Development of an Adaptive Rear-End Crash Avoidance Model

Pei-Sung Lin; Sachin Rai; Larry Hagen

Development of an Adaptive Rear-End Crash Avoidance Model

Research output: Contribution to conference › Presentation

Abstract

Rear-end collisions accounted for more than 30 percent of the 6.2 million crashes in the United States in 2004. The USDOT is currently working with the automotive industry for vehicle-to-vehicle and vehicle-to-roadside communications to improve traffic safety through crash avoidance systems. This study has developed an adaptive rear-end crash avoidance model to continually adjust the speed of the following vehicle to avoid a rear-end crash when the driver fails to respond to a collision warning. The model was developed based on motion equations, human factors, and vehicle parameters. The model was also closely examined via different vehicle travel profiles. For the covering abstract see ITRD E134653.

Original language	American English
State	Published - Oct 1 2006
Event	13th World Congress on Intelligent Transport Systems, Compendium of Technical Papers - Duration: Oct 1 2006 → …

Conference

Conference	13th World Congress on Intelligent Transport Systems, Compendium of Technical Papers
Period	10/1/06 → …

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https://trid.trb.org/View/843332

Cite this

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title = "Development of an Adaptive Rear-End Crash Avoidance Model",

abstract = " Rear-end collisions accounted for more than 30 percent of the 6.2 million crashes in the United States in 2004. The USDOT is currently working with the automotive industry for vehicle-to-vehicle and vehicle-to-roadside communications to improve traffic safety through crash avoidance systems. This study has developed an adaptive rear-end crash avoidance model to continually adjust the speed of the following vehicle to avoid a rear-end crash when the driver fails to respond to a collision warning. The model was developed based on motion equations, human factors, and vehicle parameters. The model was also closely examined via different vehicle travel profiles. For the covering abstract see ITRD E134653.",

author = "Pei-Sung Lin and Sachin Rai and Larry Hagen",

year = "2006",

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day = "1",

language = "American English",

note = "13th World Congress on Intelligent Transport Systems, Compendium of Technical Papers ; Conference date: 01-10-2006",

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T1 - Development of an Adaptive Rear-End Crash Avoidance Model

AU - Lin, Pei-Sung

AU - Rai, Sachin

AU - Hagen, Larry

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Y1 - 2006/10/1

N2 - Rear-end collisions accounted for more than 30 percent of the 6.2 million crashes in the United States in 2004. The USDOT is currently working with the automotive industry for vehicle-to-vehicle and vehicle-to-roadside communications to improve traffic safety through crash avoidance systems. This study has developed an adaptive rear-end crash avoidance model to continually adjust the speed of the following vehicle to avoid a rear-end crash when the driver fails to respond to a collision warning. The model was developed based on motion equations, human factors, and vehicle parameters. The model was also closely examined via different vehicle travel profiles. For the covering abstract see ITRD E134653.

AB - Rear-end collisions accounted for more than 30 percent of the 6.2 million crashes in the United States in 2004. The USDOT is currently working with the automotive industry for vehicle-to-vehicle and vehicle-to-roadside communications to improve traffic safety through crash avoidance systems. This study has developed an adaptive rear-end crash avoidance model to continually adjust the speed of the following vehicle to avoid a rear-end crash when the driver fails to respond to a collision warning. The model was developed based on motion equations, human factors, and vehicle parameters. The model was also closely examined via different vehicle travel profiles. For the covering abstract see ITRD E134653.

UR - https://digitalcommons.usf.edu/cutr_facpub/60

UR - https://trid.trb.org/View/843332

M3 - Presentation

T2 - 13th World Congress on Intelligent Transport Systems, Compendium of Technical Papers

Y2 - 1 October 2006

ER -

Development of an Adaptive Rear-End Crash Avoidance Model

Abstract

Conference

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