TY  - JOUR
PY  - 2023//
TI  - Autonomously steering vehicles along unmarked roads using low-cost sensing and computational systems
JO  - Vehicles (Basel)
A1  - DeRose, Giuseppe
A1  - Ramsey, Austin
A1  - Dombecki, Justin
A1  - Paul, Nicholas
A1  - Chung, Chan-Jin
SP  - 1400
EP  - 1422
VL  - 5
IS  - 4
N2  - 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>
LA  - en
SN  - 2624-8921
UR  - http://dx.doi.org/10.3390/vehicles5040077
ID  - ref1
ER  -