ALVINN: an autonomous land vehicle in a neural network
Advances in neural information processing systems 1
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Anticipatory Driving for a Robot-Car Based on Supervised Learning
Anticipatory Behavior in Adaptive Learning Systems
Template-based autonomous navigation in urban environments
Proceedings of the 2011 ACM Symposium on Applied Computing
A Road Following Approach Using Artificial Neural Networks Combinations
Journal of Intelligent and Robotic Systems
Vision and GPS-based autonomous vehicle navigation using templates and artificial neural networks
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Adaptive finite state machine based visual autonomous navigation system
Engineering Applications of Artificial Intelligence
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Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper presents a vehicle control system capable of learning behaviors based on examples from human driver and analyzing different levels of memory of the templates, which are an important capability to autonomous vehicle drive. Our approach is based on image processing, template matching classification, finite state machine, and template memory. The proposed system allows training an image segmentation algorithm and a neural network to work with levels of memory of the templates in order to identify navigable and non-navigable regions. As an output, it generates the steering control and speed for the Intelligent Robotic Car for Autonomous Navigation (CaRINA). Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.