|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/intro-self-driving-cars
课程评论：没有评论
课程名称：自驾车入门 概述：欢迎参加自驾车入门课程，这是多伦多大学自驾车专业化的第一门课程。本课程将介绍自驾车的术语、设计考虑因素和安全评估。通过本课程，您将能够： - 理解自驾车常用的硬件 - 识别自驾车软件堆栈的主要组成部分 - 编程车辆建模和控制 - 分析车辆开发的安全框架和当前行业实践 课程的最终项目将要求您开发控制代码，以在CARLA模拟环境中导航自驾车。您将构建设计车辆的纵向和横向动态模型，并使用Python创建控制器，以调节速度和路径跟踪性能。您将测试控制设计的极限，并学习在车辆性能极限驾驶中的挑战。 本课程为高级课程，适合具备机械工程、计算机电气工程或机器人学背景的学习者。成功完成本课程，您需要具备Python 3.0的编程经验、线性代数（矩阵、向量、矩阵乘法、秩、特征值和特征向量及其逆）、统计（高斯概率分布）、微积分和物理（力、力矩、惯性、牛顿定律）的基础知识。 此外，您需要具备一些硬件和软件规格，以有效运行CARLA模拟器：64位Windows 7（或更高版本）或Ubuntu 16.04（或更高版本），四核Intel或AMD处理器（2.5 GHz或更快），NVIDIA GeForce 470 GTX或AMD Radeon 6870 HD系列卡或更高，8 GB RAM，以及OpenGL 3或更高（对于Linux计算机）。 课程大纲： - 模块0：欢迎来到自驾车专业化！ 介绍专业化的主要概念和布局，并讨论过去二十年来该领域的重大进展。 - 模块1：自主驾驶的要求 介绍自驾车面临的复杂问题及其语言和结构。 - 模块2：自驾车硬件和软件架构 描述常用的系统架构及其在成本、可靠性、性能和复杂性方面的权衡。 - 模块3：自主车辆的安全保障 评估在公共道路上保证自驾车安全运行的挑战和方法。 - 模块4：车辆动态建模 定义车辆在转向、油门和刹车命令下的运动模型，进而设计车辆控制器和运动规划。 - 模块5：车辆纵向控制 学习如何跟踪固定路径上的速度配置，开发适用于大多数驾驶条件的基线控制器。 - 模块6：车辆横向控制 学习如何通过几何和动态模型预测控制方法确保准确跟踪固定路径。 - 模块7：整合所有内容 通过自驾车模拟器实践纵向和横向控制设计，跟踪预定义路径并优化圈速。 课程将结合理论与实践，提供系统的自驾车知识和技能提升机会。
Name:Module 0: Welcome to the Self-Driving Cars Specialization!
Description:This module will introduce you to the main concepts and layout of the specialization and discusses the major advances made in the field over the last two decades, highlighting the most recent progress made by major players in terms of safety and performance metrics, where available.
Name:Module 1: The Requirements for Autonomy
Description:Self-driving cars present an extremely rich and inter-disciplinary problem. This module introduces the language and structure of the problem definition, defining the most salient elements of the driving task and the driving environment.
Name:Module 2: Self-Driving Hardware and Software Architectures
Description:System architectures for self-driving vehicles are extremely diverse, as no standardized solution has yet emerged. This module describes both the hardware and software architectures commonly used and some of the tradeoffs in terms of cost, reliability, performance and complexity that constrain autonomous vehicle design.
Name:Module 3: Safety Assurance for Autonomous Vehicles
Description:As the self-driving domain matures, the requirement for safety assurance on public roads become more critical to self-driving developers. You will evaluate the challenges and approaches employed to date to tackle the immense challenge of assuring the safe operation of autonomous vehicles in an uncontrolled public road driving environment.
Name:Module 4: Vehicle Dynamic Modeling
Description:The first task for automating an driverless vehicle is to define a model for how the vehicle moves given steering, throttle and brake commands. This module progresses through a sequence of increasing fidelity physics-based models that are used to design vehicle controllers and motion planners that adhere to the limits of vehicle capabilities.
Name:Module 5: Vehicle Longitudinal Control
Description:Longitudinal control of an autonomous vehicle involves tracking a speed profile along a fixed path, and can be achieved with reasonable accuracy using classic control techniques. This week, you will learn how to develop a baseline controller that is applicable for a significant subset of driving conditions, which include most non-evasive or highly-dynamic motions.
Name:Module 6: Vehicle Lateral Control
Description:This week, you will learn about how lateral vehicle control ensures that a fixed path through the environment is tracked accurately. You will see how to define geometry of the path following control problem and develop both a simple geometric control and a dynamic model predictive control approach.
Name:Module 7: Putting it all together
Description:For the last week of the course, now you will get hands on with a simulation of an autonomous vehicle that requires longitudinal and lateral vehicle control design to track a predefined path along a racetrack with a given speed profile. You are encouraged to modify the speed profile and/or path to improve their lap time, without any requirement to do so. Work and play!
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: - Understand commonly used hardware used for self-driving cars - Identify the main components of the self-driving software stack - Program vehicle modelling and control - Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
