@article{ref1,
title="Autonomously steering vehicles along unmarked roads using low-cost sensing and computational systems",
journal="Vehicles (Basel)",
year="2023",
author="DeRose, Giuseppe and Ramsey, Austin and Dombecki, Justin and Paul, Nicholas and Chung, Chan-Jin",
volume="5",
number="4",
pages="1400-1422",
abstract="The vast majority of autonomous driving systems are limited to applications on roads with clear lane markings and are implemented using commercial-grade sensing systems coupled with specialized graphic accelerator hardware. This research reviews an alternative approach for autonomously steering vehicles that eliminates the dependency on road markings and specialized hardware. A combination of machine vision, machine learning, and artificial intelligence based on popular pre-trained Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) was used to drive a vehicle along roads lacking lane markings (unmarked roads). The team developed and tested this approach on the Autonomous Campus Transport (ACTor) vehicle--an autonomous vehicle development and research platform coupled with a low-cost webcam-based sensing system and minimal computational resources. The proposed solution was evaluated on real-world roads and varying environmental conditions. It was found that this solution may be used to successfully navigate unmarked roads autonomously with acceptable road-following behavior.<p /> <p>Language: en</p>",
language="en",
issn="2624-8921",
doi="10.3390/vehicles5040077",
url="http://dx.doi.org/10.3390/vehicles5040077"
}