Autonomous driving is one of the three new technologies that are disrupting the classical vehicle industry together with electrification and connectivity. All three are pieces in the puzzle to drastically reduce the number of fatalities and injuries from traffic accidents but also to reduce the total amount of cars, reduce the polluting greenhouse gases, reduce noise pollution and completely eliminate unwanted driving. For example would most people rather rest, read or do anything else instead of driving in congested traffic. It is not small steps to take and it will have to be done incrementally as many other things. Within the vehicle industry racing has always been the natural place to push the boundaries of what is possible. Here new technologies can be tested under controlled circumstances in order to faster find the best solution to a problem.Autonomous driving is no exception, the international student competition ”Formula Student” has introduced a driverless racing class and Formula E are slowly implementing Robo Race. The fact that race cars aim to drive at the limits of what is possible enable engineers to develop algorithms that can handle these conditions even in the every day life. Because even though the situations when normal passenger cars need to perform at the limits are rare, it is at these times it can save peoples lives. When an unforeseen event occurs and a fast manoeuvre has to be done in order to avoid the accident, that is when the normal car is driving at the limits. But the other thing to take into considerations when taking new technology into the consumer market is that the cars cannot cost as much as a race car. This means simpler computers has to be used and this in turn puts a constraint on the algorithms in the car. They can not be too computationally heavy.In this thesis a controller is designed to drive as fast as possible around the track. But in contrast to existing research it is not about how much the limit of speed can be pushed but of how simple a controller can be. The ...


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    Titel :

    Optimization based trajectory planning for autonomous racing


    Beteiligte:
    Ahlberg, Max (Autor:in)

    Erscheinungsdatum :

    2019-01-01


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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