自动驾驶模拟:用Python开发自动驾驶汽车

了解如何使用Python、PyGame和NEAT构建自动驾驶汽车模拟，欢迎来到自动驾驶模拟:用Python开发自动驾驶汽车课程。这基本上是一个广泛的基于项目的课程，在该课程中，您将一步一步地全面了解如何使用Python编程语言和Python库(如Pygame和NEAT)构建具有自动驾驶功能的自动驾驶车辆模拟，其中Pygame将用于创建模拟环境的视觉和现实表示，而NEAT(代表增强拓扑的神经进化)将用于训练神经网络来控制和设计自动驾驶行为。神经网络接受汽车传感器的输入。此外，神经网络还将通过进化算法随着时间的推移进行学习和适应，提高汽车的驾驶性能和决策技能。在介绍环节中，您将学习自动驾驶汽车的基本原理，了解其背后的技术，并了解其工作原理。然后，在学习了基本概念之后，您将逐步被引导设置所有必要的工具，例如Visual Studio Code IDE、安装Python和其他相关工具。在进入项目之前，将有一个基本的python培训会议，在那里你将学习Python中你需要知道和掌握的所有重要概念，以便为即将到来的项目做准备。基本Python培训课程是可选的，因为该课程仅面向那些对Python编程技能不太有信心的人。在基本的Python培训课程中，您将学习不同的数据类型或变量，如何构建函数并将参数传递给函数，如何构建类，以及Pygame的基础知识。然后，一旦完成基本的Python培训课程，您将进入项目，在项目中，您将一步一步地全面了解如何从头开始构建具有高级自动驾驶功能的自动驾驶汽车模拟。一旦项目已经建立，我们将进行测试，不仅测试代码是否工作，而且测试代码的输出是否是我们期望得到的。将测试三个主要目标，即汽车的决策能力、传感器集成和碰撞预防。首先，我们需要问自己这个问题。我们为什么要学习如何建立一个自动驾驶汽车模拟器？了解像特斯拉这样的汽车的自动驾驶功能是如何工作的可能会非常有趣，显然该系统非常复杂，有点难以理解，但如果你有机会从更简单的角度了解自动驾驶机制，那会怎么样，这正是你在本课程中要学习的内容。下一个问题可能是，创造我自己的像特斯拉一样的真正的自动驾驶汽车几乎不可能，也绝对不现实，这将花费你很多钱，即使你有资金，你可能也没有合适的技能和知识。在某种程度上，这实际上是正确的，我们不会建造一辆具有自动驾驶功能的全新汽车，相反，我们可能会建造一个非常酷的自动驾驶游戏，或者建造一个自动物体模拟器。Self Driving Simulations: Develop Autonomous Car with Python

以下是我们将从本课程中学到的内容:学习自动驾驶汽车的基本概念，了解自动驾驶汽车背后的技术，以及它的功能和局限性学习和理解自动驾驶汽车如何工作基本Python培训课程，为您更好地准备自动驾驶汽车项目使用Pygame和that构建自动驾驶汽车模拟项目学习如何使用GIMP绘画工具构建和设计汽车轨迹测试自动驾驶汽车，以确保汽车具有良好的决策能力、可靠的传感器集成和有效的碰撞预防系统

由Christ Raharja创造
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子教学|语言:英语|时长:30节课(4小时24分钟)|大小:1.34 GB 含课程文件

你会学到什么
学习自动驾驶汽车的基本概念，了解其背后的技术，以及其功能和局限性
学习和理解自动驾驶汽车的自动驾驶功能
使用PyGame和NEAT从头开始构建自动驾驶汽车模拟项目
测试自动驾驶汽车以确保汽车具有良好的决策能力、可靠的传感器集成和有效的碰撞预防

要求
不需要以前制造自动驾驶汽车的经验
愿意在代码中尝试很多错误

Learn how to build self driving autonomous car simulation using Python, PyGame, and NEAT

What you’ll learn
Learning the fundamental concepts of self driving autonomous car, getting to know technologies behind it, as well as its capabilities and limitations
Learning and understanding how self driving feature in autonomous car works
Building self driving autonomous car simulation project using PyGame and NEAT from scratch
Testing the self driving autonomous cars to ensures the car has a good decision making ability, solid sensor integrations, and effective collision prevention

Requirements
No previous experience in building self driving autonomous car is required
Willingness to do a lot of trial errors in the code

Description
Welcome to Self-Driving Simulations: Developing Autonomous Cars with Python course. This is basically an extensive project based course where you will be fully guided step by step on how to build autonomous vehicle simulation with self driving feature using Python programming language alongside with Python libraries, such as Pygame and NEAT where Pygame will be utilised to create a visual and realistic representation of the simulated environment while NEAT which stands for NeuroEvolution of Augmenting Topologies will be used to train the neural networks to control and design self driving behaviour. The neural network takes input from the car’s sensors. In addition, the neural network will also learn and adapt over time through evolutionary algorithms, improving the car’s driving performance and decision-making skills. In the introduction session, you will be learning the basic fundamentals of autonomous car, getting to know technologies behind it as well as understanding how it works. Then, after learning the basic concepts, you will be guided step by step to set up all necessary tools, for instance Visual Studio Code IDE, installing Python, and other relevant tools. Before getting into the project, there will be a basic python training session where you will learn all important concepts in Python that you need to know and master to prepare you for the upcoming project. The basic Python training session is optional since the session was created and intended only for those who are not very confident with their Python programming skills. In the basic Python training session, you will learn different data types or variables, how to build functions and pass down parameters to the function, how to build class, and basic fundamentals of Pygame. Then, once the basic Python training session has been completed, you will move to the project where you will be fully guided step by step on how to build an autonomous car simulation with advanced self driving features from scratch. Once the project has been built, we are going to be conducting testing, not only to test if the code works but also to test if the output of the code is what we expected to get. There will be three main objectives that will be tested, those are the car’s decision making ability, sensor integration, and collision prevention.First of all, we need to ask ourselves this question. Why should we learn how to build an autonomous car simulator? It might be very interesting to learn how the self-driving feature in cars like Tesla works, obviously the system is very complicated and a bit difficult to be understood but what if you have a chance to learn the self driving mechanism from a more simple perspective and that’s exactly what you are going to learn in this course. The next follow up question might potentially be, well it is near impossible and definitely unrealistic to create my own real autonomous vehicle like Tesla, it will cost you a lot and even if you have the funding, you might not have the right skill sets and knowledge to begin with. That is actually true to some extent, we are not going to build a brand new car with self -driving features, instead, we can potentially build a very cool self-driving game or maybe build an autonomous object simulator.Below are things that we are going to learn from this course:Learning the fundamental concepts of self driving autonomous car, getting to know technologies behind it, as well as its capabilities and limitationsLearning and understanding how autonomous car worksBasic Python training session which prepares you better for the autonomous car projectBuilding self driving autonomous car simulation project using Pygame and NEATLearning how to build and design car track using GIMP painting toolTesting the self driving autonomous cars to ensures the car has a good decision making ability, solid sensor integrations, and effective collision prevention system