China Autonomous Driving Algorithm Research Report, 2023
  • Jan.2023
  • Hard Copy
  • USD $4,200
  • Pages:220
  • Single User License
    (PDF Unprintable)       
  • USD $4,000
  • Code: WWJ003
  • Enterprise-wide License
    (PDF Printable & Editable)       
  • USD $6,000
  • Hard Copy + Single User License
  • USD $4,400
      

Autonomous Driving Algorithm Research: BEV Drives Algorithm Revolution, AI Large Model Promotes Algorithm Iteration

The core of the autonomous driving algorithm technical framework is divided into three parts: environment perception, decision planning, and control execution.

Environment perception: convert sensor data into machine language of the scenario where the vehicle is located, which can include object detection, recognition and tracking, environment modeling, motion estimation, etc.;

Decision planning: Based on the output results of perception algorithm, the final behavioral action instructions are given, including behavioral decisions (vehicle following, stopping and overtaking), action decisions (car steering, speed, etc.), path planning, etc.;

Control actuation: according to the output results of decision-making level, the underlying modules are mobilized to issue instructions to the core control components such as accelerator and brake, and promote vehicle to drive according to the planned route.

BEV drives algorithm revolution
In recent years, BEV perception has received extensive attention. BEV model mainly provides a unified space to facilitate the fusion of various tasks and sensors. It has following advantages:

BEV unifies the multimodal data processing dimension and makes multimodal fusion easier
The BEV perception system converts the information obtained from multiple cameras or radars to a bird's-eye view, and then do tasks such as object detection and instance segmentation, which can more intuitively display the dimension and direction of objects in BEV space.

In 2022, Peking University & Ali proposed a fusion framework of LiDAR and vision - BEVFusion. The processing of radar point clouds and image processing are carried out independently, using neural networks to encode, project to a unified BEV space, and then merge the two in BEV space.

自驾算法 1.png

Realize timing information fusion and build 4D space
In the 4D space, the perception algorithm can better complete the perception tasks such as speed measurement, and can transmit the results of motion prediction to the decision and control module.

PhiGent Robotics proposed BEVDet4D in 2022, which is a version based on BEVDet to increase timing fusion. BEVDet4D extends BEVDet by retaining intermediate BEV features of past frames, and then fuses features by aligning and splicing with the current frame, so that time clues can be obtained by querying two candidate features.

自驾算法 2.png

Imagine occluded objects to realize object prediction

In the BEV space, the algorithm can predict the occluded area based on prior knowledge, and imagine whether there are objects in the occluded area.

FIERY, proposed by Wayve in cooperation with the University of Cambridge in 2021, is an end-to-end road dynamic object instance prediction algorithm that does not rely on high-precision maps and is only based on aerial views of monocular cameras.

自驾算法 3.png

Promoting development of an end-to-end autonomous driving framework

In the BEV space, perception and prediction can be directly optimized end-to-end through neural networks in a unified space, and the results can be obtained at the same time. Not only the perception module, but also the BEV-based planning decision-making module is also the direction of academic research.
 
In 2022, autonomous driving team of Shanghai Artificial Intelligence Laboratory and the team of associate professor Yan Junchi of Shanghai Jiao Tong University collaborated on paper ST-P3 to propose a spatiotemporal feature learning solution that can simultaneously provide a set of more representative features for perception, prediction and planning tasks.

自驾算法 4.png

AI large model drives algorithm iteration

After 2012, deep learning algorithms are widely applied in autonomous driving field. In order to support larger and more complex AI computing needs, AI large models with the characteristics of "huge data, huge computing power, and huge algorithms" were born, which accelerated the iteration speed of algorithms.

Large Model and Intelligent Computing Center

In 2021, HAOMO.AI launched research and landing attempts on large-scale Transformer model, and then gradually applied it on a large scale in projects including multi-modal perception data fusion and cognitive model training. In December 2021, HAOMO.AI released autonomous driving data intelligence system MANA (Chinese name "Snow Lake"), which integrates perception, cognition, labeling, simulation, computing and other aspects. In January 2023, HAOMO.AI together with Volcano Engine unveiled MANA OASIS, a supercomputing center with a total computing power of 670 PFLOPS. After deploying HAOMO.AI's training platform, OASIS can run various applications including cloud large-scale model training, vehicle-side model training, annotation, and simulation. With the help of MANA OASIS, the five major models of HAOMO.AI have ushered in a new appearance and upgrade.

自驾算法 5.png

In August 2022, based on Alibaba Cloud intelligent computing platform, Xpeng Motors built an autonomous driving intelligent computing center "Fuyao", which is dedicated to training of autonomous driving models. In October 2022, Xpeng also announced the introduction of Transformer large model.

