Animal-Vehicle Encounter Naturalistic Driving Data Collection and Photogrammetric Analysis

Alden, Andrew; Mayer, Brian; McGowen, Patrick; Sherony, Rini; Takahashi, Hiroyuki

doi:10.4271/2016-01-0124

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Several studies have used both crash data and naturalistic driving data to examine AEB systems with several different focuses such as pedestrian-vehicle collisions and collisions on curves (8)(9)(10)(11)(12)(13)(14). However, vehicle-animal crashes have been somewhat less explored.…”

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confidence: 99%

“…Although infrastructure countermeasures appear to be effective, they tend to be expensive and often are slower to implement than vehicle based countermeasures. Several studies have used both crash data and naturalistic driving data to examine AEB systems with several different focuses such as pedestrian-vehicle collisions and collisions on curves ( 8 – 14 ). However, vehicle-animal crashes have been somewhat less explored.…”

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confidence: 99%

“…However, vehicle-animal crashes have been somewhat less explored. Naturalistic driving data is particularly useful in this area, and has been used in a study by Alden et al ( 14 ) to develop a potential AEB system that specifically mitigates vehicle-animal crashes. This system, however, is currently limited by how well a camera can record and recognize animals.…”

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confidence: 99%

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Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

Decker

Haus

Sherony³

et al. 2021

Transportation Research Record: Journal of the Transportation R

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In 2015, there were 319,195 police reported vehicle-animal crashes, resulting in 275 vehicle occupant fatalities. Animal-detecting automatic emergency braking (AEB) systems are a promising active safety measure which could potentially avoid or mitigate many of these crashes by warning the driver, utilizing automatic braking, or both. The purpose of this study was to develop and characterize a target population of vehicle-animal crashes applicable to AEB systems and to analyze the potential benefits of an animal-detecting AEB system. The study was based on two nationally representative databases, Fatality Analysis Reporting System and the National Automotive Sampling System’s General Estimates System, and a naturalistic driving study, SHRP 2. The target population was restricted to vehicle-animal crashes that were forward impacts or road departures and involved cars and light trucks, with no loss of control. Crash characteristics which may influence the performance of AEB such as lighting, weather, pre-crash movement, relation to junction, and first and worst harmful events, were analyzed. The study found that the major influences on the effectiveness of animal AEB systems were: weather, lighting, pre-crash movements, and the crash location. Six potential target populations were used to analyze the potential effectiveness of an animal AEB system, with effectiveness ranging between 21.6% and 97% of police reported crashes and between 4.1% and 50.8% of fatal vehicle-animal crashes. An AEB system’s ability to function in low light and poor weather conditions may enable it to avoid a substantially higher proportion of crashes.

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Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

Decker

Haus

Sherony³

et al. 2021

Transportation Research Record: Journal of the Transportation R

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A perceptual forward collision warning model using naturalistic driving data

Tawfeek

El-Basyouny

2018

Can. J. Civ. Eng.

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Rear-end collisions represent a quarter to one-third of the total number of collisions occurring on North American roads. While there are several methods to mitigate rear-end collision effects, one way is to warn drivers about impending events using forward collision warning (FCW) systems. At the core of any FCW algorithm is a trigger distance at which a message is relayed to the driver to avoid rear-end collisions. The main goal of this paper is to propose a warning distance model based on naturalistic driver following behavior. This was achieved by investigating car-following events within a critical time-to-collision range. A total of 5785 candidate car-following events were identified for the model development from 2 months of naturalistic driving study data of 63 drivers. Using regression analysis, the minimum warning distance was linked to several performance measures. It was found that the relative speed, the host vehicle speed, and the host vehicle acceleration can significantly affect the minimum warning distance. To assess the performance of the developed algorithm, it was compared to six of the existing FCW algorithms in terms of warning distances. The results of the developed algorithm were consistent with the other perceptual FCW algorithms. However, the warning distances of the proposed algorithm were less than the distances produced by the kinematic algorithms. The proposed algorithm could be used as a minimum threshold to trigger an alert for an FCW algorithm. Since the proposed algorithm is developed based on actual driving data, it is expected to be more acceptable by drivers. However, the algorithm needs further testing in real-life to validate this expectation.

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An Evaluation of Two Methodologies for Lens Distortion Removal when EXIF Data is Unavailable

Terpstra¹,

Miller²,

Hashemian³

2017

SAE Technical Paper Series

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Animal-Vehicle Encounter Naturalistic Driving Data Collection and Photogrammetric Analysis

Cited by 6 publications

References 4 publications

Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

A perceptual forward collision warning model using naturalistic driving data

An Evaluation of Two Methodologies for Lens Distortion Removal when EXIF Data is Unavailable

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