With the release of Apollo3.0, we opened the "UdacityXApollo Autonomous Driving Introductory Course" for free to more developers interested in autonomous driving, to help developers quickly get started with autonomous driving. I don’t know how the developers are learning now. Up? Here we will organize the main content of each course to provide learning directions for those who are ready to start learning Apollo. The first lesson, an overview of driverless driving This lesson will take you to a comprehensive understanding of Apollo and unmanned driving from the aspects of unmanned vehicle operation, Apollo open platform architecture, reference vehicles and hardware platforms, open source software architecture, cloud services, etc., and open a learning path for unmanned driving. 1 Comprehensive understanding of the main modules of autonomous driving The Apollo technical framework consists of four levels: reference vehicle platform, reference hardware platform, open source software platform, and cloud service platform. The main modules include high-precision maps, positioning, perception, prediction, planning, control and other modules, and subsequent courses will also focus on these modules. In the high-precision map course, we will introduce you to the core modules of unmanned vehicles. The high-precision map supports almost all other modules of the software stack, especially positioning, perception, planning and decision-making. In the positioning course, we will discuss how the vehicle confirms its location. The vehicle uses laser and radar data to compare the sensing content of these sensors with high-resolution maps. This comparison enables the vehicle to locate with single-digit centimeter accuracy. The perception course will take everyone to understand how driverless cars perceive the world. Deep learning is an important and powerful perception tool. Convolutional neural networks form the branch of deep learning, which is essential for perception tasks, such as classification, detection and segmentation. The prediction course will outline several different ways to predict how other vehicles or pedestrians might move. A method called a recurrent neural network can track the movement of other objects over time and use this time series data to predict the future. Advanced reading and learning: "Overview of Autonomous Driving Industry and Apollo" 2 Understand how unmanned vehicles work Unmanned vehicles include five core components, computer vision, sensor fusion, positioning, path planning, and control. How driverless cars work We use computer vision and sensor fusion to obtain a rich picture of our location in the world, use positioning to determine our precise location in this world, and then use path planning to draw a path through the world to the destination. Control to turn the steering wheel, open the accelerator, and then apply the brakes to drive along the trajectory and finally move the vehicle. Essentially, all other unmanned vehicles are more complex implementations of these core functions. 3 Open software stack The Apollo open software layer is divided into three sub-layers, real-time operating system, runtime framework, and application module layer. Real-time operating system (RTOS), which can ensure the completion of specific tasks within a given time. "Real-time" refers to the operating system of the unmanned vehicle, which can perform calculations, analyze and perform corresponding operations in a timely manner. It is the external data collected by the vehicle sensors After a short period of time. Real-time performance is an important requirement to ensure system stability and driving safety. ApolloRTOS is the result of the combination of the UbuntuLinux operating system and the Apollo kernel. The original Ubuntu system is not a real-time operating system. By adding the kernel designed by Apollo, we can make it an RTOS. The runtime framework is the operating environment of Apollo, a customized version of ROS, that is, the robot operating system. In order to adapt ROS to unmanned vehicles, the Apollo team improved the function and performance of shared memory, decentralization and data compatibility. Core content-application module: The Apollo software platform has various modules, including MAP engine, positioning, perception, planning, control, end-to-end driving, and human machine interface (HMI). Each module has its own algorithm library, and the relationship between them is very complicated. We will study these modules and their associated methods throughout the course. 1. Decomposition: biodegradable excess body fat, flat meat, all kinds of stone decomposition, promote the decomposition of food digestion and absorption. Enzyme Essence Liquid,Liquid Promote Digestion And Absorption,Orange Enzyme Liquid For Health Guangdong ganzhou , https://www.tlqcjs.com
This course starts from the core technology modules of autonomous driving, explains the module knowledge of high-precision maps, positioning, perception, prediction, planning, control, etc., to help zero-based students understand the basic principles and overall framework of unmanned driving, and initially grasp and use Apollo automation The autonomous driving algorithm used by the driving open platform. 
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