A Risk Level Assessment Method for Traffic Scenarios Based on BEV Perception

How to fully test the safety and functionality under different driving scenarios is a key issue for the development and application of autonomous vehicles. In this study, aimed at the test scenarios of autonomous vehicle, we propose a lidar-camera fusion approach for traffic environment sensing. Based on the successful Lift-Splat-Shoot (LSS) model, we propose a unique data enhancement strategy to develop the fusion accuracy. Through building a test dataset with the highprecision acquisition vehicle, the proposed method is verified that the new fusion authorism proposed in this paper can accurately distinguish the translation, scale, orientation and velocity of the target. This study can promote test scenario generation methods.