This work proposes a new self-driving framework that uses a human driver control model, whose feature-input values are extracted from images using deep convolutional neural networks (CNNs). The development of image processing techniques using CNNs along with accelerated computing hardware has recently enabled real-time detection of these feature-input values. The use of human driver models can lead to more "natural" driving behavior of self-driving vehicles. Specifically, we use the well-known two-point visual driver control model as the controller, and we use a top-down lane cost map CNN and the YOLOv2 CNN to extract feature-input values. This framework relies exclusively on inputs from low-cost sensors like a monocular camera and wheel speed sensors. We experimentally validate the proposed framework on an outdoor track using a 1/5th-scale autonomous vehicle platform.
@article{arxiv.1910.04862,
title = {Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model},
author = {Justin Zheng and Kazuhide Okamoto and Panagiotis Tsiotras},
journal= {arXiv preprint arXiv:1910.04862},
year = {2019}
}