The authors consider the optimal control design of an abstract autonomous vehicle (AAV). The AAV searches an area for tasks that are detected with a probability that depends on vehicle speed, and each detected task can be processed or ignored. Both searching and processing are costly, but processing also returns rewards that quantify designer preferences. They generalize results from the analysis of animal foraging behavior to model the AAV. Then, using a performance metric common in behavioral ecology, they explicitly find the optimal speed and task processing choice policy for a version of the AAV problem. Finally, in simulation, they show how parameter estimation can be used to determine the optimal controller online when density of task types is unknown.
Foraging theory for autonomous vehicle speed choice
Foraging-Theory für die Geschwindigkeitswahl autonomer Fahrzeuge
Engineering Applications of Artificial Intelligence ; 22 , 3 ; 482-489
2009
8 Seiten, 4 Bilder, 3 Tabellen, 21 Quellen
Article (Journal)
English