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市场调查报告书
商品编码
1613812

中国的汽车多模态互动开发(2024年)

China Automotive Multimodal Interaction Development Research Report, 2024
出版日期: | 出版商: ResearchInChina | 英文 270 Pages | 商品交期: 最快1-2个工作天内

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简介目录

1. 语音辨识将主导座舱交互，并与多种方式结合创造新的交互体验。

目前座舱互动应用中，语音互动是智慧座舱应用最广泛、使用频率最高的。根据水清木华研究中心最新统计，2024年1月至8月，自动语音系统安装量约1,100万辆，较去年同期成长10.9%，安装率达83%。百度Apollo智慧座舱业务总经理李涛表示， "人们使用座舱的频率最初是每天三到五次，但现在已经增加到两位数，在语音互动技术方面处于领先地位。" 模型，它达到了近三位数。

语音辨识功能的频繁使用不仅极大优化了使用者的互动体验，也推动了与触控、脸部辨识等其他互动方式融合的发展趋势。例如，蔚来榕树2.4.0的全座舱记忆功能是基于脸部识别，NOMI会主动向记录资讯的乘员打招呼（例如 "豆豆早安" ）。 Zeekr 7X整合了语音辨识和眼神交流，让驾驶者能够看到和说话，并倾斜头部以语音控制汽车。

2.比亚迪推出手掌静脉认证，Sterra车载健康监测首次亮相。

相较于语音辨识、人脸辨识等成熟的互动方式，指纹、静脉、心率等生物辨识技术仍处于探索和发展的早期阶段，但正在逐步量产和使用。例如，比亚迪于2024年推出手掌静脉认证功能，方便车辆解锁。捷恩斯和宾士分别在2025年捷恩斯GV70和2025年宾士EQE BEV上安装了指纹认证系统，使用者只需使用指纹即可执行身分识别、启动车辆、支付等各种操作。此外，Exeed Sterra还在新款ET车型中采用了虹软视觉识别技术，实现了车内智慧健康监测功能，包括五种车内健康监测：心率、血压、血氧饱和度、呼吸频率、输出包含五个关键身体指标的健康报告。

生物辨识认证技术的引进，不仅提高了驾驶便利性，也大大提高了汽车的安全防护性能，有效防范疲劳驾驶、汽车被窃等安全隐患。未来，这些生物辨识技术将更广泛地融入智慧网联汽车的发展中，为驾驶者提供更安全、更个人化的出行体验。

案例一：创世纪2025 GV70的指纹辨识系统可让使用者透过指纹辨识快速套用个人化设定（座椅、位置等），并辅助启动/驾驶车辆。它还具有便利操作、指纹支付、停车服务员模式等个人化整合功能。

范例2：比亚迪手掌静脉认证系统透过摄影机读取手掌静脉数据，并在距离8到20公分、水平360度、垂直15度的情况下进行辨识。专业影像撷取模组撷取静脉纹路影像，透过演算法撷取特征并存储，最终实现识别和识别。未来可能会先搭载于高阶品牌阳王车型上。

案例三：Exeed Sterra ET车型搭载DHS智慧健康监测功能。基于先进的视觉多模态演算法，从体表即时分析您的健康状况，测量心率、血压、血氧饱和度、呼吸频率、心率变异性五项主要身体指标，并提供健康报告。

本报告对中国汽车产业进行了研究分析，提供了多模态互动的主流方式、2024年将发布的车辆互动方式的运用情况、各整车厂/供应商的解决方案以及发展趋势等资讯.

