An Evolutionary Approach to General-Purpose Automated Speed and Lane Change Behavior
Paper i proceeding, 2017

This paper introduces a method for automatically training a general-purpose driver model, applied to the case of a truck-trailer combination. A genetic algorithm is used to optimize a structure of rules and actions, and their parameters, to achieve the desired driving behavior. The training is carried out in a simulated environment, using a two-stage process. The method is then applied to a highway driving case, where it is shown that it generates a model that matches or surpasses the performance of a commonly used reference model. Furthermore, the generality of the model is demonstrated by applying it to an overtaking situation on a rural road with oncoming traffic.

Författare

Carl-Johan Hoel

Chalmers, Tillämpad mekanik, Fordonsteknik och autonoma system

Krister Wolff

Mekanik och maritima vetenskaper

Chalmers, Tillämpad mekanik, Fordonsteknik och autonoma system

Mattias Wahde

Chalmers, Tillämpad mekanik, Fordonsteknik och autonoma system

Proceedings of 16th IEEE International Conference On Machine Learning And Applications (ICMLA)

Ämneskategorier

Annan data- och informationsvetenskap

Farkostteknik

Styrkeområden

Informations- och kommunikationsteknik

Transport

Drivkrafter

Hållbar utveckling

Innovation och entreprenörskap

DOI

10.1109/ICMLA.2017.00-70

Mer information

Senast uppdaterat

2018-03-16