Goal-Directed Occupancy Prediction for Lane-Following Actors

TL;DR

This paper introduces a goal-directed occupancy prediction method for lane-following actors that leverages road topology to improve accuracy and multi-modality in autonomous vehicle behavior prediction.

Contribution

It proposes a novel approach using mapped road topology to predict future occupancy, addressing limitations of high-level action categories and mode collapse in prior models.

Findings

Accurately predicts future occupancy consistent with lane geometry

Captures multi-modal behaviors based on scene context

Avoids mode collapse common in previous methods

Abstract

Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road actors. We show that our approach is able to accurately predict future occupancy that remains consistent with the mapped lane geometry and naturally captures multi-modality based on the local scene context while also not suffering from the mode collapse problem observed in prior work.