Zuosi Automotive Research released the “2022 ADAS Domain Controller Key Component Trend Analysis Report”.

Zuosi Automotive Research has compiled research on the current mainstream high-performance ADAS domain controller products and technical information in China, such as Huawei MDC and DJI ADAS domain control prototypes.

This article will briefly analyze key components of ADAS domain controllers, including CPU, MCU, storage, and interfaces.

CPU

In terms of CPU selection, various domain controllers are pursuing high-performance chips to achieve advanced ADAS functions such as NOP/NGP at L2+ levels. Currently, the most commonly used chips are Nvidia Xavier and Orin. Both Nvidia Xavier and Orin have built-in stereo dual-core acceleration, allowing direct hardwired output of disparity maps, along with optical flow acceleration modules. The optical flow effect of stereo dual cameras is significantly better than that of monocular systems. For stereo dual-camera companies, the core software asset is the stereo matching algorithm, most of which are semi-global matching, but achieving optimal performance requires extensive exploration.

Basic parameters of the Nvidia Orin chip, Image source: NVIDA official website

The calculation of depth maps is primarily the work of the CPU. Following the depth map is the calculation of freespace, which is mainly handled by the GPU.

MCU in Domain Controllers

The DJI ADAS domain control prototype uses a Texas Instruments MCU, image source: Zuosi Automotive Research

Generally, the MCUs used in ADAS domain controllers are provided by Infineon or NXP, especially Infineon’s TC297X/397X series, which has a high market share. The mainstream MCUs from Renesas, Infineon, and NXP all meet ASIL-D level standards. For example, the MCU used in the DJI ADAS domain controller engineering prototype is the Texas Instruments TMS570LC4357, which was launched in 2014, has not passed ASIL certification, and only has AEC Q-100 certification.

In the second half of 2024, Infineon plans to begin mass production of the new AURIX™ TC4xx series microcontrollers using 28nm technology. The AURIX™ TC4xx series microcontrollers are primarily aimed at applications with high data throughput, such as advanced driver assistance systems, various domain controllers, new energy systems, and gateway systems.

Infineon TC4xx architecture diagram, image source: Infineon official website

The MCU is very important as it is a crucial factor in ensuring that the domain controller meets ASIL-C/D certification standards.

Interfaces

ADAS domain controllers require a rich variety of interfaces (video interfaces, Ethernet interfaces, CAN interfaces, etc.) to connect various sensor devices, such as cameras, LiDAR, millimeter-wave radar, ultrasonic radar, combined navigation, IMU, and V2X modules. Camera interfaces typically use protocols such as GMSL, LVDS, and FPDLink, while millimeter-wave radar generally uses CAN/FD communication, and LiDAR, which needs to transmit large amounts of data, often uses Ethernet interfaces.

Part of the interfaces and Ethernet switch of the DJI ADAS domain control prototype, image source: Zuosi Automotive Research

Above the MCU on the DJI ADAS domain controller carrier board are two Ethernet switches, namely Marvell’s 88EA6321. On the left is a hard disk SATA interface, which uses Marvell’s 88SE9171 chip to convert SATA to PCIe. Most development boards do not come with SATA interfaces and typically use USB interfaces. Next to the 88SE9171 is a Winbond W25Q64JV NOR Flash chip with a capacity of 64Mb, which appears to store a simple hard disk driver program.

Marvell’s first-generation automotive Ethernet switch 88EA6321 is a 7-port Ethernet gigabit performance switch, fully compliant with the IEEE802.3 automotive standard, supporting AVB (Audio/Video Bridging) functionality and low-power Ethernet to reduce power consumption. This 7-port Ethernet switch integrates 2 IEEE 10/100/1000BASE-T/TX/T ports, 2 RGMII/xMII (which can be configured as 1 GMII) ports, and 1 SGMII/SerDes port. The switch provides remote management capabilities for easy connection and configuration. It typically serves as a bridge between the main processor and the MCU, specifically the Nvidia Xavier and Texas Instruments TMS570LC4357.

