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中国のインテリジェントドライビングフュージョンアルゴリズム産業（2024年）

China Intelligent Driving Fusion Algorithm Research Report, 2024

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英文 380 Pages
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即日から翌営業日
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中国のインテリジェントドライビングフュージョンアルゴリズム産業（2024年）
出版日: 2024年05月10日
発行: ResearchInChina
ページ情報: 英文 380 Pages
納期: 即日から翌営業日
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概要

2023年8月にマスク氏がFSD V12 Betaをライブ試乗してから、2024年3月にFSD V12 Supervisedを30日間無料試用するまでの8ヶ月間、都市NOAのような先進のインテリジェントドライビングが主要OEMの舞台になり始め、エンドツーエンドアルゴリズム、BEV Transformerアルゴリズム、AI基盤モデルアルゴリズムの応用事例がますます増えています。

1. スパースアルゴリズムは効率を改善し、インテリジェントドライビングコストを削減します。

現在、ほとんどのBEVアルゴリズムは高密度で、かなりの演算能力とストレージを消費します。毎秒30フレーム以上の滑らかさを実現するには、NVIDIA A100のような高価なコンピューティングリソースが必要です。それでも、2MPカメラでは5～6台しか対応できません。8MPカメラには、複数のH100 GPUのような非常に高価なリソースが必要です。

私たちの現実世界には疎な特徴があります。スパース化はセンサーがノイズを減らし、ロバスト性を向上させるのに役立ちます。さらに、距離が長くなるにつれて、グリッドはスパースになり、密なネットワークは約50メートル以内でしか維持できなくなります。クエリや特徴の相互作用を減らすことで、スパース知覚アルゴリズムは計算を高速化し、必要なストレージを減らし、知覚モデルの計算効率とシステム性能を大幅に改善し、システムの待ち時間を短縮し、知覚精度の範囲を拡大し、車速の影響を緩和します。

そのため、2021年以降、学界は密なグリッドベースのアルゴリズムではなく、スパースターゲットレベルのアルゴリズムにシフトしています。長期的な活動により、スパースターゲットレベルのアルゴリズムは、密なグリッドベースのアルゴリズムとほぼ同等の性能を発揮できます。業界もスパースアルゴリズムを反復し続けています。近年、Horizon RoboticsはSparse4Dをオープンソース化しました。Sparse4Dはビジョン専用アルゴリズムで、nuScenesのビジョン専用3D検出と3Dトラッキングの両方で1位を獲得しています。

Sparse4Dは、マルチビューテンポラルフュージョン知覚技術の範囲に属する、長い時間系列のスパース3Dターゲット検出を目指す一連のアルゴリズムです。スパース知覚の産業発展動向に直面して、Sparse4Dは純粋なスパースフュージョン知覚フレームワークを構築し、知覚アルゴリズムをより効率的かつ正確にし、知覚システムを簡素化します。密なBEVアルゴリズムと比較して、Sparse4Dは計算の複雑性を軽減し、知覚範囲における計算能力の限界を突破し、知覚効果と推論速度において密なBEVアルゴリズムを凌駕します。

スパースアルゴリズムのもう1つの重要な利点は、センサーへの依存を減らし、演算能力の消費を少なくすることで、インテリジェントドライビングソリューションのコストを削減することです。例えば、Megvii Technologyは、BEVアルゴリズムの最適化、計算能力の削減、HDマップ、RTK、LiDARの削除、アルゴリズムフレームワークの統一、自動アノテーションなどのさまざまな対策を講じることで、PETRシリーズのスパースアルゴリズムに基づくインテリジェントドライビングソリューションのコストを、市場にある従来のソリューションと比較して20％～30％削減したと述べています。

2. 4Dアルゴリズムはより高い精度を提供し、インテリジェントドライビングの信頼性を高める。

OEMのセンサー構成に見られるように、近年3年間で、インテリジェントドライビング機能と応用シナリオが増加し、これまで以上に多くのセンサーが搭載されるようになっています。ほとんどの都市型NOAソリューションは、10～12台のカメラ、3～5台のレーダー、12台の超音波レーダー、1～3台のLiDARを搭載しています。

センサーの増加に伴い、これまで以上に多くの知覚データが生成されます。データの活用をいかに向上させるかは、OEMやアルゴリズムプロバイダーの課題でもあります。各社のアルゴリズムの詳細は多少異なりますが、現在主流のBEV Transformerソリューションの一般的な考え方は基本的に同じです。

当レポートでは、中国のインテリジェントドライビングフュージョンアルゴリズム産業について調査分析し、各社のソリューションと応用事例や研究開発動向などまとめています。

