arXiv:2306.11638
Collision Avoidance Detour for Multi-Agent Trajectory Forecasting
원문 초록
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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ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting
FocalAD: Local Motion Planning for End-to-End Autonomous Driving
TrajFlow: Multi-modal Motion Prediction via Flow Matching
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method
Real-time Collision Risk Estimation based on Stochastic Reachability Spaces
GRIP: Graph-based Interaction-aware Trajectory Prediction
토론
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