Utilizing High-Definition Maps and Simulation Software to Enhance Autonomous Vehicle Safety

Abstract. The safety of autonomous vehicles (AVs) remains a major challenge, particularly within specific Operational Design Domains (ODDs). While physical testing lacks scalability, computer simulations effectively evaluate AV safety. However, most studies rely on virtual maps generated by simulation software, neglecting real-world complexities and focusing on single intersections rather than broader road networks. To fill the gap, this study lie in the development of a novel simulation workflow combining CARLA and VISSIM simulation softwares using High-definition (HD) maps while reducing data collection efforts and enabling the seamless integration of simulation results. Additionally, this study validates the effectiveness of emergency braking indicators within the AV simulation, confirming their applicability in assessing AV safety performance. 
The study area conducted near Tainan-City High-Speed Railway Station, Taiwan, analyzes emergency braking events across a network of three intersections and four road segments to identify high-risk zones. HD maps of the study area data were integrated into the CARLA to generate realistic traffic scenarios, while VISSIM modeled traffic flow and signal phases. 
The results indicate that emergency braking hotspots are concentrated at intersections, turns, and sharp curves near safety islands. This finding suggests that AVs make slower decisions than human drivers due to complex perception and computational models. Additionally, it highlights the importance of HD maps and traffic flow analysis in AV simulations and provides recommendations for improving AV safety, including simplifying road layouts, minimizing sharp turns, and restricting arbitrary lane changes.