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XuetangX

智能车辆机器学习技术

Beijing Institute of Technology via XuetangX

Overview






由北京理工大学机械与车辆学院智能车辆研究所牵头建设的《智能车辆机器学习技术》慕课,特邀了北京理工大学吕超老师、任宏斌老师和于会龙老师一起参与建设并录制了这门课。

机器学习作为人工智能的主要技术之一,已被广泛应用于各个领域。随着机器学习技术的发展,基于学习的自动驾驶相关技术也在快速崛起,并被应用到了智能车辆的设计与开发中。

本课程首先从环境感知、决策与规划、运动控制等模块出发,介绍自动驾驶技术和智能车辆的基本概念;然后结合实例,详细阐述包括监督学习、无监督学习、深度学习和强化学习等在内的常用机器学习算法的基本原理;最后,通过讲述如何构建机器学习模型,解决从高层环境感知到底层运动控制等一系列智能车辆典型问题,将理论知识与实际应用结合起来。 

       本课程可以作为智能车辆、智能交通系统、移动机器人等相关专业研究生的专业课程,帮助同学们了解智能车辆与机器学习领域的前沿知识。在课程结束时,同学们应能够理解机器学习的基本机制,并设计自己的学习算法来解决智能车辆相关问题。




Syllabus

  • Unit 1 Overview of Autonomous Vehicles
    • 1.1 Architectures for Autonomous Vehicles
    • 1.2 A Brief History of Autonomous Vehicles
    • 1.3 Exercises
  • Unit 2 Overview of Machine Learning—Supervised and Unsupervised Learning
    • 2.1 Supervised Learning
    • 2.2 Unsupervised Learning
    • 2.3 Advanced Machine Learning Methods
    • 2.4 Basics of Python
    • 2.5 Exercises
  • Unit 3 Overview of Machine Learning—Reinforcement Learning
    • 3.1 Overview of Reinforcement Learning
    • 3.2 Markov Decision Process
    • 3.3 Dynamic programming
    • 3.4 Q-iteration
    • 3.5 Q-learning
    • 3.6 Function Approximation for RL
    • 3.7 Novel RL Technologies
    • 3.8 Exercises
  • Unit 4 Learning-based Environmental Perception
    • 4.1 Overview of Environmental Perception
    • 4.2 Sensing System and Data Collection
    • 4.3 Static Object Recognition
    • 4.4 Dynamic Object
    • 4.5 SLAM
    • 4.6 Exercises
  • Unit 5 Learning-based Decision Making
    • 5.1 Overview of Decision Making
    • 5.2 Introduction to Carla
    • 5.3 Overtaking Decision Making
    • 5.4 Lane-changing Decision Making
    • 5.5 Exercises
  • Unit 6 Learning-based Motion Control
    • 6.1 Overview of Motion Control
    • 6.2 Cruise and Car Following Control
    • 6.3 Steering and Lane Keeping Control
    • 6.4 Path-tracking Control
    • 6.5 Learning from Human Drivers
    • 6.6 Exercises
  • Final examination

    Taught by

    Chao Lv, REN HONGBIN, and YU HUILONG

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