Home Technology peripherals AI Huawei Cloud and a number of companies released an action initiative: jointly build an open industrial ecosystem for autonomous driving

Huawei Cloud and a number of companies released an action initiative: jointly build an open industrial ecosystem for autonomous driving

Apr 11, 2023 am 11:25 AM
Autopilot

On November 18, 2022, the "Automotive Industry Digital Intelligence Upgrade Summit Forum" was successfully held in Suzhou under the guidance of the China Association of Automobile Manufacturers and the China Society of Automotive Engineers and hosted by Huawei Cloud and the Machinery Industry Information Research Institute. At the meeting, Huawei Cloud teamed up with dozens of autonomous driving industry partners such as Great Wall Motors, BYD, Human Horizons, NavInfo, Stardust Data, Saimu Technology, Yingyun Technology, and Luokung Technology to launch the "Co-Create Open Autonomous Driving Industry Chain Action" Initiative" (hereinafter referred to as the "Initiative"), starting from the four directions of autonomous driving ecological construction, standard system construction, network security and data security, and industrial chain technology exchanges, jointly strengthen the construction of my country's autonomous driving open industrial chain and help accelerate the acceleration of autonomous driving technology Landed. Leaders from the Machinery Industry Information Research Institute, China Association of Automobile Manufacturers, and China Society of Automotive Engineers jointly witnessed the release of the initiative.

Huawei Cloud and a number of companies released an action initiative: jointly build an open industrial ecosystem for autonomous driving

Autonomous driving has entered the deep water area, and cultivating an open industrial ecology is the key

In recent years, the Ministry of Industry and Information Technology, the National Development and Reform Commission and other central ministries and commissions have successively Introduce policies to list autonomous driving as one of the key tasks, and propose to accelerate the construction of a regulatory system for autonomous vehicles. In addition, more than 40 provinces and cities, including Beijing, Shanghai, Shenzhen, and Guangzhou, have introduced detailed management measures to safeguard the development of the domestic autonomous driving industry from a top-level design. With the support of continuously optimized industrial policies, my country's autonomous driving industry has entered a new stage of deepened development.

However, the development of autonomous driving is definitely not the output of a single function, but has a complex industrial chain and supply chain, involving data collection, data storage, data processing, data mining, data annotation, model training, and simulation. In a series of aspects such as testing, how to integrate multiple resources and promote the synergy of enterprises in the autonomous driving industry chain has become a difficult problem facing the industry.

Xu Donghai, deputy chief engineer of the China Association of Automobile Manufacturers, said that to accelerate the development of my country's autonomous driving industry, we must adhere to the principle of openness and sharing, enhance independent innovation capabilities, and promote the upstream and downstream industrial chains of autonomous driving such as chips, hosts, algorithms, and platforms. Cooperate to build an open and win-win industry.

Huawei Cloud Autonomous Driving Cloud Platform debuts, data-driven R&D, manufacturing, and distribution throughout the entire process

The greatest value of sharing and openness lies in solving the problem of scenarios and data in quantity and quality supply issues, which can directly allow technology to conduct trials and errors on massive scenarios and data, speeding up the adaptation of autonomous driving to different scenarios.

Huawei Cloud joins hands with partners from all walks of life to build an open ecosystem of collaborative cooperation, doubled benefits, and mutual benefit. At present, partners such as NavInfo, Stardust Data, and Saimu Technology have quickly integrated their respective advantages and capabilities based on the Huawei Cloud autonomous driving open API to build an ecologically open autonomous driving R&D platform. In addition, Huawei Cloud has also opened Ploto, the open source code library for autonomous driving R&D platform solutions, to support the deployment and docking of professional software service providers, significantly saving the implementation time of autonomous driving projects.

Zhang Xiuzheng, President of Huawei Cloud China, said that in order to strengthen the prosperity of the autonomous driving ecosystem, Huawei Cloud will provide an open R&D platform for all parties in the industry and upgrade various solutions to help autonomous driving companies accelerate their growth and become bigger. Be stronger.

Huawei Cloud and a number of companies released an action initiative: jointly build an open industrial ecosystem for autonomous driving

Integrating Huawei's more than 30 years of technology and experience accumulation, Huawei Cloud provides eight key capabilities for car companies, including: digital transformation experience, intelligent manufacturing capabilities, and globalization experience , cloud-cloud collaboration capabilities, autonomous driving solutions; safety compliance solutions, underlying technology innovation, and open ecological cooperation capabilities.

