Automotive Vision Algorithm Industry Research Report, 2023
  • Jan.2023
  • Hard Copy
  • USD $4,200
  • Pages:252
  • Single User License
    (PDF Unprintable)       
  • USD $4,000
  • Code: BXM149
  • Enterprise-wide License
    (PDF Printable & Editable)       
  • USD $6,000
  • Hard Copy + Single User License
  • USD $4,400

Research on automotive vision algorithms: focusing on urban scenarios, BEV evolves into three technology routes.

1. What is BEV?

BEV (Bird's Eye View), also known as God's Eye View, is an end-to-end technology where the neural network converts image information from image space into BEV space.

Compared with conventional image space perception, BEV perception can input data collected by multiple sensors into a unified space for processing, acting as an effective way to avoid error superposition, and also makes temporal fusion easier to form a 4D space. 

视觉算法 1_副本.png

BEV is not a new technology. In 2016, Baidu began to realize point cloud perception at the BEV; in 2021, Tesla’s introduction of BEV draw widespread attention in the industry. There are BEV perception algorithms corresponding to different sensor input layers, basic tasks, and scenarios. Examples include BEVFormer algorithm only based on vision, and BEVFusion algorithm based on multi-modal fusion strategy.

视觉算法 2_副本.png

2. Three technology routes of BEV perception algorithm

In terms of implementation of BEV technology, the technology architecture of each player is roughly the same, but technical solutions they adopt are different. So far, there have been three major technology routes:

Vision-only BEV perception route in which the typical company is Tesla;
BEV fused perception route in which the typical company is;
Vehicle-road integrated BEV perception route in which the typical company is Baidu.

Vision-only BEV perception technology route: Tesla is a representative company of this technology route. In 2021, it was the first one to use the pre-fusion BEV algorithm for directly transmitting the image perceived by cameras into the AI algorithm to generate a 3D space at a bird's-eye view, and output perception results in the space. This space incorporates dynamic information such as vehicles and pedestrians, and static information like lane lines, traffic signs, traffic lights and buildings, as well as the coordinate position, direction angle, distance, speed, and acceleration of each element.

视觉算法 3_副本.png

Tesla uses the backbone network to extracts features of each camera. It adopts the Transformer technology to convert multi-camera data from image space into BEV space. Transformer, a deep learning model based on the Attention mechanism, can deal with massive data-level learning tasks and accurately perceive and predict the depth of objects.

视觉算法 4_副本.png

BEV fused perception technology route: is an autonomous driving company under Great Wall Motor. In 2022, it announced an urban NOH solution that underlines perception and neglects maps. The core technology comes from MANA (Snow Lake).

In the MANA perception architecture, adopts BEV fused perception (visual Camera + LiDAR) technology. Using the self-developed Transformer algorithm, MANA not only completes the transformation of vision-only information into BEV, but also finishes the fusion of Camera and LiDAR feature data, that is, the fusion of cross-modal raw data.

视觉算法 5_副本.png

Since its launch in late 2021, MANA has kept evolving. With Transformer-based perception algorithms, it has solved multiple road perception problems, such as lane line detection, obstacle detection, drivable area segmentation, traffic light detection & recognition, and traffic sign recognition.

In January 2023, MANA got further upgraded by introducing five major models to enable the transgenerational upgrade of the vehicle perception architecture and complete such tasks as common obstacle recognition, local road network and behavior prediction. The five models are: visual self-supervision model (automatic annotation of 4D Clip), 3D reconstruction model (low-cost solution to data distribution problems), multi-modal mutual supervision model (common obstacle recognition), dynamic environment model (using perception-focused technology for lower dependence on HD maps), and human-driving self-supervised cognition model (driving policy is more humane, safe and smooth).

视觉算法 6_副本.png

Vehicle-road integrated BEV perception technology route: in January 2023, Baidu introduced UniBEV, a vehicle-road integrated solution which is the industry's first end-to-end vehicle-road integrated perception solution.