自驾算法 6_副本.png

In November 2022, Baidu released Wenxin Big Model. Leveraging more than 1 billion parameters, it recognizes thousands of objects, helping to enlarge the scope of semantic recognition. At present, it is mainly used in three aspects: distance vision, multimodality and data mining.

自驾算法 7_副本.png

1. Overview of Autonomous Driving Algorithms
1.1 Overview of Autonomous Driving Algorithms 
1.1.1 Overview of Environment Perception Algorithms - Vision
1.1.2 Overview of Environment Perception Algorithms - LiDAR
1.1.3 Overview of Environment Perception Algorithms - Radar
1.1.4 Overview of Environment Perception Algorithms - Multi-Sensor Fusion
1.2 Overview of Decision Planning and Control Actuation Algorithms 
1.3 Development of Neural Networks 
1.4 Autonomous Driving Algorithm Supply Mode

2. Research on Chip Vendor Algorithm
2.1 Huawei
2.1.1 Smart Vehicle Solutions Department 
2.1.2 ADS Autonomous Driving Full-Stack Solution 
2.1.3 Core Algorithms 
2.1.4 Autonomous Driving Algorithm Development Plan and Ecological Partners
2.2 Horizon Robotics
2.2.1 Profile 
2.2.2 Cooperation Model 
2.2.3 On-board Computing Platform and Monocular Front-View Solution Algorithm 
2.2.4 Autonomous Driving Perception Algorithm Design 
2.2.5 Core Algorithm Model 
2.2.6 Pilot Assisted Driving Solution and Super Driving Solution Algorithm 
2.2.7 Software Open API 
2.2.8 Mass Production Results and Algorithm Planning 
2.2.9 Cooperation
2.3 Black Sesame
2.3.1 Profile 
2.3.2 Perception Algorithm 
2.3.3 Latest Algorithm Achievements
2.3.4 Shanhai Tool Chain 
2.3.5 Partners 
2.3.6 Cooperation
2.4 Genesys Microelectronics
2.5 Mobileye
2.5.1 Profile 
2.5.2 Object Recognition Technology 
2.5.3 Chip Algorithm Development Process 
2.5.4 Vision Algorithms 
2.5.5 Current Development and Cooperation
2.6 Qualcomm Arriver
2.6.1 Intro of Arriver 
2.6.2 Arriver Visual Perception Algorithm
2.7 NXP
2.8 NVIDIA
2.8.1 Profile 
2.8.2 Cooperation Model 
2.8.3 Autonomous Vehicle Software Stack 
2.8.4 Perception Algorithm 
2.8.5 Perception Algorithm Model 
2.8.6 Latest Cooperation and Partners

3. Research on Tier 1 & Tier 2 Algorithm
3.1 Momenta
3.1.1 Profile 
3.1.2 Core Technology and Products 
3.1.3 Application of Momenta Algorithm 
3.1.4 Cooperation
3.2 Nullmax
3.2.1 Profile 
3.2.2 Visual Perception Module and Product Landing Process 
3.2.3 Introduction to the Latest Visual Perception Algorithm
3.2.4 The Landing Process of Algorithm Products 
3.2.5 Cooperation and Development Plan
3.3 ArcSoft
3.3.1 Profile 
3.3.2 ADAS Technology 
3.3.3 BSD and AVM Technologies 
3.3.4 One-Stop Vehicle Vision Solution 
3.3.5 Recent Dynamics and Major Customers 
3.4 JueFX
3.4.1 Profile 
3.4.2 Visual Feature Fusion Positioning Solution 
3.4.3 Development History of BEV Perception Technology 
3.4.4 LiDAR Fusion Location Solution 
3.4.5 LiDAR-based Fusion Solution 
3.4.6 Cooperation
3.5 ThunderSoft
3.6 Holomatic
3.6.1 Profile 
3.6.2 HoloPilot and Its Main Algorithms 
3.6.3 HoloParking and Its Main Algorithms 
3.6.4 Middleware 
3.7 Enjoy Move
3.7.1 Profile 
3.7.2 Autonomous Driving Software 
3.7.3 Cooperation 
3.8 Haomo.ai
3.8.1 Profile 
3.8.2 Product Portfolio
3.8.3 Latest Dynamics
3.8.4 MANA system 
3.8.5 MANA System - Vision, LiDAR Perception Module 
3.8.5 MANA System - Fusion Sensing Module 
3.8.5 MANA System - Cognitive Module 
3.8.6 Evolution of Perception 
3.8.7 Evolution of Cognitive Abilities 
3.8.8 New Technology Practice 
3.8.9 Recent Algorithm Achievements
3.9 Huanyu Zhixing
3.9.1 Profile
3.9.2 Autonomous Driving Software 
3.9.3 Athena 5.0 
3.9.4 Development Achievements and Planning
3.10 Valeo
3.10.1 Profile
3.10.2 Typical Algorithm Models
3.11 StradVision
3.11.1 Profile 
3.11.2 Vision Product Category & Customers & Timeline 
3.11.3 Autonomous Driving Algorithm 
3.11.4 Development Trends of Vision Products