目次

第1章 インテリジェントドライビングフュージョンアルゴリズムの概要

  • インテリジェントドライビングアルゴリズム：認識、決定、作動（1）
  • インテリジェントドライビングアルゴリズム：認識、決定、作動（2）
  • インテリジェントドライビングアルゴリズム：認識、決定、作動（3）
  • インテリジェントドライビングアルゴリズム：認識、決定、作動（4）
  • インテリジェントドライビングアルゴリズム：認識、決定、作動（5）
  • インテリジェントドライビングアルゴリズム：イテレーション履歴
  • インテリジェントドライビング認識アルゴリズム - 視覚認識
  • インテリジェントドライビングフュージョンアルゴリズム（1）
  • インテリジェントドライビングフュージョンアルゴリズム（2）
  • インテリジェントドライビングフュージョンアルゴリズム（3）
  • インテリジェントドライビングフュージョンアルゴリズム（4）
  • OEMのフュージョンアルゴリズムの応用事例
  • OEMのフュージョンアルゴリズムモデルの比較
  • Tier 1のフュージョンアルゴリズムモデルの比較
  • インテリジェントドライビングアルゴリズム供給モデル
  • インテリジェントドライビングフュージョンアルゴリズムの開発動向

第2章 エンドツーエンドアルゴリズム

  • エンドツーエンドのインテリジェントドライビングが長期的なコンセンサスとなる
  • 占有ネットワーク
  • エンドツーエンドアルゴリズムの応用例

第3章 BEV Transformer基盤モデルアルゴリズム

  • 小型モデルから基盤モデルまで
  • BEV+Transformerアルゴリズム
  • OEMのBEV+Transformerアルゴリズムの比較
  • Tier 1サプライヤーのBEV+Transformerアルゴリズムの比較

第4章 データはフュージョンアルゴリズムの基礎となる

  • データはフュージョンアルゴリズムの基礎となる
  • インテリジェントドライビングデータセットの比較
  • 主なデータトレーニングセットサプライヤーとその製品
  • インテリジェントドライビングにおけるデータセットの応用事例

第5章 チップベンダーのアルゴリズム

  • Huawei
  • Horizon Robotics
  • Black Sesame Technologies
  • Mobileye
  • Qualcomm Arriver
  • NXP
  • NVIDIA

第6章 Tier 1・Tier 2ベンダーのアルゴリズム

  • Momenta
  • Nullmax
  • ArcSoft
  • JueFX Technology
  • StradVision
  • iMotion
  • EnjoyMove Technology
  • Haomo.AI
  • In-driving Tech
  • Valeo

第7章 新興自動車メーカーとOEMのアルゴリズム

  • Tesla
  • NIO
  • Li Auto
  • Xpeng
  • Leapmotor
  • ZEEKR
  • BMW
  • SAIC
  • GM

第8章 L4インテリジェントドライビングのロボタクシーアルゴリズム

  • Baidu Apollo
  • Pony.ai
  • WeRide
  • DeepRoute.ai
  • QCraft
  • UISEE
  • Didi Autonomous Driving
  • Waymo
目次
Product Code: ZXF008

Intelligent Driving Fusion Algorithm Research: sparse algorithms, temporal fusion and enhanced planning and control become the trend.

China Intelligent Driving Fusion Algorithm Research Report, 2024 released by ResearchInChina analyzes the status quo and trends of intelligent driving fusion algorithms (including perception, positioning, prediction, planning, decision, etc.), sorts out algorithm solutions and cases of chip vendors, OEMs, Tier1 & Tier2 suppliers and L4 algorithm providers, and summarizes the development trends of intelligent driving algorithms.

Since the period of eight months from Musk's live test drive of FSD V12 Beta in August 2023 to the 30-day free trial of FSD V12 Supervised in March 2024, advanced intelligent driving such as urban NOA has begun to become the arena of major OEMs, and there have been ever more application cases for end-to-end algorithms, BEV Transformer algorithms, and AI foundation model algorithms.

1. Sparse algorithms improve efficiency and reduce intelligent driving cost.

At present, most BEV algorithms are dense and consume considerable computing power and storage. The smoothness of more than 30 frames per second requires expensive computing resources such as NVIDIA A100. Even so, only 5 to 6 2MP cameras can be supported. For 8MP cameras, extremely expensive resources like multiple H100 GPUs are needed.

Our real world has sparse features. Sparsification helps sensors reduce noise and improve robustness. In addition, as distance increases, grids are bound to be sparse, and a dense network can only be maintained within about 50 meters. By reducing queries and feature interactions, sparse perception algorithms speed up calculations and lower storage requirements, greatly improve the computing efficiency and system performance of the perception model, shorten the system latency, expand the perception accuracy range, and ease the impact of vehicle speed.

