DeRose, G., Jr.; Ramsey, A.; Dombecki, J.; Paul, N.; Chung, C.-J. Autonomously Steering Vehicles along Unmarked Roads Using Low-Cost Sensing and Computational Systems. Vehicles2023, 5, 1400-1422.
DeRose, G., Jr.; Ramsey, A.; Dombecki, J.; Paul, N.; Chung, C.-J. Autonomously Steering Vehicles along Unmarked Roads Using Low-Cost Sensing and Computational Systems. Vehicles 2023, 5, 1400-1422.
DeRose, G., Jr.; Ramsey, A.; Dombecki, J.; Paul, N.; Chung, C.-J. Autonomously Steering Vehicles along Unmarked Roads Using Low-Cost Sensing and Computational Systems. Vehicles2023, 5, 1400-1422.
DeRose, G., Jr.; Ramsey, A.; Dombecki, J.; Paul, N.; Chung, C.-J. Autonomously Steering Vehicles along Unmarked Roads Using Low-Cost Sensing and Computational Systems. Vehicles 2023, 5, 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 (CNN) and Recurrent Neural Networks (RNN) was used to drive a vehicle along roads lacking lane markings (unmarked roads). The team developed and tested this approach on the ACTor (Autonomous Camus TranspORt) 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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
