Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter

Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) filter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-target tracking, and that it gives a parsimonious representation of the map in contrast to grid based methods. Two original contributions address the inherent complexity issues of the algorithm: First, a data clustering algorithm is suggested to group the components of the PHD into different clusters, which structures the description of the prior and considerably improves the measurement update in the PHD filter. Second, a merging step is proposed to simplify the map representation in the PHD filter. The algorithm is applied to multi-sensor radar data collected on public roads, and the resulting map is shown to well describe the environment as a human perceives it.

[1]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[2]  Ba-Ngu Vo,et al.  A random set formulation for Bayesian SLAM , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  E. D. Dickmanns,et al.  A Curvature-based Scheme for Improving Road Vehicle Guidance by Computer Vision , 1987, Other Conferences.

[4]  Alfred O. Hero,et al.  Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion , 2000, IEEE Trans. Intell. Transp. Syst..

[5]  Hedvig Sidenbladh,et al.  Multi-target particle filtering for the probability hypothesis density , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[6]  R. Mahler.  Multitarget Bayes filtering via first-order multitarget moments , 2003 .

[7]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[8]  Jakob Roll.  Local and Piecewise Affine Approaches to System Identification , 2003 .

[9]  Martin E. Adams.  Autonomous Navigation: Achievements in Complex Environments , 2007, IEEE Instrumentation & Measurement Magazine.

[10]  A. Kirchner,et al.  Integrated obstacle and road tracking using a laser scanner , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[11]  Peter Willett,et al.  The Bin-Occupancy Filter and Its Connection to the PHD Filters , 2009, IEEE Transactions on Signal Processing.

[12]  Uwe D. Hanebeck,et al.  Progressive Gaussian mixture reduction , 2008, 2008 11th International Conference on Information Fusion.

[13]  Sridhar Lakshmanan,et al.  LEXLUTHER: an algorithm for detecting roads and obstacles in radar images , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[14]  Ba-Ngu Vo,et al.  Convergence Analysis of the Gaussian Mixture PHD Filter , 2007, IEEE Transactions on Signal Processing.

[15]  Hans P. Moravec.  Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

[16]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[17]  Sridhar Lakshmanan,et al.  An algorithm for detecting roads and obstacles in radar images , 2001, IEEE Trans. Veh. Technol..

[18]  Simon J. Godsill,et al.  Poisson models for extended target and group tracking , 2005, SPIE Optics + Photonics.

[19]  Vassilios Petridis,et al.  Identification of switching dynamical systems using multiple models , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[20]  D. Salmond.  Mixture reduction algorithms for target tracking , 1989 .

[21]  Sumeetpal S. Singh,et al.  Random finite sets and sequential Monte Carlo methods in multi-target tracking , 2003, 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695).

[22]  Ba-Ngu Vo,et al.  The Gaussian Mixture Probability Hypothesis Density Filter , 2006, IEEE Transactions on Signal Processing.

[23]  N. Nathan.  Self and will , 1997 .

[24]  Marco F. Huber,et al.  Gaussian mixture reduction via clustering , 2009, 2009 12th International Conference on Information Fusion.

[25]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[26]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[27]  H. Sorenson,et al.  Recursive bayesian estimation using gaussian sums , 1971 .

[28]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[29]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[30]  Fredrik Gustafsson,et al.  Joint road geometry estimation and vehicle tracking , 2007 .

[31]  Fredrik Gustafsson,et al.  Toward Autonomous Collision Avoidance by Steering , 2007, IEEE Transactions on Intelligent Transportation Systems.

[32]  R. Mahler.  A Theoretical Foundation for the Stein-Winter "Probability Hypothesis Density (PHD)" Multitarget Tracking Approach , 2000 .

[33]  D. Salmond,et al.  Spatial distribution model for tracking extended objects , 2005 .

[34]  Fredrik Gustafsson,et al.  Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation , 2011, IEEE Transactions on Signal Processing.

[35]  Thomas B. Schön,et al.  Estimation of the Free Space in Front of a Moving Vehicle , 2009 .

[36]  A Kirchner,et al.  MODEL BASED DETECTION OF ROAD BOUNDARIES WITH A LASER SCANNER , 1998 .

[37]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[38]  J. Bares,et al.  Three-Dimensional Map Building with MMW Radar , 2003 .

[39]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[40]  Ronald P. S. Mahler,et al.  Particle-systems implementation of the PHD multitarget-tracking filter , 2003, SPIE Defense + Commercial Sensing.

[41]  M. Nikolova,et al.  Segmentation of a road from a vehicle-mounted radar and accuracy of the estimation , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[42]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[43]  Ronald P. S. Mahler,et al.  Statistical Multisource-Multitarget Information Fusion , 2007 .

[44]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[45]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

[46]  Klaus Dietmayer,et al.  Lane detection and street type classification using laser range images , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[47]  P. S. Maybeck,et al.  Cost-function-based gaussian mixture reduction for target tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[48]  Martin David Adams,et al.  Robotic Mapping Using Measurement Likelihood Filtering , 2009, Int. J. Robotics Res..

[49]  Manfred Morari,et al.  A clustering technique for the identification of piecewise affine systems , 2001, Autom..

[50]  Peter Cheeseman,et al.  A stochastic map for uncertain spatial relationships , 1988 .

[51]  Robert Lund,et al.  An Introduction to the Theory of Point Processes, Vol. 1: Elementary Theory and Methods (2nd ed.) (Book) , 2004 .

[52]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[53]  David J. Salmond.  Mixture reduction algorithms for target tracking in clutter , 1990 .

[54]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.