中国的汽车用LiDAR产业(2024年～2025年)

2025年初，比亚迪的 "神眼" 智慧驾驶和长安汽车的 "天枢" 智慧驾驶将引发智慧驾驶大众化的浪潮，智慧驾驶的平民化趋势将愈发明显。目前，LiDAR技术正在向10-15万元价位的车型（如Galaxy E8、bZ3X、零跑B10）拓展，甚至向10万元以下的车型（如长安汽车即将搭载的车型）拓展。

此外，蔚来ET9（3个雷射雷达）、MAEXTRO S800（4个雷射雷达）、新款AITO M9（4个雷射雷达）等高阶车型均增加了安全冗余。 Zeekr千里浩瀚H9搭载了5个光达，广汽集团计画于今年第四季上市的L3级自动驾驶车型G1000也将搭载4个雷射雷达。要迈向L3/L4自动驾驶，升级到高性能光达并增加其数量至关重要。

不仅车型的起伏，性能提升与成本降低的起伏，也推动着光达的应用场景越来越多，例如人形机器人、机器狗、低空经济、物流、港口、农业等等，无论在汽车领域还是非汽车领域，光达都呈现爆发式增长。

1.到 2024 年，配备 LiDAR 的汽车数量将超过 150 万辆，渗透率将上升至 6.0%。

根据水清木华研究中心统计，2024年雷射雷达安装量将激增至152.9万台，较去年同期成长245.4%，渗透率将飙升至6.0%。配备LiDAR的车款在市场上越来越受欢迎。

从市场集中度来看，车载雷射雷达前四大厂商分别为速腾聚创、禾赛科技、华为、赛远科技。预计到2024年，这四家公司的市占率将达到99%以上，主导汽车光达市场。其他已实现量产的乘用车雷射雷达供应商包括Luminar、法雷奥、大疆Livox、天威科技等。

2.晶片和数位化推动性能持续提升和成本降低

晶片上采用 LiDAR 可满足工程需求，例如流线型外形（安装在车顶）、紧凑集成（位于挡风玻璃后方、保险桿后方、集成到前灯中）以及通过实现更细緻的环境感知来提高安全冗余度。光发射、扫描、接收零件的小型化、整合化，进一步降低了成本。同时，集中运算等数位架构设计可加快车载回应时间，并提升AEB优化等安全性能。

儘管晶片化的趋势明显，但技术瓶颈仍然存在。 SPAD晶片技术目前仍由索尼、安森美等国际公司主导，硅光子OPA的扫描精度有待进一步提高。国内厂商必须加快材料（如InGaAs探测器）与製程（如3D堆迭）的突破，以实现全供应链自给自足。

例如，Aeva 的 Atlas Ultra LiDAR 进步依赖于客製化硅片，包括 Aeva CoreVision™ LiDAR 晶片模组和 Aeva X1™ SoC 处理器。第四代 CoreVision 模组将关键的 LiDAR 组件（发射器、探测器和光学介面晶片）整合到单一汽车级设计中。利用我们独特的硅光子技术，我们取代了复杂的光纤，实现了高品质、可扩展、经济高效的大规模生产。

本报告对中国车载雷射雷达产业进行了研究分析，提供了雷射雷达概况、应用场景、国内外供应商、发展趋势等资讯。

目录

第1章 汽车用LiDAR概要

• LiDAR 简介
• LiDAR 的结构
• LiDAR 的类型
• 固态雷射雷达技术路线对比
• 固态雷射雷达成本明细
• 在严格的汽车等级要求下持续降低成本
• 汽车级光达需要研发投资
• 架构演进：透过运算能力迁移降低成本的概念
• 全新 LiDAR 晶片产品
• LiDAR晶片的製造过程
• 光达产业链

