An Approach to Generating Scenarios for Autonomous Vehicles

<div>To enhance the interpretability and coverage of high-risk scenarios in virtual test scenarios for autonomous vehicles, we propose a method for generating virtual test scenarios based on the VI-GAN (vehicle-interactive GAN) game neural network. This method constructs a converging interaction game model by capturing the interaction characteristics of vehicles converging on the ramp and those driving in the main lane. The Nash equilibrium solution of the game strategy and the convergence data are used to obtain the vehicle priority probability, and the game model is embedded in the S-GAN neural network model to propose a game trajectory generation model with the characteristics of a realistic interactive gaming behavior. Meanwhile, in order to obtain high-risk convergence scenarios, CT model is introduced to test the combination of real trajectories of interacting vehicles in the observed area and used in VI-GAN algorithm to generate more high-risk interaction trajectories with realistic game interaction behaviors. By comparing VI-GAN with LSTM, S-LSTM, S-GAN, and other trajectory generation algorithms, the results show that: (1) Compared with other algorithms such as S-GAN, the model generates converging interaction trajectories in 3.2 s and 4.8 s time domain, the ADE decreases by 25.30%/18.98%/7.02% on average, and the FDE decreases by 17.54%/16.16%/7.87% on average. Higher accuracy of interaction trajectories generated by VI-GAN algorithm. (2) The initial trajectories were generated using combinatorial testing and combined with game interaction scenarios in conflict situations. The number of generated trajectories is 150 times that of the original trajectories. The game-generated trajectories have more high-risk scenarios and higher scenario coverage compared to the original trajectories. This is of practical significance for virtual scenario-enhanced testing of self-driving cars. The virtual scene reinforcement test is of practical significance.</div>