StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving

TL;DR

StopNet is a scalable, efficient motion forecasting method for urban autonomous driving that predicts trajectories and occupancy grids, enabling better scene understanding with low latency and high accuracy.

Contribution

The paper introduces StopNet, a novel scalable model that jointly predicts trajectories and occupancy grids using a sparse scene encoder, suitable for dense urban environments.

Findings

Achieves reliable latency for predicting hundreds of agents.

Outperforms state-of-the-art in accuracy on three datasets.

Co-training trajectory and occupancy improves prediction quality.

Abstract

We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy. A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency. In addition to predicting trajectories, our scene encoder lends itself to predicting whole-scene probabilistic occupancy grids, a complementary output representation suitable for busy urban environments. Occupancy grids allow the AV to reason collectively about the behavior of groups of agents without processing their individual trajectories. We demonstrate the effectiveness of our sparse input representation and our model in terms of computation and accuracy over three datasets. We further show that co-training consistent trajectory and occupancy predictions improves upon state-of-the-art performance under standard metrics.