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

CAPE

Deep Learning,ent 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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Flounder

Book 2019urrently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to

MAZE

Introduction, 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

NOVA

Deep Learning,mples (i.e., training data) and the algorithm learns to solve the task on its own. Given enough training data, machine learning algorithms can optimize their solution to outperform traditional programming methods. Artificial neural networks are a promising tool for machine learning methods, and have

Solace

Deep Learning for Vehicle Control,eralization capability offered through learning from big data, and highly scalable properties to high-dimensional observationaction mappings enables deep learning to outperform hand-engineered control techniques. For these reasons, there has been several approaches to using deep learning to control

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