第2章 汽车用LiDAR市场与应用车辆

• 汽车光达相关标准
• 汽车光达市场分析
• 国内LiDAR应用车辆分析

第3章 LiDAR的应用情势

• LiDAR 的主要应用场景
• LiDAR 的新用途
• LiDAR 的非汽车应用
• 机器人部署范例：禾赛科技
• 机器人部署范例：RoboSense

第4章 中国的汽车用LiDAR供应商

• Hesai Technology
• RoboSense
• Seyond
• Huawei
• Zhuoyu Technology
• Livox
• Tanway Technology
• ZVISION
• LiangDao Automotive Technology
• VanJee Technology
• 其他

第5章 国外的汽车用LiDAR供应商

• Luminar
• Innoviz
• Aeva
• AEYE
• Ouster
• Valeo

第6章 汽车用LiDAR的开发趋势

In early 2025, BYD's "Eye of God" Intelligent Driving and Changan Automobile's Tianshu Intelligent Driving sparked a wave of mass intelligent driving, making the democratization of intelligent driving increasingly evident. LiDAR technology has now been extended to models priced between 100,000 and 150,000 yuan (such as the Galaxy E8, bZ3X, and Leapmotor B10), and even to a 100,000-yuan model (a Changan model will be equipped with it).

Additionally, high-end models such as the NIO ET9 (3* LiDAR), MAEXTRO S800 (4* LiDAR), and New AITO M9 (4* LiDAR) are enhancing safety redundancy. The Zeekr Qianli Haohan H9 will be equipped with 5* LiDAR, while the GAC Group's L3 autonomous driving model G1000, set to launch in Q4 this year, will feature 4* LiDAR. Upgrading to high-performance LiDAR or increasing their number has become essential for advancing to L3/L4 autonomy.

Beyond the ups and downs in vehicle models, there is also an ups and downs in performance improvement and cost reduction, which also promotes the application of LiDAR in more scenarios, such as humanoid robots, robot dogs, low-altitude economy, logistics, ports, agriculture, etc. LiDAR is experiencing an explosion in both automotive and non-automotive fields.

1. In 2024, installations of automotive LiDAR exceeded 1.5 million, and the penetration rate climbed to 6.0%

According to statistics from ResearchInChina, the installed capacity of LiDAR surged to 1.529 million units in 2024, a year-on-year increase of 245.4%; the penetration rate rapidly jumped to 6.0% in 2024. Models equipped with LiDAR are becoming more and more popular in the market.

In terms of market concentration, the top four automotive LiDAR companies include RoboSense, Hesai Technology, Huawei, and Seyond. In 2024, the combined market share of these four companies exceeded 99%, dominating the automotive LiDAR market. Other passenger car LiDAR suppliers include Luminar, Valeo, DJI Livox, Tanway Technology, etc., which are also achieving mass production.

2. Chipification and digitalization drive continuous performance improvement and cost reduction

LiDAR chipification addresses engineering demands for streamlined form factor (roof-installed), compact integration (behind windshield, bumper, or fused with headlights), and enhanced safety redundancy by enabling finer environmental perception. By miniaturizing and integrating emission, scanning, and reception components, it further reduces costs. Concurrently, digital architecture designs, such as centralized computing, enable faster onboard response times, improving safety features like AEB optimization.

Despite the clear trend toward chipification, technical bottlenecks persist: SPAD chip technology remains dominated by international players like Sony and ON Semiconductor, while silicon photonic OPA scanning accuracy requires further refinement. Domestic manufacturers must accelerate breakthroughs in materials (e.g., InGaAs detectors) and processes (e.g., 3D stacking) to achieve full supply-chain autonomy.

For example, Aeva's Atlas Ultra LiDAR advances rely on custom silicon, including the Aeva CoreVision(TM) LiDAR chip module and Aeva X1(TM) SoC processor. The fourth-gen CoreVision module integrates all critical LiDAR components - emitters, detectors, and optical interface chips - into a single automotive-grade design. Leveraging proprietary silicon photonics, it replaces complex fiber optics, ensuring quality and scalable, cost-effective mass production.

Additionally, Aeva X1, a FMCW LiDAR SoC processor, seamlessly integrates data acquisition, point-cloud processing, scanning systems, and application software into a single mixed-signal chip.

