Packt
Self-Driving Car Specialization Course
Packt

Self-Driving Car Specialization Course

包含在 Coursera Plus

深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度
深入了解一个主题并学习基础知识。
中级 等级

推荐体验

1 周 完成
在 10 小时 一周
灵活的计划
自行安排学习进度

您将学到什么

  • Build and test simulations using ROS, Gazebo, and Jetbot for autonomous driving experiments.

  • Apply computer vision and machine learning techniques to enable road scene understanding and object detection.

  • Master motion prediction, collision avoidance, and control systems for safe and efficient autonomous navigation.

  • Design and implement core systems of self-driving cars, including hardware, software, and system architecture.

要了解的详细信息

可分享的证书

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作业

7 项作业

授课语言:英语(English)

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有6个模块

In this module, we will embark on an introductory journey into the world of Self-Driving Cars. Starting with their definition and historical evolution, we’ll explore the key technologies and safety standards that underpin their functionality. Additionally, we will cover typologies, decision-making processes, and the hardware and software that bring these revolutionary vehicles to life.

涵盖的内容

9个视频1篇阅读材料

In this module, we will delve into the essential design elements of Self-Driving Car systems. We’ll explore the hardware components that provide sensory input, the software systems that process data and manage driving tasks, and the architectural design that ensures seamless interaction between these elements. This comprehensive overview will lay the foundation for understanding how Self-Driving Cars operate as a cohesive unit.

涵盖的内容

3个视频1个作业1个插件

In this module, we will explore the intricate control systems that drive Self-Driving Cars. We’ll start with longitudinal control, Adaptive Cruise Control, and state estimation techniques before delving into advanced topics such as Kalman Filters, motion prediction, and collision detection. Additionally, we’ll introduce ROS simulation tools to develop and optimize these systems. By mastering these concepts, you'll gain a comprehensive understanding of the control mechanisms essential for autonomous vehicles.

涵盖的内容

44个视频1个作业1个插件

In this module, we will explore the world of computer vision, a critical component of Self-Driving Car technology. We’ll cover foundational concepts like object detection, tracking, and semantic segmentation, before diving into advanced techniques such as 3D object detection, HydraNets, and stereo vision. Finally, we’ll examine the integration of real-time vision depth perception using platforms like Jetson Nano, showcasing their impact on autonomous vehicle navigation and safety.

涵盖的内容

10个视频1个作业1个插件

In this module, we will master ROS simulations, a cornerstone of Self-Driving Car development. Starting with the setup of ROS2 and Gazebo, we’ll explore how to create model cars, roads, and detailed environments for realistic simulations. Additionally, we’ll delve into advanced topics such as motion prediction, state estimation, obstacle avoidance, and localization, providing a comprehensive toolkit for testing and refining autonomous vehicle systems.

涵盖的内容

21个视频1个作业1个插件

In this module, we will explore the practical application of Self-Driving Car concepts using the Jetbot platform. Starting with the setup and assembly of the Jetson Nano and Jetbot hardware, we’ll dive into functionalities like collision avoidance, road and object following, and real-time vision depth perception. The module culminates in testing the fully integrated Jetbot system in real-world environments, providing hands-on experience in robotics experimentation and validation.

涵盖的内容

8个视频3个作业

位教师

Packt - Course Instructors
Packt
971 门课程229,122 名学生

提供方

Packt

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