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 language | English (US) |
|---|---|
| Title of host publication | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1136-1141 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538670248 |
| DOIs | |
| State | Published - Oct 2019 |
| Event | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Duration: Oct 27 2019 → Oct 30 2019 |
Publication series
| Name | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
|---|
Conference
| Conference | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
|---|---|
| Country/Territory | New Zealand |
| City | Auckland |
| Period | 10/27/19 → 10/30/19 |
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.
Publisher Copyright:
© 2019 IEEE.
