Difference between car-to-cyclist crash and near crash in a perpendicular crash configuration based on driving recorder analysis

Ito, Daisuke; Hayakawa, Kosei; Kondo, Yuma; Mizuno, Koji; Thomson, Robert; Piccinini, Giulio Bianchi; Hosokawa, Naruyuki

doi:10.1016/j.aap.2018.03.029

Cited by 21 publications

(7 citation statements)

References 11 publications

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“…Sai et al [7] reported the development of a current and future intelligent transportation system (ITS) in which the safety initiatives included preventing traffic crashes via cooperation between vehicles based on road infrastructure. Ito et al [8] analyzed the differences between side-impact car-and-cyclist crashes and near crashes based on an analysis of driving recorders.…”

Section: Related Workmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

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Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergencyrelated announcements is approximately 7 s. INDEX TERMS Advanced driver assistance system (ADAS), artificial intelligence over Internet of Things (AIoT), automotive, deep learning, Internet of Vehicles (IoV), head-on and single-vehicle accident detection.

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Section: Related Workmentioning

confidence: 99%

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Chang

Chen

2019

IEEE Access

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“…Here, the factors that differentiate near crashes from crashes are examined and the causes of the latter are identified. Ito et al (2018) conclude that car-to-cyclist crashes are unavoidable when the car approaching the cyclist enters an area in which the average deceleration required to stop the car is more than 4.4 m/s 2 . Finally, Sanders (2015) has analyzed the impact of near miss and collision experiences in the perceived traffic risk for cyclists.…”

Section: Introductionmentioning

confidence: 90%

“…The distinction between accidents and near-misses has also been investigated in the context of car-to-cyclist crashes and near crashes (Ito et al, 2018). Here, the factors that differentiate near crashes from crashes are examined and the causes of the latter are identified.…”

Section: Introductionmentioning

confidence: 99%

Can Automobile Insurance Telematics Predict the Risk of Near-Miss Events?

Guillén

Nielsen

Pérez-Marín

et al. 2019

North American Actuarial Journal

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Telematics data from usage-based motor insurance provide valuable information -including vehicle usage, attitude towards speeding, time and proportion of urban/non-urban driving -that can be used for ratemaking. Additional information on acceleration, braking and cornering can likewise be usefully employed to identify near-miss events, a concept taken from aviation that denotes a situation that may have resulted in an accident. We analyze near-miss events from a sample of drivers in order to identify the risk factors associated with a higher risk of near-miss occurrence. Our empirical application with a pilot sample of real usage-based insurance data reveals that certain factors are associated with a higher expected number of near-miss events, but that the association differs depending on the type of near-miss. We conclude that nighttime driving is associated with a lower risk of cornering events, urban driving increases the risk of braking events and speeding is associated with acceleration events. These results are relevant for the insurance industry in order to implement dynamic risk monitoring through telematics, as well as preventive actions.

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“…Considering the difficulty in reproducing the process of the accident, with the help of dash cam video data, the collision avoidance behaviors, environments, and road conditions before the accident could be clearly grasped. Ito et al [23] used dash cam data to compare and analyze the different factors that affect vehicle-bicycle collisions and approaching collisions. e collisions would not be avoided even if the drivers had taken some evasive actions.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Wang

Cheng

et al. 2021

Journal of Advanced Transportation

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Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

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Difference between car-to-cyclist crash and near crash in a perpendicular crash configuration based on driving recorder analysis

Cited by 21 publications

References 11 publications

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Can Automobile Insurance Telematics Predict the Risk of Near-Miss Events?

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

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