Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR

Abstract

The problem of traffic safety has become increasingly prominent owing to the increase in the number of cars. Traffic accidents often occur in an instant, which makes it necessary to obtain traffic data with high resolution. High-resolution micro traffic data (HRMTD) indicates that the spatial resolution reaches the centimeter level and that the temporal resolution reaches the millisecond level. The position, direction, speed, and acceleration of objects on the road can be extracted with HRMTD. In this paper, a LiDAR sensor was installed at the roadside for data collection. An adjacent-frame fusion method for vehicle detection and tracking in complex traffic circumstances is presented. Compared with the previous research, objects can be detected and tracked without object model extraction or a bounding box description. In addition, problems caused by occlusion can be improved using adjacent frames fusion in the vehicle detection and tracking algorithms in this paper. The data processing procedure are as follows: selection of area of interest, ground point removal, vehicle clustering, and vehicle tracking. The algorithm has been tested at different sites (in Reno and Suzhou), and the results demonstrate that the algorithm can perform well in both simple and complex application scenarios.

Get full access to this article

View all access options for this article.

References

Llop

Pinho

Ribeiro

M. C.

Pereira

M. J.

Branquinho

Traffic Represents the Main Source of Pollution in Small Mediterranean Urban Areas as Seen by Lichen Functional Groups. Environmental Science & Pollution Research, Vol. 24, No. 13, 2017, pp. 12016–12025.

Treiber

Kesting

Traffic Flow Dynamics. Springer Berlin Heidelberg, Berlin, 2013.

Wang

Zheng

Chen

Roadside Magnetic Sensor System for Vehicle Detection in Urban Environments. IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 5, 2017, pp. 1365–1374.

Mukhtar

Xia

Tang

T. B.

Vehicle Detection Techniques for Collision Avoidance Systems: A Review. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 5, 2015, pp. 2318–2338.

Huang

Cheng

Chen

Luo

Wang

Traffic Sign Occlusion Detection using Mobile Laser Scanning Point Clouds. IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 9, 2017, pp. 2364–2376.

Lindner

Richter

Wanielik

Takagi

Isogai

Multi-Channel Lidar Processing for Lane Detection and Estimation. Proc., 2009 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, 2009, pp. 1–6.

Kim

Z. W.

Robust Lane Detection and Tracking in Challenging Scenarios. IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 1, 2008, pp. 16–26.

Himmelsbach

Hundelshausen

F. V.

Wuensche

H. J.

Fast Segmentation of 3D Point Clouds for Ground Vehicles. Proc., 2010 IEEE Intelligent Vehicles Symposium, San Diego, CA, 2010, pp. 560–565.

Douillard

Underwood

Kuntz

Vlaskine

Quadros

Morton

On the Segmentation of 3D LIDAR Point Clouds. Proc., 2011 IEEE International Conference on Robotics and Automation, Shanghai, 2011, pp. 2798–2805.

10.

Shin

M. O.

Kim

Seo

Real-Time and Accurate Segmentation of 3-D Point Clouds Based on Gaussian Process Regression. IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 12, 2017, pp. 3363–3377.

11.

Streller

Furstenberg

Dietmayer

Vehicle and Object Models for Robust Tracking in Traffic Scenes Using Laser Range Images. Proc., 5th International Conference on Intelligent Transportation Systems, Singapore, 2002, pp. 118–123.

12.

Petrovskaya

Thrun

Model Based Vehicle Detection and Tracking for Autonomous Urban Driving. Autonomous Robots, Vol. 26, No. 2–3, 2009, pp. 123–139.

13.

Allodi

Broggi

Giaquinto

Patander

Prioletti

Machine Learning in Tracking Associations with Stereo Vision and Lidar Observations for an Autonomous Vehicle. Proc., 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 2016, pp. 648–653.

14.

Sun

Zheng

Yue

Automatic Background Filtering Method for Roadside LiDAR Data. Transportation Research Record Journal of the Transportation Research Board, 2018. 2672: 106–114.

15.

Alexander

Cheng

P. M.

Gorjestani

Menon

Newstrom

Shankwitz

Donath

The Minnesota Mobile Intersection Surveillance System. Proc., 2006 IEEE Intelligent Transportation Systems Conference, Toronto, Ontario, Canada, IEEE, New York, 2006, pp. 139–144.

16.

Zhao

Sha

Zhao

Cui

Zha

Shibasaki

Detection and Tracking of Moving Objects at Intersections using a Network of Laser Scanners. IEEE Transactions on Intelligent Transportation Systems, Vol. 13, No. 2, 2012, pp. 655–670.

17.

Velodyne LiDAR. User’s Manual and Programming Guide. http://www.velodynelidar.com/vlp-16.html. Accessed February 5, 2017.

18.

Hartigan

J. A.

Clustering Algorithms. John Wiley & Sons Inc., New York, 1975.

19.

Ester

Kriegel

H. P.

Sander

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proc., KDD’96 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, 1996, pp. 226–231.

20.

Steinbach

Karypis

Kumar

A Comparison of Document Clustering Techniques. Presented at TextMining Workshop at KDD2000, Minneapolis, Minn., 2000, pp. 525–526.

21.

Kanungo

Mount

D. M.

Netanyahu

N. S.

Piatko

C. D.

Silverman

A. Y.

An Efficient K-means Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, 2002, pp. 881–892.