Vehicle Plan
Vehicle plan research focuses on developing algorithms and systems for efficient and safe vehicle navigation and control, encompassing aspects like trajectory planning, behavior prediction, and sensor integration. Current research emphasizes the use of deep reinforcement learning, convolutional neural networks, and other advanced machine learning techniques to address challenges such as robust lane detection, stabilizing vehicle motion in diverse terrains, and optimizing energy management in electric vehicles. This work is crucial for advancing autonomous driving, improving traffic safety and efficiency, and enabling innovative applications in intelligent transportation systems.
Papers
Vehicles, Pedestrians, and E-bikes: a Three-party Game at Right-turn-on-red Crossroads Revealing the Dual and Irrational Role of E-bikes that Risks Traffic Safety
Gangcheng Zhang, Yeshuo Shu, Keyi Liu, Yuxuan Wang, Donghang Li, Liyan Xu
Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis
TalkMosaic: Interactive PhotoMosaic with Multi-modal LLM Q&A Interactions
Kevin Li, Fulu Li
Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles
Cui Zhang, Wenjun Zhang, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief