京都大学 / 0048 / 新制・課程博士 / 博士(文学) / 甲第18010号 / 文博第647号 / 新制||文||602(附属図書館) / 30868 / 京都大学大学院文学研究科行動文化学専攻 / (主査)教授 藤田 和生, 教授 板倉 昭二, 准教授 明和 政子 / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(薬科学) / 甲第24554号 / 薬科博第171号 / 新制||薬科||19(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 石濱 泰, 教授 加藤 博章, 教授 小野 正博 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

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Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第16819号 / 工博第3540号 / 新制||工||1535(附属図書館) / 29494 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 澤田 純男, 教授 清野 純史, 教授 林 康裕 / 学位規則第4条第1項該当

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22010号 / 工博第4622号 / 新制||工||1720(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 田中 茂信, 准教授 田中 賢治, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18246号 / 工博第3838号 / 新制||工||1588(附属図書館) / 31104 / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 宮川 豊章, 教授 河野 広隆, 教授 西山 峰広 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19509号 / 理博第4169号 / 新制||理||1599(附属図書館) / 32545 / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 向川 均, 教授 余田 成男, 准教授 榎本 剛 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20023号 / 情博第618号 / 新制||情||108(附属図書館) / 33119 / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 阿久津 達也, 教授 山本 章博, 教授 岡部 寿男 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21060号 / 工博第4424号 / 新制||工||1687(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 角 哲也, 教授 藤田 正治, 准教授 竹門 康弘 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University