On-Road Vehicle Trajectory Collection and Scene-Based Lane Change Analysis: Part II

This two-part paper aims to study lane change behaviors at the tactical level from an on-road perspective, with a special focus on analyzing the interactions between an ego and surrounding vehicles during the procedure. Part I addresses vehicle trajectory collection, whereas Part II addresses lane change extraction and scene-based behavioral analysis. Different from the general technique of moving object detection and tracking, trajectory collection for tactical driving behavior study is required to have the properties of consistency, completeness, continuity, and accuracy. This paper proposes a system of on-road vehicle trajectory collection, where an instrumented vehicle is developed with multiple horizontal 2-D lidars that have 360° coverage. The software is developed by fitting the laser points of all lidars on a vehicle model using a coupled estimation of features and reliability along frames to achieve accurate state estimations of occluded data and robust data association in multiviewpoint sensing. The performance is investigated extensively, and a large trajectory set is developed through on-road driving at the Fourth Ring Road in Beijing for a total distance of 64 km, with more than 5700 environmental trajectories with a total length of over 19 h. The performance is demonstrated to be of high quality in terms of the required properties. To the authors' knowledge, this is the first system that is able to automatically collect all-around vehicle trajectories during on-road driving and to demonstrate good performance in providing a high-quality database for driving behavior studies from an on-road perspective that addresses vehicle interactions in real-world traffic at the trajectory level.

[1]  Paul Newman,et al.  A New Approach to Model-Free Tracking with 2D Lidar , 2013, ISRR.

[2]  Mohan M. Trivedi,et al.  Learning multi-lane trajectories using vehicle-based vision , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[3]  John D. Lee,et al.  Exploratory Advanced Research: Making Driving Simulators More Useful for Behavioral Research – Simulator Characteristics Comparison and Model-based Transformation , 2013 .

[4]  Fabio Tango,et al.  Object perception for intelligent vehicle applications: A multi-sensor fusion approach , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[5]  Julius Ziegler,et al.  Optimal trajectories for time-critical street scenarios using discretized terminal manifolds , 2012, Int. J. Robotics Res..

[6]  S.P. Hoogendoorn,et al.  Traffic data collection from aerial imagery , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[7]  Pongsathorn Raksincharoensak,et al.  Lane Change Behavior Modeling for Autonomous Vehicles Based on Surroundings Recognition , 2011 .

[8]  Hongbin Zha,et al.  Probabilistic Inference for Occluded and Multiview On-road Vehicle Detection , 2016, IEEE Transactions on Intelligent Transportation Systems.

[9]  Ryosuke Shibasaki,et al.  Detection and Tracking of Moving Objects at Intersections Using a Network of Laser Scanners , 2012, IEEE Transactions on Intelligent Transportation Systems.

[10]  Francois Dion,et al.  Evaluation of Usability of IntelliDrive Probe Vehicle Data for Transportation Systems Performance Analysis , 2011 .

[11]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[12]  Raphael H. Grzebieta,et al.  The Australian 400-car Naturalistic Driving Study: innovation in road safety research and policy , 2013 .

[13]  Mohan M. Trivedi,et al.  Drive Analysis Using Vehicle Dynamics and Vision-Based Lane Semantics , 2015, IEEE Transactions on Intelligent Transportation Systems.

[14]  Ross A. Knepper,et al.  Model-Predictive Motion Planning: Several Key Developments for Autonomous Mobile Robots , 2014, IEEE Robotics & Automation Magazine.

[15]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[16]  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.

[17]  Mohan M. Trivedi,et al.  Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations , 2014, IEEE Transactions on Intelligent Transportation Systems.

[18]  Olivier Aycard,et al.  Detection, classification and tracking of moving objects in a 3D environment , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[19]  Vassili Alexiadis,et al.  Video -Based Vehicle Trajectory Data Collection , 2007 .

[20]  Mohan M. Trivedi,et al.  A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking , 2010, IEEE Transactions on Intelligent Transportation Systems.

[21]  Martin Lauer,et al.  Team AnnieWAY's Entry to the 2011 Grand Cooperative Driving Challenge , 2012, IEEE Transactions on Intelligent Transportation Systems.

[22]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Christoph Stiller,et al.  Joint self-localization and tracking of generic objects in 3D range data , 2013, 2013 IEEE International Conference on Robotics and Automation.

[24]  Hongbin Zha,et al.  Omni-directional detection and tracking of on-road vehicles using multiple horizontal laser scanners , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[25]  Marcello Montanino,et al.  Making NGSIM Data Usable for Studies on Traffic Flow Theory , 2013 .

[26]  Martial Hebert,et al.  Moving object detection with laser scanners , 2013, J. Field Robotics.

[27]  Julius Ziegler,et al.  Trajectory planning for Bertha — A local, continuous method , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[28]  Klaus-Dieter Kuhnert,et al.  A lane change detection approach using feature ranking with maximized predictive power , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[29]  Steffen Müller,et al.  Lateral trajectory tracking control for autonomous vehicles , 2014, 2014 European Control Conference (ECC).