20120114.gifFusion of all vehicle and roadside data, covering online mapping with multiple vehicle cameras and sensors, dynamic obstacle perception, and multi-intersection multi-sensor fusion from the roadside perspective;
20120114.gifSelf-developed internal and external parameters decoupling algorithm, enabling UniBEV to project the sensors into a unified BEV space regardless of how they are positioned on the vehicle and at the roadside
20120114.gifIn the unified BEV space, it is easier for UniBEV to realize multi-modal, multi-view, and multi-temporal fusion of spatial-temporal features;
20120114.gifThe big data + big model + miniaturization technology closed-loop remains superior in dynamic and static perception tasks at the vehicle side and roadside.

视觉算法 7_副本.png

Baidu’s UniBEV solution will be applied to ANP3.0, its advanced intelligent driving product planned to be mass-produced and delivered in 2023. Currently, Baidu has started ANP3.0 generalization tests in Beijing, Shanghai, Guangzhou and Shenzhen.

Baidu ANP3.0 adopts the "vision-only + LiDAR" dual redundancy solution. In the R&D and testing phase, with the "BEV Surround View 3D Perception" technology, ANP3.0 has become an intelligent driving solution that enables multiple urban scenarios solely relying on vision. In the mass production stage, ANP3.0 will introduce LiDAR to realize multi-sensor fused perception to deal with more complex urban scenarios.

3. BEV perception algorithm favors application of urban NOA.

As vision algorithms evolve, BEV perception algorithms become the core technology for OEMs and autonomous driving companies such as Tesla, Xpeng, Great Wall Motor, ARCFOX, QCraft and, to develop urban scenarios.

Xpeng Motors: the new-generation perception architecture XNet can fuse the data collected by cameras before multi-frame timing, and output 4D dynamic information (e.g., vehicle speed and motion prediction) and 3D static information (e.g., lane line position) at the BEV. In January 2023, it announced the intelligent driving solution - Pony Shitu. The self-developed BEV perception algorithm, the key feature of the solution, can recognize various types of obstacles, lane lines and passable areas, minimize computing power requirements, and enable highway and urban NOA only using navigation maps.

视觉算法 8_副本.png

1 Overview of Vision Algorithm
1.1 Vehicle Perception System Architecture
1.2 Vehicle Visual Sensors and Solutions 
1.3 Vehicle Visual Perception Tasks
1.4 Computing Architecture and Algorithms of Exterior Visual Perception Systems 
1.4.1 Mono Camera Algorithm
1.4.2 Stereo Camera Algorithm
1.4.3 Surround View Camera Algorithm
1.5 Architecture and Algorithms of In-vehicle Visual DMS 
1.5.1 Visual DMS Solution
1.5.2 Visual OMS Solution
1.6 BEV Perception Algorithm

2 Foreign Vision Algorithm Companies
2.1 Mobileye
2.1.1 Profile
2.1.2 Main Technologies
2.1.3 Visual Solutions
2.1.4 Major Customers

2.2 Continental
2.2.1 Profile
2.2.2 Vision Algorithm Layout 
2.2.3 DMS Vision and Algorithm
2.2.4 In-cabin Vision and Algorithm
2.2.5 Surround View Camera and Algorithm

2.3 Bosch
2.3.1 Profile
2.3.2 Front View Camera and Algorithm
2.3.3 Surround View Camera and Algorithm
2.3.4 In-cabin Vision and Algorithm
2.3.5 Fused Perception Algorithm

2.4 StradVision
2.4.1 Profile
2.4.2 Products
2.4.3 Vision Algorithm
2.4.4 Dynamics

2.5.1 Profile
2.5.2 Core Algorithms for Autonomous Driving
2.5.3 Autonomous Vehicle Software Stack
2.5.4 DRIVE Perception
2.5.5 Perception Algorithm for Driving Scenario
2.5.6 Perception Algorithm for Parking Scenario
2.5.7 In-cabin Perception Algorithm
2.5.8 Cooperation Dynamics and Partners