The 88EA6321 is an early product from Marvell, and the company’s Ethernet switches have since evolved to the third generation. However, Marvell’s more advanced products usually collaborate only with major manufacturers. The target market for the 88EA6321 is scenarios with lower safety requirements, such as body controllers, infotainment controllers, and gateways. It supports a maximum of only 1G. Although Tesla also uses this chip, traditional automakers like Volkswagen do not use such low-bandwidth switches in new products. Currently, the most advanced designs support up to 10G, which is gigabit Ethernet, and generally support up to 2.5G.

For certain LiDAR systems with high point cloud density, peak rates may exceed 100M per second. The 88EA6321 is not suitable for connecting high point cloud density LiDAR (it is even less feasible with CAN, which only supports a maximum bandwidth of 0.5M). Currently, mainstream millimeter-wave radars typically use CAN or CAN-FD interfaces, with very few 4D millimeter-wave radars offering Ethernet output, and the default is generally CAN-FD.

Storage

Similar to the consumer electronics sector, automotive systems are beginning to adopt high-speed storage devices such as LPDDR DRAM, UFS, and eMMC on a large scale to meet the data transmission and storage requirements of ADAS domain controllers’ system and software-level algorithms. Currently, the mainstream storage combination for domain controllers mainly adopts the form of “LPDDR+UFS”, consistent with storage combinations in mobile phones.

Micron automotive-grade LPDDR5, image source: Micron official website

Micron is at the forefront of automotive storage. In June 2021, Micron launched the first automotive UFS 3.1 memory device product combination, offering advantages in cost/density. The data reading performance of Micron’s UFS 3.1 is twice that of the previous generation UFS 2.1, with continuous write performance improved by 50%, meeting the growing demand for real-time local storage of sensor and camera data in ADAS systems and black box applications at level 3 and above.The Ideal L9 ADAS domain controller is equipped with Micron automotive-grade LPDDR5 DRAM and UFS 3.1 storage chips based on 3D TLC NAND technology.As of now, Micron’s LPDDR5 is the only memory product in the industry that has achieved ASIL-D certification for automotive safety integrity levels.

With the increasing levels of intelligent driving assistance and the gradual application of features such as high-speed/city NGP and autonomous parking AVP, the demand for automotive-grade DRAM capacity, bandwidth, and product requirements will further increase.

• In terms of capacity, according to Micron’s data, the single vehicle DRAM capacity requirement for level 1/2 is about 8GB, while levels 3 and 5 increase to 16GB and 74GB, respectively.

• In terms of bandwidth, level 2 DRAM bandwidth is generally 25-50GB/s, while at level 3, bandwidth can reach 200GB/s, and after level 4, bandwidth will increase to 1TB/s.

• In terms of products, level 2 primarily uses basic DDR2/DDR3, while currently level 2 is beginning to upgrade to level 3, and DRAM will gradually transition to DDR4/LPDDR4/LPDDR5/GDDR5.

In terms of UFS, UFS is specifically defined by JEDEC as a high-performance storage alternative to e-MMC. It has become the main solution for smartphones and is continuously migrating to automotive applications and other areas. UFS is expected to surpass e-MMC and become the primary storage solution for automotive applications.

Deserialization

The representative model for deserialization is Texas Instruments’ DS90UB960. 360-degree panoramic fisheye lens surround view is usually completed by infotainment, with an effective distance generally within 10 meters, making it unsuitable for long-range ADAS. It is typically used only for parking. ADAS domain controllers do not consider 360 surround view, and one DS90UB960 is sufficient to handle 4 surround view image sensors.

