0476597 20240103214322.720170731235959.9 85007579596 000407655600029 10.1016/j.ijar.2016.11.018 20 s. P cav_un_epca*02567740888-613X881 (2017)567-586Elsevier Influence diagrams Optimal control Vehicle control cav_un_auth*0216188 Kratochvíl Václav Matematická teorie rozhodování Department of Decision Making Theory MTR MTR UTIA-B Department of Decision Making Theory CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101228 Vomlel Jiří Matematická teorie rozhodování Department of Decision Making Theory MTR MTR UTIA-B Department of Decision Making Theory Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/MTR/kratochvil-0476597.pdf cav_un_auth*0332303 GA16-12010S GA ČR CZ Influence diagrams have been applied to diverse decision problems. In this paper, we describe their application to the speed profile optimization problem – a problem traditionally solved by the methods of optimal control theory. Influence diagrams appeared to be well-suited to these types of problems. It is mainly due to their ability to perform computations efficiently if the utility function is additively decomposed along the vehicle path, which is the case for utility functions based on, e.g., the total driving time or the total fuel consumption. Also, driving constraints can be efficiently included in the influence diagram. If the vehicle speed deviates from the optimal speed profile during the real drive, a new optimal speed profile can be quickly computed in the compiled influence diagram. The theory of influence diagrams has not yet been sufficiently developed for continuous variables and nonlinear utility functions. JD 10000 10200 10201 2018 2 4 A hod 4ah 20231122142550.3 RVO:67985556 http://hdl.handle.net/11104/0273647 1 Department of Decision Making Theory UTIA-B 10103 STATISTICS & PROBABILITY S 3+4 Article|Proceedings Paper Computer Science Artificial Intelligence 3+4 Article|Proceedings Paper Computer Science Artificial Intelligence 3+4 Article|Proceedings Paper Computer Science Artificial Intelligence COMPUTERSCIENCE.ARTIFICIALINTELLIGENCE 2.504 0.687 7.8 0.0042 0.658 3384 0.866 1.343 182 Q2 44.33 51.1 Q3 Q2 0.9 Q2 51.136 2017 Soubory v repozitáři: kratochvil-0476597.pdf 85007579596 SCOPUS PUBMED 000407655600029 WOS cav_un_epca*0256774 International Journal of Approximate Reasoning 0888-613X 1873-4731 Roč. 88 č. 1 2017 567 586 Elsevier