Vehicle Mode and Driving Activity Detection Based on Analyzing Sensor Data of Smartphones

Abstract

In this paper, we present a flexible combined system, namely the Vehicle mode-driving Activity Detection System (VADS), that is capable of detecting either the current vehicle mode or the current driving activity of travelers. Our proposed system is designed to be lightweight in computation and very fast in response to the changes of travelers’ vehicle modes or driving events. The vehicle mode detection module is responsible for recognizing both motorized vehicles, such as cars, buses, and motorbikes, and non-motorized ones, for instance, walking, and bikes. It relies only on accelerometer data in order to minimize the energy consumption of smartphones. By contrast, the driving activity detection module uses the data collected from the accelerometer, gyroscope, and magnetometer of a smartphone to detect various driving activities, i.e., stopping, going straight, turning left, and turning right. Furthermore, we propose a method to compute the optimized data window size and the optimized overlapping ratio for each vehicle mode and each driving event from the training datasets. The experimental results show that this strategy significantly increases the overall prediction accuracy. Additionally, numerous experiments are carried out to compare the impact of different feature sets (time domain features, frequency domain features, Hjorth features) as well as the impact of various classification algorithms (Random Forest, Naïve Bayes, Decision tree J48, K Nearest Neighbor, Support Vector Machine) contributing to the prediction accuracy. Our system achieves an average accuracy of 98.33% in detecting the vehicle modes and an average accuracy of 98.95% in recognizing the driving events of motorcyclists when using the Random Forest classifier and a feature set containing time domain features, frequency domain features, and Hjorth features. Moreover, on a public dataset of HTC company in New Taipei, Taiwan, our framework obtains the overall accuracy of 97.33% that is considerably higher than that of the state-of the art.

Dynamic Basic Activity Sequence Matching Method in Abnormal Driving Pattern Detection Using Smartphone Sensors

“…In the parameter optimization phase, the sensor data, including accelerometer, gyroscope, and magnetometer, are collected and labeled corresponding to the normal or abnormal driving patterns. Then, such data are passed to activity detection module (ADM) (described in [23]) to transform into two sequences of basic driving activities-containing stop (S), going straight (G), turning left (L), turning right (R)-corresponding to the long duration of activities in normal driving scenarios (representing by the window size W) and the short duration of activities in abnormal driving scenarios (representing by the window size W ). Thus, each basic driving activity is detected by ADM in a sliding data window with a specific overlapping ratio that determines the overlap between two consecutive data windows.…”

“…. , n. In our previous work [23], it has been shown that in the normal driving scenarios, the optimal window size for accurately identifying basic activities of car and motorcycle drivers usually ranges from 4 to 6 s. Hence, in the later experiments, W will be tested in this range.…”

“…Supposing that ADM, using the window size seconds, transforms a sensor data segment into a sequence of basic activities , … where ∈ { , , , }, =1, 2…n. In our previous work [23], it has been shown that in the normal driving scenarios, the optimal window size for accurately identifying basic activities of car and motorcycle drivers usually ranges from 4 seconds to 6 seconds. Hence, in the later experiments, will be tested in this range.…”

“…The time-domain features provide statistic metrics of driving activities in time domain; the frequency-domain features provide information about cyclic characteristics of activities; and Hjorth features can provide additional information of activities in both time and frequency domains. A set of formulas for computing such features is presented in [23]. The resulting feature vectors are then used to train the abnormal driving detection model.…”

“…This factor absolutely leads to a high computational time and resource. Thus, our proposed framework utilizes the smaller feature set consisting of 34 time-domain features and seven frequency-domain features and 18 Hjorth features previously described in [23].…”