Title :
Artificial neural network for predictions of vehicle drivable range and period
Author :
Chen, Yuan-Lin ; Chih Hsien Huang ; Kuo, Yao-Wen ; Wang, Shung-Sung
Author_Institution :
Inst. of Electro-Mech. Eng., Ming Chi Univ. of Technol., New Taipei, Taiwan
Abstract :
An artificial neural network for predicting the drivable range and period for vehicles under remaining fuel is presented in this paper. The driver´s driving behaviors, vehicle condition and traffic route are all taking into consideration. The presented method could learn from the statuses of vehicle such as vehicle speed and engine speed and traffic route to make a prediction of vehicle drivable range and period under remaining fuel. The experimental results are presented for to guarantee the artificial neural network could estimate the vehicle drivable range and period accurately.
Keywords :
neural nets; road traffic; traffic engineering computing; artificial neural network; driving behaviors; engine speed; remaining fuel; traffic route; vehicle condition; vehicle drivable range; vehicle speed; Artificial neural networks; Engines; Estimation; Fuels; Neurons; Training; Vehicles; artificial neural network; driver´s driving behaviors; vehicle drivable period; vehicle drivable range;
Conference_Titel :
Vehicular Electronics and Safety (ICVES), 2012 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4673-0992-9
DOI :
10.1109/ICVES.2012.6294324