目录

第1章 驾驶座多模态互动概要

  • 多模态互动定义
  • 多模态互动开发系统
  • 多模态互动产业链
  • 多模态互动政策环境

第2章 驾驶座单一模态互动

  • 驾驶座模态互动系统的装载数
  • 触觉互动
  • 听觉互动
  • 视觉互动
  • 嗅觉互动
  • 其他的生物识别功能

第3章 OEM的驾驶座多模态互动解决方案

  • SAIC
  • BYD
  • Changan Automobile
  • GAC
  • Geely
  • NIO
  • Xpen Motors
  • Li Auto
  • Leapmotor
  • Xiaomi Auto
  • BMW
  • Mercedes-Benz
  • Volkswagen

第4章 供应商的驾驶座多模态互动解决方案

  • Desay SV
  • Joyson Electronics
  • SenseTime
  • iFLYTEK
  • ThunderSoft
  • AISpeech
  • Huawei
  • Baidu
  • Tencent
  • NavInfo
  • Continental
  • MediaTek

第5章 车型的基准的多模态互动解决方案的应用案例

  • 老舖子品牌的案例
    • Yangwang U9
    • IM L6
    • Geely Galaxy E8
    • Zeekr 7X
    • Jiyue 07
    • Changan UNI-Z
    • Changan Deepal G318
    • Avatr 07
    • Dongfeng e-phi-007
    • ARCFOX aS5
    • Exeed Sterra ET
  • 新兴品牌的案例
    • Xiaomi SU7
    • Luxeed R7
    • STELATO S9
    • Li Auto MEGA Ultra
    • Xpeng MONA 03
    • ONVO L60
    • Leapmotor C16
  • 合资企业品牌的案例
    • Volvo EX30
    • Lotus EMEYA
    • 2024 Buick E5
    • 2025 BMW i4
    • 2025 Mercedes-Benz All-electric EQE
    • 2025 Genesis GV70

第6章 多模态互动的摘要与开发趋势

  • 智慧型驾驶座的多模态互动的融合用途
  • 趋势1
  • 趋势2(1):座舱交互载具扩大，交互范围延伸至车外
  • 趋势2(2)
  • 趋势2(3)
  • 趋势3(1)
  • 趋势3(2)
  • 趋势3(3)
  • 趋势4
简介目录
Product Code: LYX011

Multimodal interaction research: AI foundation models deeply integrate into the cockpit, helping perceptual intelligence evolve into cognitive intelligence

China Automotive Multimodal Interaction Development Research Report, 2024 released by ResearchInChina combs through the interaction modes of mainstream cockpits, the application of interaction modes in key vehicle models launched in 2024, and the cockpit interaction solutions of OEMs/suppliers, and summarizes the development trends of cockpit multimodal interaction fusion.

1. Voice recognition dominates cockpit interaction, and integrates with multiple modes to create a new interaction experience.

Among current cockpit interaction applications, voice interaction is used most widely and most frequently in intelligent cockpits. According to the latest statistics from ResearchInChina, from January to August 2024, the automate voice systems were installed in about 11 million vehicles, a year-on-year increase of 10.9%, with an installation rate of 83%. Li Tao, General Manager of Baidu Apollo's intelligent cockpit business, pointed out that "the frequency of people using cockpits has increased from 3-5 times a day at the beginning to double digits today, and has even reached nearly three digits on some models with leading voice interaction technology."

The frequent use of voice recognition function not only greatly optimizes user interactive experience, but also promotes the development trend of fusing with other interactive modes such as touch and face recognition. For example, the full-cabin memory function of NIO Banyan 2.4.0 is based on face recognition, and NOMI actively greets occupants who have recorded information (e.g., "Good morning, Doudou"); Zeekr 7X integrates voice recognition with eye contact to enable the driver to see and speak to control, and tilt his/her head to control the car via voice.

2. BYD launched palm vein recognition, and Sterra in-cabin health monitoring debuted

Compared with the mature interaction modes such as voice and face recognition, biometric technologies such as fingerprint, vein, and heart rate are still in the early stage of exploration and development, but they are gradually being mass-produced and applied. For example, BYD launched a palm vein recognition function in 2024, which can realize convenient vehicle unlocking; Genesis and Mercedes-Benz introduced fingerprint recognition systems in the 2025 Genesis GV70 and 2025 Mercedes-Benz EQE BEV respectively, allowing users to complete a range of operations such as identification, vehicle start and payment only with fingerprints; in addition, Exeed Sterra still uses visual perception technology provided by ArcSoft in new ET model, realizing in-cabin intelligent health monitoring function, and outputting health reports for users including five major physical indicators, i.e., heart rate, blood pressure, blood oxygen saturation, respiratory rate and heart rate variability.

