VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction

• URL: http://arxiv.org/abs/2208.04530v1
• Date: Tue, 9 Aug 2022 03:49:04 GMT
• Title: VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction
• Authors: Xin Huang, Xiaoyu Tian, Junru Gu, Qiao Sun, Hang Zhao
• Abstract summary: We propose a novel occupancy flow fields predictor to produce accurate occupancy and flow predictions. Our model ranks 3rd place on the Open dataset Occupancy and Flow Prediction Challenge, and achieves the best performance in the occluded occupancy and flow prediction task.
• Score: 18.277777620073685
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Predicting future behaviors of road agents is a key task in autonomous driving. While existing models have demonstrated great success in predicting marginal agent future behaviors, it remains a challenge to efficiently predict consistent joint behaviors of multiple agents. Recently, the occupancy flow fields representation was proposed to represent joint future states of road agents through a combination of occupancy grid and flow, which supports efficient and consistent joint predictions. In this work, we propose a novel occupancy flow fields predictor to produce accurate occupancy and flow predictions, by combining the power of an image encoder that learns features from a rasterized traffic image and a vector encoder that captures information of continuous agent trajectories and map states. The two encoded features are fused by multiple attention modules before generating final predictions. Our simple but effective model ranks 3rd place on the Waymo Open Dataset Occupancy and Flow Prediction Challenge, and achieves the best performance in the occluded occupancy and flow prediction task.

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