Dynamic Speed Adaptation for Curves using Machine Learning

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

Author: Kirilll Narmack; [2018]

Keywords: Machine; Learning; Artificial; Intelligence; MachineLearning; Machine-Learning; AI; ML; MLP; Neural; Networks; Multilayer-Perceptron; RFB; RBF-Network; Vehicle; Automation; AutonomousVehicles; Autonomous; SelfDriving; Self-Driving; Self; Driving; Curve; Speed; Adaptation; Adaption; CSA; ACC; Adaptive; Cruise; Control; Driving; Style; Driver; Behavior; Behaviour; Sample; Samples; Distance; Derivative; Speed; Velocity; Curvature; Road; Inclination; Lane; Width; Type; Acceleration; Longitudinal; Lateral; Data; Training; Test; Validation; Results; Discussion; Sustainability; Ethics; Ethical; Sustainable; Future; Today; Tomorrow; Yesterday; Robot; Robotics; System; Systems; Class; Classes; Bin; Bins; Tree; One; A; Dynamic; Using; Time; Delay; Volvo; Car; Cars; Corporation; Zenuity; School; Computer; Science; Master; Degree; Project; Thesis; Paper; Object; GPS; Map; Length; Research; Advanced; Машинное; Обучение; Искусственный; Интеллект; AI; ML; MLP; RBF; Автомобиль; Машына; Сеть; Робот; Водитель; Заворот; Дорога; Сам; Сама; Едет; Ехать; Учить; Учит; Учится; Автоматизация; Адаптация; Результат; Один; Одна; Одно; Вчера; Сегодня; Завтра; Дерево; Карта; ГПС; Информатика; Компьютер; Наука; Научная; Работа; Школа; Вольво; Завод; Транспорт; Maskininlärning; Artificiell; Intelligens; Inlärning; Maskin; AI; ML; MLP; RBF; Neural; Neurala; Nätverk; Artificiella; Automation; Själv; Självkörande; Körande; Bil; Fordon; Robot; Robotik; Körstil; Stil; Beteende; Adaption; Kurva; Lutning; Väg; CSA; ACC; Farthållare; Hastighet; Fart; Hållare; Inclination; Körfält; Fält; Spår; Bredd; Typ; Acceleration; Longitudinell; Lateral; Data; Traingin; Test; Validation; Resultat; Diskussion; Längd; Hållbarhet; Etik; Etiskt; Framtid; Dåtid; Idag; Imorgon; System; Class; Klass; Classer; Klasser; Träd; En; Ett; Fler; Flera; Dynamisk; Använda; Tid; Fördröjning; Tids; Volvo; Car; Corporation; Object; Zenuity; Skola; Dator; Vetenskap; Forkning; Avancerad; Projekt; Exjobb; Examensarbete; Betyg; GPS; Karta;

Abstract: The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated.

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