Therefore, the academia has shifted to sparse target-level algorithms rather than dense grid-based algorithms since 2021. With long-term efforts, sparse target-level algorithms can perform almost as well as dense grid-based algorithms. The industry also keeps iterating sparse algorithms. Recently, Horizon Robotics has open-sourced Sparse4D, its vision-only algorithm which ranks first on both nuScenes vision-only 3D detection and 3D tracking lists.

Sparse4D is a series of algorithms moving towards long-time-sequence sparse 3D target detection, belonging to the scope of multi-view temporal fusion perception technology. Facing the industry development trend of sparse perception, Sparse4D builds a pure sparse fusion perception framework, which makes perception algorithms more efficient and accurate and simplifies perception systems. Compared with dense BEV algorithms, Sparse4D reduces the computational complexity, breaks the limit of computing power on the perception range, and outperforms dense BEV algorithms in perception effect and reasoning speed.

Another significant advantage of sparse algorithms is to cut down the cost of intelligent driving solutions by reducing dependence on sensors and consuming less computing power. For example, Megvii Technology mentioned that taking a range of measures, for example, optimizing the BEV algorithm, reducing computing power, removing HD maps, RTK and LiDAR, unifying the algorithm framework, and automatic annotation, it has lowered the costs of its intelligent driving solutions based on PETR series sparse algorithms by 20%-30%, compared with conventional solutions on the market.

2. 4D algorithms offer higher accuracy and make intelligent driving more reliable.

As seen from the sensor configurations of OEMs, in recent three years ever more sensors have been installed, with increasing intelligent driving functions and application scenarios. Most urban NOA solutions are equipped with 10-12 cameras, 3-5 radars, 12 ultrasonic radars and 1-3 LiDARs.

With the increasing number of sensors, ever more perception data are generated. How to improve the utilization of the data is also placed on the agenda of OEMs and algorithm providers. Although the algorithm details of companies are a little different, the general ideas of the current mainstream BEV Transformer solutions are basically the same: conversion from 2D to 3D and then to 4D.

Temporal fusion can greatly improve the algorithm continuity, and the memory of obstacles can handle occlusion and allows for better perception the speed information. The memory of road signs can improve the driving safety and the accuracy of vehicle behavior prediction. The fusion of information from historical frames can improve the perception accuracy of the current object, while the fusion of information from future frames can verify the object perception accuracy, thereby enhancing the algorithm reliability and accuracy.

Tesla's Occupancy Network algorithm is a typical 4D algorithm.

Tesla adds the height information to the vector space of 2D BEV+ temporal information output by the original Transformer algorithm to build the 4D space representation form of 3D BEV + temporal information. The network runs every 10ms on the FSD, that is, it runs at 100FPS, which greatly improves the speed of model detection.

3. End-to-end algorithms integrating perception, planning and control enable more anthropomorphic intelligent driving.

Mainstream intelligent driving algorithms have adopted the "BEV+Transformer" architecture, and many innovative perception algorithms have emerged. However, rule-based algorithms still prevail among planning and control algorithms. Some OEMs face technical and practical challenges in both perception and planning & control systems, which are sometimes in a "split" state. In some complex scenarios, the perception module may fail to accurately recognize or understand the environmental information, and the decision module may make incorrect driving decisions due to improper handling of the perception results or algorithm limitations. This restricts the development of advanced intelligent driving to some extent.

UniAD, an end-to-end intelligent driving algorithm jointly released by SenseTime, OpenDriveLab and Horizon Robotics, was rated as the Best Paper in CVPR2023. UniAD integrates three main tasks (perception, prediction and planning) and six sub-tasks (target detection, target tracking, scene mapping, trajectory prediction, grid prediction and path planning) into a unified end-to-end network framework based on Transformer for the first time to attain a general model of full-stack task-critical driving. Under the nuScenes real scene dataset, UniAD performs all tasks best in the field, especially in terms of the prediction and planning results far better the previous best solution.

The basic end-to-end algorithm enables direct inputs from sensors and predictive control outputs, but it is difficult to optimize, because of lacking effective feature communication between network modules and effective interaction between tasks and needing to output results in phases. The decision-oriented perception and decision integrated design proposed by the UniAD algorithm uses token features for deep fusion according to the perception-prediction-decision process, so that the indicators of all tasks targeting decision are consistently improved.

In terms of planning and control algorithms, Tesla adopts an approach of interactive search + evaluation model to enable a comfortable and effective algorithm that combines conventional search algorithms with artificial intelligence:

Firstly, candidate objects are obtained according to lane lines, occupancy networks and obstacles, and then decision trees and candidate object sequences are generated.

