Abstract
In the last decade, significant advances have been made in sensing and communication technologies. Such progress led to a considerable growth in the development and use of intelligent transportation systems. Characterizing driving styles of drivers using in-vehicle sensor data is an interesting research problem and an essential real-world requirement for automotive industries. A good representation of driving features can be extremely valuable for anti-theft, auto insurance, autonomous driving, and many other application scenarios. This paper addresses the problem of driver identification using real driving datasets consisting of measurements taken from in-vehicle sensors. The paper investigates the minimum learning and classification times that are required to achieve a desired identification performance. Further, feature selection is carried out to extract the most relevant features for driver identification. Finally, in addition to driving pattern related features, driver related features (e.g., heart-rate) are shown to further improve the identification performance.
Highlights
In the era of the Internet of Things (IoT), every object can be made smart with embedded sensors, and connected to the internet through wireless technologies
Classification algorithms The machine learning algorithms considered in the classification task are Decision Tree, Random Forest, Extra Trees, k-nearest neighbor (KNN), Support vector machine (SVM), Gradient Boosting, AdaBoost based on Decision Tree, and multi-layer perception (MLP)
We proposed a time-optimized driver fingerprinting method based on the driving patterns
Summary
In the era of the Internet of Things (IoT), every object can be made smart with embedded sensors, and connected to the internet through wireless technologies. Vehicle-based performance technologies infer driver behavior by monitoring car systems such as lane deviation, steering or speed variability. Such systems are critical to detect and avoid driver drowsiness, which is related to around 20% of severe car injuries. Most methods in the literature on driving style modeling rely on a human-defined driving behavior feature set, which consists of handcrafted vehicle movement features derived from sensor data. These features are used by machine learning methods (supervised classification, unsupervised clustering, or reinforcement learning) to solve problems such as driver classification/identification, driver performance assessment, and individual driving style learning
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