DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks

TL;DR

DiGNet introduces a graph neural network approach for scalable self-driving that effectively handles diverse traffic scenarios, demonstrating high safety and rule compliance over extensive simulated driving distances.

Contribution

The paper presents a novel graph-based deep learning model that scales to large traffic scenarios, outperforming traditional methods in generalization and safety in diverse environments.

Findings

Successfully navigates over 7,000 km in simulation

Handles complex scenarios like roundabouts and pedestrian crossings

Maintains traffic rule compliance and safety

Abstract

Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable cases. Recently, self-driving methods based on deep learning have shown promising results with better generalization capability but less hand engineering effort. However, most of the previous learning-based methods are trained and evaluated in limited driving scenarios with scattered tasks, such as lane-following, autonomous braking, and conditional driving. In this paper, we propose a graph-based deep network to achieve scalable self-driving that can handle massive traffic scenarios. Specifically, more than 7,000 km of evaluation is conducted in a high-fidelity driving simulator, in which our method can obey the traffic rules and safely navigate the vehicle in a large variety of urban, rural, and highway environments, including unprotected left turns, narrow roads, roundabouts, and pedestrian-rich intersections. Demonstration videos are available at https://caipeide.github.io/dignet/.