The trajectory for reaching the above objects is constructed synchronously using conventional search and neural networks;

The interaction between the vehicle and other participants in the scene is predicted to form a new trajectory. After multiple evaluations, the final trajectory is selected. During the trajectory generation, Tesla applies conventional search algorithms and neural networks, and then scores the generated trajectory according to collision check, comfort analysis, the possibility of the driver taking over and the similarity with people, to finally decide the implementation strategy.

XBrain, the ultimate architecture of Xpeng's all-scenario intelligent driving, is composed of XNet 2.0, a deep vision neural network, and XPlanner, a planning and control module based on a neural network. XPlanner is a planning and control algorithm based on a neural network, with the following features:

Rule algorithm

Long time sequence (minute-level)

Multi-object (multi-agent decision, gaming capability)

Strong reasoning

The previous advanced algorithms and ADAS functional architectures were separated and consisted of many small logic planning and control algorithms for sub-scenes, while XPlanner has a unified planning and control algorithm architecture. XPlanner is supported by a foundation model and a large number of extreme driving scenes for simulation training, thus ensuring that it can cope with various complex situations.

Table of Contents

1 Overview of Intelligent Driving Fusion Algorithms

  • 1.1 Intelligent Driving Algorithms: Perception, Decision, Actuation (1)
  • 1.1 Intelligent Driving Algorithms: Perception, Decision, Actuation (2)
  • 1.1 Intelligent Driving Algorithms: Perception, Decision, Actuation (3)
  • 1.1 Intelligent Driving Algorithms: Perception, Decision, Actuation (4)
  • 1.1 Intelligent Driving Algorithms: Perception, Decision, Actuation (5)
  • 1.2 Intelligent Driving Algorithms: Iteration History
  • 1.3 Intelligent Driving Perception Algorithms - Visual Perception
    • 1.3.1 Visual Perception Algorithms (1)
    • 1.3.2 Visual Perception Algorithms (2)
    • 1.3.3 Intelligence Driving Perception Algorithms - LiDAR Perception (1)
    • 1.3.3 Intelligence Driving Perception Algorithms - LiDAR Perception (2)
    • 1.3.3 Intelligence Driving Perception Algorithms - LiDAR Perception (3)
    • 1.3.3 Intelligence Driving Perception Algorithms - LiDAR Perception (4)
    • 1.3.3 Intelligence Driving Perception Algorithms - LiDAR Perception (5)
    • 1.3.3 Intelligence Driving Perception Algorithms - LiDAR Perception (6)
    • 1.3.3 Intelligence Driving Perception Algorithms - LiDAR Perception (7)
    • 1.3.4 Intelligence Driving Perception Algorithms - Radar Perception
    • 1.3.5 Intelligent Driving Decision Algorithms
    • 1.3.6 Intelligent Driving Control Algorithms
  • 1.4 Intelligent Driving Fusion Algorithms (1)
  • 1.4 Intelligent Driving Fusion Algorithms (2)
  • 1.4 Intelligent Driving Fusion Algorithms (3)
  • 1.4 Intelligent Driving Fusion Algorithms (4)
    • 1.4.1 Temporal Fusion Algorithms
    • 1.4.2 DNN Algorithms
    • 1.4.3 CNN Algorithms
    • 1.4.4 YOLO V3 Algorithms
    • 1.4.5 RNN Algorithms
    • 1.4.6 3D Bounding Box Algorithms
    • 1.4.7 6D-Vision Algorithms
    • 1.4.8 VFM Algorithms
    • 1.4.9 Pseudo-LiDAR
    • 1.4.10 Algorithm Solutions Integrating Traditional Algorithms and Neural Networks
    • 1.4.11 DETR3D Algorithms
    • 1.4.12 Far3D Algorithms
    • 1.4.13 Sparse BEV Algorithms
    • 1.4.14 PETR Algorithms
    • 1.4.15 Sparse 4D Algorithms (1)
    • 1.4.15 Sparse 4D Algorithms (2)
    • 1.4.15 Sparse 4D Algorithms (3)
    • 1.4.15 Sparse 4D Algorithms (4)
  • 1.5 Application Cases of OEM Fusion Algorithms
    • 1.5.1 Application Cases of OEM Fusion Algorithms (1)
    • 1.5.2 Application Cases of OEM Fusion Algorithms (2)
    • 1.5.3 Application Cases of OEM Fusion Algorithms (3)
  • 1.6 Comparison among OEM Fusion Algorithm Models
  • 1.7 Comparison among Tier 1 Fusion Algorithm Models
  • 1.8 Intelligent Driving Algorithm Supply Models
  • 1.9 Development Trends of Intelligent Driving Fusion Algorithms
    • 1.9.1 Development Trends of Intelligent Driving Fusion Algorithms (1)
    • 1.9.2 Development Trends of Intelligent Driving Fusion Algorithms (2)
    • 1.9.3 Development Trends of Intelligent Driving Fusion Algorithms (3)
    • 1.9.4 Development Trends of Intelligent Driving Fusion Algorithms (4)
    • 1.9.5 Development Trends of Intelligent Driving Fusion Algorithms (5)
    • 1.9.6 Development Trends of Intelligent Driving Fusion Algorithms (6)
    • 1.9.7 Development Trends of Intelligent Driving Fusion Algorithms (7)
    • 1.9.8 Development Trends of Intelligent Driving Fusion Algorithms (8)
    • 1.9.9 Development Trends of Intelligent Driving Fusion Algorithms (9)

