A lot of studies have been conducted for object classification on the road. Biljecki et al. [
7] developed a fuzzy-based method for classifying the trajectories obtained from GPS into different transportation model. The testing results showed that this method can achieve 91.6% of accuracy by comparing them with the reference data derived from manual classification. Fuerstenberg et al. [
8] developed a speed-based method for pedestrian and non-pedestrian objects detection. An accuracy of more than 80% can be achieved in an urban road environment. Shape-based methods have been well developed for object classification in transportation [
9]. A lot of efforts have been conducted to use the features extracted from videos for object classification [
10]. Gupte et al. developed a rule-based method to classify vehicles into two categories: Trucks and other vehicles [
9]. The authors assumed that trucks have a length greater than 550 cm and a height greater than 400 cm. Vehicles with parameters out of the range will be classified as non-trucks. Though they claimed a correct classification rate of 90% can be achieved in the test, this simple rule-based algorithm can only work for a pre-defined zone, and the error went high when there were multiple vehicles existing in the scene. Zhang et al. [
11] used pixel-represented lengths extracted from uncalibrated video cameras to distinguish long vehicles from short vehicles. The results can achieve the accuracy of more than 91% for vehicle classification. Mithun et al. [
12] used multiple time-spatial images (TSIs) obtained from the video streams for vehicle detection and classification. The overall accuracy in counting vehicles using the TSIs method was above 97% in the test. Chen compared two feature-based classifiers and a model-based approach by classifying the objects from the static roadside CCTV camera [
13]. It was found that the support vector machine (SVM) can achieve the highest classification performance. Zangenehpour et al. [
14,
15] used the shape and speed information extracted from the video to classify the object into one of the three classes: pedestrians, cyclists, and motor vehicles. The results showed that the overall accuracy of more than 90% can be achieved using the SVM classifier. Liang and Juang [
16] proposed a static and spatiotemporal features-based method to classify the object into one of the four classes: Pedestrians, cars, motorcycles, and bicycles. Experimental results from 12 test video sequences showed that their method can achieve relatively high accuracy. The above-mentioned studies showed that camera/video-based detection has been well studied. However, since the performance of the camera/video can be greatly influenced by light conditions, researchers are looking for other sensors for object classification [
17].
Light Detection and Ranging (LiDAR) has been widely used for different transportation areas [
18]. The typical LiDAR system is developed based on the Time of Flight (ToF) method. The ToF method is used to determine the time that a laser pulse needs to overcome a certain distance in a particular medium. The performance of the LiDAR is not influenced by the light condition, indicating that LiDAR can be a supplement of the camera for object classification [
19]. Cui et al. [
20] developed a vehicle classification method to distinguish pedestrians, vehicles, and bicycles using random forest serving the connected-vehicle systems. Six features were extracted from the point cloud and the testing results showed that the accuracy is 84%. Khan et al. [
21] developed a two-stage big data analytics framework with real-world applications using spare machine learning and long short-term memory network. Wu et al. [
22] developed a real-time queue length detection method with roadside LiDAR Data. The method developed by Cui et al. [
20] was used for vehicle classification before detecting the queue length. Song et al. [
23] developed a CNN-based 3D object classification using Hough space of LiDAR point clouds. Premebida et al. [
24] developed a Gaussian Mixture Model classifier to distinguish vehicles and pedestrians from the LiDAR data. The selected features included segment centroid, normalized Cartesian dimension, internal standard deviation of points, and radius of points cloud. The testing results showed that the false rates for pedestrians and vehicles were 21.1% and 16.5%, respectively. Lee and Coifman [
25] trained a rule-based classifier to sort the vehicles into six classes using the LiDAR data. Eight features including length, height, detection of middle drop, vehicle height at middle drop, front vehicle height and length, and rear vehicle height and length were extracted. An overall accuracy of 97% was achieved in the testing data. However, this algorithm can only class the object at the specific pre-defined location and could not solve the challenge of vehicle classification with the occlusion issue. Zhang et al. [
26] used the SVM to classify vehicles and non-vehicles from the LiDAR using 13 descriptors representing the shape of the object. The success rate for vehicle and non-vehicle classification was 91%. Yao et al. [
27] also applied the SVM to distinguish vehicles and non-vehicles from the LiDAR data. The polynomial function was selected as the kernel function of SVM. The testing showed that 87% of the vehicles can be successfully extracted. Song et al. [
28] developed a SVM method for object classification for the roadside LiDAR data. Six features extracted from the object trajectories were involved to distinguish different objects. The height-profile was innovatively used as a feature for classification. The testing results showed that the RF method can achieve an accuracy of 89%. Fuerstenbery and Willhoeft [
8] used the geometric data and the information from the past to get a classification of one object from on-board LiDAR. The past information can overcome the limitation that only a partial car can be scanned in one frame. Gao et al. [
29] presented an object classification for LiDAR data based on convolutional neural network (CNN). The average accuracy to classify the object into five classes (pedestrian, cyclist, car, truck, and others) can reach a value of 96%. Wang et al. [
30] used the SVM with radial basis function to distinguish the pedestrian and non-pedestrian objects. Four features representing the shape profile were used for training. All pedestrians except for those in the occlusion areas can be recognized. Though there have been a lot of efforts for object classification using LiDAR data. There are several problems need to be fixed. The first one is how to automatically extract the required features from the LiDAR data. Though a lot of features were used in previous studies, many of them required manual or semi-manual selection which could not meet the requirement of many advanced applications, such as connected-vehicles and autonomous vehicles. The second challenge is how to reduce the computation load of the data processing to achieve a real-time classification goal. PNN is derived from Radial Basis Function (RBF) network and has fast speed and simple structure. PNN assigns the weights and uses matrix manipulation for training and testing, which makes it possible for real-time classification. This paper used developed a PNN based method for object classification.
Table 1 shows the comparison of the methods used in this paper and the methods used in previous work. It can be seen that previous studies did not test the PNN method using the roadside LiDAR. It is therefore necessary to verify the performance of PNN for object classification using the roadside LiDAR.