Driver behavior profiling: An investigation with different smartphone sensors and machine learning

Driver behavior profiling: An investigation with different smartphone sensors and machine learning

Abstract: Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement.

Results: We executed all combinations of the 4 MLAs and their configurations described on Table 1 over the 15 data sets described in Section 4.3 using 5 different nf values. We trained, tested, and assessed every evaluation assembly with 15 different random seeds. Finally, we calculated the mean AUC for these executions, grouped them by driving event type, and ranked the 5 best performing assemblies in the boxplot displayed in Fig 6. This figure shows the driving events on the left-hand side and the 5 best evaluation assemblies for each event on the right-hand side, with the best ones at the bottom. The assembly text identification in Fig 6 encodes, in this order: (i) the nf value; (ii) the sensor and its axis (if there is no axis indication, then all sensor axes are used); and (iii) the MLA and its configuration identifier.

Conclusions and future work: In this work we presented a quantitative evaluation of the performances of 4 MLAs (BN, MLP, RF, and SVM) with different configurations applied in the detection of 7 driving event types using data collected from 4 Android smartphone sensors (accelerometer, linear acceleration, magnetometer, and gyroscope). We collected 69 samples of these event types in a real-world experiment with 2 drivers. The start and end times of these events were recorded serve as the experiment ground-truth. We also compared the performances when applying different sliding time window sizes.

We performed 15 executions with different random seeds of 3865 evaluation assemblies of the form EA = {1:sensor, 2:sensor axis(es), 3:MLA, 4:MLA configuration, 5:number of frames in sliding window}. As a result, we found the top 5 performing assemblies for each driving event type. In the context of our experiment, these results show that (i) bigger window sizes perform better; (ii) the gyroscope and the accelerometer are the best sensors to detect our driving events; (iii) as general rule, using all sensor axes perform better than using a single one, except for aggressive left turns events; (iv) RF is by far the best performing MLA, followed by MLP; and (v) the performance of the top 35 combinations is both satisfactory and equivalent, varying from 0.980 to 0.999 mean AUC values.

As future work, we expect to collect a greater number of driving events samples using different vehicles, Android smartphone models, road conditions, weather, and temperature. We also expect to add more MLAs to our evaluation, including those based on fuzzy logic and DTW. Finally, we intend use the best evaluation assemblies observed in this work to develop an Android smartphone application which can detect driving events in real-time and calculate the driver behavior profile.