Collision Avoidance Detour for Multi-Agent Trajectory Forecasting

• URL: http://arxiv.org/abs/2306.11638v1
• Date: Tue, 20 Jun 2023 16:05:24 GMT
• Title: Collision Avoidance Detour for Multi-Agent Trajectory Forecasting
• Authors: Hsu-kuang Chiu and Stephen F. Smith
• Abstract summary: We present Collision Avoidance Detour (CAD), which won the 3rd place award in the 2023 Open dataset Challenge - Sim Agents. To satisfy the motion prediction factorization requirement, we partition all the valid objects into three mutually exclusive sets: Autonomous Driving Vehicle (ADV), World-tracks-to-predict, and World-others.
• Score: 8.680676599607123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present our approach, Collision Avoidance Detour (CAD), which won the 3rd place award in the 2023 Waymo Open Dataset Challenge - Sim Agents, held at the 2023 CVPR Workshop on Autonomous Driving. To satisfy the motion prediction factorization requirement, we partition all the valid objects into three mutually exclusive sets: Autonomous Driving Vehicle (ADV), World-tracks-to-predict, and World-others. We use different motion models to forecast their future trajectories independently. Furthermore, we also apply collision avoidance detour resampling, additive Gaussian noise, and velocity-based heading estimation to improve the realism of our simulation result.

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