Typical application diagram of DS90UB960, image source: Texas Instruments

The image above shows the typical application of Texas Instruments’ DS90UB960, which connects 4 2-megapixel sensors at 30Hz frame rate or 4 2-megapixel sensors at 60Hz frame rate, with the latter being more likely. DS90UB954 is a simplified version of DS90UB960, reducing from 4Lane to 2Lane. Typically paired with it is DS90UB953. It is speculated that Tesla uses this chip for in-car driver state monitoring because the LVDS output from cameras is unsuitable for long-distance transmission. Cameras generally need to be equipped with a deserialization chip to convert parallel data into serial data for transmission via coaxial or STP, allowing for longer transmission distances and easier compliance with automotive EMI standards.

It should be noted that the data formats of cameras are usually RAW RGB and YUV. There are three common levels of YUV: YUV444, YUV422, and YUV420. The formula for calculating bandwidth for RAW RGB is pixels × frame rate × bits × 4. For example, if a camera outputs at 30Hz with 2 million pixels, the bandwidth is 2 million × 30 × 8 × 4, which equals 1.92Gbps, a very high bandwidth. YUV444 is calculated as pixels × frame rate × bits × 3, resulting in 1.44Gbps; YUV422 is pixels × frame rate × bits × 2, resulting in 0.96Gbps; and YUV420 is pixels × frame rate × bits × 1.5, resulting in 0.72Gbps. ADAS systems typically do not focus much on color, making YUV420 sufficient. In non-automotive applications, YUV422 is generally more common.

Table of Contents for the “2022 ADAS Domain Controller Key Component Trend Report”

