1. New and improved PnC tool chain
(1) Support for local PnC simulation: Apollo 8.0 provides a local debugging function for PnC simulation testing. Developers can simulate vehicle driving and reproduce various scenarios locally through the Dreamview simulator.
(2) Support cloud-based scene creation and management: Apollo Studio provides cloud-based simulation scene management capabilities. Developers can freely create scenes and obstacles that meet their own test simulation requirements, and can classify and manage the scenes. (3) Support one-click download: In Apollo 8.0, Dreamview introduced a Studio plug-in. Through the plug-in, developers can download scenes and dynamic models in Studio locally, more than doubling the PnC debugging efficiency.
2. Convenient visualization tool for perception results
Apollo 8.0 provides a perception result visualization tool for the perception module, which supports the simultaneous visualization of the raw data and boundingbox of Lidar and Camera detection results, making it convenient for developers to view and debug model detection effects locally in a timely manner.
3. Dreamview Configuration Center
As common resources, in order to facilitate developers' use and access, we integrated data packages, scene sets, and dynamic models into the Dreamview configuration center in Apollo 8.0. Developers can download data packages, scene sets, and dynamic models through the configuration center without having to pull them in the local terminal wget, which greatly reduces the cost of getting started for novice developers. In 8.0, we provide two typical data packages demo_3.5 and sensor_rgb, which contain more comprehensive channel messages, making it easier for developers to view perception effects and PnC effects.
4. Documentation
In order to enable more novice developers to quickly learn Apollo, and to enable more experienced developers to quickly and conveniently find module source code analysis related to them, in Apollo 8.0, we optimized and reconstructed the technical documentation of Apollo Github and the Apollo community official website.
1. Clearer Apollo Github open source documentation
Apollo Github open source documentation mainly provides detailed source code interpretation for senior developers who want to study the source code and conduct extended development. Compared with the previous Github document classification, it is more vague, and it is more difficult for developers to find the documents they need. In version 8.0, we have restructured the Apollo Github document structure and classified the original documents according to the autonomous driving module. Developers can directly enter and search according to the modules they want to know [Installation Instructions] [Quick Start] [CyberRT] [Localization] [Perception] [Prediction] [Planning] [Decider] [Control], etc.
Github document address: https://github.com/ApolloAuto/apollo/tree/master/docs
2. More comprehensive Apollo community official website documentation
The Apollo community official website documents mainly provide novice developers with an introduction to Apollo, as well as practical instructions for on-board and on-vehicle scenarios, so that novice developers can quickly understand Apollo and get started. In 8.0, we optimized the structure of the community official website documents, starting from the developer's usage scenarios, and providing application practice case guidance and extended development guidance for different scenarios.
Apollo 8.0 has been comprehensively upgraded from two important aspects: "new architecture" and "new capabilities". It has been improved based on the actual needs of developers to help developers better and faster familiarize themselves with and use the Apollo open platform. The launch of Apollo open platform 8.0 has once again made the Apollo open platform take a big step forward in engineering usability, reducing the threshold of operational difficulty and cost, allowing more developers to easily and conveniently get started with the Apollo open platform and devote themselves to the field of autonomous driving technology.
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