Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data

Feng, Fenling; Bao, Shan; Sayer, James R.; Flannagan, Carol A. C.; Manser, Michael; Wunderlich, Robert

doi:10.1016/j.aap.2017.04.012

Cited by 141 publications

(52 citation statements)

References 16 publications

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“…Our findings largely support the first hypothesis, that positive valence led to better takeover performance, reflected by a smaller maximum resulting acceleration and a smaller maximum resulting jerk. Smaller maximum resulting acceleration and maximum resulting jerk are associated with a higher level of safety (Hergeth et al, 2017), shift quality, and ride comfort (Huang & Wang, 2004), and lower driving aggressiveness (Bagdadi & Várhelyi, 2011, 2013Feng et al, 2017). The results are in line with some studies examining the effects of valence in manual driving, where positive valence led to better vehicle control (Chan & Singhal, 2013;Groeger, 2013;Hancock et al, 2012;Trick et al, 2012),…”

Section: Discussionsupporting

confidence: 79%

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers' takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers' takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.

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Section: Discussionsupporting

confidence: 79%

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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“…Most of the studies have used common vehicle kinematics such as speed, longitudinal and lateral acceleration to measure driving aggressiveness [69]. An attempt in [70] has investigated that whether vehicle longitudinal jerk could be potentially used to identify aggressive drivers. The authors hypothesized that the vehicle jerk indicates how smoothly a driver accelerates and decelerates the vehicle, and aggressive drivers may use large jerk more often by operating the gas and brake pedal compared to normal drivers.…”

Section: B Human Driver Aggressive Driving Behavior (Hadb)mentioning

confidence: 99%

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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“…The paper presents a modeling framework that incorporates driver's reactions to events inside and outside the vehicle. A methodology to measure the aggressiveness of driving behavior is presented in [11].…”

Section: Related Workmentioning

confidence: 99%

Clusters of Driving Behavior From Observational Smartphone Data

Warren

Lipkowitz

Sokolov

2019

IEEE Intell. Transport. Syst. Mag.

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Understanding driving behaviors is essential for improving safety and mobility of our transportation systems. Data is usually collected via simulator-based studies or naturalistic driving studies. Those techniques allow for understanding relations between demographics, road conditions and safety. On the other hand, they are very costly and time consuming. Thanks to the ubiquity of smartphones, we have an opportunity to substantially complement more traditional data collection techniques with data extracted from phone sensors, such as GPS, accelerometer gyroscope and camera. We developed statistical models that provided insight into driver behavior in the San Francisco metro area based on tens of thousands of driver logs. We used novel data sources to support our work. We used cell phone sensor data drawn from five hundred drivers in San Francisco to understand the speed of traffic across the city as well as the maneuvers of drivers in different areas. Specifically, we clustered drivers based on their driving behavior. We looked at driver norms by street and flagged driving behaviors that deviated from the norm.

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Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data

Cited by 141 publications

References 16 publications

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

Clusters of Driving Behavior From Observational Smartphone Data

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