Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving

TL;DR

This paper proposes a modular, distribution-aware approach to autonomous driving that combines perception, goal prediction, and model-based planning to improve generalisability and safety, achieving state-of-the-art results in simulation.

Contribution

It introduces a modular framework with distribution-aware goal prediction and obstacle perception, enhancing robustness over end-to-end models in autonomous driving.

Findings

Achieved state-of-the-art results on the CARNOVEL benchmark in CARLA.

Demonstrated improved generalisability to novel scenarios.

Validated the effectiveness of distribution-aware goal prediction.

Abstract

The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the driving behaviour from experts. However, exclusively relying on imitation can limit agents' generalisability to novel scenarios that are outside the support of the training data. In this paper, we address this challenge by factorising the driving task, based on the intuition that modular architectures are more generalisable and more robust to changes in the environment compared to monolithic, end-to-end frameworks. Specifically, we draw inspiration from the trajectory forecasting community and reformulate the learning-to-drive task as obstacle-aware perception and grounding, distribution-aware goal prediction, and model-based planning. Firstly, we train the obstacle-aware perception module to extract salient representation of the visual context. Then, we learn a multi-modal goal distribution by performing conditional density-estimation using normalising flow. Finally, we ground candidate trajectory predictions road geometry, and plan the actions based on on vehicle dynamics. Under the CARLA simulator, we report state-of-the-art results on the CARNOVEL benchmark.