Abstract-Prior autonomous navigation systems focused on the demonstration of the technological feasibility. But as the technology evolves, improving user experience through learning expert's or individual's driving pattern emerges as a promising research direction. As a first step toward this goal, we investigate methods to learn from human demonstrations in urban scenarios without any environmental disturbances (traffic-free). We propose a path model that generates a reference path with smooth and peak-value-reduced curvature, and a parameterized speed model to be fitted by human driving data. Model parameters are then learned through regression methods, and certain statistical human driving patterns are revealed. The learned model is then evaluated by comparing the generated plan with the collected data by the same human driver.
Gu and Dolan REF , proposed a geometry based speed planning approach in which a reference path was generated for the autonomous vehicle by combining the smooth and peak-value-reduced curvature and a parameterized speed model that was fitted from human driving data.
Toward human-like motion planning in urban environments
2014 IEEE Intelligent Vehicles Symposium Proceedings
2014 IEEE Intelligent Vehicles Symposium Proceedings
Computer Science