Collision Avoidance Detour for Multi-Agent Trajectory Forecasting
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingCollision Avoidancemotion predictionTrajectory ForecastingvalidSimilar Papers 제목 키워드 기반
Predictive Collision Management for Time and Risk Dependent Path Planning
Autonomous agents such as self-driving cars or parcel robots need to recognize and avoid possible collisions with obstacles in order to move successfully in their environment. Humans, however, have learned to predict mov…
Collision AvoidanceManagementSelf-Driving CarsStandby-Based Deadlock Avoidance Method for Multi-Agent Pickup and Delivery Tasks
The multi-agent pickup and delivery (MAPD) problem, in which multiple agents iteratively carry materials without collisions, has received significant attention. However, many conventional MAPD algorithms assume a specifi…
Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance
Predicting agents' future trajectories plays a crucial role in modern AI systems, yet it is challenging due to intricate interactions exhibited in multi-agent systems, especially when it comes to collision avoidance. To …
Collision AvoidancePredictionTrajectory PredictionMulti-agent systems with CBF-based controllers -- collision avoidance and liveness from instability
Assuring system stability is typically a major control design objective. In this paper, we present a system where instability provides a crucial benefit. We consider multi-agent collision avoidance using Control Barrier …
Collision AvoidanceHuman-Inspired Multi-Agent Navigation using Knowledge Distillation
Despite significant advancements in the field of multi-agent navigation, agents still lack the sophistication and intelligence that humans exhibit in multi-agent settings. In this paper, we propose a framework for learni…
Collision AvoidanceKnowledge Distillationreinforcement-learningReinforcement Learning (RL)