4. Algorithm Research of Emerging Automakers and OEMs  
4.1 Tesla 
4.1.1 Profile
4.1.2 Tesla Algorithm 
4.1.3 Multi-camera Fusion Algorithm 
4.1.4 Environment Awareness Algorithm 
4.1.5 Latest Planning and Decision-making Algorithm 
4.2 NIO 
4.2.1 Profile
4.2.2 Evolution of NIO Autonomous Driving System 
4.2.3 Comparison of NIO Pilot System and NAD System 
4.3 Li Auto
4.3.1 Profile
4.3.2 Intelligent Driving Route 
4.3.3 Algorithm History 
4.3.4 AD Max Intelligent Driving Algorithm Architecture 
4.3.5 Layout in Intelligent Driving 
4.3.6 Future Development Plan
4.4 Xpeng 
4.4.1 Profile
4.4.2 Algorithm and Autonomous Driving Ability Evolution Route 
4.4.3 Autonomous Driving Algorithm Architecture 
4.4.4 New Perception Architecture 
4.4.5 Data Collection, Labeling and Training 
4.5 Rising Auto 
4.5.1 Profile 
4.5.2 RISING PILOT 
4.5.3 Full Fusion Algorithm 
4.5.4 Full Fusion Algorithm: Application Effect
4.6 Leapmotor
4.6.1 Profile
4.6.2 Full Domain Self-Research 
4.6.3 Algorithm Capabilities and Future Planning
4.7 ZEEKR
4.7.1 Profile
4.7.2 ZEEKR's Mobileye Solution 
4.7.3 Cooperation between ZEEKR and Waymo and Self-Developed Algorithm Solution
4.8 BMW 
4.8.1 Profile
4.8.2 Algorithms for BMW 
4.8.3 Cooperation in Autonomous Driving
4.9 SAIC 
4.9.1 SAIC Motor Autonomous Driving Layout 
4.9.2 Introduction to Z-ONE Tech
4.9.3 Z-ONE Tech Computing Platform 
4.9.4 SAIC Artificial Intelligence Laboratory
4.10 General Motors 
4.10.1 General Motors Autonomous Driving Layout 
4.10.2 Introduction to Cruise 
4.10.3 Cruise perception Algorithm 
4.10.4 Cruise Decision Algorithm 
4.10.5 Cruise Autonomous Driving Development Tool Chain 
4.10.6 Cruise's Robotaxi and Future Plans

5. Research on Robtaxi Algorithm for L4 Autonomous Driving
5.1 Baidu Apollo 
5.1.1 Profile 
5.1.2 Driverless Technology Architecture History 
5.1.3 Introduction to Perception Algorithm 
5.1.4 Autonomous Vehicle Positioning Technology 
5.1.5 Latest Highlights Technology
5.2 Pony.ai
5.2.1 Profile 
5.2.2 Main Business and Business Model 
5.2.3 Core Technology and the Latest Autonomous Driving System Configuration 
5.2.4 Sensor Fusion Solution 
5.2.5 Cooperation
5.3 WeRide
5.3.1 Profile 
5.3.2 WeRide One 
5.3.3 Algorithm Modules for WeRide One 
5.3.4 Cooperation
5.4 Deeproute.ai
5.4 Deeproute.ai 
5.4.1 Profile
5.4.2 Technology 
5.4.3 Self-Developed Algorithm 
5.4.4 Cooperation and Latest Dynamics
5.5 QCraft
5.5.1 Profile 
5.5.2 Products 
5.5.3 Hyperfusion Perception Solution
5.5.4 Prediction Algorithm 
5.5.5 Planning Algorithm 
5.5.6 Classical Algorithm Model
5.5.7 Cooperation
5.6 UISEE Technology
5.6.1 Profile 
5.6.2 U-Drive Intelligent Driving System 
5.6.3 Visual Positioning Technology 
5.6.4 Latest Algorithm
5.6.5 R & D Planning and Partners
5.7 AutoX
5.7.1 Profile 
5.7.2 Self-Driving Technology 
5.7.3 Self-Driving Fusion Perception System xFusion
5.8 DiDi Autonomous Driving
5.8.1 Profile
5.8.2 Autonomous Driving Technology
5.9 Waymo
5.9.1 Profile
5.9.2 Sensor Product Portfolio
5.9.3 Technology 
5.9.4 Behavior Prediction Algorithm 
5.9.5 Latest News

6. Development Trend of Autonomous Driving Algorithms 

6.1 Algorithm Trend I
6.2 Algorithm Trend II
6.3 Algorithm Trend III 
6.4 Algorithm Trend IV 
6.5 Algorithm Trend V
6.6 Algorithm Trend VI 
6.7 Algorithm Trend VII
 

In-vehicle Payment and ETC Market Research Report, 2024

Research on in-vehicle payment and ETC: analysis on three major application scenarios of in-vehicle payment In-vehicle payment refers to users selecting and purchasing goods or services in the car an...

