Automated Selection of Automobile Crash Countermeasures

  March 1, 2003      Motor Vehicles, Traffic Safety
Wang, H., A. Parrish and H. C. Chen. Proceedings of 41st ACM Southeast Regional Conference, pp. 268-273, 2003.

This paper describes a neural network approach to automatically select crash countermeasures. The approach uses as a base the CARE IMPACT analysis that mines the crash database for the most critical attributes. However, while CARE bases its countermeasure selection on the “maximum gain” that can be obtained by eliminating attribute value over-representations, two additional algorithms were introduced to determine the most significant variables and to rank the attributes, namely: (1) a Euclidean distance approach, and (2) an ellipse distance approach. An evaluation function is constructed from the neural network learning, capturing the decision making strategy and then used continuously to select countermeasures.