Cyber-Human-Physical Heterogeneous Traffic Systems for Enhanced Safety

Automated vehicles have immense potentials for improving the safety, efficiency and environmental problems in our existing transportation systems. Despite the tremendous ongoing efforts from both industry and academia, fully autonomous vehicles have not yet been widely deployed in public traffic. In the foreseeable future, automated vehicles will very likely be expected to operate in traffic that involve heterogeneous agents including automated vehicles, human-driven vehicles and pedestrians. Such heterogeneity will bring new challenges to the safety of the traffic system. This paper reviews some existing works related to heterogeneous traffic systems and presents a vision of cyber-human-physical heterogeneous traffic systems that can substantially enhance overall safety.

[1]  P. G. Gipps,et al.  A MODEL FOR THE STRUCTURE OF LANE-CHANGING DECISIONS , 1986 .

[2]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[3]  Nakayama,et al.  Dynamical model of traffic congestion and numerical simulation. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[5]  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.

[6]  K. Ahmed Modeling drivers' acceleration and lane changing behavior , 1999 .

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

[8]  Bruce H. Krogh,et al.  Distributed model predictive control , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[9]  Bruce H. Krogh,et al.  Stability-constrained model predictive control , 2001, IEEE Trans. Autom. Control..

[10]  I. Couzin,et al.  Collective memory and spatial sorting in animal groups. , 2002, Journal of theoretical biology.

[11]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[12]  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).

[13]  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.

[14]  Chris M.J. Tampère,et al.  Human-kinetic multiclass traffic flow theory and modelling. With application to Advanced Driver Assistance Systems in congestion , 2004 .

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

[16]  A. Richards,et al.  A decentralized algorithm for robust constrained model predictive control , 2004, Proceedings of the 2004 American Control Conference.

[17]  Christophe F. Wakim,et al.  A Markovian model of pedestrian behavior , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[18]  B. Steux,et al.  Hardware-friendly pedestrian detection and impact prediction , 2004, IEEE Intelligent Vehicles Symposium, 2004.

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

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

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

[22]  Lars Petersson,et al.  Threat assessment for general road scenes using monte carlo sampling , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[23]  William B. Dunbar,et al.  Distributed receding horizon control for multi-vehicle formation stabilization , 2006, Autom..

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

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

[26]  Dirk Helbing,et al.  General Lane-Changing Model MOBIL for Car-Following Models , 2007 .

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

[28]  A. Kummert,et al.  The unscented Kalman filter for pedestrian tracking from a moving host , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[29]  Martin Treiber,et al.  Calibrating Car-Following Models by Using Trajectory Data , 2008, 0803.4063.

[30]  Francesco Borrelli,et al.  Decentralized Receding Horizon Control and Coordination of Autonomous Vehicle Formations , 2008, IEEE Transactions on Control Systems Technology.

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

[32]  Denis Gillet,et al.  A hamilton-jacobi formulation for cooperative control of multi-agent systems , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

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

[34]  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.

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

[36]  Rainald Löhner,et al.  On the modeling of pedestrian motion , 2010 .

[37]  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.

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

[39]  John M. Dolan,et al.  A point-based MDP for robust single-lane autonomous driving behavior under uncertainties , 2011, 2011 IEEE International Conference on Robotics and Automation.

[40]  Klaus C. J. Dietmayer,et al.  Analysis of V2X communication parameters for the development of a fusion architecture for cooperative perception systems , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

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

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

[43]  Denis Gillet,et al.  Decentralized Coordination of Autonomous Vehicles at intersections , 2011 .

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

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

[46]  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.

[47]  Klaus C. J. Dietmayer,et al.  Car2X-based perception in a high-level fusion architecture for cooperative perception systems , 2012, 2012 IEEE Intelligent Vehicles Symposium.

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

[49]  Klaus C. J. Dietmayer,et al.  Early detection of the Pedestrian's intention to cross the street , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[50]  Victor L. Knoop,et al.  Integrated Lane Change Model with Relaxation and Synchronization , 2012 .

[51]  Martin Fellendorf,et al.  Modeling Concepts for Mixed Traffic , 2012 .

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

[53]  Wuhong Wang,et al.  A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow , 2013 .

[54]  Takayuki Kanda,et al.  Potential for the dynamics of pedestrians in a socially interacting group. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[55]  Dirk Wollherr,et al.  A prediction-based reactive driving strategy for highly automated driving function on freeways , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[56]  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.

[57]  Kyongsu Yi,et al.  Probabilistic and Holistic Prediction of Vehicle States Using Sensor Fusion for Application to Integrated Vehicle Safety Systems , 2014, IEEE Transactions on Intelligent Transportation Systems.

[58]  Fabien Moutarde,et al.  Priority-based coordination of autonomous and legacy vehicles at intersection , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[59]  Ioannis Karamouzas,et al.  Universal power law governing pedestrian interactions. , 2014, Physical review letters.

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

[61]  Franz Kummert,et al.  Pedestrian crossing prediction using multiple context-based models , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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

[63]  Petros A. Ioannou,et al.  Personalized Driver/Vehicle Lane Change Models for ADAS , 2015, IEEE Transactions on Vehicular Technology.

[64]  Santokh Singh,et al.  Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey , 2015 .

[65]  Hirokatsu Kataoka,et al.  Predicting driving behavior using inverse reinforcement learning with multiple reward functions towards environmental diversity , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[66]  Washington Y. Ochieng,et al.  Modelling shared space users via rule-based social force model , 2015 .

[67]  Xiaogang Wang,et al.  Pedestrian Behavior Understanding and Prediction with Deep Neural Networks , 2016, ECCV.

[68]  Masayoshi Tomizuka,et al.  Enabling safe freeway driving for automated vehicles , 2016, 2016 American Control Conference (ACC).

[69]  Weilong Song,et al.  Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection , 2016 .

[70]  Washington Y. Ochieng,et al.  Calibration and Validation of a Shared Space Model: Case Study , 2016 .

[71]  Cristina Olaverri-Monreal,et al.  P2V and V2P communication for Pedestrian warning on the basis of Autonomous Vehicles , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[72]  Hannes Sommer,et al.  Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[73]  Xiaobo Qu,et al.  A recurrent neural network based microscopic car following model to predict traffic oscillation , 2017 .

[74]  Fawzi Nashashibi,et al.  Fusion of Perception and V2P Communication Systems for the Safety of Vulnerable Road Users , 2017, IEEE Transactions on Intelligent Transportation Systems.

[75]  Irfan Khan,et al.  Rethinking cooperative awareness for future V2X safety-critical applications , 2017, 2017 IEEE Vehicular Networking Conference (VNC).

[76]  Girish Chowdhary,et al.  Intent Communication between Autonomous Vehicles and Pedestrians , 2017, ArXiv.

[77]  A. Yamashita,et al.  Trajectory Prediction of Surrounding Vehicles Using LSTM Network , 2018 .

[78]  Yi Guo,et al.  Learning How Pedestrians Navigate: A Deep Inverse Reinforcement Learning Approach , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[79]  Yunyi Jia,et al.  Modeling, Learning and Prediction of Longitudinal Behaviors of Human-Driven Vehicles by Incorporating Internal Human DecisionMaking Process using Inverse Model Predictive Control , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[80]  Mohan M. Trivedi,et al.  Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[81]  Beshah Ayalew,et al.  A Model for Vehicular Interactions Extracted From Real-World Traffic Data , 2019, Volume 3: 21st International Conference on Advanced Vehicle Technologies; 16th International Conference on Design Education.