A survey on motion prediction and risk assessment for intelligent vehicles

With the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model.

[1]  Max Donath,et al.  American Control Conference , 1993 .

[2]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  A. Galip Ulsoy,et al.  Vehicle dynamics and external disturbance estimation for vehicle path prediction , 2000, IEEE Trans. Control. Syst. Technol..

[4]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

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

[6]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[7]  I. Dagli,et al.  Motivation-based approach to behavior prediction , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[8]  Qingfeng Huang,et al.  An adaptive peer-to-peer collision warning system , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).

[9]  Hajime Asama,et al.  Inevitable collision states — a step towards safer robots? , 2004, Adv. Robotics.

[10]  Thierry Fraichard,et al.  Motion prediction for moving objects: a statistical approach , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[11]  K.C.J. Dietmayer,et al.  IMM object tracking for high dynamic driving maneuvers , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[12]  George Kollios,et al.  Extraction and clustering of motion trajectories in video , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  D. Aubert,et al.  A collision mitigation system using laser scanner and stereovision fusion and its assessment , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[14]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[15]  Y. Liu,et al.  Performance evaluation of intersection warning system using a vehicle traffic and wireless simulator , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[16]  Hiren M. Mandalia,et al.  Using Support Vector Machines for Lane-Change Detection , 2005 .

[17]  Sebastien Glaser,et al.  Kalman filters predictive steps comparison for vehicle localization , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  T. Kanade,et al.  Monte Carlo road safety reasoning , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[19]  Kristian Kroschel,et al.  A Multilevel Collision Mitigation Approach—Its Situation Assessment, Decision Making, and Performance Tradeoffs , 2006, IEEE Transactions on Intelligent Transportation Systems.

[20]  Han-Shue Tan,et al.  Vehicle future trajectory prediction with a DGPS/INS-based positioning system , 2006, 2006 American Control Conference.

[21]  A. Lambert,et al.  Reducing Navigation Errors by Planning with Realistic Vehicle Model , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[22]  Ching-Yao Chan,et al.  Defining Safety Performance Measures of Driver-Assistance Systems for Intersection Left-Turn Conflicts , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[23]  Nikolaos Papanikolopoulos,et al.  Deterministic sampling-based switching kalman filtering for vehicle tracking , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[24]  Tarek Sayed,et al.  Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[25]  Han-Shue Tan,et al.  DGPS-Based Vehicle-to-Vehicle Cooperative Collision Warning: Engineering Feasibility Viewpoints , 2006, IEEE Transactions on Intelligent Transportation Systems.

[26]  Tieniu Tan,et al.  A system for learning statistical motion patterns , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Angelos Amditis,et al.  Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems , 2007, IEEE Transactions on Intelligent Transportation Systems.

[28]  Joaquín Salas,et al.  Detecting Unusual Activities at Vehicular Intersections , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[29]  Gerd Wanielik,et al.  Comparison and evaluation of advanced motion models for vehicle tracking , 2008, 2008 11th International Conference on Information Fusion.

[30]  Lars Petersson,et al.  Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling , 2008, IEEE Transactions on Intelligent Transportation Systems.

[31]  Angelos Amditis,et al.  Cooperative Path Prediction in Vehicular Environments , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[32]  A. Barth,et al.  Where will the oncoming vehicle be the next second? , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[33]  Klaus C. J. Dietmayer,et al.  Continuous Driver Intention Recognition with Hidden Markov Models , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[34]  Christoph Hermes,et al.  Long-term vehicle motion prediction , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[35]  Christian Laugier,et al.  Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion , 2009, Int. J. Robotics Res..

[36]  Fawzi Nashashibi,et al.  Real time trajectory prediction for collision risk estimation between vehicles , 2009, 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing.

[37]  Klaus C. J. Dietmayer,et al.  Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios , 2009, IEEE Transactions on Intelligent Transportation Systems.

[38]  Mohan M. Trivedi,et al.  Learning trajectory patterns by clustering: Experimental studies and comparative evaluation , 2009, CVPR.

[39]  Matthias Althoff,et al.  Model-Based Probabilistic Collision Detection in Autonomous Driving , 2009, IEEE Transactions on Intelligent Transportation Systems.

[40]  Christopher Tay.  Analysis of Dynamic Scenes: Application to Driving Assistance. (Analyses des scènes dynamiques: Application à l'assistance à la conduite) , 2009 .

