Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
This addresses a critical safety issue for autonomous driving systems by enabling prediction of vehicles not directly observed, which is incremental as it builds on existing trajectory prediction tasks.
The paper tackles the problem of predicting motion for unseen vehicles in autonomous driving by proposing a safety-aware deep learning model that predicts an occupancy map indicating the earliest time each location can be occupied, and it significantly outperforms state-of-the-art baselines on the nuScenes dataset.
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}}.