Report Pages: 109

01

High-Performance Domain Controller Product Solutions

1.1 Huawei

1.1.1 Huawei Intelligent Driving Domain Controller Product Matrix

1.1.2 Huawei Intelligent Driving Domain Controller Product Features

1.1.3 Huawei Intelligent Driving Domain Controller Internal Architecture

1.1.4 Huawei MDC610 Domain Controller

1.1.5 Huawei MDC610 Logical Architecture

1.1.6 Huawei MDC610 Low Power State

1.1.7 Huawei MDC610 Appearance and Interfaces

1.1.8 Huawei MDC610 Interface Scheme – CAN/Ethernet/Video Interfaces

1.1.9 Huawei MDC610 Typical Deployment Scheme – Debugging/Mass Production Scheme

1.1.10 Huawei MDC610 Cooling Specifications – Liquid Cooling/Air Cooling

1.1.11 Huawei MDC610 Deployment Location

1.1.12 Huawei MDC610 Box Dimensions and Installation Space – Liquid Cooling/Air Cooling

1.1.13 Huawei MDC610 Thermal Management Deployment Requirements

1.1.14 Huawei MDC610 Compatible Sensors

1.1.15 Huawei MDC300/F Domain Controller

1.1.16 Huawei MDC300/F Appearance and Interfaces

1.1.17 Huawei MDC300/F Internal Structure

1.1.18 Huawei MDC300/F Technical Specifications – Interfaces/System/Computing Power and Power Supply

1.1.19 Huawei MDC300/F Hardware Design Block Diagram

1.2 Desay SV Automotive

1.2.1 Desay SV Intelligent Driving Domain Controller Product Planning

1.2.2 Desay SV Autonomous Driving Domain Controller IPU: Product Matrix and Comparison

1.2.3 Desay SV Autonomous Driving Domain Controller: Software and Hardware Architecture and Division of Logic

1.2.4 Desay SV Autonomous Driving Domain Controller IPU04: Internal Structure/Software Architecture

1.2.5 Desay SV Domain Fusion Central Computing Platform Aurora: Performance Features/Design Scheme

1.3 DJI Automotive

1.3.1 Domain Controller Appearance and Interfaces

1.3.2 Internal Structure of Domain Controller – Domain Controller Mainboard/SOM Slot/Chip Module

1.3.3 Domain Controller Mainboard Component Introduction

1.3.4 Domain Controller Ethernet Switch

1.3.5 Domain Controller Deserialization

1.3.6 Domain Controller Debugging PCB

1.3.7 Domain Controller System Block Diagram

1.3.8 Domain Controller Key Components – Summary/CPU/MCU/Deserialization

1.3.9 Control ECU Appearance and Interfaces

1.3.10 Control ECU Internal Structure

1.3.11 Control ECU System Block Diagram

1.3.12 Control ECU Key Components

1.4 Tesla

1.4.1 Tesla Autopilot Function Upgrade Path: HW1.0→HW3.0

1.4.2 Tesla Domain Controller HW: Development Path

1.4.3 Tesla Domain Controller HW2.5 VS HW3.0

1.4.4 Tesla Domain Controller HW3.0: Internal Structure

1.5 Haomo Zhixing

1.5.1 Haomo Zhixing Intelligent Driving Domain Controller: Product Development Planning

1.5.2 Haomo Zhixing Intelligent Driving Domain Controller: “Little Magic Box 3.0” (IDC 3.0)

1.6 Furukawa Tech

1.6.1 Furukawa Tech Intelligent Driving Domain Controller Layout

1.6.2 Furukawa Tech Third Generation Intelligent Driving Domain Controller ADC30 Architecture Diagram

1.6.3 Furukawa Tech Third Generation Intelligent Driving Domain Controller ADC30 Sensor Configuration and Function Support

1.6.4 Furukawa Tech Next Generation Autonomous Driving Domain Controller ADC-X

1.7 Domain Controller Technology Benchmarking

1.7.1 Domain Controller Product Technology Benchmarking (1)

1.7.2 Domain Controller Product Technology Benchmarking (2)

1.7.3 Domain Controller Product Technology Benchmarking (3)

02

Comparison of Key Components in High-End ADAS Domain Controllers

2.1 Chips

2.1.1 Chips: Nvidia

2.1.2 Chips: Horizon

2.1.3 Chips: Black Sesame

2.1.4 Chips: Qualcomm Snapdragon RIGE

2.1.5 ADAS Domain Controller Chip Manufacturers Software Stack Solutions

2.1.6 ADAS Domain Controller Chip Manufacturers Product Performance Parameter Comparison (1)

2.1.7 ADAS Domain Controller Chip Manufacturers Product Performance Parameter Comparison (2)

2.1.8 ADAS Domain Controller Chip Manufacturers Product Performance Parameter Comparison (3)

2.1.9 ADAS Domain Controller Chip Manufacturers Product Performance Parameter Comparison (4)

2.1.10 ADAS Domain Controller Chip Manufacturers Product Performance Parameter Comparison (5)

2.2 MCU

2.2.1 Main ADAS Domain Controller MCU Manufacturers

2.2.2 MCU: Infineon TC Series Comparison

2.2.3 MCU: Infineon TC2xx

2.2.4 MCU: Infineon TC3xx

2.2.5 MCU: Infineon TC4xx

2.2.6 Trend 1

2.2.7 Trend 2

2.2.8 Trend 3

2.3 Storage

2.3.1 Storage: Micron

2.3.2 Storage: Samsung

2.3.3 Storage: Hynix

2.3.4 Storage: Kioxia (formerly Toshiba Storage)

2.3.5 Storage: ISSI (a wholly-owned subsidiary of Beijing Junzheng)

2.3.6 Storage Trend 1

2.3.7 Storage Trend 2

2.3.8 Storage Trend 3

2.4 I/O Interfaces

2.4.1 LVDS/CAN/ETH

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Panoramic View of the Intelligent Connected Vehicle Industry Chain (December 2022 Edition)

