Titlebook: Deep Learning for Autonomous Vehicle Control; Algorithms, State-of Sampo Kuutti,Saber Fallah,Richard Bowden Book 2019 Springer Nature Switz

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Kristina H??k,David Benyon,Alan J. Munro 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

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Designing Instruction For Open Sharingts to later chapters were also presented. The review of control techniques was broken into three sections: lateral, longitudinal, and full vehicle control. The lateral control systems were shown to favor using supervised learning to predict steering angles from image inputs, while the dominant trend

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Deep Learning for Autonomous Vehicle Control978-3-031-01502-1Series ISSN 2576-8107 Series E-ISSN 2576-8131

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Kristina H??k,David Benyon,Alan J. Munroent years and has shown great promise in fields such as computer vision [24], speech recognition [25], and language processing [26]. The aim of this chapter is to provide the reader with a brief background on neural networks and deep learning methods which are discussed in the later sections.

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Designing Instruction For Open Sharingake recommendations for the direction of future research. Since multiple research projects have focussed on learning a single driving action, the discussion on control techniques in this chapter is broken into three sections: lateral (steering), longitudinal (acceleration and braking), and full vehicle control.