Abstract
In this paper, we focus on detection of speed changes from audio data, representing recordings of cars passing a microphone placed near the road. The goal of this work is to observe the behavior of drivers near control points, in order to check whether their driving is safe both when approaching the speed camera and after passing it. The audio data were recorded in controlled conditions, and they are publicly available for downloading. They represent one of three classes: car accelerating, decelerating, or maintaining constant speed. We used SVM, random forests, and artificial neural networks as classifiers, as well as the time series based approach. We also tested several approaches to audio data representation, namely: average values of basic audio features within the analyzed time segment, parametric description of the time evolution of these features, and parametric description of curves (lines) in the spectrogram. Additionally, the combinations of these representations were used in classification experiments. As a final step, we constructed an ensemble classifier, consisting of the best models. The proposed solution achieved an accuracy of almost 95%, without mistaking acceleration with deceleration, and very rare mistakes between stable speed and speed changes. The outcomes of this work can become a basis for campaigns aiming at improving traffic safety.
Highlights
Some drivers will not admit it, driving at excessive speed increases the difficulty of carrying out the tasks they face while driving
The best results are obtained for lines calculated using Method9; good results were obtained for Method4
We proposed the methodology for detecting speed changes based on audio data, recorded for vehicles passing the audio recorder
Summary
Some drivers will not admit it, driving at excessive speed increases the difficulty of carrying out the tasks they face while driving. They have less time to properly evaluate the traffic situation and respond . I.e., unmatched to road and traffic conditions, is the cause of many road accidents and their consequences, as evidenced in numerous works on the subject [1,2,3,4,5,6,7,8] To improve this situation, careful investigation and monitoring of trends in road safety is needed. A network of road safety observatories has been build
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