Importance Is in Your Attention: Agent Importance Prediction for Autonomous Driving

Christopher Hazard, Akshay Bhagat, Balarama Raju Buddharaju, Zhongtao Liu, Yunming Shao, Lu Lu, Sammy Omari, Henggang Cui; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 2532-2535

Abstract

Trajectory prediction is an important task in autonomous driving. State-of-the-art trajectory prediction models often use attention mechanisms to model the interaction between agents. In this paper, we show that the attention information from such models can also be used to measure the importance of each agent with respect to the ego vehicle's future planned trajectory. Our experiment results on the nuPlans dataset show that our method can effectively find and rank surrounding agents by their impact on the ego's plan.

Related Material

[pdf] [arXiv]
[bibtex]
@InProceedings{Hazard_2022_CVPR, author = {Hazard, Christopher and Bhagat, Akshay and Buddharaju, Balarama Raju and Liu, Zhongtao and Shao, Yunming and Lu, Lu and Omari, Sammy and Cui, Henggang}, title = {Importance Is in Your Attention: Agent Importance Prediction for Autonomous Driving}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2022}, pages = {2532-2535} }