Abstract
The integration of Internet of things (IoT) and intelligent transportation system (ITS) is expected to improve the traffic efficiency and enhance the driving experience. However, due to the dynamic traffic environment and various types of vehicles, it is a challenge to perform vehicle classification and speed estimation with a single magnetic sensor. In this paper, based on a single low-cost magnetic sensor, a scheme is proposed to achieve vehicle classification and speed interval estimation by designing a two-dimensional convolutional neural network (CNN). Specifically, we extract the magnetic field data of each vehicle and then convert the collected data into a two-dimensional grayscale image. In this way, the images of vehicle signals with different types and driving speeds can be used as the input data to train the designed CNN model. With the designed CNN model, we classify the vehicles into 7 types and estimate the speed interval of each vehicle, where the speeds in the range of 10km/h-70km/h are divided into 6 intervals of size 10km/h. The performance of the proposed vehicle classification and speed estimation scheme is evaluated by experiments, where the experimental results show that the accuracy of vehicle classification and the accuracy of speed interval estimation are 97.83% and 96.85%, respectively.
Highlights
The intelligent transportation system (ITS), which aims to achieve efficient traffic management, is expected to improve traffic efficiency and traveling experience of drivers [1]–[3]
A camera is used to record the type of each vehicle and a radar speedometer is used to record the speed of the vehicle passing by the detection coverage of the magnetic sensor
The signals of vehicles are converted into images and treated as the input of the convolutional neural network (CNN) model for speed interval estimation
Summary
The intelligent transportation system (ITS), which aims to achieve efficient traffic management, is expected to improve traffic efficiency and traveling experience of drivers [1]–[3]. When the collected data is no smaller than the threshold (i.e., M (k) ≥ Dth(k)), the vehicle may enter the detection coverage of the sensor At this time, the value of ID is 1 and the state machine moves to S2. Extract vehicle signals Based on the designed state machine for vehicle detection, the time points when the vehicle enters and exits the detection range of the magnetic sensor can be determined. It can be seen in this figure that the obvious difference is the length of the waveform when the same vehicle moving at different speeds.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
