Hazardous Scenario Enhanced Generation for Automated Vehicle Testing Based on Optimization Searching Method

The scenario-based test method is the research hotspot of automated vehicle (AV) validation and verification (V&V), and testing with hazardous scenarios is of important means. An Optimization Searching (OS) method for enhanced generation in hazardous scenarios is proposed in this paper to efficiently explore functional boundary scenarios in a huge logical state space. The method is computationally tractable, and its generated experimental parameters are optimized using past test results. The method includes five essential modules. The Exploration and Exploitation module uses the Multi-arm bandit method to obtain the greatest sum of the TTC-1 (Time To Collision). The Parameter Moving Probability Determination module uses an analytic hierarchy process to ensure that influential parameters are more likely to move. The Step Size Determination module is built with Levy-step to find a greater number of hazardous scenarios. The Memory Function module is used to avoid repeat experiments that can reduce computing efficiency. The Result Analysis module creates a hazard parameter space for subsequent tests. We tested an ACC (Adaptive Cruise Control) algorithm with a specified logical scenario in the virtual environment built by PreScan. The results showed that the OS method can effectively discover the dangerous range with the tested ACC algorithm, and its test speed can reach more than five times that of an exhaustive algorithm without prior knowledge.

Automated summary

The paper proposes an Optimization Searching method for enhanced generation in hazardous scenarios, utilizing modules such as Exploration and Exploitation, Parameter Moving Probability Determination, Step Size Determination, Memory Function, and Result Analysis. The method effectively explores functional boundaries in the automated vehicle context and shows significant improvement in test speed in ACC algorithm testing.