Test Scenario Generation and Optimization Technology for Intelligent Driving Systems

In this paper, we propose a new scenario generation algorithm called Combinatorial Testing Based on Complexity (CTBC) based on both combinatorial testing (CT) method and Test Matrix (TM) technique for intelligent driving systems. To guide the generation procedure in the algorithm and evaluate the validity of the generated scenarios, we further propose a concept of complexity of test scenario. CTBC considers both overall scenario complexity and cost of testing, and the reasonable balance between them can be found by using the Bayesian optimization algorithm on account of the black box property of CTBC. The effectiveness of this method is validated by applying it to the lane departure warning (LDW) system on a hardware-in-the-loop (HIL) test platform. The result shows that the bigger the complexity index is, the easier it is to reveal system defects. Furthermore, the proposed algorithm can significantly improve the integrated complexity of the generated test scenarios while ensuring the coverage, which can help to find potential faults of the system more and faster, and further enhance the test efficiency.

Automated summary

In this paper, a new scenario generation algorithm called Combinatorial Testing Based on Complexity (CTBC) is proposed for intelligent driving systems. The algorithm considers both overall scenario complexity and cost of testing, and uses the Bayesian optimization algorithm to find a reasonable balance between them. The effectiveness of the method is validated by applying it to the lane departure warning (LDW) system on a hardware-in-the-loop (HIL) test platform. The proposed algorithm significantly improves the integrated complexity of the generated test scenarios while ensuring coverage, enhancing the efficiency of system fault detection.