To address the challenges of recognition lag and limited representational capacity of single-modal physiological information in driver intention recognition under human–machine co-driving environments, this study constructed a highway driving takeover experimental scenario. Typical takeover tasks including obstacle avo...
Min Duan, Jin Shu, Lian Xie et al.· Vehicles· 0 citations
At autonomous intersections in mixed traffic, where Connected and Autonomous Vehicles (CAVs) coexist with Human-Driven Vehicles (HDVs), scheduling must remain effective even when a reliable explicit HDV turning intention is not available sufficiently early for the scheduling decision. This paper proposes a learning-bas...
Xu-Hao Yue, Feng Peng, Ze-Jian Deng et al.· Machines· 0 citations
Numerical simulations show that SC-OVM-controlled CAVs can smooth vehicle trajectories, reduce average delay, improve end-of-green passing performance, and achieve a balanced performance in efficiency, stability, and safety compared with the Full Velocity Difference Model (FVDM), Virtual Leading Vehicle model (VLV), an...
Meng-Han Du, Jiang-Chen Li, Mengyuan Sun et al.· Electronics· 0 citations
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