Regression Based Model for Autosteering of a Car with Delayed Steering Response

Nikulin, Vsevolod; Podusenko, Albert; Tanev, Ivan; Shimohara, Katsunori

doi:10.1109/dsaa.2017.16

Cited by 2 publications

(1 citation statement)

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“…Several related publications ( [4][5][6]) are discussing a similar topic. However, in the current work we investigate whether introducing a domain-specific knowledge [7][8] into GP -used to evolve the optimal steering function -would facilitate either an improved efficiency of evolution or better quality of the obtained steering (or both).…”

Section: Introductionmentioning

confidence: 99%

Evolving the autosteering of a car featuring a realistically simulated steering response

Nikulin

Podusenko

Tanev

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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We consider the area of intelligent road vehicles, especially, the topic of automated vehicles. Focusing on the importance of the automated steering, we address the challenge of automated keeping of a car in the middle of the driving lane. Our objective is to investigate the feasibility of employing genetic programming (GP) to evolve the automated steering of a car. The latter is implemented in the Open Source Racing Car Simulator (TORCS) with a realistically modeled steering featuring both a delay of response and a rate limit. We propose two approaches aimed at improving the efficiency of evolution via GP. In the first approach we implement an incremental evolution of the steering function by commencing the evolution with an ideal car and gradually increasing the degree of its realism (i.e., the amount of steering delay) in due course of evolution. The second approach is based on incorporating expert knowledge about the (expected) structure of the steering function according to the servo control model of steering. The experimental results verify that the proposed approaches yield an improved efficiency of evolution in that the obtained solutions are both of a better quality and could be obtained faster than those of the canonical GP.

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Section: Introductionmentioning

confidence: 99%