[41]  Kym Watson,et al.  Recognition of dangerous situations within a cooperative group of vehicles , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[42]  Nikolaos Papanikolopoulos,et al.  Clustering of Vehicle Trajectories , 2010, IEEE Transactions on Intelligent Transportation Systems.

[43]  Rüdiger Dillmann,et al.  A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[44]  Ümit Özgüner,et al.  Hybrid-state driver/vehicle modelling, estimation and prediction , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[45]  Nicholas Roy,et al.  A Bayesian Nonparametric Approach to Modeling Mobility Patterns , 2010, AAAI.

[46]  Jonathan P. How,et al.  Threat assessment design for driver assistance system at intersections , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[47]  Helge J. Ritter,et al.  Recognition of situation classes at road intersections , 2010, 2010 IEEE International Conference on Robotics and Automation.

[48]  Stewart Worrall,et al.  Improving vehicle safety using context based detection of risk , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[49]  Jonas Sjöberg,et al.  Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions , 2010, IEEE Transactions on Intelligent Transportation Systems.

[50]  Javier Ibanez Guzman,et al.  Vehicle to vehicle communications applied to road intersection safety, field results , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[51]  Markus Maurer,et al.  Object tracking in urban intersections based on active use of a priori knowledge: Active interacting multi model filter , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[52]  Thao Dang,et al.  Handling uncertainties in criticality assessment , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[53]  Franz Kummert,et al.  Behavior prediction at multiple time-scales in inner-city scenarios , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[54]  Albert S. Huang,et al.  A Bayesian nonparametric approach to modeling motion patterns , 2011, Auton. Robots.

[55]  Anup Doshi,et al.  Lane change intent prediction for driver assistance: On-road design and evaluation , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[56]  Amaury Nègre,et al.  Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety , 2011, IEEE Intelligent Transportation Systems Magazine.

[57]  N. Roy,et al.  Mobile Agent Trajectory Prediction using Bayesian Nonparametric Reachability Trees , 2011 .

[58]  Juan Liu,et al.  An Efficient Computational Architecture for a Collision Early-Warning System for Vehicles, Pedestrians, and Bicyclists , 2011, IEEE Transactions on Intelligent Transportation Systems.

[59]  Matthias Althoff,et al.  Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars , 2011, IEEE Transactions on Intelligent Transportation Systems.

[60]  Eduardo Mario Nebot,et al.  A bayesian approach for driving behavior inference , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[61]  Thao Dang,et al.  A flexible method for criticality assessment in driver assistance systems , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[62]  Christian Laugier,et al.  Risk assessment at road intersections: Comparing intention and expectation , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[63]  Christian Laugier,et al.  Evaluating risk at road intersections by detecting conflicting intentions , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[64]  Christoph Stiller,et al.  Driver intent inference at urban intersections using the intelligent driver model , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[65]  Eduardo Mario Nebot,et al.  Estimation of Multivehicle Dynamics by Considering Contextual Information , 2012, IEEE Transactions on Robotics.

[66]  Jonathan P. How,et al.  Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set , 2012, IEEE Transactions on Intelligent Transportation Systems.

[67]  Ulrich Kressel,et al.  Incorporating Categorical Information for Enhanced Probabilistic Trajectory Prediction , 2013, 2013 12th International Conference on Machine Learning and Applications.

[68]  Mathias Perrollaz,et al.  Learning-based approach for online lane change intention prediction , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[69]  Martin Buss,et al.  Interactive scene prediction for automotive applications , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[70]  Christian Laugier,et al.  Intention-Aware Risk Estimation for General Traffic Situations, and Application to Intersection Safety , 2013 .

[71]  Stewart Worrall,et al.  Vehicle collision probability calculation for general traffic scenarios under uncertainty , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[72]  Jonas Firl,et al.  Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[73]  Horst-Michael Groß,et al.  Combining behavior and situation information for reliably estimating multiple intentions , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[74]  Ruzena Bajcsy,et al.  Lane Keeping Assistance with Learning-Based Driver Model and Model Predictive Control , 2014 .

[75]  Karl-Heinz Hoffmann,et al.  Prediction of driver intended path at intersections , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[76]  Alexandra Neukum,et al.  Advisory warnings based on cooperative perception , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.