Domestic Brand OEM Autonomous Driving	Automotive Vision (Domestic)	High-Precision Maps
Joint Venture Brand OEM Autonomous Driving	Automotive Vision (Foreign)	High-Precision Positioning
ADAS and Autonomous Driving Tier 1 – Domestic	Surround View Market Research (Local)	Automotive Gateway
ADAS and Autonomous Driving Tier 1 – Foreign	Surround View Market Research (Joint Venture)	Data Closed Loop Research
Autonomous Driving and Cabin Domain Controllers	Infrared Night Vision	Automotive Information Security Hardware
Multi-Domain Computing and Regional Controllers	Autonomous Driving Simulation (Foreign)	Automotive Information Security Software
Passenger Vehicle Chassis Domain Control	Autonomous Driving Simulation (Domestic)	OEM Digital Transformation
Domain Controller Ranking Analysis	LiDAR – Domestic	Wireless Communication Modules
E/E Architecture	LiDAR – Foreign	Automotive 5G Integration
L4 Autonomous Driving	Millimeter-Wave Radar	800V High-Voltage Platform
L2/L2+ Autonomous Driving	Automotive Ultrasonic Radar	Fuel Cells
Passenger Vehicle Camera Quarterly Report	Radar Disassembly	Integrated Battery
ADAS Data Annual Report	LiDAR and Millimeter-Wave Radar Ranking	Integrated Die Casting
Joint Venture Brand Vehicle Networking	Dedicated Vehicle Autonomous Driving	Automotive Operating Systems
Domestic Brand Vehicle Networking	Mining Autonomous Driving	Steer-by-Wire Chassis
Autonomous Driving Heavy Trucks	Unmanned Shuttle Vehicles	Skateboard Chassis
Commercial Vehicle ADAS	Unmanned Delivery Vehicles	Electronic Control Suspension
Commercial Vehicle Intelligent Cockpit	Unmanned Retail Vehicle Research	Steering Systems
Commercial Vehicle Vehicle Networking	Agricultural Machinery Autonomous Driving	Line Control Braking Research
Commercial Vehicle Intelligent Chassis	Port Autonomous Driving	Charging and Swapping Infrastructure
Automotive Intelligent Cockpit	Modular Reports	Automotive Motor Controllers
Intelligent Cockpit Tier 1	V2X and Vehicle-Road Coordination	Hybrid Power Reports
Multi-Screen and Linked Screens in Cockpits	Roadside Intelligent Perception	Automotive PCB Research
Intelligent Cockpit Design	Roadside Edge Computing	IGBT and SiC Research
Instrument and Central Control Display	Automotive eCall Systems	EV Thermal Management Systems
Intelligent Rearview Mirrors	Automotive EDR Research	Automotive Power Electronics
Dash Cameras	Personalization of Smart Vehicles	Electric Drive and Power Domain
Automotive Digital Keys	Multimodal Interaction in Vehicles	Automotive Wiring Harnesses
Automotive UWB Research	In-Vehicle Voice	Automotive Audio Systems
HUD Industry Research	TSP Manufacturers and Products	Automotive Seats
Human-Machine Interaction	Autonomous Driving Regulations	Automotive Lighting
In-Vehicle DMS	Autonomous Driving Standards and Certifications	Automotive Magnesium Alloy Die Casting
OTA Research	Intelligent Connected Testing Base	Denso’s New Four Modernizations
Automotive Cloud Service Research	PBV and Automotive Robots	New Forces in Car Manufacturing – NIO
Automotive Functional Safety	Flying Cars	New Forces in Car Manufacturing – XPeng
AUTOSAR Research	Integrated Driving Research	New Forces in Car Manufacturing – LI AUTO
Software-Defined Vehicles	Smart Parking Research	Autonomous Driving Chips
Software Suppliers	Automotive Car-Sharing	Chassis SOC
Passenger Vehicle T-Box	Shared Mobility and Autonomous Driving	Automotive VCU Research
Commercial Vehicle T-Box	Digital Transformation of Automakers	Automotive MCU Research
T-Box Ranking Analysis	Intelligent Surfaces	Sensor Chips
XPeng G9 Feature Disassembly	Model Supplier Research	In-Vehicle Storage Chips
LI AUTO L8/L9 Feature Disassembly	NIO ET5/ET7 Intelligent Function Disassembly	Automotive CIS Research
DJI Front Dual Camera and Tida Tong LiDAR Disassembly	Autonomous Driving Fusion Algorithms

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