Automatic Generation of Intelligent Vehicle Testing Scenarios at Intersections Based on Natural Driving Datasets

Turning right at a junction without the aid of traffic signals is a frequent cause of intersection accidents and a situation that intelligent automobiles will need to handle well in the future. This allows this study to choose the traffic circumstances of turning right at junctions and traveling straight through them. It then suggests an automatic approach for creating test scenarios for intelligent vehicles based on Monte Carlo simulation. Through the examination of scene feature parameters and a natural driving dataset, a Markov chain-based right-turn trajectory model was created. Then, test vehicle trajectories and right-turn trajectories were produced using Monte Carlo simulations. By figuring out the average DTW (Dynamic Time Warping) value between the created and actual right-turn trajectories in the four speed intervals with the highest occurrence probabilities, the authenticity of the generated right-turn trajectories was confirmed. The produced trajectories were simultaneously loaded into the software for cooperative simulation. The AEB (Automatic Emergency Braking) system's shortcomings were shown by extracting the relative positions of the two cars before to the incident. The two popular varieties of AEB algorithm models were then compared using safety and comfort parameters. Furthermore, the test scenario's risk level can be managed by changing the scenario feature settings. There are some potential uses for the suggested test scenario generation method in the testing and assessment of algorithms for intelligent vehicles.