How Smartphone Accelerometers Reveal Aggressive Driving Behavior?—The Key is the Representation

Carlos, Manuel Ricardo; González, Luis; Wahlström, Johan; Ramirez, Graciela; Martínez, Fernando; Runger, George C.

doi:10.1109/tits.2019.2926639

Cited by 37 publications

(15 citation statements)

References 28 publications

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“…Carlos et al proposed a second-order representation, based on the bag-of-words' strategy, to model accelerometer timestamps associated with aggressive driving maneuvers. The results show that this novel representation outperformed both state-of-the-art works with 6% and 15% in F-measure for each scenario, respectively [38]. The other method is to evaluate a driver's aggressive driving behavior by experts.…”

Section: Objective Study Of Aggressive Driving Behaviormentioning

confidence: 90%

Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China

et al. 2021

Sustainability

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Aggressive driving is common across the world. While most aggressive driving is conscious, some aggressive driving behavior may be unconscious on part of motor vehicle drivers. Perceptual bias of aggressive driving behavior is one of the main causes of traffic accidents. This paper focuses on identifying impact factors related to aggressive driving perceptual bias. Questionnaire data from 690 drivers, collected from a drivers’ retraining course administered by the Traffic Management Bureau in Nanjing, China, were used to collect drivers’ socioeconomic characteristics, personality traits, and external environment data. Actual penalty points were considered as an objective indicator and Gaussian mixture model (GMM) was used to cluster an objective indicator into different levels. The driving anger expression (DAX) was used to measure drivers’ self-assessment of aggressive driving behavior and then to identify perceptual biases. Then a binary logistic model was estimated to explore the influence of different factors on drivers’ perceptual bias of aggressive driving behavior. Results showed that bus drivers were less likely to have perceptual bias of aggressive driving behavior. Truck drivers, drivers with an extraversion characteristic, and drivers who have dissatisfaction with road infrastructure and actual work were likely to have a perceptual bias. The findings are potentially beneficial for proposing targeted countermeasures to identify dangerous drivers and improve drivers’ safety awareness.

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Section: Objective Study Of Aggressive Driving Behaviormentioning

confidence: 90%

Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China

et al. 2021

Sustainability

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show abstract

“…Longitudinal speed, lateral speed, and gap are chosen to recognize risky drivers. Discrete Fourier Transform (DFT) or Fast Fourier Transform (FFT) has been applied in many driving behavior studies [ 16 , 34 , 35 , 36 ] to convert the time series of driving features to signal amplitude in the frequency domain. Xue et al [ 10 ] found that DFT is a better feature extraction method than statistical parameters, such as mean, standard deviation, maximum, and minimum.…”

Section: Methodsologymentioning

confidence: 99%

“…A smartphone is equipped with multiple sensors, including an accelerometer, gyroscope, magnetometer, microphone, cameras, thermometer, and Global Positioning System (GPS). Thus, the smartphone has the ability to track a vehicle’s motion and location and has been applied to detect abnormal driving behaviors [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework

Wang

Xue

2021

IJERPH

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Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classification algorithms. Instead of applying preset sampling or cost-sensitive learning, this paper proposes a novel automated machine learning framework that simultaneously and automatically searches for the optimal sampling, cost-sensitive loss function, and probability calibration to handle class-imbalance problem in recognition of risky drivers. The hyperparameters that control sampling ratio and class weight, along with other hyperparameters, are optimized by Bayesian optimization. To demonstrate the performance of the proposed automated learning framework, we establish a risky driver recognition model as a case study, using video-extracted vehicle trajectory data of 2427 private cars on a German highway. Based on rear-end collision risk evaluation, only 4.29% of all drivers are labeled as risky drivers. The inputs of the recognition model are the discrete Fourier transform coefficients of target vehicle’s longitudinal speed, lateral speed, and the gap between the target vehicle and its preceding vehicle. Among 12 sampling methods, 2 cost-sensitive loss functions, and 2 probability calibration methods, the result of automated machine learning is consistent with manual searching but much more computation-efficient. We find that the combination of Support Vector Machine-based Synthetic Minority Oversampling TEchnique (SVMSMOTE) sampling, cost-sensitive cross-entropy loss function, and isotonic regression can significantly improve the recognition ability and reduce the error of predicted probability.

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“… 5 Aggressive violations mainly indicate aggressive driving behaviors against other road users, such as dangerous overtaking, prevent other vehicles from overtaking, honking to display anger or aggressive stance. 6 , 7 Driving mistakes means the driver’s improper driving behaviors due to misjudgment, improper operation or other reasons. For example, the driver’s inattention caused him or her to have a rear-end collision with the vehicle in front.…”

Section: Introductionmentioning

confidence: 99%

The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors

Liu

Wang

Guo

2021

PRBM

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Purpose Researches addressing driving behaviors have not fully revealed how emotions affect risky driving behaviors and tend to focus on the effects of some negative emotions rather than those of more specific emotions. This study aimed to test the potential moderating effects of eight common driving emotions on the relationship between self-reported individual traits (sensation seeking and driving style) and actual risky driving behaviors, sequentially providing some implications for the risky driving behavior prevention. Participants and Methods A total of 78 licensed drivers were recruited from undergraduate students, company employees and taxi drivers in China. The participants’ data on self-reported driving style (SDBS) and self-reported sensation seeking (SSSS) were obtained through questionnaires. The participants’ data on actual risky driving behaviors (ARD) in eight driving emotional activation states were obtained through a series of emotion induction experiments and driving experiments. The Structural Equation Modeling (SEM) and moderating effect tests were employed to investigate the relationships of driving emotions, SDBS, SSSS and ARD. Results Results showed that anger and pleasure affected risky driving behaviors positively by enhancing the relationship between SDBS and ARD, while surprise and fear were negatively related to risky driving behaviors by weakening this relationship. Anxiety positively affected risky driving behaviors by synchronously enhancing the relationship between SDBS and ARD and the relationship between SSSS and ARD, while helplessness and relief affected risky driving behaviors negatively by weakening the two relationships. Contempt affected risky driving behaviors positively by enhancing the relation between SSSS and ARD. Conclusion The results illustrated the effects of different emotions on risky driving behaviors, and also partly explained the reasons for these effects. This research provided a source of reference for reducing traffic accidents caused by risky driving behaviors.

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How Smartphone Accelerometers Reveal Aggressive Driving Behavior?—The Key is the Representation

Cited by 37 publications

References 28 publications

Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China

Identification of Contributing Factors for Driver’s Perceptual Bias of Aggressive Driving in China

Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework

The Moderating Effects of Emotions on the Relationship Between Self-Reported Individual Traits and Actual Risky Driving Behaviors

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