SEIDM: A Safe and Efficient Intelligent Driver Model for Autonomous Driving Behavior
For autonomous driving systems, SEIDM improves traffic flow efficiency while maintaining safety, addressing a known limitation of IDM.
SEIDM enhances the Intelligent Driver Model (IDM) for autonomous driving by introducing an adaptive safety factor, achieving shorter stabilization spacing and faster convergence to traffic flow equilibrium in simulations, outperforming IDM and its variants.
The Intelligent Driver Model (IDM) is a cornerstone of Adaptive Cruise Control (ACC), valued for its interpretable parameters and effectiveness in car-following behavior modeling. However, its inherent conservatism leads to prolonged stabilization and reduced traffic efficiency, which have received limited attention. In this paper, we propose SEIDM (Safe and Efficient Intelligent Driver Model), an enhanced IDM extension designed to improve traffic flow efficiency without sacrificing safety. SEIDM introduces an adaptive safety factor to dynamically modulate the impact of the safe deceleration term in acceleration decisions. This allows vehicles to follow more assertively under safe conditions while behaving more cautiously in potential hazards. Extensive urban traffic simulations show that SEIDM achieves significantly shorter stabilization spacing and faster convergence to traffic flow equilibrium, outperforming the original IDM and its variants in traffic stability and efficiency.