Driving Scenario Generation

Generating realistic and diverse driving scenarios for autonomous vehicle (AV) testing and training is crucial for ensuring safety and reliability. Current research focuses on developing methods that leverage real-world data and simulation, employing techniques like diffusion models, trajectory optimization, and reinforcement learning to create scenarios encompassing a wide range of conditions, including rare but critical safety-critical events. These advancements aim to address the limitations of relying solely on real-world data, which often lacks sufficient diversity and representation of high-risk situations, ultimately improving the robustness and safety of AV systems. The resulting improvements in scenario generation directly impact the development and validation of safer and more reliable autonomous driving technologies.