Tianhang Wang (王天航)

Hi there! Welcome to my homepage. I am currently a 3rd-year Ph.D. student in Intelligent Sensing, Perception and Computing (ISPC) Gruop led by Prof. Guang Chen at Tongji University, Shanghai, China. Before that, I received my bachelor degree of Automotive Engineering at Dalian University of Technology in 2021.

My research interests include autonomous driving, multi-agents intelligence.

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Selected Publications

* indicates equal contribution

Multi-agent collaborative perception (MCP) has recently attracted much attention. It includes three key processes: communication for sharing, collaboration for integration, and reconstruction for different downstream tasks. Existing methods pursue designing the collaboration process alone, ignoring their intrinsic interactions and resulting in suboptimal performance. In contrast, we aim to propose a Unified Collaborative perception framework named UMC, optimizing the communication, collaboration, and reconstruction processes with the Multi-resolution technique. The communication introduces a novel trainable multi-resolution and selective-region (MRSR) mechanism, achieving higher quality and lower bandwidth. Then, a graph-based collaboration is proposed, conducting on each resolution to adapt the MRSR. Finally, the reconstruction integrates the multi-resolution collaborative features for downstream tasks. Since the general metric can not reflect the performance enhancement brought by MCP systematically, we introduce a brand-new evaluation metric that evaluates the MCP from different perspectives. To verify our algorithm, we conducted experiments on the V2X-Sim and OPV2V datasets. Our quantitative and qualitative experiments prove that the proposed UMC greatly outperforms the state-of-the-art collaborative perception approaches.

Accident anticipation attempts to predict whether an accident may occur in advance, which is greatly significant for improving the safety of intelligent vehicles. Most existing approaches integrate the features of accident-relevant agents with spatial information for accident anticipation. However, these approaches ignore the actual spatio-temporal state of missing agents, whether they are occluded or left, which is relevant to accident occurrence. To address this issue, we propose a Graph and Spatio-temporal Continuity based framework for accident anticipation called GSC, which takes the missing agents into account. Specifically, the proposed GSC maintains the spatio-temporal continuity of missing agents, which are in the occluded spatial state in the process of the graph convolution operation. Besides, we define a novel adjacent matrix to add dynamic features to graph learning. In particular, our adjacent matrix utilizes the historical trajectory of each agent to integrate dynamic information into the original static interaction, which improves the quality of the spatial relation feature for accident anticipation. Experimental results on public datasets demonstrate state-of-the-art performance in the correctness of identifying an accident, which is found to reach over 60%.

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