A Pattern-Recognition Approach for Driving Skill Characterization

Zhang, Yilu; Lin, William C.; Chin, Yuen-Kwok

doi:10.1109/tits.2010.2055239

Cited by 98 publications

(50 citation statements)

References 25 publications

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“…Our driving behavior characterization algorithms are based on a pattern-recognition approach. Although the modeling relies on an idea proposed by Zhang [77], the difference between the proposals is the fact that Zhang's work aims to identify the driver's skill level (e.g., expert or novice) through receiving driver behavior measurements as input. Our research aims to identify driver behavior based on online outlier detection through measurements of the signals from different sensors embedded onboard the vehicle, as well as sensors of the mobile device onboard the vehicle.…”

Section: Online Cep-based Outlier Detection Algorithmsmentioning

confidence: 99%

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Vasconcelos

Olivieri

et al. 2017

J Internet Serv Appl

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The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones can be used to online detect and classify the driver's behavior in near real-time. To do so, we first modeled and performed an intrinsic evaluation to assess the performance of three outlier detection algorithms formulated as a data stream processing network which receives as input and processes data streams of smartphone and vehicle sensors. Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers' maneuvers as either cautious or reckless. Thus, we adapted a data mining mechanism which takes into account a sensor's data rates and power to determine driver behavior in the scoring process. Finally, as the intrinsic evaluation does not necessarily reveal how well an algorithm will perform in a real-world scenario, we evaluated the algorithm that achieved the best result in a real-world case study to assess drivers' driving behavior. Our results indicate that the algorithm performs quickly and accurately; the algorithm classifies driver behavior with 95.45% accuracy. Moreover, such results are obtained within 100 milliseconds of processing time on average.

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Section: Online Cep-based Outlier Detection Algorithmsmentioning

confidence: 99%

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Vasconcelos

Olivieri

et al. 2017

J Internet Serv Appl

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“…A wide variety of research on driver modeling has been reported [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. However, very few studies have been explicitly characterized driving skill in terms of driving action [14][15][16][17][18][19][20] and applied this using machine learning methods.…”

Section: Related Workmentioning

confidence: 99%

Driving skill classification in curve driving scenes using machine learning

Chandrasiri

Nawa²,

Ishii³

2016

J. Mod. Transport.

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Driver support and infotainment systems can be adapted to the specific needs of individual drivers by assessing driver skill and state. In this paper, we present a machine learning approach to classifying the skill at maneuvering by drivers using both longitudinal and lateral controls in a vehicle. Conceptually, a model of drivers is constructed on the basis of sensor data related to the driving environment, the drivers' behaviors, and the vehicles' responses to the environment and behavior together. Once the model is built, the driving skills of an unknown driver can be classified automatically from the driving data. In this paper, we demonstrate the feasibility of using the proposed method to assess driving skill from the results of a driving simulator. We experiment with curve driving scenes, using both full curve and segmented curve scenarios. Six curves with different radii and angular changes were set up for the experiment. In the full curve driving scene, principal component analysis and a support vector machine-based method accurately classified drivers in 95.7 % of cases when using driving data about high-and low/average-skilled driver groups. In the cases with segmented curves, classification accuracy was 89 %.

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“…In particular, when a driver is performing additional tasks unrelated to driving and is under higher workload, changes can be observed in features of the steering wheel angle (SWA) (Mehler et al, 2012). To determine the skill level of drivers, Zhang et al (2010) use vehicle simulator telemetry data from typical and expert drivers as they performed several manoeuvres. As typical drivers were more numerous than experts, the data was re-sampled so that it included the same number of typical drivers as experts, although under-sampling of manoeuvres from all drivers may have been more appropriate.…”

Section: Related Workmentioning

confidence: 99%

Data Mining for Vehicle Telemetry

Taylor

Griffiths

Bhalerao

et al. 2016

Applied Artificial Intelligence

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This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.

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A Pattern-Recognition Approach for Driving Skill Characterization

Cited by 98 publications

References 25 publications

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Smartphone-based outlier detection: a complex event processing approach for driving behavior detection

Driving skill classification in curve driving scenes using machine learning

Data Mining for Vehicle Telemetry

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