Vehicle Tracking for Heavy Road Vehicle Collision Avoidance with an Inexpensive Solid State Laser Sensor

Vimal Kumar, Shankar C. Subramanian, Rajesh Rajamani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The focus of this paper is on the development of a vehicle-tracking algorithm using a solid-state LIDAR sensor for application to Collision Avoidance Systems (CAS) for Heavy Commercial Road Vehicles. Solid State LIDARs are relatively inexpensive compared to RADARs and point cloud LIDARs, and hence could accelerate commercialization of Advanced Driver Assistance Systems (ADAS) especially in cost-sensitive markets. The suitability of an inexpensive LIDAR sensor for Rear End Collision Avoidance application is analyzed first. Then, using the measurements from the sensor, an Interacting Multiple Model filter and a linear Kalman Filter are used for estimating the longitudinal and the lateral motion variables respectively, for various classes of road vehicles. Good tracking accuracy is achieved in the lateral direction despite the sensor's low angular resolution. The proposed estimation algorithm is first evaluated in a vehicle dynamics software, IPG TruckMaker®, and then through experiments, and the results are presented.

Original languageEnglish (US)
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1136-1141
Number of pages6
ISBN (Electronic)9781538670248
DOIs
StatePublished - Oct 2019
Event2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand
Duration: Oct 27 2019Oct 30 2019

Publication series

Name2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
CountryNew Zealand
CityAuckland
Period10/27/1910/30/19

Bibliographical note

Funding Information:
*This research was supported by the Ministry of Labor and Employment (now Ministry of Skill Development and Entrepreneurship), Government of India, through the grant EDD/14-15/023/MOLE/NILE.

Funding Information:
This research was supported by the Ministry of Labor and Employment (now Ministry of Skill Development and Entrepreneurship), Government of India, through the grant EDD/14-15/023/MOLE/NILE.

Funding Information:
The authors acknowledge the funding provided by the Ministry of Skill Development and Entrepreneurship, Government of India, through the grant EDD/14-15/ 023/MOLE/NILE. The authors also thank the Security section of IIT Madras for providing the speed gun for the experiments.

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