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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Nikulin:2017:ieeeDSAA,

• author = "Vsevolod Nikulin and Albert Podusenko and Ivan Tanev and Katsunori Shimohara",
• booktitle = "2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)",
• title = "Regression Based Model for Autosteering of a Car with Delayed Steering Response",
• year = "2017",
• pages = "412--419",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/DSAA.2017.16",
• month = oct,
• abstract = "In our work we consider the area of intelligent road vehicles, especially, the topic of automated vehicles. Our objective is to automatically develop - by applying a supervised learning approach - the steering of a realistically simulated car featuring a steering delay and rate limit of turning of front wheels. Due to the adopted physical constrains, the typically used steering mechanisms (based on servo-control model) result in a non-stable, oscillating behaviour of the controlled car. The proposed approach of automated development of steering for such a realistically simulated car employs the perception-action relationship obtained from the sample runs of an ideal car (featuring instant steering response) steered by the servo-control model. Then, implying that offsetting the perceptions in the obtained relationship back in time to the value equal to the steering delay would mimic the behaviour of the car featuring steering delays, we used the so modified perception-action relationship to train the proposed regression based model. The experimental results verify that the derived automated steering controls the car featuring steering delays of 100 ms and 200 ms in a much similar way as the servo-control steers an ideal car. Moreover, for delay of 400 ms the steering, developed via proposed approach provides a better quality of control than that obtained from one of the most versatile unsupervised machine learning approaches - genetic programming.",
• notes = "Also known as \cite{8259802}",

}

Genetic Programming entries for Vsevolod Nikulin Albert Podusenko Ivan T Tanev Katsunori Shimohara

Citations