2 End-to-end Algorithms

  • 2.1 End-to-end Intelligent Driving Becomes a Long-Term Consensus
    • 2.1.1 How to Build an End-to-end Neural Network Foundation Model of Intelligent Driving?
    • 2.1.2 End-to-end Algorithms (1)
    • 2.1.3 End-to-end Algorithms (2)
    • 2.1.4 End-to-end Algorithms (3)
    • 2.1.5 End-to-end Algorithms (4)
  • 2.2 Occupancy Networks
    • 2.2.1 Occupancy Networks (1)
    • 2.2.2 Occupancy Networks (2)
    • 2.2.3 Occupancy Networks (3)
    • 2.2.4 Occupancy Networks (4)
    • 2.2.5 Occupancy Networks (5)
    • 2.2.6 Occupancy Networks (6)
  • 2.3 Application Cases of End-to-end Algorithms
    • 2.3.1 Application Cases of End-to-end Algorithms (1)
    • 2.3.2 Application Cases of End-to-end Algorithms (2)
    • 2.3.3 Application Cases of End-to-end Algorithms (3)
    • 2.3.4 Application Cases of End-to-end Algorithms (4)
    • 2.3.5 Application Cases of End-to-end Algorithms (5)
    • 2.3.6 Application Cases of End-to-end Algorithms (6)
    • 2.3.7 Application Cases of End-to-end Algorithms (7)
    • 2.3.8 Application Cases of End-to-end Algorithms (8)

3 BEV Transformer Foundation Model Algorithms

  • 3.1 From Small Models to Foundation Models
    • 3.1.1 BEV Perception Systems
    • 3.1.2 Three Common Transformers
    • 3.1.3 BEV Det
    • 3.1.3 BEV Stereo
    • 3.1.3 SOLOFusion
    • 3.1.3 VideoBEV
    • 3.1.4 Inverse Perspective Mapping
    • 3.1.4 BEV Former
  • 3.2 BEV+Transformer Algorithms
    • 3.2.1 BEV + Transformer Foundation Models (1)
    • 3.2.2 BEV + Transformer Foundation Models (2)
    • 3.2.3 BEV + Transformer Foundation Models (3)
  • 3.3 Comparison among OEM BEV+Transformer Algorithms
    • 3.3.1 Progress of OEM BEV+Transformer Algorithms
    • 3.3.2 Cases of OEM BEV+Transformer Algorithms (1)
    • 3.3.3 Cases of OEM BEV+Transformer Algorithms (2)
    • 3.3.4 Cases of OEM BEV+Transformer Algorithms (3)
  • 3.4 Comparison among BEV+Transformer Algorithms of Tier 1 Suppliers
    • 3.4.1 Cases of Tier 1 BEV+Transformer Algorithms (1)
    • 3.4.2 Cases of Tier 2 BEV+Transformer Algorithms (1)
    • 3.4.3 Cases of Tier 3 BEV+Transformer Algorithms (1)
    • 3.4.4 Cases of Tier 4 BEV+Transformer Algorithms (1)

