X. Chen, W. Zhang, H. Bai*. Two-Dimensional Following Lane-Changing (2DF-LC): A Framework for Dynamic Decision-Making and Rapid Behavior Planning
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DOI码:10.1109/TIV.2023.3324305
发表刊物:IEEE Transactions on Intelligent Vehicles
关键字:Small-scale traffic models, Lane change, Twodimensional car-following, Social forces, Human-like driving
摘要:Lane changes require dynamic decision-making and rapid behavior planning, which are challenging for traffic modeling. We propose a two-dimensional following lane-changing framework (2DF-LC) that exploits the benefits of car-following (CF) models for computational efficiency, collision avoidance, and human-like behavior. This framework uses a sigmoid-based intelligent driver model (SIDM) with both longitudinal and lateral following. To avoid excessive acceleration at start-up, we develop an SIDM that ensures a smooth start-up. In the longitudinal plane, we introduce a transition function to create a double-target car-following model (DT-SIDM) that can handle sudden acceleration changes due to target switching, thereby guaranteeing stable longitudinal motion and dynamic collision avoidance. In the lateral plane, we develop a lateral movement car-following model (LM-SIDM) inspired by a social force model. The LM-SIDM defines both lane and gap forces, resulting in effective lateral motion and collision avoidance during lane changes. Simulations and tests in three typical scenarios show that 2DF-LC has high computational efficiency: it completes calculations within milliseconds. Compared with the widely used hierarchical motion planning system (HMPS) and integrated model and learning combined algorithm (IMLC) methods, 2DF-LC based on real trajectories reduces the errors by 49.5% and 16.1%, respectively, and achieves a 28.63% lower time-integrated anticipated collision time (TI-ACT) than the original trajectories, indicating improved safety. Moreover, 2DF-LC produces a smooth acceleration curve, with an average jerk value of 0.358 m/s 3 . The lane-change trajectory generated by 2DF-LC can also be followed and executed effectively in CarSim tests.
论文类型:期刊论文
学科门类:工学
文献类型:J
卷号:9
期号:1
页面范围:427-445
ISSN号:2379-8904
是否译文:否
发表时间:2024-01-10
收录刊物:SCI
发布期刊链接:https://ieeexplore.ieee.org/abstract/document/10285029
上一条: X. Chen, W. Zhang, H. Bai*. A sigmoid-based car-following model to improve acceleration stability in traffic oscillation and following failure in free flow
下一条: Hai-Jian Bai*, Chen-Chen Guo, Heng Ding. Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism