A framework for evaluating aggressive driving behaviors based on in-vehicle driving records

Lee, Jooyoung; Jang, Kitae

doi:10.1016/j.trf.2017.11.021

Cited by 44 publications

(31 citation statements)

References 16 publications

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“…Moreover, the number of accidents and aberrations increased with the agressiveness of the driving style. Consistent with previous studies (Reason et al, 1990; Martinussen et al, 2014; Lee and Jang, 2017), these results demonstrate the close relationship between driving behavior characteristics and driving styles.…”

Section: Discussionsupporting

confidence: 92%

“…Compared with the moderate and conservative drivers, the drivers with the aggressive driving styles had more intensive emotion fluctuations and difficulties in emotion regulation (Trógolo et al, 2014; Zhang et al, 2016), which was associated with the delta and theta power in the temporal area (Knyazev et al, 2008, 2009). The aggressive drivers were more likely to engage in aberrational driving behaviors (Reason et al, 1990; Martinussen et al, 2014; Lee and Jang, 2017), which was resultant from the poor cognitive states and cognitive failures (Wickens et al, 2008) and related with the theta/beta ratio of the EEG signal in the frontal area (Angelidis et al, 2018; Puma et al, 2018). Besides, some studies suggested that high beta power in the parietal area was associated with the pro-active driving state, which was related with a better anticipation and active use of ongoing information, and a more proactive planning of future responses (Tao et al, 2010; Garcia et al, 2017; Getzmann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

et al. 2019

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Driving style is a very important indicator and a crucial measurement of a driver's performance and ability to drive in a safe and protective manner. A dangerous driving style would possibly result in dangerous behaviors. If the driving styles can be recognized by some appropriate classification methods, much attention could be paid to the drivers with dangerous driving styles. The driving style recognition module can be integrated into the advanced driving assistance system (ADAS), which integrates different modules to improve driving automation, safety and comfort, and then the driving safety could be enhanced by pre-warning the drivers or adjusting the vehicle's controlling parameters when the dangerous driving style is detected. In most previous studies, driver's questionnaire data and vehicle's objective driving data were utilized to recognize driving styles. And promising results were obtained. However, these methods were indirect or subjective in driving style evaluation. In this paper a method based on objective driving data and electroencephalography (EEG) data was presented to classify driving styles. A simulated driving system was constructed and the EEG data and the objective driving data were collected synchronously during the simulated driving. The driving style of each participant was classified by clustering the driving data via K-means. Then the EEG data was denoised and the amplitude and the Power Spectral Density (PSD) of four frequency bands were extracted as the EEG features by Fast Fourier transform and Welch. Finally, the EEG features, combined with the classification results of the driving data were used to train a Support Vector Machine (SVM) model and a leave-one-subject-out cross validation was utilized to evaluate the performance. The SVM classification accuracy was about 80.0%. Conservative drivers showed higher PSDs in the parietal and occipital areas in the alpha and beta bands, aggressive drivers showed higher PSD in the temporal area in the delta and theta bands. These results imply that different driving styles were related with different driving strategies and mental states and suggest the feasibility of driving style recognition from EEG patterns.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

et al. 2019

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“…Some scholars applied machine-learning models to evaluate driver's aggressive style between different characteristics using smartphone technology [36]. Lee and Jang used 43 taxis' in-vehicle driving records to evaluate aggressive driving behaviors and proposed three steps of a data analytic method [37]. Carlos et al proposed a second-order representation, based on the bag-of-words' strategy, to model accelerometer timestamps associated with aggressive driving maneuvers.…”

Section: Objective Study Of Aggressive Driving Behaviormentioning

confidence: 99%

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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“…Während der "Driving Style" eher schwierig veränderbar ist, kann sich der "Driving Skill" durch wachsende Fahrerfahrung und Trainings verbessern [19]. In wissenschaftlichen Studien wurde nachgewiesen, dass "Driving Skill" und "Driving Style" miteinander korrelieren [5,11,21,38]. So zeigte sich, dass ein aggressiverer Fahrstil mit variableren Fahrkompetenzen einhergeht [38], während mehr Fahrerfahrung und Trainings mit einem vorsichtigeren, sichereren Fahrstil einhergehen [11].…”

Section: Fahrertypen Im Kontext Des Automatisierten Fahrensunclassified

Identifikation von Fahrertypen im Kontext des automatisierten Fahrens

Schlüter

Hellmann

Weyer

2021

Forsch Ingenieurwes

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ZusammenfassungIm Straßenverkehr existieren unterschiedliche Konzepte zur Identifikation von Fahrertypen, die sich hinsichtlich Fahrverhalten und Einstellung zum Fahren unterscheiden. Im Rahmen der Automatisierung von Fahraufgaben gilt es zu überprüfen, wie diese Konzepte an die Herausforderungen veränderter Mensch-Maschine-Interaktion angepasst werden müssen und ob sich neuartige Fahrertypen identifizieren lassen. Auf Basis bestehender Typisierungen aus der Verkehrspsychologie sowie Erkenntnissen der Automationsforschung werden dazu die Konzepte des „Driving Style“ und „Driving Skill“ weiterentwickelt, um Fahrertypen im Kontext des automatisierten Fahrens zu identifizieren. In einer großzahligen Online-Umfrage wurden drei Fahrertypen identifiziert, die sich insbesondere hinsichtlich ihrer Einstellung zum automatisierten Fahren unterscheiden. In einer experimentellen Studie im Fahrsimulator kann anschließend gezeigt werden, dass diese Fahrertypen die Automation im Fahrzeug jeweils anders erleben und daher differenzierte Ansprüche an diese richten. Insgesamt deuten die Studienergebnisse darauf hin, dass die Akzeptanz des automatisierten Fahrens durch nutzergerechte Technik gefördert werden könnte. Die Ergebnisse dienen dazu, die jeweiligen Fahrertypen, ihre Einstellungen und ihre Nutzungspräferenzen im Kontext des automatisierten Fahrens besser zu verstehen und erste Ansatzpunkte für deren Berücksichtigung in der adaptiven Technikentwicklung zu identifizieren.

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A framework for evaluating aggressive driving behaviors based on in-vehicle driving records

Cited by 44 publications

References 16 publications

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

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

Identifikation von Fahrertypen im Kontext des automatisierten Fahrens

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