2.6 Qualcomm
2.6.1 Snapdragon Ride Platform
2.6.2 Snapdragon Ride Vison System
2.6.3 Vision Algorithm Layout
2.6.4 Partners

2.7 Valeo 
2.7.1 Profile
2.7.2 Core Algorithm Layout
2.7.3 Drive4U Fully Autonomous Driving Solution
2.7.4 Remote Park4U Fully Automated Parking System
2.7.5 Major Customers

2.8 Seeing Machines
2.8.1 Profile
2.8.2 DMS Product Roadmap
2.8.3 DMS Technology
2.8.4 DMS Algorithm and Solution
2.8.5 OMS Algorithm and Solution
2.8.6 Cooperation Dynamics

2.9 Smart Eyes
2.9.1 Profile
2.9.2 DMS General Development Platform
2.9.3 Eye Tracking Technology and System Solutions
2.9.4 DMS Algorithm
2.9.5 IMS Perception Algorithm
2.9.6 Software and Hardware Integrated Driver Monitoring System (AIS)
2.9.7 Cooperation Dynamics

2.10 Cipia
2.10.1 Profile
2.10.2 DMS Solution
2.10.3 In-cabin Solution
2.10.4 Fleet Solution
2.10.5 Cooperation Dynamics

2.11 XPERI
2.11.1 Profile
2.11.2 DMS Solution
2.11.3 New Generation DMS Solution
2.11.4 OMS Solution
2.11.5 Partners and Dynamics
2.12 Tesla
2.12.1 Overview of AI Algorithms for Autopilot Systems 
2.12.2 Occupancy Networks Algorithm
2.12.3 New Lane Detection Algorithm 
2.12.4 HydarNet Algorithm
2.12.5 Autopilot Solutions 
3 Chinese Vision Algorithm Companies
3.1 Momenta
3.1.1 Profile
3.1.2 Visual Perception Algorithm
3.1.3 Mass-produced Autonomous Driving Solutions
3.1.4 Fully Intelligent Driving Solution
3.1.5 Dynamics in Autonomous Driving

3.2.1 Profile
3.2.2 Development Strategy
3.2.3 Core Business
3.2.4 Intelligent Data System MANA
3.2.5 Intelligent Data System MANA - Perception Algorithm 
3.2.6 Intelligent Data System MANA - Cognition Algorithm
3.2.7 Urban Scenario Solutions
3.2.8 Service Model and Implemented Projects

3.3 Nullmax 
3.3.1 Profile
3.3.2 Core Technologies
3.3.3 MaxView Perception Technology System
3.3.4 Multi-camera BEV Solution
3.3.5 MaxFlow Data Closed Loop
3.3.6 Autonomous Driving Solutions
3.3.7 Competitive Edges and Major Partners

3.4 Motovis
3.4.1 Profile
3.4.2 Main Products and Solutions
3.4.3 Core Algorithm Team and Technologies
3.4.4 Visual Perception Based on Deep Learning
3.4.5 BEV-based Fused Perception Algorithm

3.5.1 Profile
3.5.2 Autonomous Driving Solutions
3.5.3 Out-cabin Perception Solution 
3.5.4 Out-cabin Algorithm and Capabilities 
3.5.5 Improvements in Out-cabin Algorithm
3.5.6 In-cabin Perception Solution
3.5.7 In-cabin Perception Algorithm and Capabilities
3.5.8 Partners and Dynamics

3.6 JIMU Intelligent
3.6.1 Profile
3.6.2 Out-cabin Perception Algorithm
3.6.3 The Work Done by JIMU to Improve Algorithm Accuracy 
3.6.4 Application of Out-cabin Detection Algorithm
3.6.5 In-cabin Driver Monitoring Technology
3.6.6 Cooperation Dynamics and Future Development

