Behavior Cloning for Mini Autonomous Car Path Following

TL;DR

This paper demonstrates a behavior cloning approach using neural networks to enable mini autonomous cars to follow routes smoothly by mimicking human driving behavior from camera data, simplifying control systems.

Contribution

The paper introduces a behavior cloning method for route following in autonomous cars, utilizing camera data and neural networks to directly mimic human driving actions, simplifying control complexity.

Findings

Achieved smooth and precise route following in a 13-meter test route.

Validated the approach on mini-autonomous cars as a practical benchmark.

Showed potential for scaling from small experiments to real-world vehicles.

Abstract

This article presents the implementation and evaluation of a behavior cloning approach for route following with autonomous cars. Behavior cloning is a machine-learning technique in which a neural network is trained to mimic the driving behavior of a human operator. Using camera data that captures the environment and the vehicle's movement, the neural network learns to predict the control actions necessary to follow a predetermined route. Mini-autonomous cars, which provide a good benchmark for use, are employed as a testing platform. This approach simplifies the control system by directly mapping the driver's movements to the control outputs, avoiding the need for complex algorithms. We performed an evaluation in a 13-meter sizer route, where our vehicle was evaluated. The results show that behavior cloning allows for a smooth and precise route, allowing it to be a full-sized vehicle and enabling an effective transition from small-scale experiments to real-world implementations.