Collision Avoidance Detour for Multi-Agent Trajectory Forecasting
Collision Avoidance Detour for Multi-Agent Trajectory Forecasting
著者: Hsu-kuang Chiu, Stephen F. Smith
分類: cs.CV, cs.RO
原文アブストラクト
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.