Pushing the Limit of Learning-based Planning for Autonomous Driving

Abstract

We present PLUTO, a powerful framework that Pushes the Limit of imitation learning-based planning for aUTOnomous driving. Our improvements stem from three pivotal aspects: a longitudinal-lateral aware model architecture that enables flexible and diverse driving behaviors; An innovative auxiliary loss computation method that is broadly applicable and efficient for batch-wise calculation; A novel training framework that leverages contrastive learning, augmented by a suite of new data augmentations to regulate driving behavior and facilitate the understanding of underlying interactions. We assessed our framework using the large-scale real-world nuPlan dataset and its associated standardized planning benchmark. Impressively, PLUTO achieves state-of-the-art closed-loop performance, beating other competing learning-based methods and surpassing the current top-performed rule-based planner for the first time.

Method Overview

Model Architecture

We introduce a query-based model architecture that simultaneously addresses lateral and longitudinal planning maneuvers, enabling flexible and diverse driving behaviors.

Auxiliary Loss Computation Method

We propose a novel method for calculating auxiliary loss based on differential interpolation. This method is applicable to a broad spectrum of auxiliary tasks and allows for efficient batch-wise computation in vector-based models.

Contrastive Imitation Learning Framework

We present the Contrastive Imitation Learning (CIL) framework, accompanied by a new set of data augmentations. The CIL framework is aimed at regulating driving behaviors and enhancing interaction learning, without significantly increasing the complexity of training.