An Evolutionary Approach to General-Purpose Automated Speed and Lane Change Behavior
Paper in proceedings, 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.

Author

Carl-Johan E Hoel

Volvo Group

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Krister Wolff

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Mechanics and Maritime Sciences

Mattias Wahde

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

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

Subject Categories

Other Computer and Information Science

Vehicle Engineering

Areas of Advance

Information and Communication Technology

Transport

Driving Forces

Sustainable development

Innovation and entrepreneurship

DOI

10.1109/ICMLA.2017.00-70

More information

Latest update

1/22/2019