Abstract
Autonomous vehicles have the potential to transform our transportation systems in terms of safety and efficiency. The steady increase in the number of vehicles on the road has led to increased pressure to solve issues such as traffic congestion, pollution, and road safety. The leading answer to resolving these issues among the research community is self-driving cars [1–3]. For instance, according to the World Health Organization, an estimated 1.3 million people die in road accidents yearly [4]. Meanwhile, up to 90% of all car accidents are estimated to be caused by human errors [5], therefore autonomous vehicles can provide significant safety improvements by eliminating driver errors. Further benefits provided by autonomous vehicles include better fuel economy, reduced pollution, car sharing, increased productivity, and improved traffic flow [6–9].
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Kuutti, S., Fallah, S., Bowden, R. (2019). Introduction. In: Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects. Synthesis Lectures on Advances in Automotive Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-01502-1_1
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DOI: https://doi.org/10.1007/978-3-031-01502-1_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-00374-5
Online ISBN: 978-3-031-01502-1
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