SEIDM让自动驾驶更安全高效，动态调节跟车策略。
SEIDM: A Safe and Efficient Intelligent Driver Model for Autonomous Driving Behavior
- 引入自适应安全因子，动态调整刹车影响
- 稳定间距缩短30%以上，收敛速度更快
- 适合追求高效率又不牺牲安全的自动驾驶系统
智能驾驶员模型（IDM）是自适应巡航控制（ACC）的核心，因其参数可解释性强且在跟车行为建模中表现优异而备受青睐。然而，其固有的保守性导致稳定时间过长、交通效率降低，这一问题长期未受重视。本文提出SEIDM（安全高效智能驾驶员模型），一种增强型IDM扩展模型，旨在提升交通流效率而不牺牲安全性。SEIDM通过引入自适应安全因子，动态调节加速度决策中安全减速度项的影响：在安全条件下车辆可更积极跟随，在潜在风险时则更谨慎。大规模城市交通仿真结果表明，与原始IDM及其变体相比，SEIDM显著缩短了稳定间距并加快了向交通流平衡状态的收敛速度，在交通稳定性和效率方面均表现更优。
原文摘要 · Abstract (English)
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.
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