the quantitative indicators and evaluation models pertinent to automated vehicles are classified and elaborated in detail. The quantitative indicators are classified into four categories： subjective indicators， indicators derived from vehicle parameters， indicators based on physiological signals， and indicators related to driver behaviour. The evaluation models encompass psychophysical models， biomechanical models， statistical models， and learning-based evaluation models. Finally， prospective trends in the research of comfort in automated vehicles is brought forward， thereby offering a technical framework for further studies on the system design and user experience in this domain.