TY  - Type of reference 
TI  - Multi-criteria optimization for autonomous vehicles in a dynamic environment 
AU  - Jean-Baptiste Receveur 
AU  - Pierre Melchior 
AU  - Stéphane Victor 
AB  - In the last few years much effort has been made towards more autonomous vehicles and fuel consumption reduction. This article deals with the issue trajectory optimization of unmanned terrestrial vehicles so as to reduce consumption, travel time or to improve comfort. Main focuses are set on testing different criteria and the possibility of using a genetic algorithm to improve the potential field methods. The main idea of this article is that potential field methods could be improved by adding a dynamic target in it. Two improvements are brought to the potential field method : the generation of an optimal path in the environment, and the joint generation an optimal motion. 
DO  - 10.21494/ISTE.OP.2018.0283 
JF  - Control 
KW  -  Optimization, Autonomous vehicles,  Path planning,  Potential fields,  Optimal trajectory,  Genetic algorithms,  Fractional differentiation,  Algorithmes génétiques,  Optimisation, Véhicules autonomes,  Planification de trajectoire,  Champs de potentiel,  Trajectoire optimale,  Dérivée fractionnaire,  
L1  - http://openscience.fr/IMG/pdf/iste_auto18v1n2.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2018/08/23 
SN  - 2631-4924 
TT  - Optimisation multi-critère pour véhicules autonomes en environnement dynamique 
UR  - http://openscience.fr/Multi-criteria-optimization-for-autonomous-vehicles-in-a-dynamic-environment 
IS  - Issue 1 
VL  - 2 
ER  -