RoboSense restructured LiDAR architecture through chipification, consolidating discrete components into chips to slash assembly costs. Its MX product, for instance, replaces FPGAs with ASICs, reducing costs to under USD200 and enabling adoption in RMB150,000-RMB200,000 vehicles.

The MX also features RoboSense's self-developed SoC, the M-Core, with powerful processing capabilities and multi-threshold TDC (Time-to-Digital Converter), boosting weak-echo detection by 4 times and range resolution by 32 times. RoboSense has achieved chipified scanning, integrated data processing, and iterative transceiver upgrades.

Hesai's AT512 LiDAR employs chipified control to achieve 400m detection range while improving optical efficiency via integrated VCSEL and single-photon detectors.

In January 2025, Hesai launched the world's first 1,440-channel ultra-long-range LiDAR, powered by its Gen4 chip. It leverages advanced high-efficiency sensing and ultra-parallel processing to deliver unprecedented perception, producing image-grade point clouds that capture road imperfections, pedestrians, and vehicle details with precision.

Key features of Hesai's Gen4 chip include: 1. 3D stacking technology enabling single-board integration of 512 channels. 2. A 256-core Intelligent Point-cloud Engine (IPE) and 8-core APU, achieving 24.6 billion samples per second. 3. 130% higher detector sensitivity and 85% lower per-point power consumption. 4. Support for all-solid-state e-scanning, photon anti-interference, and smart optical zoom.

Digitalization is also a key focus in the LiDAR industry. Digital LiDAR employs digital methods to detect and process photon information, eliminating the "analog-to-digital" conversion process. This preserves more detection data, enhances resolution, accuracy, integration, and perception fusion capabilities, while delivering additional system-level benefits.

Digital LiDAR utilizes Single-Photon Avalanche Diode (SPAD) devices, which detect laser signals at the single-photon level. The output digital signals can proceed directly to processing without requiring intermediate transmission components. Meanwhile, signal processing, storage, and even laser control can be integrated into chips via algorithms, improving computational efficiency while reducing reliance on physical hardware.

Current SPAD chip players include Sony, as well as domestic entrants like Sophoton, FortSense, and Adaps Photonics. Companies adopting SPAD-based digital architectures include Ouster, ZVISION, and RoboSense. For example, the ZVISION EZ6, which uses SPAD chips, achieves a 20%-30% cost reduction compared to previous generations, making it suitable for forward long-range applications (passenger cars/intelligent transportation).

The EM4, the first product under RoboSense's new digital EM platform, integrates a SPAD-SoC chip and a 940nm VCSEL chip. As the world's first 1080-channel LiDAR, it can precisely identify distant small objects like tires, traffic cones, and cartons, raising the safety ceiling for autonomous driving systems. It can improve system response time by up to 70%, enabling more confident decision - supported by direct integration with automotive Ethernet systems in smart vehicles. RoboSense's digital LiDAR will accelerate adoption across automotive, robotics, and drone markets.

In terms of algorithm and architecture innovation, take VanJee Technology's 192-channel LiDAR WLR-760 as an example. It adopts a VCSEL+SPAD design, combined with VanJee's self-developed FOC vector control algorithm for rotating mirrors and multi-channel VCSEL drivers. This not only significantly improves product performance but also simplifies the internal structure. Compared to traditional solutions, the number of component types is reduced by over 60%, the quantity of components by over 80%, and production steps by 30%.

In information processing, there is a trend toward shifting computing power upward. For instance, ZVISION's SPAD product architecture retains only the optoelectronic front end, transmitting raw signals directly to the domain controller. The EZ-Key algorithm suite is deployed on the domain controller side, moving LiDAR computing tasks to the domain controller. This approach modularizes the LiDAR's optoelectronic front end, minimizes its power consumption, and standardizes LiDAR data. It also enables the use of massive amounts of raw corner-case data to iteratively upgrade point cloud algorithms.

