Abstract:

In order to accelerate the speed and efficiency of autonomous systems testing, the method of generating a scene database for unmanned driving in campus environments was proposed. Firstly, the simulation test scenarios in complex campus environment were analyzed, and the campus scenes were simplified as a combination of road network structure, ground properties, interacting members and environmental factors. Secondly, the method of generating the scene database based on importance indicators was proposed to solve the boundedness of the campus scenario database. Then, the complexity indicators and interest probability indicators were used to describe the importance indicators of scenarios. The fuzzy analytic hierarchy process(FAHP) was used to evaluate the complexity of the scenario. The interest probability of the scenario was calculated by combining the kernel density estimation method and the interested weight calculation method. Next, the parameter space was segmented to obtain the set of similar scenarios, and the scenario sets were sorted according to test priority and importance indicators. The filtered scenarios were gradually added to the test scenario database, and the scenario database with test sequences was generated. Finally, the test evaluations based on the real-world campus scenario database were conducted to verify the effectiveness of the scenario database generation method proposed in this paper. The results show that the campus test scenes can be effectively described using four scene elements and the tree structure. The method proposed in this paper can generate a campus test scene library with high test efficiency, high coverage, conformity to natural probability, and interest interval, which is helpful to improve the efficiency of unmanned simulation test in complex campus environment.

Key words: complex campus environment, unmanned driving, generation of scenario library, scenario model, importance indicators