Introduction of biometric technology not only improves driving convenience, but also significantly enhances the safety protection performance of vehicles, effectively preventing potential safety hazards such as tired driving and car theft. In the future, these biometric technologies will be more widely integrated into the development of intelligent and connected vehicles, providing drivers with a safer and more personalized mobility experience.

Case 1: Fingerprint recognition system of Genesis 2025 GV70 allows users to quickly apply personalized settings (seats, positions, etc.) through fingerprint authentication, and also supports vehicle start/drive. In addition, there are personalized linkage functions such as easy to use, fingerprint payment, and valet mode.

Case 2: BYD's palm vein recognition system uses a camera to read palm vein data for recognition at a distance of 8-20cm, 360 degrees horizontally and 15 degrees vertically. It uses professional image acquisition module to obtain images of vein patterns, extracts characteristics through algorithms and stores them, and finally realizes identification and recognition. In the future, it may be first installed in high-end brand Yangwang models.

Case 3: Exeed Sterra ET model is equipped with DHS intelligent health monitoring function. Based on advanced visual multimodal algorithm, it can analyze health status in real time according to the surface of the human body, measure the five major physical indicators of heart rate, blood pressure, blood oxygen saturation, respiratory rate and heart rate variability, and output a health report.

3. AI foundation models lead cockpit interaction innovation, and perceptual intelligence evolves into cognitive intelligence

China Society of Automotive Engineers clearly defines and classifies intelligent cockpits in its jointly released white paper. The classification system is based on capabilities achieved by intelligent cockpits, comprehensively considers the three dimensions of human-machine interaction capabilities, scenario expansion capabilities, and connected service capabilities, and subdivides intelligent cockpits into five levels from L0 to L4.

With the wide adoption of AI foundation models in intelligent cockpits, HMI capabilities have crossed the boundary of L1 perceptual intelligence and entered a new stage of L2 cognitive intelligence.

Specifically, in the stage of perceptual intelligence, intelligent cockpit mainly relies on the in-cabin sensor system, such as cameras, microphones and touch screens, to capture and identify the behavior, voice and gesture information of driver and passengers, and then convert the information into machine-recognizable data. However, limited by established rules and algorithm framework, the cockpit interaction system in this stage still lacks the capability of independent decision and self-optimization, which is mainly reflected in the passive response to input information.

After entering the cognitive intelligence stage, intelligent cockpits can comprehensively analyze multiple data types such as voice, vision and touch by virtue of powerful multimodal processing capabilities of foundation model technology. This feature makes intelligent cockpits highly intelligent and humanized, able to actively think and serve, as well as keenly perceive actual needs of the driver and passengers, providing users with personalized HMI services. perceives

Case 1: SenseAuto introduced an intelligent cockpit AI foundation model product, A New Member For U, at the 2024 SenseAuto AI DAY. It can be regarded as the "Jarvis" on the vehicle, which can weigh up occupants' words and observe their expressions, actively think, serve, and plan. For example, on the road, it can actively turn up the air conditioner temperature and lower music volume for the sleeping children in the rear seat, and adjust the chassis and driving mode to the comfort mode to create a more comfortable sleeping environment. In addition, it can actively detect the physical condition of occupants, find the nearest hospital for the sick ones, and plan the route.

Case 2: NOMI Agents, NIO's multi-agent framework, uses AI foundation models to reconstruct NOMI's cognition and complex task processing capabilities, allowing it to learn to use tools, for example, calling search, navigation, and reservation services. Meanwhile, according to complexity and time span of the task, NOMI is able to perform complex planning and scheduling. For example, among NOMI's six core multi-agent functions, "NOMI DJ" recommends a playlist that suits the context to users based on their needs, and actively creates an atmosphere; "NOMI Exploration" understands based on spatial orientation, matches map data and world knowledge, and answers children's questions, for example, "what is the tower on the side?".

