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市场调查报告书
商品编码
1482383

中国智慧驾驶融合演算法产业（2024年）

China Intelligent Driving Fusion Algorithm Research Report, 2024
出版日期: | 出版商: ResearchInChina | 英文 380 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

从2023年8月马斯克先生现场试驾FSD V12 Beta到2024年3月FSD V12监督30天免费试用8个月，像城市NOA这样的先进智慧驾驶开始成为各大主机厂的焦点，端对端演算法、BEV Transformer演算法、基于AI的模型演算法的应用越来越多。

1.稀疏演算法提高效率，降低智慧驾驶成本。

目前，大多数 BEV 演算法都是密集的，并且消耗大量的运算能力和储存空间。实现每秒 30 帧或更高的流畅度需要昂贵的运算资源，例如 NVIDIA A100。不过，仅支援 5-6 个 2MP 相机。 8MP 相机需要非常昂贵的资源，例如多个 H100 GPU。

我们的现实世界具有稀疏特征。稀疏化有助于感测器降低杂讯并提高稳健性。而且，随着距离的增加，网格变得稀疏，只能在50公尺左右的范围内维持密集的网路。透过减少查询和特征交互，稀疏感知演算法加快了计算速度，减少了所需的储存空间，显着提高了感知模型的计算效率和系统性能，减少了系统延迟，提高了感知精度范围并降低了车辆速度的影响。

因此，从 2021 年开始，学术界正在转向稀疏目标层级演算法，而不是基于密集网格的演算法。透过长期活动，稀疏目标层级演算法的表现几乎与基于网格的密集演算法一样好。业界也不断迭代稀疏演算法。近年来，地平线机器人公司开源了Sparse4D。 Sparse4D 是一种视觉特定演算法，在 nuScenes 视觉特定 3D 检测和 3D 追踪中排名第一。

Sparse4D是一组针对长时间序列中的稀疏3D目标侦测的演算法，属于多视点时间融合感知技术的范围。面对稀疏感知的产业发展趋势，Sparse4D建构了纯粹的稀疏融合感知框架，使感知演算法更有效率且准确，简化了感知系统。与稠密BEV演算法相比，Sparse4D降低了计算复杂度，打破了感知范围内的算力限制，在感知有效性和推理速度上优于稠密BEV演算法。

稀疏演算法的另一个重要优势是，它们透过减少对感测器的依赖并消耗更少的运算能力来降低智慧驾驶解决方案的成本。例如，旷视科技透过优化BEV演算法、降低算力、去除高清地图、RTK、LiDAR、统一演算法框架、自动标註等多种措施，对PETR系列的稀疏演算法进行了改进。比，其智慧驾驶解决方案的成本降低了20%至30%。

2. 4D演算法提供更高的精度，增加智慧驾驶的可靠性。

过去三年，智慧驾驶功能和应用场景不断增加，从 OEM 感测器配置来看，现在配备的感测器数量比以往任何时候都多。大多数城市 NOA 解决方案包括 10-12 个摄影机、3-5 个雷达、12 个超音波雷达和 1-3 个雷射雷达。

随着感测器的增加，产生的感知数据比以往任何时候都多。如何提高数据的利用，对于整车厂和演算法提供者来说也是一个课题。虽然每家公司的演算法细节可能略有不同，但目前主流的BEV Transformer解决方案的整体思路基本上相同。

本报告针对中国智慧驾驶融合演算法产业进行调查分析，并总结各公司的解决方案、应用实例以及研发趋势。

目录

第一章智慧驾驶融合演算法概述

  • 智慧驾驶演算法：辨识、决策、行动 (1)
  • 智慧驾驶演算法：辨识、决策、行动 (2)
  • 智慧驾驶演算法：辨识、决策、行动 (3)
  • 智慧驾驶演算法：辨识、决策、行动 (4)
  • 智慧驾驶演算法：辨识、决策、行动 (5)
  • 智慧驾驶演算法：迭代历史
  • 智慧驾驶辨识演算法-视觉识别
  • 智慧驾驶融合演算法（一）
  • 智慧驾驶融合演算法（二）
  • 智慧驾驶融合演算法（三）
  • 智慧驾驶融合演算法（4）
  • OEM融合演算法应用范例
  • OEM融合演算法模型比较
  • Tier 1融合演算法模型比较
  • 智慧驾驶演算法供应模型
  • 智慧驾驶融合演算法发展趋势

第2章端对端演算法

  • 端到端智慧驾驶成为长期共识
  • 占用的网络
  • 端对端演算法应用范例

第三章 BEV Transformer 基本模型演算法

  • 从小型号到基本型号
  • BEV+Transformer演算法
  • OEM BEV+Transformer 演算法比较
  • 一级供应商BEV+Transformer演算法对比

第四章资料是融合演算法的基础

  • 资料是融合演算法的基础
  • 智慧驾驶资料集对比
  • 主要资料训练集供应商及其产品
  • 资料集在智慧驾驶中的应用范例

第五章晶片厂商演算法

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

第 6 章第 1 层与第 2 层供应商演算法

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

第 7 章新兴汽车製造商和 OEM 演算法

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

第八章L4智慧驾驶机器人轴演算法

  • Baidu Apollo
  • Pony.ai
  • WeRide
  • DeepRoute.ai
  • QCraft
  • UISEE
  • Didi Autonomous Driving
  • Waymo
简介目录
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