Robust Autonomous Vehicle Pursuit without Expert Steering Labels

TL;DR

This paper introduces a robust learning-based pursuit control system for autonomous vehicles that does not require expert steering labels, using classical controllers for offline training and data augmentation for robustness, validated in simulation.

Contribution

The work presents a novel approach combining classical controller-based offline labeling with deep learning for pursuit without expert labels, enhancing robustness and real-time performance.

Findings

Effective pursuit control in CARLA simulator across diverse scenarios

Robustness to unseen trajectories and high route completion

Real-time operation demonstrated in simulation

Abstract

In this work, we present a learning method for lateral and longitudinal motion control of an ego-vehicle for vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while maintaining a safety distance. To train our model, we do not rely on steering labels recorded from an expert driver but effectively leverage a classical controller as an offline label generation tool. In addition, we account for the errors in the predicted control values, which can lead to a loss of tracking and catastrophic crashes of the controlled vehicle. To this end, we propose an effective data augmentation approach, which allows to train a network capable of handling different views of the target vehicle. During the pursuit, the target vehicle is firstly localized using a Convolutional Neural Network. The network takes a single RGB image along with cars' velocities and estimates the target vehicle's pose with respect to the ego-vehicle. This information is then fed to a Multi-Layer Perceptron, which regresses the control commands for the ego-vehicle, namely throttle and steering angle. We extensively validate our approach using the CARLA simulator on a wide range of terrains. Our method demonstrates real-time performance and robustness to different scenarios including unseen trajectories and high route completion. The project page containing code and multimedia can be publicly accessed here: https://changyaozhou.github.io/Autonomous-Vehicle-Pursuit/.