ALVINN: an autonomous land vehicle in a neural network
Advances in neural information processing systems 1
Model based vehicle detection and tracking for autonomous urban driving
Autonomous Robots
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
Template-based autonomous navigation and obstacle avoidance in urban environments
ACM SIGAPP Applied Computing Review
The Driving School System: Learning Basic Driving Skills From a Teacher in a Real Car
IEEE Transactions on Intelligent Transportation Systems
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This paper presents a vehicle control system capable of learning to navigate autonomously. Our approach is based on image processing, road and navigable area identification, template matching classification for navigation control, and trajectory selection based on GPS way-points. The vehicle follows a trajectory defined by GPS points avoiding obstacles using a single monocular camera. The images obtained from the camera are classified into navigable and non-navigable regions of the environment using neural networks that control the steering and velocity of the vehicle. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.