Smartphone-Based Adaptive Driving Maneuver Detection: A Large-Scale Evaluation Study

Castignani, German; Derrmann, Thierry; Frank, Raphaël; Engel, Thomas

doi:10.1109/tits.2016.2646760

Cited by 53 publications

(24 citation statements)

References 10 publications

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“…The machine‐learning, anomaly detection approach follows that of Castignani, Derrmann, Frank, and Engel (), who were first to apply an MVG model to detect anomalous driving behavior. We utilize this approach to identify anomalies in accelerometer and GPS sensor readings and objectively determine vehicle risk levels.…”

Section: Methodsmentioning

confidence: 99%

“…State graphs are employed to model behavior and determine abnormal driving (Zhang et al., ). Castignani, Derrmann, Frank, and Engel () propose an adaptive driving maneuver detection system, utilizing a multivariate normal‐based anomaly detection to detect risky driving maneuvers. Wu, Chen, and Yeh () present a driving behavior orientated event data recorder (EDR), employing hidden Markov models to identify maneuvers and fuzzy logic to determine danger levels.…”

Section: Introductionmentioning

confidence: 99%

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Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

2018

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The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics-based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location-based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor-generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

2018

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“…This paper [15], builds a statistical model of the driver, vehicle and the smart phone and an adaptive driving maneuver detection methods is proposed. This combination is done using multivariate normal model.…”

Section: Litrature Surveymentioning

confidence: 99%

A Survey Paper on Non Intrusive Texting While Driving Detection Using Smart Phone

2018

IJMTER

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Abstract-Many interesting systems on mobile phone are introduced to prevent the dangerous activity of texting while driving (T&D). However these systems require user's manual input to block the texting activity or some extra devices are used to identify user's location and also some localization techniques are used. Here the idea is to propose a method which is able to detect T&D without any user intervention. To implement this idea various smart phone embedded sensors such as gyroscopes, accelerometers and GPS are used. They are used to collect the information such as touch strokes, holding orientation and vehicle speed. Using these patterns the whether the T&D is existing or not identified.

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“…When combined with geographic information, the method is widely used in caring for the old and the young, and for sports pattern recognition [19][20][21][22] . In the field of automobile driving, accelerometers, and gyroscopes are widely used in driving style and driving safety recognition [23][24][25] , such as sudden acceleration, sudden braking, speeding, and sharply turning [24,26,27] . These behaviors can be identified according to the features of the accelerations and the change of angles [24] .…”

Section: Introduction mentioning

confidence: 99%

Behavior modelling and sensing for machinery operations using smartphone’s sensor data: A case study of forage maize sowing

Wu¹,

Chen²,

Wang³

et al. 2019

International Journal of Agricultural and Biological Engineering

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Large-scale agricultural machinery cooperatives require technical statistic report of agricultural machinery operations to improve the efficiency of fleet management. This research proposed a smartphone-based solution to build the behavior model for agricultural machinery operations by using the embedded sensors including the GNSS, the accelerometer, and the microphone. The whole working process of agricultural machinery operation was divided into four stages: preparation, operation, U-turn, and transfer, each of which may contain the behaviors of stalling and idling. Field experiments were carried out by skilled operators, whose operations were typical agricultural machinery operations that could be used to extract behavior features. Butterworth low-pass filter was used to smooth the output from the accelerometer. Then, the operating data were collected through an APP when sowing the forage maize as a case study. Four stages of machinery operation can be preliminarily classified by using GNSS speed, while the identification of behaviors such as sudden acceleration and longtime idling that may increase fuel consumption, reduce machinery life, or decrease the working efficiency, requires extra information such as acceleration and sound intensity. The results showed that the jerk of accelerating can describe the severity of the sudden acceleration, the standard deviation of forward acceleration can reflect the smoothness of operation, the upward acceleration can be used to identify behaviors of stalling and idling, and the sound intensity during idling can capture the behavior of goosing the throttle. Further, the operating behavior figure can be drawn based on the above parameters. In conclusion, this research constructed several behavior models of agricultural machinery and operators by using smartphone's sensor data and established the base of the online assessing and scoring system for agricultural machinery operations. modelling and sensing for machinery operations using smartphone's sensor data: a case study of forage maize sowing. Int J Agric & Biol Eng, 2019; 12(6): 66-74. Biographies: Caicong Wu, PhD, Associate Professor, research interest: location-based service of farm machinery, Email: wucc@cau.edu.cn; Zhibo Chen, PhD candidate, research interest: location-based service of farm machinery, Email: bobodi@qq.com; Dongxu Wang, Master candidate, research interest: location-based service of farm machinery, Email: dongxu_wang@ cau.edu.cn; Zhihong Kou, Master candidate, research interest: location-based service of farm machinery, Email: 603040848@qq.com; Yaping Cai, PhD candidate, research interest: mobile information collection, Email: caicai_pku@ 163.com.

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Smartphone-Based Adaptive Driving Maneuver Detection: A Large-Scale Evaluation Study

Cited by 53 publications

References 10 publications

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach

A Survey Paper on Non Intrusive Texting While Driving Detection Using Smart Phone

Behavior modelling and sensing for machinery operations using smartphone’s sensor data: A case study of forage maize sowing

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