Modelling Stop Intersection Approaches using Gaussian Processes

Alexandre Armand (1, 2) , David Filliat (1, 3) , Javier Ibanez-Guzman (2)

1 RV - Robotique et Vision
2 Technocentre Renault [Guyancourt]
3 Flowers - Flowing Epigenetic Robots and Systems

Each driver reacts differently to the same traffic conditions, however, most Advanced Driving Assistant Systems (ADAS) assume that all drivers are the same. This paper proposes a method to learn and to model the velocity profile that the driver follows as the vehicle decelerates towards a stop intersection. Gaussian Processes (GP), a machine learning method for non-linear regressions are used to model the velocity profiles. It is shown that GP are well adapted for such an application, using data recorded in real traffic conditions. It consists of the generation of a normally distributed speed, given a position on the road. By comparison with generic velocity profiles, benefits of using individual driver patterns for ADAS issues are presented.

Domaines: Robotique [cs.RO]

Alexandre Armand, David Filliat, Javier Ibanez-Guzman. Modelling Stop Intersection Approaches using Gaussian Processes. 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), Oct 2013, The Hague, Netherlands. ⟨10.1109/ITSC.2013.6728466⟩. ⟨hal-00919680⟩