Automotive Audio System Industry Report, 2024

Automotive audio systems in 2024: intensified stacking, and involution on number of hardware and software tuning   Sales of vehicle models equipped with more than 8 speakers have made stea...

China Passenger Car Highway & Urban NOA (Navigate on Autopilot) Research Report, 2024

NOA industry research: seven trends in the development of passenger car NOA In recent years, the development path of autonomous driving technology has gradually become clear, and the industry is acce...

Automotive Cloud Service Platform Industry Report, 2024

Automotive cloud services: AI foundation model and NOA expand cloud demand, deep integration of cloud platform tool chainIn 2024, as the penetration rate of intelligent connected vehicles continues to...

OEMs’ Passenger Car Model Planning Research Report, 2024-2025

Model Planning Research in 2025: SUVs dominate the new lineup, and hybrid technology becomes the new focus of OEMs OEMs’ Passenger Car Model Planning Research Report, 2024-2025 focuses on the medium ...

Passenger Car Intelligent Chassis Controller and Chassis Domain Controller Research Report, 2024

Chassis controller research: More advanced chassis functions are available in cars, dozens of financing cases occur in one year, and chassis intelligence has a bright future.  The report combs th...

New Energy Vehicle Thermal Management System Market Research Report, 2024

xEV thermal management research: develop towards multi-port valve + heat pump + liquid cooling integrated thermal management systems. The thermal management system of new energy vehicles evolves fro...

New Energy Vehicle Electric Drive and Power Domain industry Report, 2024

OEMs lead the integrated development of "3 + 3 + X platform", and the self-production rate continues to increase The electric drive system is developing around technical directions of high integratio...

Global and China Automotive Smart Glass Research Report, 2024

Research on automotive smart glass: How does glass intelligence evolve  ResearchInChina has released the Automotive Smart Glass Research Report 2024. The report details the latest advances in di...

Passenger Car Brake-by-Wire and AEB Market Research Report, 2024

1. EHB penetration rate exceeded 40% in 2024H1 and is expected to overshoot 50% within the yearIn 2024H1, the installations of electro-hydraulic brake (EHB) approached 4 million units, a year-on-year ...

Autonomous Driving Data Closed Loop Research Report, 2024

Data closed loop research: as intelligent driving evolves from data-driven to cognition-driven, what changes are needed for data loop? As software 2.0 and end-to-end technology are introduced into a...

Research Report on Intelligent Vehicle E/E Architectures (EEA) and Their Impact on Supply Chain in 2024

E/E Architecture (EEA) research: Advanced EEAs have become a cost-reducing tool and brought about deep reconstruction of the supply chain The central/quasi-central + zonal architecture has become a w...

Automotive Digital Power Supply and Chip Industry Report, 2024

Research on automotive digital power supply: looking at the digital evolution of automotive power supply from the power supply side, power distribution side, and power consumption side This report fo...

Automotive Software Business Models and Suppliers’ Layout Research Report, 2024

Software business model research: from "custom development" to "IP/platformization", software enters the cost reduction cycle According to the vehicle software system architecture, this report classi...

Passenger Car Intelligent Steering Industry Research Report, 2024

Intelligent Steering Research: Steer-by-wire is expected to land on independent brand models in 2025 The Passenger Car Intelligent Steering Industry Research Report, 2024 released by ResearchInChina ...

China Passenger Car Mobile Phone Wireless Charging Research Report, 2024

China Passenger Car Mobile Phone Wireless Charging Research Report, 2024 highlights the following:Passenger car wireless charging (principle, standards, and Qi2.0 protocol);Passenger car mobile phone ...

Automotive Smart Exteriors Research Report, 2024

Research on automotive smart exteriors: in the trend towards electrification and intelligence, which exteriors will be replaced by intelligence The Automotive Smart Exteriors Research Report, 2024 r...

Automotive Fragrance and Air Conditioning System Research Report, 2024

Research on automotive fragrance/air purification: With surging installations, automotive olfactory interaction is being linked with more scenarios. As users require higher quality of personalized, i...

2005- www.researchinchina.com All Rights Reserved 京ICP备05069564号-1 京公网安备1101054484号