A Comprehensive Study and Applications of Risk Assessment Based on Potential Risk Field for Intelligent Transportation

This thesis develops two new models to assess driving risks for vehicles. The first model (DRS) uses "virtual energy" instead of vehicle mass to better predict driving behavior and plan safer merging maneuvers on highways. The second model (IPF) helps platoons (groups of vehicles) change lanes safely by determining the safe gap required between other cars. Together, these models improve both safety and efficiency, providing a better foundation for managing traffic with both human-driven and automated vehicles.