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私たちは、モビリティ自動化技術に取り組んでいる開発者と愛好家のコミュニティです。私たちのイベントは、安全で信頼性の高い自動運転車の開発を加速するための知識、経験、ベスト プラクティスを共有することを目的としています。オープンソースの Autoware と ROS に関する学術的進歩、企業、およびテクノロジの成長するエコシステムは、このテクノロジ スタックの研究から商用生産への道を提供します。


Practical Memory Pool Based Allocators For Modern C++ - Misha Shalem - CppCon 2020

Practical Memory Pool Based Allocators For Modern C++ - Misha Shalem - CppCon 2020 --- Runtime-deterministic memory allocations are a crucial aspect of any safety-critical real-time system. One of the simplest and widely adopted allocation mechanisms used in such systems is a memory pool with fixed block sizes. Unfortunately, the need to know the exact sizes of the memory blocks makes any practical usage of memory pools with standard C++ allocator-based approach rather problematic since users often “hide” real properties of allocations which are made under the hood. For example: STL’s node-based containers like 'std::map' as well as other standard mechanisms like 'std::promise' or 'std::allocate_shared'. Being a company which focuses on real-time safety-critical applications, we still see a significant value in keeping compatibility with the standard allocator model as well as in following common conventions which are familiar to every C++ developer. This talk presents an approach which uses a combination of a memory allocator implementation which instruments the code, and an external LLVM-based tool which extracts the instrumentation information and generates static memory pool definitions, allowing the allocator to switch from the heap to a memory pool without any further changes to the code. The presentation will walk through a simplest possible implementation of this approach. --- Misha Shalem C++ Architect, Apex.AI C++ developer with 16+ years of experience. Currently holds position of C++ Architect at Apex.AI, Palo Alto, CA --- Streamed & Edited by Digital Medium Ltd - *-----* Register Now For CppCon 2022: *-----*
ROS World 2020: Autoware Parallel Session

ROS World 2020: Autoware Parallel Session

Autoware.Auto is an open-source autonomous driving stack built on ROS 2. This track aims at providing an overview of the capabilities to the ROS 2 community. Autonomous Valet Parking The Autoware Foundation has completed its first software demonstration with Autoware.Auto - Autonomous Valet Parking! But what is Autonomous Valet Parking and how did we achieve this milestone? How close are we to roaming the streets with a fully autonomous vehicle? What's next for the Foundation? Get the answers to the questions and more! Localization and State Estimation in Autoware.Auto This talk is about algorithms for localization and state estimation implemented in Autoware.Auto. It explains what localization and state estimation are, goes into details of the typical methods used to implement these concepts, as well as presents architecture decisions specific to the Autoware.Auto implementation. Object Detection and Controls in Autoware.Auto This talk gives a holistic overview over the 3D object detection stack available in Autoware.Auto. In addition it provides insight into the Model Predictive Controller and the Pure Pursuit Controller in Autoware.Auto. Behavioral and Motion Planning in Autoware.Auto This talk provides details about the architecture and algorithms for the planning module in Autoware.Auto. It explains how the behavior planner makes decisions from information provided by other modules in Autoware.Auto and how the motion planner plans the trajectory to a given goal. 200mph ROS - How Indy Autonomous Speeds Open Source University teams are exploring high speed autonomous driving via Indy Autonomous Challenge. We discuss technical challenges and how the open source community is rallying with autonomous driving stack of Autoware.Auto, ROS 2, OpenCV, Eclipse CycloneDDS with iceoryx and Zenoh V2X. #iac2021
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