Developing an autosteering of road motor vehicles in slippery road conditions

Alekseeva, Natalia Mihajlovna

WEKO3

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Developing an autosteering of road motor vehicles in slippery road conditions

https://doi.org/10.14988/00027629

名前 / ファイル	ライセンス	アクション
要旨/abstaract (205.9 kB)
zk1102.pdf (4.6 MB)

Item type

学位論文 / Thesis or Dissertation(1)

公開日

2020-10-05

タイトル

Developing an autosteering of road motor vehicles in slippery road conditions

言語

eng

キーワード

言語

主題Scheme

Other

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自律走行車

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自動操舵

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滑りやすい路面状況

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PDコントローラー

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予測モデル

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遺伝的プログラミング

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言語

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autonomous vehicles

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automated steering

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slippery road conditions

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PD controllers

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predictive model

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Genetic programming

資源タイプ

資源タイプ識別子

http://purl.org/coar/resource_type/c_db06

資源タイプ

doctoral thesis

ID登録

10.14988/00027629

ID登録タイプ

JaLC

アクセス権

open access

アクセス権URI

http://purl.org/coar/access_right/c_abf2

その他（別言語等）のタイトル

その他のタイトル

滑りやすい路面条件における自動車の自動操縦に関する研究

言語

その他（別言語等）のタイトル

その他のタイトル

スベリヤスイロメンジョウケンニオケルジドウシャノジドウソウジュウニカンスルケンキュウ

言語

ja-Kana

著者

Alekseeva, Natalia Mihajlovna

WEKO 28580
CiNii ID 8000001877607
ORCID 0000-0002-8269-2324

en	Alekseeva, Natalia Mihajlovna Alekseeva, Natalia

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著者所属

言語

値

理工学研究科

著者所属（英）

言語

値

Graduate School of Science and Engineering

抄録

内容記述タイプ

Abstract

内容記述

In the nearest future, the human driver is viewed as a reliable backup even for the fully automated road motor vehicles (cars). Indeed, the driver is assumed to swiftly take the control of the car in cases of suddenly occurring (i) challenging environmental conditions, (ii) complex unforeseen driving situations, or (iii) degradation of performance of the car. However, due to the cognitive overload in such a sudden, stressful takeover of the control, the driver would often experience the startle effect, which usually results in an unconscious, instinctive, yet incorrect response. An extreme case of startle is freezing, in which the driver might be incapable to respond to the sudden takeover of control at all.
The possible approaches to alleviate the startle during the takeover of control (i.e., the automation startle) include an offset- (i.e., either early- or delayed-), gradual yielding the controls to the driver. In the cases considered above, however, these approaches are hardly applicable because of (i) the presumed unpredictability of the events that result in the need of takeover of control, and (ii) the severe time constraints of the latter.
Conversely, the objective of our research is to propose an approach of minimizing the need of yielding the control to the driver in challenging environmental conditions by guaranteeing an adequate automated control in these conditions. Focusing on slippery roads as an instance of challenging conditions, and steering control as an instance of control, we aim at developing such an automated steering that controls the car adequately in various road surfaces featuring low friction coefficients without the need of driver’s intervention.
In order to develop such an automated steering we employed an in-house evolutionary computation framework – XML-based genetic programming (XGP) – which offers a flexible, portable, and human readable representation of the evolved optimal steering functions. The trial runs of the evolved steering functions were performed in the Open Source Racing Car Simulator (TORCS), which features a realistic, yet computationally efficient simulation of the car and its environment.
The obtained experimental results indicate that due to the challenging dynamics of the unstable car on slippery roads, neither the canonical (tuned) servo-control (as a variant of PD) nor the (tuned) PID-controller could control the car adequately on slippery roads. On the other hand, the controller, featuring a relaxed, arbitrary structure evolved by XGP outperforms both the servo- and PID controllers in that it results in a minimal deviation of the car from its intended trajectory in rainy, snowy, and icy road conditions. Moreover, the evolved steering that employs anticipated perceptions is even superior as it could anticipate the imminent understeering of the car at the entry of the turns and consequently – to compensate for such an understeering by proactively turning the steering wheels in advance – well before entering the turn. The obtained results suggest a human competitiveness of the evolved automated steering as it outperforms the commonly used alternative steering controllers proposed by human experts.
The research could be viewed as a step towards the evolutionary development of automated steering of cars in challenging environmental conditions.

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2023-07-27 18:41:55.479948

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Developing an autosteering of road motor vehicles in slippery road conditions

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