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X-WR-CALDESC:Events for WASP
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BEGIN:VEVENT
DTSTART;TZID=+01:00:20211112T100000
DTEND;TZID=+01:00:20211112T235900
DTSTAMP:20260601T155640
CREATED:20211021T082605Z
LAST-MODIFIED:20260601T083859Z
UID:10000295-1636711200-1636761540@wasp-sweden.org
SUMMARY:PhD defense: Decision-Making in Autonomous Driving using Reinforcement Learning
DESCRIPTION:Welcome to Doctoral Defense of Carl-Johan Hoel\n\nDate and time: November 12\, 10:00 – 12:00\n \nLocation: Lecture room FB\, Fysikgården 4\, Chalmers \nDoctoral student: Carl-Johan Hoel\, Chalmers\, Mechanics and Maritime Sciences\, Vehicle Engineering and Autonomous Systems \nTitel: Decision-Making in Autonomous Driving using Reinforcement Learning \nSupervisor: Krister Wolff\, Chalmers \nOpponent:  Professor Ville Kyrki\, Department of Electrical Engineering and Automation\, Aalto University\, Finland \n\nOriginal location: Lecture room FB\, Fysikgården 4\, Chalmers
URL:https://wasp-sweden.org/event/phd-defense-decision-making-in-autonomous-driving-using-reinforcement-learning/
LOCATION:Chalmers University of Technology\, Chalmers University of Technology\, Göteborg\, Sweden
CATEGORIES:Defense
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BEGIN:VEVENT
DTSTART;TZID=+01:00:20210917T130000
DTEND;TZID=+01:00:20210917T235900
DTSTAMP:20260601T155640
CREATED:20210901T070203Z
LAST-MODIFIED:20260601T083902Z
UID:10000301-1631883600-1631923140@wasp-sweden.org
SUMMARY:PhD Defense: Privacy expectations and challenges of smart home ecosystems
DESCRIPTION:Welcome to Doctoral Defense of Tomasz Kosinski\n\nDate and time: September 17\, 13:00 \nLocation: Room CSE 473\, Jupiter building\, Hörselgången 5\, Campus Lindholmen and online \nDoctoral student: Tomasz Kosinski\, Department of Computer Science and Engineering\, Chalmers University of Technology \nTitel: Privacy expectations and challenges of smart home ecosystems \nSupervisor: Professor Morten Fjeld\, Department of Computer Science and Engineering \nOpponent:  Professor Florian Alt\, Professor of Usable Security at the Bundeswehr University Munich \n\nOriginal location: Chalmers University of Technology / online
URL:https://wasp-sweden.org/event/phd-defense-privacy-expectations-and-challenges-of-smart-home-ecosystems/
LOCATION:Chalmers University of Technology\, Chalmers University of Technology\, Göteborg\, Sweden
CATEGORIES:Defense
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BEGIN:VEVENT
DTSTART;TZID=+01:00:20210609T133000
DTEND;TZID=+01:00:20210609T235900
DTSTAMP:20260601T155640
CREATED:20210528T112344Z
LAST-MODIFIED:20260601T083903Z
UID:10000305-1623245400-1623283140@wasp-sweden.org
SUMMARY:PhD Defense: Designing Trustworthy Autonomous Systems
DESCRIPTION:Welcome to Doctoral Defense of Piergiuseppe Mallozzi\nDate and Time: June 9th\, 13:30\n \nLocation: Room 473\, Jupiter Building \nTitel: Designing Trustworthy Autonomous Systems \nDoctoral student: Piergiuseppe Mallozzi\, PhD Software Engineering\, Chalmers University of Technology \nSupervisor: Patrizio Pelliccione\, Chalmers University of Technology \nOpponent:  Cristina Seceleanu\, Mälardalen University\, Sweden \nOriginal location: Chalmers Universtity of Technology
URL:https://wasp-sweden.org/event/phd-defense-designing-trustworthy-autonomous-systems/
LOCATION:Chalmers University of Technology\, Chalmers University of Technology\, Göteborg\, Sweden
CATEGORIES:Defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=+01:00:20190605T100000
DTEND;TZID=+01:00:20190605T235900
DTSTAMP:20260601T155640
CREATED:20190531T082636Z
LAST-MODIFIED:20260601T083919Z
UID:10000347-1559728800-1559779140@wasp-sweden.org
SUMMARY:Tactical decision-making for autonomous driving: A reinforcement learning approach
DESCRIPTION:Defense by Carl-Johan Hoel\nFor: Licentiate of Engineering in Adaptive systems\nLocation: Lecture hall FB\, Fysikgården 4\, Chalmers University of Technology\, Göteborg \nDiscussion leader: Associate Professor Christos Dimitrakakis\, Department of Computer Science and Engineering\, Chalmers.\nSupervisors: Associate Professor Krister Wolff (Chalmers)\, Adjunct Professor Leo Laine (AB Volvo).\nExaminer: Professor Mattias Wahde \nThe tactical decision-making task of an autonomous vehicle is challenging\, due to the diversity of the environments the vehicle operates in\, the uncertainty in the sensor information\, and the complex interaction with other road users. This thesis introduces and compares three general approaches\, based on reinforcement learning\, to creating a tactical decision-making agent. The first method uses a genetic algorithm to automatically generate a rule based decision-making agent\, whereas the second method is based on a Deep QNetwork agent. The third method combines the concepts of planning and learning\, in the form of Monte Carlo tree search and deep reinforcement learning. The three approaches are applied to several highway driving cases in a simulated environment and outperform a commonly used baseline model by taking decisions that allow the vehicle to navigate 5% to 10% faster through dense traffic. However\, the main advantage of the methods is their generality\, which is indicated by applying them to conceptually different driving cases. Furthermore\, this thesis introduces a novel way of applying a convolutional neural network architecture to a high level state description of interchangeable objects\, which speeds up the learning process and eliminates all collisions in the test cases. \nOriginal location: Chalmers University of Technology\, Göteborg
URL:https://wasp-sweden.org/event/tactical-decision-making-for-autonomous-driving-a-reinforcement-learning-approach/
LOCATION:Chalmers University of Technology\, Chalmers University of Technology\, Göteborg\, Sweden
CATEGORIES:Defense
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