Abstract:

Onboard camera images are the main data sources for constructing the intelligent vehicle test scenario library， but the probability of critical test scenarios occurring in the actual collected onboard camera images is very low， and most of the scenarios have little test value. If it is directly applied to the intelligent vehicle test， it will waste a lot of test resources. In this paper， a criticality assessment model for the intelligent vehicle test scenario based on the onboard camera images is proposed. Firstly， the images collected from real vehicles are processed based on the camera parameters to output parameters that have impact on driving safety. Then， the parameters are integrated using the risk field theory to output the criticality assessment results of the intelligent vehicle test scenario. Finally， the criticality assessment validation is conducted on the images collected from the actual vehicle. The results show that the proposed model can accurately output the specific values of the criticality of the test scenarios in order to compare the test values of different scenarios， proving that the model proposed in this paper can effectively screen the intelligent vehicle critical test scenarios.

Key words: intelligent vehicle, test scenario, criticality assessment model, onboard camera images, risk field theory