Behavioral Cloning Models Reality Check for Autonomous Driving

TL;DR

This paper validates the real-world performance of behavior cloning perception systems for autonomous driving, showing they can predict steering angles accurately in practical scenarios.

Contribution

It provides the first real-world validation of vision-based behavior cloning models for autonomous vehicle lateral control.

Findings

Low error in steering angle prediction

Effective real-time performance

Potential for real-world deployment

Abstract

How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control? While numerous vision-based autonomous vehicle systems have been trained and evaluated in simulated environments, there is a notable lack of real-world validation for these systems. This paper addresses this gap by presenting the real-world validation of state-of-the-art perception systems that utilize Behavior Cloning (BC) for lateral control, processing raw image data to predict steering commands. The dataset was collected using a scaled research vehicle and tested on various track setups. Experimental results demonstrate that these methods predict steering angles with low error margins in real-time, indicating promising potential for real-world applications.