DocumentCode :
529535
Title :
Collision risk assessment for pedestrians´ safety : Neural network with interacting multiple model apporach
Author :
Park, Seongkeun ; Choi, Baehoon ; Baehoon Choi ; Kim, Euntai
Author_Institution :
Dept. of Electr. & Electron. Eng., Comput. Intell. Lab., Seoul, South Korea
fYear :
2010
fDate :
18-21 Aug. 2010
Firstpage :
2897
Lastpage :
2900
Abstract :
In this paper, we propose a alarm system for pedestrian protection. We usually do not know that pedestrians may or may not be in dangerous situation, and to know whether pedestrians are in dangerous situation or not. In this paper, we construct collision probability system between vehicle and pedestrian. By using monte carlo simulation, we calculate the collision probability, and it is hard to know collision probability of all area, we recover collision probability of all area using neural networks. And, the collision probabilities are different according to tendency of pedestrian movement, we understand the tendency of pedestrian movement using interacting multiple model tracking method. Computer simulation will be show the validity of our proposed method.
Keywords :
Monte Carlo methods; alarm systems; collision avoidance; neural nets; risk management; road safety; traffic engineering computing; alarm system; collision probability system; collision risk assessment; interacting multiple model tracking method; monte carlo simulation; neural network; pedestrian movement; pedestrian protection; pedestrian safety; Artificial neural networks; Computational modeling; Driver circuits; Legged locomotion; Monte Carlo methods; Probability; Safety; Collision probability; Intelligent vehicle; Pedestrian protection system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
SICE Annual Conference 2010, Proceedings of
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7642-8
Type :
conf
Filename :
5602858
Link To Document :
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