Detecting empty and loaded platform semi-trailers using side-fire LiDAR data for supporting freight analysis

Abstract

Empty truck trips constitute an important aspect of commodity-based freight planning and modeling. But this information is generally not available to state Departments of Transportation (DOTs) or Metropolitan Planning Organizations (MPOs) since detecting empty trips is a challenge with traditional vehicle sensors as they do not provide the body type of the truck and trailer. In this study, we propose a method for detecting empty and loaded platform semi-trailers using data from a multi-array Light Detection and Ranging (LiDAR) sensor. From the LiDAR cloud points, 3D profiles of trucks can be generated, and these profiles allow extracting useful information (e.g., body type, empty and loaded platforms). Since only platform semi-trailers’ payload is observable from their 3D profiles, we only consider open platform trailers which constitute about 20% of the truck trailer population in the USA. This paper shows how point-cloud data from a 16-beam LiDAR sensor are processed to extract useful information and features to distinguish between empty and loaded platform semi-trailers versus all other major truck body types (e.g., dry van, container, tank, automobile transport). Several machine learning (ML) models, in particular, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost.M2), and Support Vector Machines (SVM) are implemented on the field data collected on a freeway segment that includes over 9,200 trucks. The results show that all major semi-trailers and empty platform semi-trailers can be distinguished with high levels of accuracies of 99% and 97% respectively.