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

Date Published: April 10, 2017

Publisher: Public Library of Science

Author(s): Jair Ferreira, Eduardo Carvalho, Bruno V. Ferreira, Cleidson de Souza, Yoshihiko Suhara, Alex Pentland, Gustavo Pessin, Houbing Song.


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.

Partial Text

Driver behavior strongly impacts traffic security [1] and causes the vast majority of motor vehicle accidents [2]. In 2010, the total economic cost of motor vehicle crashes in the United States was 242 billion [3]. This figure represents the costs for approximately 33 thousand fatalities, 4 million nonfatal injuries, and 24 million damaged vehicles. Driver behavior adaptations might increase overall security and lessen vehicle fuel/energy consumption and gas emissions [4, 5]. In this context, driver behavior profiling tries to better understand and potentially improve driver behavior, leveraging a safer and more energy aware driving.

In this section we describe recent driver behavior profiling work. It is worth noting that several driver behavior profiling solutions are commercially available mowadays, mostly in the insurance telematics and freight management domains. Examples include Aviva Drive (, Greenroad (, Ingenie (, Snapshot (, and SeeingMachines ( However, technical details of these solutions are not publicly available.

In our evaluation, we compare the performance of four MLAs: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Bayesian Network (BN). Those MLAs were chosen given their great presence in the literature of classification problems, and the fact that they represent different machine learning “tribes” [27], which ensures a machine learning algorithmic diversity. In this section, basic concepts of the aforementioned MLAs are explained.

We modeled this work as a multi-label supervised learning classification problem where the labels are driving events types. The goal of this work is to identify the best combination of motion sensor (and its axes), learning algorithm (and its parameters), and number of frames in the sliding window (nf) to detect individual driving event types. To this end, we define an evaluation assembly in the form EA = {1:sensor, 2:sensor axis(es), 3:MLA, 4:MLA configuration, 5:nf}.

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.

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.




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