부산대학교 도서관

Driving behavior is an important aspect of maintaining and sustaining safe transport on the roads. It also directly affects fuel consumption, traffic flow, public health, and air pollution along with psychology and personal mental health. For advanced driver assistance systems (ADASs) and autonomous vehicles, predicting driver behavior helps to facilitate interaction between ADAS and the human driver. Consequently, driver behavior prediction has emerged as an important research topic and has been investigated largely during the past few years. Often, the investigations are based on simulators and controlled environments. Driving behavior can be inferred using control actions, visual monitoring, and inertial measurement unit (IMU) data. This study leverages the IMU data recorded using a smartphone placed inside the vehicle. The dataset contains the accelerometer and gyroscope data recorded from the real traffic environment. Extensive experiments are performed regarding the use of a different set of features, the combination of original and derived features, and binary versus multiclass classification problems; a total of six scenarios are considered. Results reveal that “timestamp” is the most important feature and using it with accelerometer and gyroscope features can lead to a 100% accuracy for driver behavior prediction. Without using the “timestamp” feature, the number of wrong predictions for “slow” and “normal” classes is high due to the feature space overlap. Although derived features can help elevate the performance of the models, the models show inferior performance to that of using the “timestamp” feature. Deep learning models tend to show poor performance than machine learning models where random forest and extreme gradient boosting machines show a 100% accuracy for multiclass classification.