The EZ-Key suite can be deployed either on the LiDAR unit itself or flexibly integrated into customer's domain controller. Its functions include dirt detection, rain/fog/dust/exhaust detection, line-drawing algorithms, ghosting removal algorithms, and bloated-point suppression algorithms, effectively addressing the impact of false point clouds on data quality in various scenarios.

As LiDAR point cloud quality approaches the pixel-level clarity of cameras, and with LiDAR's zoom capability mirroring that of camera lenses, parameters can be dynamically adjusted based on driving scenarios and needs. This enhances recognition in the central field of view, with finer resolution for clearer perception. For example, the focal length can be extended for highway driving to detect distant obstacles earlier, or narrowed for urban congestion to better perceive nearby vehicles and pedestrians. Zoom-capable LiDARs have already been deployed in mass-produced models like the Hyptec GT.

For cost reduction, companies can improve system integration through chip-based and digital architecture designs. By enhancing production processes and introducing highly automated equipment, they can cut labor calibration costs-which account for about 20% of LiDAR costs. Additionally, higher integration reduces the number of key suppliers, improving supply chain stability and enabling faster large-scale automation, further lowering manufacturing costs.

Economies of scale drive marginal cost reductions. Hesai plans to deliver 1.2 to 1.5 million LiDAR units in 2025, with over 80% allocated to ADAS applications. RoboSense aims to penetrate the mid- to low-price vehicle market in 2025 with its MX series (priced below $200) and accelerate expansion into emerging sectors like robotics and industrial applications.

3. LiDAR accelerates its penetration into humanoid robots and other fields

In non-automotive applications, LiDAR is being widely adopted in industrial control, robotics, drones, measurement & ranging, ports, logistics, agriculture, and other sectors. In December 2024, Hesai delivered over 20,000 LiDAR units for the robotics market in a single month.

Hesai Technology stated that its LiDAR shipments in 2025 are projected to reach 1.2 to 1.5 million units, with approximately 200,000 units designated for robotics applications-covering mobile robots, delivery robots, cleaning robots, and more. Its new production line is set to commence operations in Q3 2025, with annual capacity expected to reach 2 million units by year-end. Hesai's XT series currently provides 3D perception technology for Unitree's robots and is deployed in scenarios such as BMW's Automated Factory Driving (AFD) system.

Meanwhile, RoboSense officially announced its robotics platform company strategy in early 2025, positioning itself as a "robotics technology platform company" to supply incremental components and solutions for the AI robotics industry. Products like the E1R and Airy LiDARs for robots, along with new robotics vision offerings such as the Active Camera and the dexterous hand Papert 2.0, are rapidly being implemented in AI robotics applications.

Seyond is also actively expanding in the robotics market, with its products already deployed across major applications including robotic dogs, logistics robots, industrial robots, and agricultural robots. The company continues to see growing shipments in this sector.

Finally, let's examine how other LiDAR companies are advancing product applications in non-automotive fields.

