Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

TL;DR

This paper introduces a novel safety-aware motion prediction model for autonomous vehicles that predicts occupancy maps including unseen vehicles, enhancing safety by addressing occlusions and sensor limitations.

Contribution

It proposes the first deep learning approach capable of predicting unseen vehicles' occupancy, using new loss functions and outperforming existing methods on large-scale datasets.

Findings

Significantly outperforms state-of-the-art baselines

First approach to predict unseen vehicles in most cases

Effective in complex, occluded environments

Abstract

Motion prediction of vehicles is critical but challenging due to the uncertainties in complex environments and the limited visibility caused by occlusions and limited sensor ranges. In this paper, we study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map that indicates the earliest time when each location can be occupied by either seen and unseen vehicles. The ability to predict unseen vehicles is critical for safety in autonomous driving. To tackle this challenging task, we propose a safety-aware deep learning model with three new loss functions to predict the earliest occupancy map. Experiments on the large-scale autonomous driving nuScenes dataset show that our proposed model significantly outperforms the state-of-the-art baselines on the safety-aware motion prediction task. To the best of our knowledge, our approach is the first one that can predict the existence of unseen vehicles in most cases. Project page at {\url{https://github.com/xrenaa/Safety-Aware-Motion-Prediction}}.