In addition, Huawei Cloud has also released a "1 3 M N" global automotive industry cloud infrastructure layout, uniting with mainstream tool chain vendors such as data annotation, training, simulation and graph merchants to build a safe, compliant, open Decoupled autonomous driving cloud platform.

Currently, Chinese Horizons (Gaohe Automobile) has joined hands with Huawei Cloud to build a dedicated cloud platform for autonomous driving, which can significantly improve research and development efficiency and reduce costs while meeting data compliance.

From bamboo cane shoes to horse-drawn bicycles, from traditional horse-drawn carriages to steam, electrification and even the evolution of intelligence, human beings have never stopped trying and exploring travel methods. Currently, autonomous driving is still the benchmark for industry development. Facing the future, Huawei Cloud will continue to deeply explore the autonomous driving industry, and work hand in hand with partners with full-process digital capabilities and open ecological services to jointly promote the rapid development of the autonomous driving field and help China's automobile industry become bigger and stronger.

The above is the detailed content of Huawei Cloud and a number of companies released an action initiative: jointly build an open industrial ecosystem for autonomous driving. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Why is Gaussian Splatting so popular in autonomous driving that NeRF is starting to be abandoned? Why is Gaussian Splatting so popular in autonomous driving that NeRF is starting to be abandoned? Jan 17, 2024 pm 02:57 PM

Written above & the author’s personal understanding Three-dimensional Gaussiansplatting (3DGS) is a transformative technology that has emerged in the fields of explicit radiation fields and computer graphics in recent years. This innovative method is characterized by the use of millions of 3D Gaussians, which is very different from the neural radiation field (NeRF) method, which mainly uses an implicit coordinate-based model to map spatial coordinates to pixel values. With its explicit scene representation and differentiable rendering algorithms, 3DGS not only guarantees real-time rendering capabilities, but also introduces an unprecedented level of control and scene editing. This positions 3DGS as a potential game-changer for next-generation 3D reconstruction and representation. To this end, we provide a systematic overview of the latest developments and concerns in the field of 3DGS for the first time.

How to solve the long tail problem in autonomous driving scenarios? How to solve the long tail problem in autonomous driving scenarios? Jun 02, 2024 pm 02:44 PM

Yesterday during the interview, I was asked whether I had done any long-tail related questions, so I thought I would give a brief summary. The long-tail problem of autonomous driving refers to edge cases in autonomous vehicles, that is, possible scenarios with a low probability of occurrence. The perceived long-tail problem is one of the main reasons currently limiting the operational design domain of single-vehicle intelligent autonomous vehicles. The underlying architecture and most technical issues of autonomous driving have been solved, and the remaining 5% of long-tail problems have gradually become the key to restricting the development of autonomous driving. These problems include a variety of fragmented scenarios, extreme situations, and unpredictable human behavior. The "long tail" of edge scenarios in autonomous driving refers to edge cases in autonomous vehicles (AVs). Edge cases are possible scenarios with a low probability of occurrence. these rare events

Choose camera or lidar? A recent review on achieving robust 3D object detection Choose camera or lidar? A recent review on achieving robust 3D object detection Jan 26, 2024 am 11:18 AM

0.Written in front&& Personal understanding that autonomous driving systems rely on advanced perception, decision-making and control technologies, by using various sensors (such as cameras, lidar, radar, etc.) to perceive the surrounding environment, and using algorithms and models for real-time analysis and decision-making. This enables vehicles to recognize road signs, detect and track other vehicles, predict pedestrian behavior, etc., thereby safely operating and adapting to complex traffic environments. This technology is currently attracting widespread attention and is considered an important development area in the future of transportation. one. But what makes autonomous driving difficult is figuring out how to make the car understand what's going on around it. This requires that the three-dimensional object detection algorithm in the autonomous driving system can accurately perceive and describe objects in the surrounding environment, including their locations,

Have you really mastered coordinate system conversion? Multi-sensor issues that are inseparable from autonomous driving Have you really mastered coordinate system conversion? Multi-sensor issues that are inseparable from autonomous driving Oct 12, 2023 am 11:21 AM