Table of Contents

1 Overview of Automotive LiDAR

• 1.1 Introduction to LiDAR
• 1.2 LiDAR Structure
  • 1.2.1 Transmitter System
  • 1.2.2 Scanning System
• Comparison of Advantages and Disadvantages of LiDAR with Different Scanning Methods
• Development Trends of LiDAR Scanning Systems
• Mechanical
• Semi-solid-state - Rotating Mirror Type
• Semi-solid-state - Galvanometer Mirror Type
• Semi-solid-state - Prism Type
• All-solid-state - Flash
• All-solid-state - OPA
• 1.2.3 Receiver System
• SPAD-SoC Technology Development Trends
• Localization of SPAD-SoC Facilitates Adoption of Pure Solid-State LiDAR in Vehicles
• Case 1:
• Case 2:
• Case 3:
• Case 4:
• Case 5:
• 1.2.4 Information Processing System
• Trend 1:
• Trend 2:
• Case 1:
• Case 2:
• Case 3:
• 1.3 LiDAR Types
  • 1.3.1 By Ranging Method:
• ToF Is Currently Mainstream, FMCW Is the Future Development Direction
• Comparison of Mass Production Implementation between ToF LiDAR and FMCW LiDAR
• Detailed technical optimization directions for ToF LiDAR R&D
• Lightweight and miniaturization of FMCW LiDAR
• 1.3.2 By Wavelength:
• Summary and Analysis of Current LiDAR Technical Routes
• 1.3.3 By Optical Control: Solid-state development trend
• Is OPA the Ideal Scanning Solution?
• Is OPA+FMCW the Ultimate Technical Evolution Direction for LiDAR?
• Comparative Analysis of Mainstream Automotive LiDAR Product Technical Routes
• Analysis of Key Automotive LiDAR Technology Trends
• 1.4 Comparison of Solid-State LiDAR Technical Routes
• 1.5 Cost Breakdown of Solid-State LiDAR
• 1.6 Continuous Cost Reduction Under Stringent Automotive-Grade Requirements
• 1.7 R&D Investment Required for Automotive-Grade LiDAR
• 1.8 Architecture Evolution: Cost Reduction Concept Through Computing Power Migration
• Architecture Simplification Case 1:
• Chip-Based Cost Reduction Case 1:
• Chip-Based Cost Reduction Case 2:
• Main Development Directions for LiDAR Chipification in 2025 (1)
• Main Development Directions for LiDAR Chipification in 2025 (2)
• 1.9 New LiDAR Chip Products
• Case 1:
• Case 2:
• Case 3:
• Case 4:
• Case 5:
• Case 6:
• 1.10 LiDAR Chip Manufacturing Process
• The Chip Manufacturing Process Evolves from Front-Side Illumination (FSI) to Back-Side Illumination with Stacking (BSI+Stack)
• Miniaturization Trend Case 1:
• Miniaturization Trend Case 2:
• Digitalization Progress Case 1:
• Digitalization Progress Case 2:
• 1.11 LiDAR Industry Chain

2 Automotive LiDAR Market and Application Vehicles

• 2.1 Automotive LiDAR Related Standards
• 2.2 Automotive LiDAR Market Analysis
  • 2.2.1 Automotive LiDAR Price Development Trends
  • 2.2.2 Global and China Automotive LiDAR Market Size
  • 2.2.3 Domestic Automotive LiDAR Installations and Installation Rate (by Year)
  • 2.2.4 Domestic Passenger Vehicle LiDAR Installations and Installation Rate (by Month)
  • 2.2.5 Installations Share Trends of Four Major LiDAR Suppliers
  • 2.2.6 Domestic Passenger Car LiDAR Installation Share (by Price)
  • 2.2.7 LiDAR Installations and Share by Autonomous Driving Level
  • 2.2.8 Passenger Car LiDAR Installations and Share by Number of LiDAR Units
  • 2.2.9 Top 10 Brands of Domestic Passenger Car by LiDAR Installations
  • 2.2.10 Automotive LiDAR Installations and Year-over-Year Growth
  • 2.2.11 Top 11 OEMs and Suppliers by Automotive Pre-installed LiDAR Installations
  • 2.2.12 Installation Share of Leading Automotive LiDAR Manufacturers by Partner OEMs
  • 2.2.13 Installation Share of Other Automotive LiDAR Manufacturers by Partner OEMs
• 2.3 Domestic LiDAR Application Vehicle Analysis
• Case 1: MAEXTRO S800
• Case 2:
• Case 3:
• Case 18: GAC Aion 520 LiDAR Edition
• Case 19: GAC Toyota bZ3X
• Case 20: Leapmotor B10
• Partial Models Equipped with LiDAR in Overseas Markets

