Titlebook: Creating Autonomous Vehicle Systems, Second Edition; Shaoshan Liu,Liyun Li,Jean-Luc Gaudiot Book 2020Latest edition Springer Nature Switze

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https://doi.org/10.1007/978-3-540-76707-7ject detection, semantic segmentation, etc. Most state-of-the-art algorithms now apply one type of neural network that is based on convolution operation, and the field is rapidly progressing. In this chapter, we will cover selected deep-learning-based algorithms for perception in autonomous driving.

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Perception in Autonomous Driving,sensory data. In order for autonomous driving vehicles to be safe and intelligent, perception modules must be able to detect any obstacle, to recognize road surface, lane dividers, traffic signs and lights, to track moving objects in 3D, etc. Since all subsequent driving decisions, planning, and con

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Decision, Planning, and Control, feedback .. Decision, planning, and control are the modules that compute how the autonomous vehicle should maneuver. These modules constitute the traditional narrow concept of planning and control. While they all solve the same problem of how the autonomous vehicle should behave, they operate at di

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Reinforcement Learning-Based Planning and Control,me increasingly popular with recent developments in artificial intelligence. Even though current state-of-the-art learning-based approaches to planning and control have their limitations, we feel they will become extremely important in the future and that, as technical trends, they should not be ove