4 Data Is the Cornerstone of Fusion Algorithms

  • 4.1 Data Is the Cornerstone of Fusion Algorithms
    • 4.1.1 Datasets: How to Collect
    • 4.1.2 Datasets: Evolution from Single-vehicle Intelligence to Vehicle-city Integration
    • 4.1.3 Datasets: From Perception to Prediction and Planning
    • 4.1.4 Datasets: Multimodal, End-to-end
    • 4.1.5 Next-generation Datasets
  • 4.2 Intelligent Driving Dataset Comparison
    • 4.2.1 Intelligent Driving Dataset Comparison (1)
    • 4.2.2 Intelligent Driving Dataset Comparison (2)
    • 4.2.3 Intelligent Driving Dataset Comparison (3)
    • 4.2.4 Intelligent Driving Dataset Comparison (4)
    • 4.2.5 Intelligent Driving Dataset Comparison (5)
    • 4.2.6 Intelligent Driving Dataset Comparison (6)
  • 4.3 Major Data Training Set Suppliers and Their Products
    • 4.3.1 Major Data Training Set Suppliers and Their Products (1)
    • 4.3.2 Major Data Training Set Suppliers and Their Products (2)
    • 4.3.3 Major Data Training Set Suppliers and Their Products (3)
    • 4.3.4 Major Data Training Set Suppliers and Their Products (4)
    • 4.3.5 Major Data Training Set Suppliers and Their Products (5)
  • 4.4 Application Cases of Datasets in Intelligent Driving
    • 4.4.1 Application Cases of Datasets in Intelligent Driving (1)
    • 4.4.2 Application Cases of Datasets in Intelligent Driving (2)
    • 4.4.3 Application Cases of Datasets in Intelligent Driving (3)
    • 4.4.4 Application Cases of Datasets in Intelligent Driving (4)
    • 4.4.5 Application Cases of Datasets in Intelligent Driving (5)
    • 4.4.6 Application Cases of Datasets in Intelligent Driving (6)
    • 4.4.7 Application Cases of Datasets in Intelligent Driving (7)
    • 4.4.8 Application Cases of Datasets in Intelligent Driving (8)
    • 4.4.9 Application Cases of Datasets in Intelligent Driving (9)

5 Algorithms of Chip Vendors

  • 5.1 Huawei
    • 5.1.1 Intelligent Automotive Solution (IAS) Business Unit (BU)
    • 5.1.2 Cooperation Modes
    • 5.1.3 Intelligent Driving Full Stack Solutions (1)
    • 5.1.4 Intelligent Driving Full Stack Solutions (2)
    • 5.1.5 Intelligent Driving Perception Algorithms: GOD 2.0&RCR 2.0
    • 5.1.6 Intelligent Driving Perception Algorithms: Occupancy
    • 5.1.7 Intelligent Driving Perception Algorithms: Transfusion
  • 5.2 Horizon Robotics
    • 5.2.1 Profile
    • 5.2.2 Cooperation Modes
    • 5.2.3 Automotive Computing Platforms and Monocular Front View Solution Algorithms
    • 5.2.4 Intelligent Driving Perception Algorithm Design (1)
    • 5.2.4 Intelligent Driving Perception Algorithm Design (2)
    • 5.2.4 Intelligent Driving Perception Algorithm Design (3)
    • 5.2.5 Core Algorithm Libraries (1)
    • 5.2.5 Core Algorithm Libraries (2)
    • 5.2.5 Core Algorithm Libraries (3)
    • 5.2.6 NOA Solutions and Super Driving Solution Algorithms
    • 5.2.7 Open Software Platforms
    • 5.2.8 Official Open Source Sparse4D Algorithms
    • 5.2.9 Algorithm Planning
    • 5.2.10 Recent Dynamics in Cooperation
  • 5.3 Black Sesame Technologies
    • 5.3.1 Profile
    • 5.3.2 Visual Perception Algorithms
    • 5.3.3 4D Radar and Visual Perception Fusion Algorithms
    • 5.3.4 LiDAR DSP
    • 5.3.5 PointPillars Algorithms
    • 5.3.6 Parking Visual Perception Algorithms
    • 5.3.7 Driving Visual Perception Algorithms
    • 5.3.8 Shanhai Toolchain
    • 5.3.9 Partners
    • 5.3.10 Recent Dynamics in Cooperation
  • 5.4 Mobileye
    • 5.4.1 Profile
    • 5.4.2 Full Stack Intelligent Driving Solutions
    • 5.4.3 Object Recognition Technology
    • 5.4.4 Chip Algorithm Development Process
    • 5.4.5 Vision Algorithms
    • 5.4.6 Recent Dynamics in Cooperation
  • 5.5 Qualcomm Arriver
    • 5.5.1 Profile
    • 5.5.2 Visual Perception Algorithms
  • 5.6 NXP
    • 5.6.1 Profile
    • 5.6.2 ADAS Software and Hardware Solutions
    • 5.6.3 Object Detection Algorithms
    • 5.6.4 CNN Algorithms for Object Detection
  • 5.7 NVIDIA
    • 5.7.1 Profile
    • 5.7.2 Cooperation Mode
    • 5.7.3 Intelligent Vehicle Software Stacks
    • 5.7.4 DRIVE Perception Algorithms (1)
    • 5.7.4 DRIVE Perception Algorithms (2)
    • 5.7.4 DRIVE Perception Algorithms (3)
    • 5.7.5 Perception Algorithm End-to-end Models: PiloNet to NVRadarNet
    • 5.7.6 Recent Dynamics in Cooperation
    • 5.7.7 Automotive Partner Technology Exhibition and Ecological Cooperation at CES 2024