The first pilot and key article mainly introduces several commonly used coordinate systems in autonomous driving technology, and how to complete the correlation and conversion between them, and finally build a unified environment model. The focus here is to understand the conversion from vehicle to camera rigid body (external parameters), camera to image conversion (internal parameters), and image to pixel unit conversion. The conversion from 3D to 2D will have corresponding distortion, translation, etc. Key points: The vehicle coordinate system and the camera body coordinate system need to be rewritten: the plane coordinate system and the pixel coordinate system. Difficulty: image distortion must be considered. Both de-distortion and distortion addition are compensated on the image plane. 2. Introduction There are four vision systems in total. Coordinate system: pixel plane coordinate system (u, v), image coordinate system (x, y), camera coordinate system () and world coordinate system (). There is a relationship between each coordinate system,

SIMPL: A simple and efficient multi-agent motion prediction benchmark for autonomous driving SIMPL: A simple and efficient multi-agent motion prediction benchmark for autonomous driving Feb 20, 2024 am 11:48 AM

Original title: SIMPL: ASimpleandEfficientMulti-agentMotionPredictionBaselineforAutonomousDriving Paper link: https://arxiv.org/pdf/2402.02519.pdf Code link: https://github.com/HKUST-Aerial-Robotics/SIMPL Author unit: Hong Kong University of Science and Technology DJI Paper idea: This paper proposes a simple and efficient motion prediction baseline (SIMPL) for autonomous vehicles. Compared with traditional agent-cent

This article is enough for you to read about autonomous driving and trajectory prediction! This article is enough for you to read about autonomous driving and trajectory prediction! Feb 28, 2024 pm 07:20 PM

Trajectory prediction plays an important role in autonomous driving. Autonomous driving trajectory prediction refers to predicting the future driving trajectory of the vehicle by analyzing various data during the vehicle's driving process. As the core module of autonomous driving, the quality of trajectory prediction is crucial to downstream planning control. The trajectory prediction task has a rich technology stack and requires familiarity with autonomous driving dynamic/static perception, high-precision maps, lane lines, neural network architecture (CNN&GNN&Transformer) skills, etc. It is very difficult to get started! Many fans hope to get started with trajectory prediction as soon as possible and avoid pitfalls. Today I will take stock of some common problems and introductory learning methods for trajectory prediction! Introductory related knowledge 1. Are the preview papers in order? A: Look at the survey first, p

Let's talk about end-to-end and next-generation autonomous driving systems, as well as some misunderstandings about end-to-end autonomous driving? Let's talk about end-to-end and next-generation autonomous driving systems, as well as some misunderstandings about end-to-end autonomous driving? Apr 15, 2024 pm 04:13 PM

In the past month, due to some well-known reasons, I have had very intensive exchanges with various teachers and classmates in the industry. An inevitable topic in the exchange is naturally end-to-end and the popular Tesla FSDV12. I would like to take this opportunity to sort out some of my thoughts and opinions at this moment for your reference and discussion. How to define an end-to-end autonomous driving system, and what problems should be expected to be solved end-to-end? According to the most traditional definition, an end-to-end system refers to a system that inputs raw information from sensors and directly outputs variables of concern to the task. For example, in image recognition, CNN can be called end-to-end compared to the traditional feature extractor + classifier method. In autonomous driving tasks, input data from various sensors (camera/LiDAR

FisheyeDetNet: the first target detection algorithm based on fisheye camera FisheyeDetNet: the first target detection algorithm based on fisheye camera Apr 26, 2024 am 11:37 AM

Target detection is a relatively mature problem in autonomous driving systems, among which pedestrian detection is one of the earliest algorithms to be deployed. Very comprehensive research has been carried out in most papers. However, distance perception using fisheye cameras for surround view is relatively less studied. Due to large radial distortion, standard bounding box representation is difficult to implement in fisheye cameras. To alleviate the above description, we explore extended bounding box, ellipse, and general polygon designs into polar/angular representations and define an instance segmentation mIOU metric to analyze these representations. The proposed model fisheyeDetNet with polygonal shape outperforms other models and simultaneously achieves 49.5% mAP on the Valeo fisheye camera dataset for autonomous driving

See all articles