Year: 2024
Type: conference-paper
Abstract: Predicting future motions of road participants is an important task for driving autonomously. Most existing models excel at predicting the marginal trajectory of a single agent, but predicting joint trajectories for multiple agents that are consistent within a scene remains a challenge. Previous research has often focused on marginal predictions, but the importance of joint predictions has become increasingly apparent. Joint prediction aims to generate trajectories that are consistent across the entire scene. Our research builds upon the SIMPL baseline to explore methods for generating scene-consistent trajectories. We tested our algorithm on the Argoverse 2 dataset, and experimental results demonstrate that our approach can generate scene-consistent trajectories. Compared to the SIMPL baseline, our method significantly reduces the collision rate of joint trajectories within the scene.
Source: China Automation Congress (CAC)
Authors: Mingyi Wang, Hongqun Zou, Yifan Liu, You Wang, Guang Li
Institution: Zhejiang University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4407467463