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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