no code implementations • 17 Mar 2024 • Xiaoji Zheng, Lixiu Wu, Zhijie Yan, Yuanrong Tang, Hao Zhao, Chen Zhong, Bokui Chen, Jiangtao Gong
Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks.
2 code implementations • 17 Mar 2024 • Ruoxuan Yang, Xinyue Zhang, Anais Fernandez-Laaksonen, Xin Ding, Jiangtao Gong
Recently, LLM-powered driver agents have demonstrated considerable potential in the field of autonomous driving, showcasing human-like reasoning and decision-making abilities. However, current research on aligning driver agent behaviors with human driving styles remains limited, partly due to the scarcity of high-quality natural language data from human driving behaviors. To address this research gap, we propose a multi-alignment framework designed to align driver agents with human driving styles through demonstrations and feedback.
no code implementations • 25 Feb 2024 • Zhipeng Ma, Zheyan Tu, Xinhai Chen, Yan Zhang, Deguo Xia, Guyue Zhou, Yilun Chen, Yu Zheng, Jiangtao Gong
The experimental results demonstrate that JGRM outperforms existing methods in both road segment representation and trajectory representation tasks.
1 code implementation • ICCV 2023 • Zhijie Yan, Pengfei Li, Zheng Fu, Shaocong Xu, Yongliang Shi, Xiaoxue Chen, Yuhang Zheng, Yang Li, Tianyu Liu, Chuxuan Li, Nairui Luo, Xu Gao, Yilun Chen, Zuoxu Wang, Yifeng Shi, Pengfei Huang, Zhengxiao Han, Jirui Yuan, Jiangtao Gong, Guyue Zhou, Hang Zhao, Hao Zhao
One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents.
no code implementations • 17 Oct 2022 • Longrui Chen, Yan Zhang, Wenjie Jiang, Jiangtao Gong, Jiahao Shen, Mengdi Chu, Chuxuan Li, Yifeng Pan, Yifeng Shi, Nairui Luo, Xu Gao, Jirui Yuan, Guyue Zhou, Yaqin Zhang
This paper proposes a high-fidelity simulation framework that can estimate the potential safety benefits of vehicle-to-infrastructure (V2I) pedestrian safety strategies.
1 code implementation • 11 Oct 2022 • Lin Ma, Jiangtao Gong, Hao Xu, Hao Chen, Hao Zhao, Wenbing Huang, Guyue Zhou
In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database.
1 code implementation • 11 Oct 2022 • Yang Li, Xiaoxue Chen, Hao Zhao, Jiangtao Gong, Guyue Zhou, Federico Rossano, Yixin Zhu
Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line.