Collision Avoidance Detour for Multi-Agent Trajectory Forecasting

Hsu-kuang Chiu; Stephen F. Smith

Collision Avoidance Detour for Multi-Agent Trajectory Forecasting

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arxiv 2306.11638 v1 pith:QFFFTTQ2 submitted 2023-06-20 cs.CV cs.RO

Hsu-kuang Chiu , Stephen F. Smith This is my paper

classification cs.CVcs.RO

keywords avoidancecollisiondetourautonomousdrivingmotionadditiveagents

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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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