Identification of Autonomous Driving Volatility Hotspots on Urban Roads

Abstract

The development and deployment of autonomous driving technology are crucial for enhancing future traffic safety. However, road safety challenges persist due to the behavioral differences between autonomous vehicles and human drivers in mixed traffic conditions. A comprehensive understanding of the distinct behaviors of autonomous vehicles is essential to improve traffic safety. This study aims to evaluate the driving volatility of autonomous vehicles and identify volatility hotspots using real-world data. Various volatility indicators commonly used in existing studies were further processed to derive an integrated driving volatility measure based on principal component analyses. Driving volatility was analyzed by comparing autonomous and manual driving modes. Volatility hotspots were identified by comparing driving volatility of each data point. The findings indicate that the average driving volatility in autonomous mode was approximately 45% lower than in manual mode. Factors such as uphill grades were found to increase the instability of autonomous vehicles more significantly than in manual driving. Complex road alignments, such as reverse horizontal curves, increased manual driving volatility. The results of this study provide insights for designing road environments that are more compatible with autonomous vehicles in the future.