Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach

TL;DR

This paper presents a scene-specific trajectory set generation method for motion forecasting in autonomous driving, improving efficiency and plausibility by leveraging map and actor data, and demonstrating significant performance gains.

Contribution

Introduces a novel scene-specific trajectory set generation approach using map info and actor dynamics, with a recursive subsampling technique to improve trajectory plausibility.

Findings

Achieves up to 10% improvement in Driving Area Compliance (DAC).

Maintains competitive displacement errors.

Effectively captures complex actor behaviors in real-world scenarios.

Abstract

Representing diverse and plausible future trajectories is critical for motion forecasting in autonomous driving. However, efficiently capturing these trajectories in a compact set remains challenging. This study introduces a novel approach for generating scene-specific trajectory sets tailored to different contexts, such as intersections and straight roads, by leveraging map information and actor dynamics. A deterministic goal sampling algorithm identifies relevant map regions, while our Recursive In-Distribution Subsampling (RIDS) method enhances trajectory plausibility by condensing redundant representations. Experiments on the Argoverse 2 dataset demonstrate that our method achieves up to a 10% improvement in Driving Area Compliance (DAC) compared to baseline methods while maintaining competitive displacement errors. Our work highlights the benefits of mining such scene-aware trajectory sets and how they could capture the complex and heterogeneous nature of actor behavior in real-world driving scenarios.