# Collision Avoidance Detour for Multi-Agent Trajectory Forecasting

arXiv: https://arxiv.org/abs/2306.11638 · HTML: https://mlsanity.com/paper/2306.11638/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction](https://mlsanity.com/paper/2209.10033/) (2022)

## Built on by (most cited first, 2 of 2)

- [BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction](https://mlsanity.com/paper/2405.17372/) (2024)
- [Autonomous Vehicle Controllers From End-to-End Differentiable Simulation](https://mlsanity.com/paper/2409.07965/) (2024)

## Abstract (arXiv)

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.