Driver classification and driving style recognition using inertial sensors

Ly, Minh Van; Martin, Sujitha; Trivedi, Mohan M.

doi:10.1109/ivs.2013.6629603

Cited by 224 publications

(114 citation statements)

References 16 publications

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“…Lastly, Van Ly et al attempted to perform driver identification distinguishing between two drivers using sensor data collected from inertial sensors in a mobile device [28]. This work initially shows that a mounted phone sensor's accelerometer is highly correlated with acceleration and braking activity, and subsequently the authors use the phone data to distinguish between the two drivers along a diverse multi-hour course involving residential and highway segments (using a modern sedan).…”

Section: Related Workmentioning

confidence: 99%

Automobile Driver Fingerprinting

Enev

Takakuwa

Koscher

et al. 2015

Proceedings on Privacy Enhancing Technologies

214

130

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Today's automobiles leverage powerful sensors and embedded computers to optimize efficiency, safety, and driver engagement. However the complexity of possible inferences using in-car sensor data is not well understood. While we do not know of attempts by automotive manufacturers or makers of after-market components (like insurance dongles) to violate privacy, a key question we ask is: could they (or their collection and later accidental leaks of data) violate a driver's privacy? In the present study, we experimentally investigate the potential to identify individuals using sensor data snippets of their natural driving behavior. More specifically we record the in-vehicle sensor data on the controllerarea-network (CAN) of a typical modern vehicle (popular 2009 sedan) as each of 15 participants (a) performed a series of maneuvers in an isolated parking lot, and (b) drove the vehicle in traffic along a defined ∼ 50 mile loop through the Seattle metropolitan area. We then split the data into training and testing sets, train an ensemble of classifiers, and evaluate identification accuracy of test data queries by looking at the highest voted candidate when considering all possible one-vs-one comparisons. Our results indicate that, at least among small sets, drivers are indeed distinguishable using only incar sensors. In particular, we find that it is possible to differentiate our 15 drivers with 100% accuracy when training with all of the available sensors using 90% of driving data from each person. Furthermore, it is possible to reach high identification rates using less than 8 minutes of training data. When more training data is available it is possible to reach very high identification using only a single sensor (e.g., the brake pedal). As an extension, we also demonstrate the feasibility of performing driver identification across multiple days of data collection.

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Section: Related Workmentioning

confidence: 99%

Automobile Driver Fingerprinting

Enev

Takakuwa

Koscher

et al. 2015

Proceedings on Privacy Enhancing Technologies

214

130

View full text Add to dashboard Cite

show abstract

“…A number of previous studies have shown that drivers can be uniquely identified using driving behavior signals [7,8,11,16,17]. A variety of sensors can be found in off-the-shelf devices Journal of Advanced Transportation 3 available in current market.…”

Section: Driver Identification In Real-world Situationsmentioning

confidence: 99%

“…More recently proposed algorithms use Support Vector Machine (SVM) and are more focused in using driving behavior signals as inputs [10,16]. In [16], signals from inertial sensors, namely, accelerometer and gyroscope, are processed in order to detect accelerating, braking, and wheel-turning events.…”

Section: Driver Identification In Real-world Situationsmentioning

confidence: 99%

Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

Tanprasert

Saiprasert

Thajchayapong

2017

Journal of Advanced Transportation

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This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the sevensecond-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

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“…Firstly, obtaining a gravity vector with the vehicle stopped and, secondly, obtaining a driving directional vector and correcting it by a Principal Component Analysis (PCA) approach. Other authors try to use a fixed Inertial Measurement Unit on the vehicle [12] or they use a smartphone in a fixed and calculated position in [13,14].…”

Section: Introductionmentioning

confidence: 99%

Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

et al. 2018

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Air pollution and climate change are some of the main problems that humankind is currently facing. The electrification of the transport sector will help to reduce these problems, but one of the major barriers for the massive adoption of electric vehicles is their limited range. The energy consumption in these vehicles is affected, among other variables, by the driving behavior, making range a value that must be personalized to each driver and each type of electric vehicle. In this paper we offer a way to estimate a personalized energy consumption model by the use of the vehicle dynamics and the driving events detected by the use of the smartphone inertial sensors, allowing an easy and non-intrusive manner to predict the correct range for each user. This paper proposes, for the classification of events, a deep neural network (Long-Short Time Memory) which has been trained with more than 22,000 car trips, and the application to improve the consumption model taking into account the driver behavior captured across different trips, allowing a personalized prediction. Results and validation in real cases show that errors in the predicted consumption values are halved when abrupt events are considered in the model.

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Driver classification and driving style recognition using inertial sensors

Cited by 224 publications

References 16 publications

Automobile Driver Fingerprinting

Automobile Driver Fingerprinting

Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

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