Fast prototyping of a Highly Autonomous Cooperative Driving System for public roads

This paper presents a framework for a fast prototyping of Advanced Driving Assistance Systems (ADAS). The simulation tool SiVIC is proposed for drastically reducing development time and costs of a vehicle system design. RTMaps® is used as a platform for easily encapsulating the system component algorithms and for effortlessly transferring them from a simulation environment to a physical vehicle. With these tools a Highly Autonomous Cooperative Driving System (HACS) has been designed. A perception component uses a combination of sensors to map the environment. In this paper a cooperative, extended perception with infrastructure-to-vehicle communication (I2V) will be proposed. A co-pilot integrates a fast Total Trajectory Exploration (TTE) method that finds a trajectory that is optimal with respect to the sensed environment. A simple controller on the vehicle actuators is used for guiding the vehicle on this trajectory. The cooperation between human and automation is managed by a Driving Mode Selection Unit (MSU) and a Human Machine Interface (HMI). In this paper a vehicle system which allows highly autonomous driving with human cooperation is called a co-system.

[1]  L. Nouveliere,et al.  Backstepping control synthesis for both longitudinal and lateral automated vehicle , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[2]  Mathias Perrollaz,et al.  Obstacle Detection Based on Fusion Between Stereovision and 2D Laser Scanner , 2007 .

[3]  Sebastien Glaser,et al.  New likelihood updating for the IMM approach application to outdoor vehicles localization , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Daniel Zeng,et al.  Advanced urban transport: Automation is on the way , 2007 .

[5]  Dominique Gruyer,et al.  Cooperative Fusion for Multi-Obstacles Detection With Use of Stereovision and Laser Scanner , 2005, Auton. Robots.

[6]  Bruno Steux,et al.  RT Maps, un environnement logiciel dédié à la conception d'applications embarquées temps-réel : utilisation pour la détection automatique de véhicules par fusion radar / vision , 2001 .

[7]  Benoit Vanholme,et al.  Manoeuvre-based trajectory planning for highly autonomous vehicles on real road with traffic , 2009, 2009 European Control Conference (ECC).

[8]  D. Gruyer,et al.  A new multi-lanes detection using multi-camera for robust vehicle location , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[9]  B. Lusetti,et al.  Driver steering assistance for lane departure avoidance , 2009 .

[10]  Sebastien Glaser,et al.  Experimental comparison of Kalman Filters for vehicle localization , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[11]  R Gillie,et al.  ADVANCED URBAN TRANSPORT , 1975 .

[12]  B. Mourllion,et al.  Variance Behavior and Signification in Probabilistic Framework Applied to Vehicle Localization , 2006, 2006 IEEE Intelligent Vehicles Symposium.