Paper Info
Title
Full-range adaptive cruise control based on supervised adaptive dynamic programming
Abstract
The paper proposes a supervised adaptive dynamic programming (SADP) algorithm for a full-range adaptive cruise control (ACC) system, which can be formulated as a dynamic programming problem with stochastic demands. The suggested ACC system has been designed to allow the host vehicle to drive both in highways and in Stop and Go (SG) urban scenarios. The ACC system can autonomously drive the host vehicle to a desired speed and/or a given distance from the target vehicle in both operational cases. Traditional adaptive dynamic programming (ADP) is a suitable tool to address the problem but training usually suffers from low convergence rates and hardly achieves an effective controller. A SADP algorithm which introduces the concept of inducing region is here introduced to overcome such training drawbacks. The SADP algorithm performs very well in all simulation scenarios and always better than more traditional controllers. The conclusion is that the proposed SADP algorithm is an effective control methodology able to effectively address the full-range ACC problem.
Year
DOI
Venue
2014
10.1016/j.neucom.2012.09.034
Neurocomputing
Keywords
Field
DocType
target vehicle,acc system,proposed sadp algorithm,supervised adaptive dynamic programming,host vehicle,full-range adaptive cruise control,sadp algorithm,traditional adaptive dynamic programming,dynamic programming problem,full-range acc problem,neural networks
Convergence (routing),Dynamic programming,Control theory,Control theory,Cruise control,Artificial intelligence,Artificial neural network,Mathematics,Machine learning
Journal
Volume
ISSN
Citations 
125,
0925-2312
21
PageRank 
References 
Authors
0.79
8
6
Name
Order
Citations
PageRank
Dongbin Zhao1102582.21
Zhaohui Hu2210.79
Zhongpu Xia3241.57
Cesare Alippi41040115.84
Yuanheng Zhu525213.51
Ding Wang6187068.16