MPA: MultiPath++ Based Architecture for Motion Prediction
Stepan Konev

TL;DR
This paper introduces a MultiPath++ based architecture for motion prediction in autonomous driving, achieving third place in the Waymo Challenge 2022, emphasizing safety and reliability.
Contribution
The paper presents a novel MultiPath++ based architecture for motion prediction, tailored for autonomous driving, with publicly available source code.
Findings
Achieved third place in Waymo Motion Prediction Challenge 2022
Demonstrated effectiveness of MultiPath++ architecture
Contributed a publicly available implementation
Abstract
Autonomous driving technology is developing rapidly and nowadays first autonomous rides are being provided in city areas. This requires the highest standards for the safety and reliability of the technology. Motion prediction part of the general self-driving pipeline plays a crucial role in providing these qualities. In this work we present one of the solutions for Waymo Motion Prediction Challenge 2022 based on MultiPath++ ranked the 3rd as of May, 26 2022. Our source code is publicly available on GitHub.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques · Advanced Neural Network Applications
