By summarizing the laws, regulations and standards formulated in China and foreign countries during the development of intelligent and connected vehicles, this paper sorted out the standardization construction system of intelligent and connected vehicles, which included 4 parts: admission, term definition, testing system and accident liability of intelligent and connected vehicles. Through the interpretation of standards, the understanding of the evolution of relevant laws and regulations in the process of the transformation of traditional vehicles to intelligent and connected vehicles was deepened, the technical requirements and social problems faced by the current intelligent and connected vehicle industry were clarified.

The existing passive safety related standard restricts the development of new intelligent vehicle technologies. To solve this problem, this research team developed China construction scheme of intelligent vehicles passive safety standards and the development roadmap according to the revision of the relevant passive safety standards of intelligent vehicles by the international standard organizations and the American standards institution, and then took one national mandatory standards as an example. This research can provide necessary support for the applicability developing and revising China’s passive safety standards.

In order to realize the accurate prediction of the trajectory of surrounding vehicles, a driving intention recognition and trajectory prediction model based on graph neural network and Gated Recurrent Unit (GRU) was designed by using deep learning method. The driving intention recognition model constructed the interaction relationship between vehicles into a space-time graph, used the graph neural network to learn its interaction rules and used Softmax function to calculate the probability of different driving intentions. The trajectory prediction model adopted an encoded-decoded GRU network and the encoder encoded the vehicle history trajectory information and fused the recognized driving intention information and then realized trajectory prediction through the decoder. Finally, the Next Generation Simulation (NGSIM) dataset was used to train and verify the model and the results show that the proposed model can better identify the driving intention of the vehicle, and the vehicle trajectory prediction model considering the driving intention can effectively improve the prediction accuracy.

Based on the multi-point preview model, a trajectory tracking model optimized by Grated Re-circulated Unit (GRU) neural network was designed to improve trajectory tracking accuracy of intelligent vehicle. Firstly, on the vehicle’s 2 degree of freedom model, 3 preview trajectory tracking models were established based on the preview theory. The simulation results show that the multi-point preview model has the best tracking effect. Then, the parameters such as multi-point preview lateral displacement deviation and steering wheel angle were used as the inputs of GRU neural network. After training, the optimized steering wheel angle was as output to control the driving direction of the vehicle. The verification results show that the trajectory tracking model optimized by GRU has better tracking effect under double shift path and S-curve path.

To solve the problem that the path tracking controller has a large tracking error under different road adhesion coefficients and vehicle speeds based on traditional Model Predictive Control (MPC), this paper proposed a path tracking control strategy for autonomous vehicles based on Particle Swarm Optimization (PSO)-BP neural network optimization MPC. Firstly, a path tracking controller based on MPC was designed; Secondly, PSO-BP was used to optimize MPC, and the controller accuracy and vehicle stability were taken as evaluation functions to obtain the offline optimal time domain parameters of PSO. Finally four conditions were selected for comparison and simulation verification of double shift lane tracking. The results show that the lateral control accuracy of double shift lane tracking under the 4 conditions, including low adhesion at low speed, high adhesion at low speed, high adhesion at high speed and medium adhesion at medium speed, is improved by 50%, 55%, 9% and 20% respectively.

In order to improve the accuracy and robustness of intelligent vehicle path tracking, a robust feedback path tracking controller was designed based on Lyapunov Stability Theory. By using Schur complement lemma and solving Linear Matrix Inequality (LMI), the feedback matrix of the control system was obtained. By building a CarSim/Simulink joint simulation platform, the robust feedback controller was compared with the Linear Quadratic Regulator (LQR). The performance of the designed controller was verified by simulation tests on the roads with different adhesion coefficients at different speeds. The simulation results show that, compared with LQR controller, the robust feedback controller based on Lyapunov stability theory not only has higher control accuracy, but also has stronger robustness when the vehicle is running on different roads at different speeds.

For the active obstacle avoidance of vehicle under different road conditions, this paper proposed an obstacle avoidance path planning method in driving risk field considering road adhesion coefficient. Firstly, the driving risk fields including road boundary risk field, target gravitational field and obstacle risk field were established. The road adhesion coefficient was estimated in real time based on the volumetric Kalman filter algorithm, and the driving risk field function considering the road adhesion coefficient was derived with negative gradient derivative, and the obstacle avoidance path with the lowest risk was obtained. Then, the obstacle avoidance reference path satisfying the vehicle constraints was obtained by 5-degree polynomial fitting optimization. Finally, the model predictive control algorithm was utilized to track the obstacle avoidance path. The simulation results show that at the same speed, the smaller the road adhesion coefficient, the smaller the lateral acceleration is, the smaller the standard deviation of the lateral acceleration is, and the more stable the obstacle avoidance effect will be.