Exploring Driving Parameter Settings for Autonomous Vehicles: Considering Travel Efficiency and Safety in Urban Traffic Environments

This study addresses the imperative yet understudied aspect of exploring the simulated driving parameters for autonomous vehicles (AV) to achieve a harmonious balance between safety and travel efficiency. While previous research has predominantly focused on enhancing the safety performance of AV, the paramount facet of fast and efficient travel efficiency has been relatively neglected. Leveraging microscopic traffic simulations, this study delves into the impact of diverse combinations of driving parameters, specifically 216 sets derived from the Enhanced Intelligent Driver Model and lane-change model embedded in simulated AV. These parameters are strategically adjusted to address scenarios emphasizing safety, travel efficiency, and a harmonious integration of both objectives. The study's findings provide a comprehensive insight into AV performance under varying driving parameter sets, considering travel efficiency and safety. Additionally, it contributes to identifying the optimal range of parameter sets to enhance travel efficiency while maintaining a high level of safety. Furthermore, the study introduces a performance index map, elucidating the effectiveness of different parameter combinations in facilitating the safe and efficient movement of AV. This index map serves as a valuable reference for formulating autonomous driving algorithms for diverse purposes, ultimately enhancing the role of AV as efficient travel service providers in contemporary transportation systems.