3 LiDAR Application Scenarios

• 3.1 Main Application Scenarios of LiDAR
• 3.2 Emerging Applications of LiDAR
• 3.3 Non-Automotive Applications of LiDAR
• Comparison Between Automotive LiDAR and Robotic LiDAR
• Perception Solutions for Humanoid Robots and Robotic Dogs
• LiDAR Installation Rate in Robotic Dogs
• LiDAR Installation Rate in Humanoid Robots
• Pilot Implementations of Domestic and International Humanoid Robots in Automotive Industry
• 3.4 Robotic Deployment Case: Hesai Technology
• LiDAR Sales in Robotics Market
• Designed Specifically for Robotics Field: Mini High-Performance 3D LiDAR
• LiDAR for Robotics Scenarios: QT128
• LiDAR for Robotics Scenarios: XT32
• 3.5 Robotic Deployment Case: RoboSense
• Active Camera - Robotic Eye: Integrating LiDAR Digital Signals with Camera Data
• Digital LiDAR Enables Comprehensive Upgrade for Both Automotive and Robotic Perception Capabilities
• Robotics Business Dynamics

4 Chinese Automotive LiDAR Suppliers

• 4.1 Hesai Technology
  • 4.1.1 Profile
  • 4.1.2 R&D Patents
  • 4.1.3 Chipification Roadmap
  • 4.1.4 System Security Development History
  • 4.1.5 Overall LiDAR Supporting
  • 4.1.6 LiDAR Supporting, 2024
  • 4.1.7 Performance, 2021 - 2024
  • 4.1.8 Product Matrix
  • 4.1.9 AT1440
  • 4.1.10 AT512
  • 4.1.11 ATX
• ATX Designation Case 1:
• ATX Application Case 1:
• 4.1.12 AT 128
• AT128 Designation Case 1:
• AT128 Designation Case 2:
• AT128 Designation Case 3:
• 4.1.13 OT128 (1)
• 4.1.13 OT128 (2)
• 4.1.13 OT128 (3)
• 4.1.14 ET25
• 4.1.15 FTX Series
• 4.1.16 FT120 (1)
• 4.1.16 FT120 (2)
• 4.1.17 JT Series
• 4.1.18 Cooperation Case (1)
• 4.1.18 Cooperation Case (2)
• 4.2 RoboSense
  • 4.2.1 Profile
• 4.2.2R&D Breakthrough (1)
• 4.2.2R&D Breakthrough (2)
• 4.2.3 Core Technology (1):
• 4.2.3 Core Technology (2):
• 4.2.3 Core Technology (3):
• 4.2.4 LiDAR Platforms and Products
• 4.2.5 Comparison of Main Parameters for LiDAR Platforms and Products
• 4.2.6LiDAR Supporting, 2024
• 4.2.7 EM4 (1)
• 4.2.7 EM4 (2)
• 4.2.7 EM4 (3)
• 4.2.8 E1R (1)
• 4.2.8 E1R (2)
• 4.2.9 E1
• 4.2.10 Airy
• 4.2.11 MX
• 4.2.12 M3
• 4.2.13 Cooperation Case
• 4.3 Seyond
  • 4.3.1 Comprehensive Product Series Analysis (1)
  • 4.3.2 Comprehensive Product Series Analysis (2)
  • 4.3.3 Application Status and Trends in Non-Automotive Fields
  • 4.3.4 Sales and Customer Share, 2022-2025
  • 4.3.5 Cooperation Dynamics
  • 4.3.6 Operational Risks and Improvement Recommendations
• 4.4 Huawei
  • 4.4.1 LiDAR Development History
  • 4.4.2 LiDAR Product Comparison (1)
  • 4.4.2 LiDAR Product Comparison (2)
  • 4.4.3 LiDAR D2
  • 4.4.4 LiDAR D3
  • 4.4.5 LiDAR D5
  • 4.4.5 LiDAR Core Technology
  • 4.4.6 LiDAR and Autonomous Driving Solutions
  • 4.4.7 Challenges and Countermeasures (1)
  • 4.4.7 Challenges and Countermeasures (2)
  • 4.4.8 Detailed LiDAR Supporting, 2024 (1)
  • 4.4.8 Detailed LiDAR Supporting, 2024 (2)
• 4.5 Zhuoyu Technology
  • 4.5.1 Comparison of Chengxing Platform Configurations
  • 4.5.2 Comparison of Chengxing Platform's LiDAR Configurations and Performance
  • 4.5.3 Advantages of LiDAR-Vision Solution (1)
  • 4.5.3 Advantages of LiDAR-Vision Solution (2)
• 4.6 Livox
  • 4.6.1 Profile
  • 4.6.2 High-Performance 3D LiDAR Series Implementation Status (1)
  • 4.6.2 High-Performance 3D LiDAR Series Implementation Status (2)
• 4.7 Tanway Technology
  • 4.7.1 Profile
  • 4.7.2 Perception Algorithms
  • 4.7.3 Product Series Comparison
  • 4.7.4 Automotive Application Products
  • 4.7.5 Non-Automotive Applications (1)
  • 4.7.5 Non-Automotive Applications (2)
• 4.8 ZVISION
  • 4.8.1 Profile
  • 4.8.2 LiDAR Technological Innovation
  • 4.8.3 LiDAR Product Series Comparison
  • 4.8.4 Price of LiDAR Series and Selection Recommendations
  • 4.8.5 Partners
• 4.9 LiangDao Automotive Technology
  • 4.9.1 Profile
  • 4.9.2 3D Perception Technology
  • 4.9.3 AI Perception Function Development and Data Training
  • 4.9.4 Gen2 Mini
  • 4.9.5 Next-Generation LDSatellite(R)
  • 4.9.6 Cooperation Dynamics
  • 4.9.7 Customers
• 4.10 VanJee Technology
  • 4.10.1 Designations and Application Expansion of LiDAR
  • 4.10.2 Comparison of LiDAR Series
  • 4.10.3 WLR-760
  • 4.10.4 WLR-750
  • 4.10.5 WLR-720/719E
  • 4.10.6 WLR-718H/722
  • 4.10.7 Applications of LiDAR in Automotive ADAS
  • 4.10.8 Competitiveness of LiDAR in Automotive ADAS Field
  • 4.10.9 LiDAR Mass Production Capability
• 4.11 Others
  • 4.11.1 Benewake
  • 4.11.2 WHST
  • 4.11.3 Rayz Technologies

