Autonomously Steering Vehicles along Unmarked Roads Using Low-Cost Sensing and Computational Systems

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.

Keywords

• autonomous vehicles
• Self-Drive vehicles
• deep learning
• convolutional neural networks
• recurrent neural networks
• image histogram matching