[30]  B. V. K. Vijaya Kumar,et al.  A multi-sensor fusion system for moving object detection and tracking in urban driving environments , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[31]  Takatsugu Hirayama,et al.  Integrated modeling of driver gaze and vehicle operation behavior to estimate risk level during lane changes , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[32]  Praveen Edara,et al.  Modeling Mandatory Lane Changing Using Bayes Classifier and Decision Trees , 2014, IEEE Transactions on Intelligent Transportation Systems.

[33]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[34]  Hongbin Zha,et al.  A vehicle model for micro-traffic simulation in dynamic urban scenarios , 2011, 2011 IEEE International Conference on Robotics and Automation.

[35]  Seiichi Mita,et al.  Evaluating human & computer for expressway lane changing , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[36]  Alberto Broggi,et al.  The VisLab Intercontinental Autonomous Challenge: An Extensive Test for a Platoon of Intelligent Vehicles , 2012 .

[37]  Minglu Li,et al.  A Compressive Sensing Approach to Urban Traffic Estimation with Probe Vehicles , 2013, IEEE Transactions on Mobile Computing.

[38]  John H. L. Hansen,et al.  International Large-Scale Vehicle Corpora for Research on Driver Behavior on the Road , 2011, IEEE Transactions on Intelligent Transportation Systems.

[39]  Ragunathan Rajkumar,et al.  Towards a viable autonomous driving research platform , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[40]  John M. Dolan,et al.  Motion planning under uncertainty for on-road autonomous driving , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[41]  Dimitar Petrov Filev,et al.  Real-time Determination of Driver's Driving Behavior during Car Following , 2015 .

[42]  Ulrich Hofmann,et al.  360 Degree multi sensor fusion for static and dynamic obstacles , 2012, 2012 IEEE Intelligent Vehicles Symposium.

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

[44]  Mahmoud Mesbah,et al.  Impact of heavy vehicles on surrounding traffic characteristics , 2015 .

[45]  Mohan M. Trivedi,et al.  Drive quality analysis of lane change maneuvers for naturalistic driving studies , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[46]  Johannes Huber,et al.  Online trajectory optimization for nonlinear systems by the concept of a model control loop — Applied to the reaction wheel pendulum , 2013, 2013 IEEE International Conference on Control Applications (CCA).

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

[48]  Haneen Farah,et al.  Latent class model for car following behavior , 2012 .

[49]  Ali Ghaffari,et al.  Lane Change Trajectory Model Considering the Driver Effects Based on MANFIS , 2012 .

[50]  Yvonne Barnard,et al.  UDRIVE: the European naturalistic driving study , 2014 .

[51]  Mohan M. Trivedi,et al.  Understanding vehicular traffic behavior from video: a survey of unsupervised approaches , 2013, J. Electronic Imaging.

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

[53]  R. D. Ervin,et al.  System for Assessment of the Vehicle Motion Environment (SAVME): volume II , 2000 .

[54]  Feng Guo,et al.  Individual driver risk assessment using naturalistic driving data. , 2013, Accident; analysis and prevention.

[55]  Tarak Gandhi,et al.  Vehicle Surround Capture: Survey of Techniques and a Novel Omni-Video-Based Approach for Dynamic Panoramic Surround Maps , 2006, IEEE Transactions on Intelligent Transportation Systems.

[56]  Mohan M. Trivedi,et al.  Overtaking & receding vehicle detection for driver assistance and naturalistic driving studies , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[57]  Alexandra Kondyli,et al.  Exploratory Analysis of Lane Changing on Freeways Based on Driver Behavior , 2015 .

[58]  Mohan M. Trivedi,et al.  Vehicle Iconic Surround Observer: Visualization platform for intelligent driver support applications , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[59]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014, ROBOMECH Journal.

[60]  Nathan P Belz,et al.  Implementation, Driver Behavior, and Simulation: Issues Related to Roundabouts in Northern New England , 2014 .

[61]  Kenneth L Campbell The SHRP 2 Naturalistic Driving Study: Addressing Driver Performance and Behavior in Traffic Safety , 2012 .

[62]  Hongbin Zha,et al.  Learning lane change trajectories from on-road driving data , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[63]  Nick Reed,et al.  Driving next to automated vehicle platoons: How do short time headways influence non-platoon drivers’ longitudinal control? , 2014 .

[64]  Holger Rootzén,et al.  Accident Analysis and Prevention , 2013 .

[65]  Majid Sarvi,et al.  Effect of Surrounding Traffic Characteristics on Lane Changing Behavior , 2010 .

[66]  Hugh F. Durrant-Whyte,et al.  Simultaneous Localization, Mapping and Moving Object Tracking , 2007, Int. J. Robotics Res..

[67]  Alex Pentland,et al.  Driver behavior recognition and prediction in a SmartCar , 2000, Defense, Security, and Sensing.

[68]  John A. Michon,et al.  A critical view of driver behavior models: What do we know , 1985 .