3.7 Smarter Eye
3.7.1 Profile
3.7.2 Core Technologies
3.7.3 Developments and Cooperation

3.8 SenseTime
3.8.1 Profile
3.8.2 Intelligent Vehicle Business Layout
3.8.3 SenseAuto Pilot Solution
3.8.4 SenseAuto Cabin Solution
3.8.5 Core Technologies

3.9 ArcSoft 
3.9.1 Profile
3.9.2 Strategic Layout
3.9.3 Vehicle Visual Perception Algorithm
3.9.4 VisDrive Vehicle Vision Solution
3.9.5 Software and Hardware Integrated Vehicle Vision Solution for OEMs: Tahoe
3.9.6 Customers and Partners

3.10 Baidu Apollo
3.10.1 Profile
3.10.2 Development History of Baidu Autonomous Driving Perception 
3.10.3 Baidu’s Main Algorithms in Perception 1.0 Stage
3.10.4 Baidu’s Main Algorithms in Perception 2.0 Stage
3.10.5 Baidu’s Autonomous Driving System Solutions 
3.10.6 Baidu’s Vision-only Solution - Apollo Lite
3.10.7 Baidu’s Fused Perception Solution - Apollo Lite++
3.10.8 Baidu’s End-to-end 3D Perception Development Kit - Paddle3D
3.10.9 Major Clients and Partners of Baidu Apollo

3.11 UISEE 
3.11.1 Profile
3.11.2 U-Drive Intelligent Driving Platform
3.11.3 U-Pilot Solution for Mass Production
3.11.4 Visual Positioning Technology
3.11.5 R&D Plan and Partners

3.12 Horizon Robotics
3.12.1 Profile
3.12.2 Technologies and Solutions
3.12.3 Chip Iteration History
3.12.4 AI Algorithm Layout
3.12.5 BEV Perception Solution
3.12.6 AIDI Development Platform
3.12.7 Intelligent Driving Solutions
3.12.8 Intelligent Driving Solution: Front View Mono 
3.12.9 Intelligent Driving Solution: Driving and Parking Integrated Solution
3.12.10 Intelligent Driving Solution: SuperDrive
3.12.11 Partners

3.13 Juefx 
3.13.1 Profile
3.13.2 Products
3.13.3 Fused Location Production Solution 
3.13.4 Fused Location Solution with Visual Features
3.13.5 Development History of BEV Perception Technology
3.13.6 Cooperation Ecosystem

3.14 ZongMu Technology
3.14.1 Profile
3.14.2 Visual Products and Systems
3.14.3 Vision Algorithm
3.14.4 Major Customers

3.15 ThunderSoft
3.15.1 Profile
3.15.2 Intelligent Vision Products and Core Technologies
3.15.3 Surround View Camera + DMS Vision Algorithms 

3.16 iVICAR
3.16.1 Profile
3.16.2 Surround View Camera Algorithm Layout

4 Summary and Trends
4.1 Summary on Companies  
4.1.1 List of Foreign Vision Algorithm Companies
4.1.2 List of Chinese Vision Algorithm Companies
4.2 Development Trends
4.2.1 Trend 1
4.2.2 Trend 2
4.2.3 Trend 3
4.2.4 Trend 4
4.2.5 Trend 5
4.2.6 Trend 6
4.2.7 Trend 7
4.2.8 Trend 8


Automotive Microcontroller Unit (MCU) Industry Report, 2024

With policy support, the localization rate of automotive MCU will surge. Chinese electric vehicle companies are quickening their pace of purchasing domestic chips to reduce their dependence on impor...

Automotive Digital Key Industry Trends Research Report, 2024

Automotive Digital Key Industry Trends Research Report, 2024 released by ResearchInChina highlights the following: Forecast for automotive digital key market;Digital key standard specifications and co...

Automotive XR (VR/AR/MR) Industry Report, 2024

Automotive XR (Extended Reality) is an innovative technology that integrates VR (Virtual Reality), AR (Augmented Reality) and MR (Mixed Reality) technologies into vehicle systems. It can bring drivers...

