Fusion of infrared vision and radar for estimating the lateral dynamics of obstacles

Abstract Automotive forward collision warning systems are based on range finders to detect the obstacles ahead and warn or intervene when a dangerous situation occur. However, the radar information by itself is not adequate to predict the future path of vehicles in collision avoidance systems due to the poor estimation of their lateral attribute. In order to face this problem, this paper proposes the utilization of a new Kalman based filter, whose measurement space includes data from a radar and a vision system. Given the superiority of vision systems in estimating azimuth and lateral velocity, the filter proves to be robust in vehicle maneuvers and curves. Results from simulated and real data are presented, providing comparative results with stand alone tracking systems and the cross-covariance technique in multisensor architectures.

[1]  Massimo Bertozzi,et al.  Vehicle detection and localization in infra-red images , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[2]  Takeo Kato,et al.  An obstacle detection method by fusion of radar and motion stereo , 2002, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[3]  Paul Levi,et al.  Robust vehicle tracking fusing radar and vision , 2001, Conference Documentation International Conference on Multisensor Fusion and Integration for Intelligent Systems. MFI 2001 (Cat. No.01TH8590).

[4]  Werner von Seelen,et al.  FUSION OF DIFFERENT SENSORS AND ALGORITHMS FOR SEGMENTATION , 1998 .

[5]  A. Polychronopoulos,et al.  Centralized data fusion for obstacle and road borders tracking in a collision warning system , 2004 .

[6]  Yaakov Bar-Shalom,et al.  The Effect of the Common Process Noise on the Two-Sensor Fused-Track Covariance , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Sridhar Lakshmanan,et al.  CLARK: a heterogeneous sensor fusion method for finding lanes and obstacles , 2000, Image Vis. Comput..

[8]  J R Treat,et al.  TRI-LEVEL STUDY OF THE CAUSES OF TRAFFIC ACCIDENTS: FINAL REPORT , 1979 .

[9]  A. Polychronopoulos,et al.  Multiple sensor collision avoidance system for automotive applications using an IMM approach for obstacle tracking , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[10]  Frank Dellaert,et al.  Model-based car tracking integrated with a road-follower , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[11]  Stefan Gehrig,et al.  A TRAJECTORY-BASED APPROACH FOR THE LATERAL CONTROL OF VEHICLE FOLLOWING SYSTEMS , 1998 .

[12]  Gerd Wanielik,et al.  A new driving supporting system, integrating an infrared camera and an anti-collision micro-wave radar: the EUCLIDE project , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[13]  Junbin Gao,et al.  Some remarks on Kalman filters for the multisensor fusion , 2002, Inf. Fusion.

[14]  Samuel S. Blackman,et al.  Multiple-Target Tracking with Radar Applications , 1986 .