5 Foreign Automotive LiDAR Suppliers

• 5.1 Luminar
  • 5.1.1 Profile
  • 5.1.2 Development History
  • 5.1.3 Technical Advantages
  • 5.1.4 Ecosystem
  • 5.1.5 Product Roadmap
  • 5.1.6 Iris
  • 5.1.7 Halo
  • 5.1.8 Sentinel(TM)
  • 5.1.9 LiDAR Supporting
  • 5.1.10 Customer Expansion
• 5.2 Innoviz
  • 5.2.1 Profile & Product Portfolio
  • 5.2.2 Core Technology of LiDAR Matrix
  • 5.2.3 Two Long-Range LiDAR
  • 5.2.4 Vehicle Installations of Two Long-Range Version
  • 5.2.5 Two Mid/Short-Range LiDAR
  • 5.2.6 One's Specifications and Applications
  • 5.2.7 One's Performance in Specific Vehicle Models
  • 5.2.8 Revenue and Net Profit Trend, 2023-2025
  • 5.2.9 Commercialization Progress
• 5.3 Aeva
  • 5.3.1 Latest Dynamics
  • 5.3.2 Revenue and Mass Production Designations
  • 5.3.3 Atlas(TM) Ultra 4D LiDAR
  • 5.3.4 Atlas
  • 5.3.5 Aeries(TM) II
• 5.4 AEYE
  • 5.4.1 Profile
  • 5.4.2 Performance Trend
  • 5.4.3 Light Asset Mode
  • 5.4.4 Comparison of LiDAR Product Series
  • 5.4.5 Mass Production and Future Capacity Plan of Apollo LiDAR
• 5.5 Ouster
  • 5.5.1 Profile
  • 5.5.2 Comparison of LiDAR Product Series
  • 5.5.3 Performance
• 5.6 Valeo
  • 5.6.1 LiDAR Supporting (1)
  • 5.6.2 LiDAR Supporting (2)

6 Development Trends of Automotive LiDAR

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