MultiXNet: Multiclass Multistage Multimodal Motion Prediction

• URL: http://arxiv.org/abs/2006.02000v4
• Date: Mon, 24 May 2021 04:31:50 GMT
• Title: MultiXNet: Multiclass Multistage Multimodal Motion Prediction
• Authors: Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, Alyssa Dayan, Sidney Zhang, Brian C. Becker, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington
• Abstract summary: MultiXNet is an end-to-end approach for detection and motion prediction based directly on lidar sensor data. The method was evaluated on large-scale, real-world data collected by a fleet of SDVs in several cities.
• Score: 27.046311751308775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future. To address this task we propose MultiXNet, an end-to-end approach for detection and motion prediction based directly on lidar sensor data. This approach builds on prior work by handling multiple classes of traffic actors, adding a jointly trained second-stage trajectory refinement step, and producing a multimodal probability distribution over future actor motion that includes both multiple discrete traffic behaviors and calibrated continuous position uncertainties. The method was evaluated on large-scale, real-world data collected by a fleet of SDVs in several cities, with the results indicating that it outperforms existing state-of-the-art approaches.

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