Vehicle Auto-Classification Using Machine Learning Algorithms Based on Seismic Fingerprinting

Abstract

Most vehicle classification systems now use data from images or videos. However, these approaches violate drivers’ privacy and reveal their identities. Due to various disruptions, detecting automobiles using seismic ambient noise signals is challenging. This study uses seismic surface waves to compare time series data between different vehicle types. We applied various artificial intelligence approaches using raw data from three different vehicle sizes (Bus/Truck, Car, and Motorcycle) and background noise. By using vertical component seismic data, this study compares the decoding abilities of Logistic Regression, Support Vector Machine, and Naïve Bayes (NB) approaches to determine the class of automobiles. The Multiclass classifiers were trained on 4185 samples and tested on 1395 randomly chosen from actual and synthetic data sets. Additionally, we used the convolutional neural network approach as a baseline to assess the effectiveness of machine learning (ML) methods. The NB method showed relatively high classification accuracy during training for the three multiclass classification situations. Overall, we investigate an ML-based decoding technique that can be used for security and traffic analysis across large geographic areas without endangering driver privacy and is more effective and economical than conventional methods.

Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise

“…Similar to our proposed work for earthquake identification, some research efforts have also been made for the event detection of earthquakes with machine learning algorithms, applying time wave series data analogous to those used for different vehicle types. The implementation of different machine learning algorithms determines the class of automobiles [24] for distinguishing between earthquake and non-earthquake, vandalism vibrations [25], even for event detection, phase identification, and the onset picking time [26]. In all such cases, the results indicate that the use of deep neural networks was superior in distinguishing and provided high classification accuracy during training, as well as in the event and phase detection of earthquakes.…”

“…In addition, as mentioned above, a 24-bit A/D high-speed analog-to-digital precision board (ADS1256), as shown in Figure 3, is connected to the microcomputer board using the 40-pin GPIO connector. The A/D board has 24 bits of accuracy and has a quantization error 1/2 LSB of 2 24 /max input voltage. It can be adjusted to operate with a max input voltage of 3.3 or 5 volts.…”

Insurance Amount Prediction Based on Accidental Car Damage Level Using CNN

“…Ahmad Bahaa Ahmad et al [5] explored machine learning for vehicle classification via seismic fingerprinting. Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB) algorithms were used.…”

Application of an ensemble CatBoost model over complex dataset for vehicle classification

“…Edge-empowered Cooperative Multi-Camera sensing system is proposed for vehicle tracking over two vehicle datasets with the maximum accuracy of 92.43%. But the limitation are, it does not work on severe climatic conditions [ 44 , 45 ]. Using classifier like Naïve Bayes, vehicle faults [ 46 ] are identified with the features of temperature, noise and vibration of the vehicles even parking management system [ 47 ] are performed.…”