Methods for classification of truck trailers using side-fire light detection and ranging (LiDAR) Data
Olcay Sahin, Reza Vatani Nezafat, Mecit Cetin
Abstract
Classification of vehicles into distinct groups is critical for a number of applications including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The main goal of this paper is to demonstrate how data from Light Detection and Ranging (LiDAR) sensors could be leveraged to distinguish between specific types of truck trailers beyond what is generally possible by the traditional vehicle classification sensors (e.g., piezoelectric sensors and inductive loop detectors). A multi-array LiDAR sensor enables the construction of 3D-profiles of vehicles since it measures the distance to the object reflecting its emitted light. This paper shows how point-cloud data from a 16-beam LiDAR sensor are processed to extract useful information and features to classify semi-trailer trucks hauling ten different types of trailers: a reefer and non-reefer dry van, 20 ft and 40 ft intermodal containers, 40 ft reefer intermodal container, platforms, tanks, car transporter, open-top van/dump and aggregated other types (i.e., livestock, logging). K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost.M2), and Support Vector Machines (SVM) supervised machine learning algorithms are implemented on the field data collected on a freeway segment that includes over seven-thousand trucks. The results show that with the SVM model, different trailer body types can be distinguished with a very high level of accuracy ranging from 85% to 98%.