OEMs’ Next-generation In-vehicle Infotainment (IVI) System Trends Report, 2024

OEMs’ Next-generation In-vehicle Infotainment (IVI) System Trends Report, 2024 released by ResearchInChina systematically analyzes the iteration process of IVI systems of mainstream automakers in Chin...

Global and China Automotive Lighting System Research Report, 2023-2024

Installations of intelligent headlights and interior lighting systems made steady growth. From 2019 to 2023, the installations of intelligent headlights and interior lighting systems grew steadily. I...

Automotive Display, Center Console and Cluster Industry Report, 2024

Automotive display has become a hotspot major automakers compete for to create personalized and differentiated vehicle models. To improve users' driving experience and meet their needs for human-compu...

Global and China Passenger Car T-Box Market Report, 2024

Global and China Passenger Car T-Box Market Report, 2024 combs and summarizes the overall global and Chinese passenger car T-Box markets and the status quo of independent, centralized, V2X, and 5G T-B...

AI Foundation Models’ Impacts on Vehicle Intelligent Design and Development Research Report, 2024

AI foundation models are booming. The launch of ChapGPT and SORA is shocking. Scientists and entrepreneurs at AI frontier point out that AI foundation models will rebuild all walks of life, especially...

Analysis on Geely's Layout in Electrification, Connectivity, Intelligence and Sharing

Geely, one of the leading automotive groups in China, makes comprehensive layout in electrification, connectivity, intelligence and sharing. Geely boasts more than ten brands. In 2023, it sold a tota...

48V Low-voltage Power Distribution Network (PDN) Architecture Industry Report, 2024

Automotive low-voltage PDN architecture evolves from 12V to 48V system. Since 1950, the automotive industry has introduced the 12V system to power lighting, entertainment, electronic control units an...

Automotive Ultrasonic Radar and OEMs’ Parking Route Research Report, 2024

1. Over 220 million ultrasonic radars will be installed in 2028. In recent years, the installations of ultrasonic radars in passenger cars in China surged, up to 121.955 million units in 2023, jumpin...

Automotive AI Foundation Model Technology and Application Trends Report, 2023-2024

Since 2023 ever more vehicle models have begun to be connected with foundation models, and an increasing number of Tier1s have launched automotive foundation model solutions. Especially Tesla’s big pr...

Qualcomm 8295 Based Cockpit Domain Controller Dismantling Analysis Report

ResearchInChina dismantled 8295-based cockpit domain controller of an electric sedan launched in December 2023, and produced the report SA8295P Series Based Cockpit Domain Controller Analysis and Dism...

Global and China Automotive Comfort System (Seating system, Air Conditioning System) Research Report, 2024

Automotive comfort systems include seating system, air conditioning system, soundproof system and chassis suspension to improve comfort of drivers and passengers. This report highlights seating system...

Automotive Memory Chip and Storage Industry Report, 2024

The global automotive memory chip market was worth USD4.76 billion in 2023, and it is expected to reach USD10.25 billion in 2028 boosted by high-level autonomous driving. The automotive storage market...

Automotive AUTOSAR Platform Research Report, 2024

AUTOSAR Platform research: the pace of spawning the domestic basic software + full-stack chip solutions quickens. In the trend towards software-defined vehicles, AUTOSAR is evolving towards a more o...

China Passenger Car Electronic Control Suspension Industry Research Report, 2024

Research on Electronic Control Suspension: The assembly volume of Air Suspension increased by 113% year-on-year in 2023, and the magic carpet suspension of independent brands achieved a breakthrough ...

Global and China Hybrid Electric Vehicle (HEV) Research Report, 2023-2024

1. In 2025, the share of plug-in/extended-range hybrid electric passenger cars by sales in China is expected to rise to 40%. In 2023, China sold 2.754 million plug-in/extended-range hybrid electric p...

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