6 Algorithms of Tier 1 & Tier 2 Vendors

  • 6.1 Momenta
    • 6.1.1 Profile
    • 6.1.2 Core Algorithms
    • 6.1.3 Algorithm Application
    • 6.1.4 Mapless Intelligent Driving Algorithms
    • 6.1.5 DDLD Lane Line Recognition Algorithm
    • 6.1.6 DDPF Location Fusion Algorithm
    • 6.1.7 DLP Planning and Control Algorithm
    • 6.1.8 Algorithm Development Route
    • 6.1.9 Recent Dynamics in Cooperation
  • 6.2 Nullmax
    • 6.2.1 Profile
    • 6.2.2 Algorithms and Modules
    • 6.2.3 Core Algorithms (1)
    • 6.2.3 Core Algorithms (2)
    • 6.2.3 Core Algorithms (3)
    • 6.2.4 Application Process of Algorithm Products
    • 6.2.5 Recent Dynamics in Cooperation
  • 6.3 ArcSoft
    • 6.3.1 Profile
    • 6.3.2 Intelligent Driving Technology (1)
    • 6.3.3 Intelligent Driving Technology (2)
    • 6.3.4 One-stop Automotive Vision Solution: VisDrive
    • 6.3.5 Recent Dynamics and Development Planning
  • 6.4 JueFX Technology
    • 6.4.1 Profile
    • 6.4.2 Visual Feature Fusion Positioning Solutions
    • 6.4.3 BEV Perception Technology
    • 6.4.4 BEV+Transformer Algorithms (1)
    • 6.4.4 BEV+Transformer Algorithms (2)
    • 6.4.4 BEV+Transformer Algorithms (3)
    • 6.4.5 LiDAR Fusion Positioning Solutions
    • 6.4.6 Architecture of Highway NOA Solutions with Low-weight Maps
    • 6.4.7 Real-time Online Mapping
    • 6.4.8 Automatic Annotation Systems
    • 6.4.9 Multi-sensor Fusion Positioning Algorithms (1)
    • 6.4.9 Multi-sensor Fusion Positioning Algorithms (2)
    • 6.4.9 Multi-sensor Fusion Positioning Algorithms (3)
    • 6.4.10 Different Fusion Algorithm Solutions Based on LiDAR
    • 6.4.11 Perception Foundation Model Algorithms Based on Data Closed Loop
    • 6.4.12 Cooperation Ecology
  • 6.5 StradVision
    • 6.5.1 Profile
    • 6.5.2 Intelligent Driving Algorithms (1)
    • 6.5.2 Intelligent Driving Algorithms (2)
    • 6.5.3 Next-generation "3D Perception Network"
    • 6.5.4 Development Dynamics of Vision Products
  • 6.6 iMotion
    • 6.6.1 Profile
    • 6.6.2 Core Intelligent Driving Algorithms
    • 6.6.3 Mass Production
  • 6.7 EnjoyMove Technology
    • 6.7.1 Profile
    • 6.7.2 Intelligent Driving Software
    • 6.7.3 Recent Dynamics
  • 6.8 Haomo.AI
    • 6.8.1 Profile
    • 6.8.2 Product Matrix
    • 6.8.3 Status Quo of Intelligent Driving
    • 6.8.4 MANA System
    • 6.8.5 Perception Module of MANA System
    • 6.8.5 Cognitive Module of MANA System
    • 6.8.6 Intelligent Computing Center
    • 6.8.7 Perception Algorithm Optimization
    • 6.8.8 Cognitive Algorithm Optimization
  • 6.9 In-driving Tech
    • 6.9.1 Profile
    • 6.9.2 Intelligent Driving Algorithms (1)
    • 6.9.3 Intelligent Driving Algorithms (2)
    • 6.9.4 Algorithm Achievements and Planning
  • 6.10 Valeo
    • 6.10.1 Profile
    • 6.10.2 Typical Algorithm Models (1)
    • 6.10.2 Typical Algorithm Models (2)

