Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan

Abstract

The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is significantly simplified by this system’s capability to swiftly and accurately generate a vast array of specific scenarios through the input of scene parameters. The results indicate that this system achieves SG at a rate 2.5-fold faster than manual methods, alongside substantial improvements in accuracy. This system introduces a novel approach to virtual simulation, which is vital for the progress of autonomous driving safety. It offers a new paradigm for quickly and precisely generating test scenarios for autonomous driving.