Dynamic Car-Following Model With Jerk Suppression for Highway Autonomous Driving

被引:0
作者
Liu, Ke [1 ]
Ma, Jing [1 ]
Lai, Edmund M. -K. [1 ]
机构
[1] Auckland Univ Technol, Sch Engn Comp & Math Sci, Auckland 1010, New Zealand
来源
IEEE ACCESS | 2025年 / 13卷
关键词
Safety; Vehicle dynamics; Autonomous vehicles; Adaptation models; Mathematical models; Roads; Real-time systems; Driver behavior; Data models; Computational modeling; Autonomous driving; car-following; jerk reduction; adaptive headway threshold; VEHICLE;
D O I
10.1109/ACCESS.2025.3535596
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study proposes a dynamic safe car-following strategy that is based on dynamic adjustment of headway time with jerk suppression. Reinforcement learning models trained with this strategy result in enhanced safety and driving comfort, validated using real driving data from the Next Generation Simulation (NGSIM) I-80 and HighD datasets. Simulation results demonstrate significant reduction in the risk of collisions. More importantly, low collision rates are maintained with driving speed profiles that are different from the training data, exhibiting cross-dataset generalizability. It also significantly improves driving comfort, with a 10% jerk reduction compared to existing models.
引用
收藏
页码:23111 / 23119
页数:9
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