Motion Transformer with Global Intention Localization and Local Movement Refinement

By Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele. Neural Information Processing Systems, 2022

459 citations recorded by the source corpus.

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions. Existing works explore to directly predict future trajectories based on latent features or utilize dense goal candidates to identify agent's destinations, where the former strategy converges slowly since all motion modes are derived from the same feature while the latter strategy has efficiency issue since its performance highly relies on the density of goal candidates. In this paper, we propose Motion TRansformer (MTR) framework that models motion prediction as the joint optimization of global intention localization and local movement refinement. Instead of using goal candidates, MTR incorporates spatial intention priors by adopting a small set of learnable motion query pairs. Each motion query pair takes charge of trajectory prediction and refinement for a specific motion mode, which stabilizes the training process and facilitates better multimodal predictions. Experiments show that MTR achieves state-of-the-art performance on both the marginal and joint motion prediction challenges, ranking 1st on the leaderboards of Waymo Open Motion Dataset. The source code is available at https://github.com/sshaoshuai/MTR.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d532ade62543bfc52a4827f90fa4128dbfffb12f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Steven L. Waslander
2. Hao Shao
3. Yu Liu
4. Letian Wang
5. Hongsheng Li

In Computer Vision and Pattern Recognition

1. Yu Liu
2. Hongsheng Li
3. Steven L. Waslander
4. Hao Shao
5. Letian Wang
6. Yuxuan Hu
7. Xiaogang Wang
8. Wanli Ouyang

Citations in the corpus

• LMDrive: Closed-Loop End-to-End Driving with Large Language Models (2023, background): “LMDrive takes as input the language instruction and multi-modal multi-view sensor data, and outputs control signals in real time to drive in complex scenarios. together [2, 26, 41, 57]; and 2) an end-to-end approach that directly converts sensor data to control signals via a neural network [18, 39].”