Methods for classification of truck trailers using side-fire light detection and ranging (LiDAR) Data

Sahin, Olcay; Nezafat, Reza; Cetin, Mecit

doi:10.1080/15472450.2020.1733999

Cited by 21 publications

(10 citation statements)

References 18 publications

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“…Figure 8 shows the recall distribution—which is also referred to as “correct classification rate” (CCR) in previous research ( 38 , 39 )—for the DNN models built with 10 sets of bootstrapped samples. Minority classes such as Classes 4, 7, 10, 12, and 13 resulted in relatively high prediction variances since limited training instances were used to learn from the features for every single model.…”

Section: Model Resultsmentioning

confidence: 99%

Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops

Tok

Ritchie

2021

Transportation Research Record

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The Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve various transportation needs such as pavement design, emission estimation, and transportation planning. Many transportation agencies rely on Weigh-In-Motion and Automatic Vehicle Classification sites to collect these essential vehicle classification counts. However, the spatial coverage of these detection sites across the highway network is limited by high installation and maintenance costs. One cost-effective approach has been the use of single inductive loop sensors as an alternative to obtaining FHWA vehicle classification data. However, most data sets used to develop such models are skewed since many classes associated with larger truck configurations are less commonly observed in the roadway network. This makes it more difficult to accurately classify under-represented classes, even though many of these minority classes may have disproportionately adverse effects on pavement infrastructure and the environment. Therefore, previous models have been unable to adequately classify under-represented classes, and the overall performance of the models is often masked by excellent classification accuracy of majority classes, such as passenger vehicles and five-axle tractor-trailers. To resolve the challenge of imbalanced data sets in the FHWA vehicle classification, this paper constructed a bootstrap aggregating deep neural network model on a truck-focused data set using single inductive loop signatures. The proposed method significantly improved the model performance on several truck classes, especially minority classes such as Classes 7 and 11 which were overlooked in previous research. The model was tested on a distinct data set obtained from four spatially independent sites and achieved an accuracy of 0.87 and an average F1 score of 0.72.

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Section: Model Resultsmentioning

confidence: 99%

Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops

Tok

Ritchie

2021

Transportation Research Record

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“…In recent years, the research results of roadside LiDAR object detection methods based on traditional machine learning have been the most numerous. Usually, the object detection task is divided into four main steps: background filtering, object point clustering, feature extraction, and object classification [ 35 , 36 , 37 , 38 ]. When processing the point clouds captured by roadside LiDAR, some reasonable clusters are generated by background filtering and density-based spatial clustering methods, and then object detection is achieved by feature extraction and the classification of each cluster.…”

Section: Object Detection Based On Roadside Lidarmentioning

confidence: 99%

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Sun

Wang

et al. 2022

Sensors

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Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.

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“…In summary, the vertical orientation of the LiDAR sensor provided a denser representation of each vehicle object while the field-of-view of the sensor was restricted. Meanwhile, the model performance could vary with traffic conditions due to the constant-speed assumption made in those methods (16,17). Conversely, the horizontal orientation of the sensor broadens the LDZ, but such placement only captures a sparse point cloud for each vehicle object which gives insufficient information to classify trucks in detail.…”

Section: Introductionmentioning

confidence: 99%

“…vehicle height, vehicle length, vehicle middle drop, the point density of truck refrigeration unit, etc.) of each predetermined vehicle class from the regularized point cloud, such as 2D projections (9)(10)(11) and rectangular voxels (17), or directly from the clustered sparse point cloud (15). Subsequently, classic machine learning algorithms were used to map high-level feature vectors to the predetermined vehicle classes.…”

Section: Introductionmentioning

confidence: 99%

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Truck body type classification using a deep representation learning ensemble on 3D point sets

Allu

Sun

et al. 2021

Transportation Research Part C: Emerging Technologies

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Methods for classification of truck trailers using side-fire light detection and ranging (LiDAR) Data

Cited by 21 publications

References 18 publications

Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops

Deep Ensemble Neural Network Approach for Federal Highway Administration Axle-Based Vehicle Classification Using Advanced Single Inductive Loops

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Truck body type classification using a deep representation learning ensemble on 3D point sets

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