How machine learning drive the development of autonomous cars - A study of emerging technologies and the evolving value chain Master’s Thesis in the Master’s Programme

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/257036
Download file(s):
File Description SizeFormat 
257036.pdfFulltext3.44 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: How machine learning drive the development of autonomous cars - A study of emerging technologies and the evolving value chain Master’s Thesis in the Master’s Programme
Authors: Lundqvist, Filip
de Richelieu, Robin
Abstract: Autonomous cars have gained much attention in the past few years. Partially automated features are sold to consumers, and advance autonomous test vehicles are driving on public roads. These features and vehicles have caused a debate about their safety as they have been involved in a couple of fatal crashes in recent years. To simultaneously develop autonomous cars and features without compromising safety is a big challenge for the automotive industry, which is investing heavily in technology. This thesis aims to describe constraints in advanced driver-assistance systems (ADAS) and autonomous driving (AD) developed with machine learning techniques, how companies are adapting to this new technology demand, and current development paths towards autonomous cars. A model of the development of ADAS/AD was created by interviewing industry experts and a literature review. Companies efforts in the area were identified by searching databases and reading news articles and press releases. Data is a valuable asset but requires extensive work to be useful when developing ADAS/AD. Collecting data per se is not considered as a challenging task. Instead, the difficulty is to collect data about edge-cases, that is, rare situations occurring maybe once in a human lifetime of driving. Another key takeaway is the inherent difficulty of creating consistent datasets to train neural networks with, as it requires humans to interpret subjective situations the same way. Incumbent firms in the automotive industry are accompanied by startups and established technology companies from outside the industry in trying to develop and capture value from ADAS/AD. Collaborations and acquisitions are prominent ways to get desired know-how and secure supply of critical components. A schematic overview of the relationships in the industry is mapped to give the reader an idea of the sophisticated ecosystem companies are part of through investments, acquisitions, spin-offs and collaborations. Efforts to capture value from ADAS/AD can be divided into two branches. One being OEMs developing and offering ADAS in consumer products aiming to develop autonomous driving gradually. The second is characterized by companies targeting autonomous robo-taxi solutions in geo-fenced areas. Although both approaches are facing many similar challenges, they also differ in specific areas such as operational design domain.
Keywords: Transport;Grundläggande vetenskaper;Hållbar utveckling;Innovation och entreprenörskap (nyttiggörande);Övrig industriell teknik och ekonomi;Transport;Basic Sciences;Sustainable Development;Innovation & Entrepreneurship;Other industrial engineering and economics
Issue Date: 2019
Publisher: Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation
Chalmers University of Technology / Department of Technology Management and Economics
Series/Report no.: Master thesis. E - Department of Technology Management and Economics, Chalmers University of Technology, Göteborg, Sweden : E2019:100
URI: https://hdl.handle.net/20.500.12380/257036
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.