7 Algorithms of Emerging Automakers and OEMs

  • 7.1 Tesla
    • 7.1.1 Profile
    • 7.1.2 End-to-end Algorithms
    • 7.1.3 Multi-camera Fusion Algorithms
    • 7.1.4 Environment Perception Algorithms
    • 7.1.5 Computing Power Development Planning
  • 7.2 NIO
    • 7.2.1 Profile
    • 7.2.2 Intelligent Driving System Evolution
    • 7.2.3 Comparison between Pilot System and NAD System
  • 7.3 Li Auto
    • 7.3.1 Profile
    • 7.3.2 Intelligent Driving Route
    • 7.3.3 Algorithm Evolution
    • 7.3.4 Intelligent Driving Algorithm Architecture of AD Max 3.0
    • 7.3.5 Layout in Intelligent Driving
    • 7.3.6 Future Automotive Development Plan
  • 7.4 Xpeng
    • 7.4.1 Profile
    • 7.4.2 Intelligent Driving System and Algorithm Evolution
    • 7.4.3 Intelligent Driving Algorithm Architecture
    • 7.4.4 New Perception Architecture (1)
    • 7.4.4 New Perception Architecture (2)
    • 7.4.4 New Perception Architecture (3)
    • 7.4.5 Recent Cooperation Dynamics and Development Planning
  • 7.5 Leapmotor
    • 7.5.1 Profile
    • 7.5.2 Global Independent R&D
    • 7.5.3 Intelligent Driving Technology Planning
  • 7.6 ZEEKR
    • 7.6.1 Profile
    • 7.6.2 ZEEKR & Mobileye Intelligent Driving Solution
    • 7.6.3 ZEEKR & Waymo Intelligent Driving Solution
  • 7.7 BMW
    • 7.7.1 Profile
    • 7.7.2 Intelligent Driving
    • 7.7.3 Intelligent Driving Implementation and Development Planning
    • 7.7.4 Dynamics in Recent Intelligent Driving
  • 7.8 SAIC
    • 7.8.1 Intelligent Driving Layout
    • 7.8.2 Profile of Z-One
    • 7.8.3 Computing Platform of Z-One
    • 7.8.4 SAIC AI LAB
  • 7.9 GM
    • 7.9.1 Intelligent Driving Layout
    • 7.9.2 Profile and Recent Dynamics of Cruise
    • 7.9.3 Perception Algorithms of Cruise
    • 7.9.4 Decision Algorithms of Cruise
    • 7.9.5 Intelligent Driving Development Toolchain of Cruise
    • 7.9.6 Development Planning of Cruise

8 Robtaxi Algorithms of L4 Intelligent Driving

  • 8.1 Baidu Apollo
    • 8.1.1 Profile
    • 8.1.2 Architecture of Apollo 9.0
    • 8.1.3 Perception Algorithms (1)
    • 8.1.3 Perception Algorithms (2)
    • 8.1.3 Perception Algorithms (3)
    • 8.1.4 CVIS Solutions
    • 8.1.5 The Latest Intelligent Driving Solutions (1)
    • 8.1.5 The Latest Intelligent Driving Solutions (2)
    • 8.1.6 Intelligent Driving Solutions (1)
    • 8.1.6 Intelligent Driving Solutions (2)
  • 8.2 Pony.ai
    • 8.2.1 Profile
    • 8.2.2 Main Businesses and Business Models
    • 8.2.3 Core Technology and the Latest Intelligent Driving System Configuration
    • 8.2.4 Sensor Fusion Solutions
    • 8.2.5 Intelligent Driving Solutions
    • 8.2.6 Recent Dynamics in Cooperation
  • 8.3 WeRide
    • 8.3.1 Profile
    • 8.3.2 Intelligent Driving Platform
    • 8.3.3 WeRide One Algorithm Module
    • 8.3.4 Recent Dynamics in Cooperation
  • 8.4 DeepRoute.ai
    • 8.4.1 Profile
    • 8.4.2 Full Stack Solutions for L4 Autonomous Driving
    • 8.4.3 Self-developed Algorithms
    • 8.4.4 Intelligent Driving Solutions
    • 8.4.5 Recent Dynamics in Cooperation
  • 8.5 QCraft
    • 8.5.1 Profile
    • 8.5.2 Intelligent Driving Solutions
    • 8.5.3 Hyper-converged Perception Solutions
    • 8.5.4 Prediction Algorithms
    • 8.5.5 Planning Algorithms
    • 8.5.6 Classic Algorithm Models
  • 8.6 UISEE
    • 8.6.1 Profile
    • 8.6.2 Intelligent Driving System
    • 8.6.3 Vision Positioning Technology
    • 8.6.4 The Latest Algorithm
    • 8.6.5 Recent Cooperation Dynamics and Partners
  • 8.7 Didi Autonomous Driving
    • 8.7.1 Profile
    • 8.7.2 Intelligent Driving Technology
    • 8.7.3 Application of Intelligent Driving Technology
  • 8.8 Waymo
    • 8.8.1 Profile
    • 8.8.2 Sensor Matrix
    • 8.8.3 Intelligent Driving Algorithms
    • 8.8.4 Behavior Prediction Algorithms
    • 8.8.5 Recent Dynamics