Generation of High-Coverage Traffic Scenarios for Efficient Simulation Testing of Automated Driving Systems

Virtual simulation testing is crucial for ensuring automated vehicles safety, which offers low cost and good repeatability. The key is to test in various virtual driving scenarios, but often fails to strike a balance between scenario coverage and test efficiency. To address this issue, we propose a scenario generation method based on a Genetic Algorithm optimized Hamiltonian Monte Carlo sampling approach. Specifically, a Markov chain is constructed converging to the joint probability density dis