Highway vehicle accident reconstruction using Cooperative Collision Warning based Motor Vehicle Event Data Recorder

Author(s):  
Chung-Ping Young ◽  
Bao Rong Chang ◽  
Ting-Ying Wei
Author(s):  
John C. Glennon

As litigation has mushroomed in the 1970s and 807s, more and more varied types of people have proclaimed their expertise to practice motor-vehicle accident reconstruction. A vast number of those who have claimed to be experts have nothing more than a high-school education and a short course in accident reconstruction. Unfortunately, the courts, more often than not, have qualified these people as experts. Another large group of practitioners are college educated, but come to accident reconstruction by way of education and experience in non-related fields such as chemistry, nuclear physics, aeronautical engineering, air-conditioning design, plastics manufacture, and other distant disciplines. These people usually know the basic physics associated with accident reconstruction, but often do not appreciate or understand the idiosyncrasies of motor-vehicle collisions. But, they too are usually qualified as experts by the courts.


2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.


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