Skip to Main Content
 

Global Search Box

 
 
 

ETD Abstract Container

Abstract Header

Vehicle Classification Using LiDAR Returns from an Instrumented Probe Vehicle

Danford, Hunter Vladimir
http://rave.ohiolink.edu/etdc/view?acc_num=osu172139019636617

Abstract Details

2024, Master of Science, Ohio State University, Civil Engineering.
This thesis explores vehicle classification using returns from LiDAR sensors mounted on a moving instrumented probe vehicle (IPV). The first methodology is a pre-existing scheme that classifies vehicles based on height and length, referred to as the height and length method (HLM). The second is a novel scheme that is developed in this thesis that uses the upper envelope of the vehicle's side view silhouette to classify vehicles, referred to as the shape based classification method (SBCM). The pre-existing HLM was developed using stationary sensors and has already been shown to be robust to data imperfections. The present work demonstrates that HLM also works well when the LiDAR sensors are moving. The main limitation of HLM is that it can only achieve a coarse classification: passenger vehicle, single unit truck, multi-unit truck, or vehicle pulling trailer. The novel SBCM is intended to provide finer gradations among classes. The SBCM collects profiles of the vehicle height from LiDAR returns and normalizes the profiles to 100 points equally distanced along the vehicle (i.e., relative percentage distance from the front to the back of the vehicle). A training set of the empirical LiDAR data collected was used to develop prototype vehicle profiles representative of seven classes: passenger car, SUV, mini-van, pickup truck, van, single unit truck and multi-unit truck. Generally, several sub-class height profiles were developed to capture various vehicle shapes within a class. To develop the profiles, vehicles in the sub-class were chosen from concurrent video imagery as being representative of the sub-class. Then the prototype vehicle height profile for the sub-class was determined by taking the average height across all the representative vehicles at each of the 100 points in the normalized profiles. An eighth classification of vehicles pulling trailers is also employed but does not rely on a prototype height profile. Two variants of the 8-class scheme SBCM were also considered: one that consolidated four classes into a single class, and a second that consolidated five classes into a single class, yielding a 5-class and 4-class scheme, respectively. The classes of the SBCM 4-class scheme are directly comparable to those of the HLM. LiDAR returns from 8,656 vehicles were used in this study, with returns from 1,365 vehicles used to train the SBCM height profiles and returns from the other 7,291 used to evaluate both HLM and SBCM. A detailed breakdown of performance is conducted on both the development and evaluation data sets. Summarizing just the results from the evaluation data set here, the SBCM achieved 93.6% accuracy with the 8-class scheme, 97.5% accuracy with the 5-class scheme, and 99.2% accuracy with the 4-class scheme. The progressive improvement reflects the fact that many misclassifications obtained when using the 8-class scheme became correct classifications after some classes were combined. The HLM has an innate 4-class scheme with which it achieved 99.0% accuracy, slightly worse than the 99.2% SBCM accuracy, while not allowing for the high accuracy finer classification of the SBCM.
Benjamin Coifman (Advisor)
Rabi Mishalani (Committee Member)
Mark McCord (Committee Member)
78 p.
Civil Engineering
vehicle classification; LiDAR; IPV

Recommended Citations

Citations

  • Danford, H. V. (2024). Vehicle Classification Using LiDAR Returns from an Instrumented Probe Vehicle [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu172139019636617

    APA Style (7th edition)

  • Danford, Hunter. Vehicle Classification Using LiDAR Returns from an Instrumented Probe Vehicle. 2024. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu172139019636617.

    MLA Style (8th edition)

  • Danford, Hunter. "Vehicle Classification Using LiDAR Returns from an Instrumented Probe Vehicle." Master's thesis, Ohio State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu172139019636617

    Chicago Manual of Style (17th edition)

osu172139019636617
53
© 2024, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.