Table of Contents

1 Overview of Cockpit Multimodal Interaction

  • 1.1 Definition of Multimodal Interaction
  • 1.2 Multimodal Interaction Development System
  • 1.3 Multimodal Interaction Industry Chain
    • 1.3.1 Multimodal Interaction Industry Chain - Chip Vendors
    • 1.3.2 Multimodal Interaction Industry Chain - Algorithm Providers
    • 1.3.3 Multimodal Interaction Industry Chain - System Integrators
  • 1.4 Multimodal Interaction Policy Environment
    • 1.4.1 Summary of Laws and Regulations Related to Network Data Security of Intelligent Connected Vehicle
    • 1.4.2 Multimodal Interaction Laws and Regulations (1)
    • 1.4.2 Multimodal Interaction Laws and Regulations (2)
    • 1.4.2 Multimodal Interaction Laws and Regulations (3)

2 Cockpit Single-modal Interaction

  • 2.1 Installation of Cockpit Modal Interaction System
    • 2.1.1 Installations & Installation Rate of In-vehicle Voice Recognition, 2024
    • 2.1.2 Installations & Installation Rate of In-vehicle Voiceprint Recognition, 2024
    • 2.1.3 Installations & Installation Rate of Exterior Voice Recognition, 2024
    • 2.1.4 Installations & Installation Rate of In-vehicle Gesture Recognition, 2024
    • 2.1.5 Installations & Installation Rate of In-vehicle Face Recognition (FACE ID), 2024
    • 2.1.6 Installations & Installation Rate of In-vehicle DMS, 2024
    • 2.1.7 Installations & Installation Rate of In-vehicle OMS, 2024
  • 2.2 Haptic Interaction
    • 2.2.1 Haptic Interaction Development Route
    • 2.2.2 Application Cases of Haptic Interaction in Vehicle Models
    • 2.2.3 Haptic Feedback Technology
    • 2.2.4 Summary of Haptic Interaction Suppliers
  • 2.3 Auditory Interaction
    • 2.3.1 Voice Recognition Development Route
    • 2.3.2 Application Cases of Voice Recognition in Vehicle Models
    • 2.3.3 Application Cases of Voiceprint Recognition in Vehicle Models
    • 2.3.4 Application Cases of External Voice Recognition in Vehicle Models
    • 2.3.5 Summary of Voice Interaction Suppliers
  • 2.4 Visual Interaction
    • 2.4.1 Gesture Recognition Development Route
    • 2.4.2 Application Cases of Gesture Recognition in Vehicle Models
    • 2.4.3 Facial Recognition Development Route
    • 2.4.4 Application Cases of Face Recognition in Vehicle Models
    • 2.4.5 Application Case of Line of Sight Recognition Vehicle Models
    • 2.4.6 Application Case of Lip Movement Recognition Vehicle Models
    • 2.4.7 Summary of Visual Interaction Suppliers (1) - Gesture Recognition
    • 2.4.7 Summary of Visual Interaction Suppliers (2) - Face Recognition
    • 2.4.7 Summary of Visual Interaction Supplier (3) - Lip Movement Recognition
  • 2.5 Olfactory Interaction
    • 2.5.1 Olfactory Interaction Development Route
    • 2.5.2 Application Cases of Olfactory Interaction in Vehicle Models
    • 2.5.3 Summary of Automotive Smart Fragrance/Air Purification Suppliers
  • 2.6 Other Biometric Functions
    • 2.6.1 Iris Recognition Development Route
    • 2.6.2 Application Case of Iris Recognition Vehicle Models
    • 2.6.3 Iris Recognition AR/VR Applications
    • 2.6.4 Solutions of Iris Recognition Suppliers
    • 2.6.5 Summary of Iris Recognition Suppliers
    • 2.6.6 Fingerprint Recognition Development Route
    • 2.6.7 Application Cases of Fingerprint Recognition in Vehicle Models
    • 2.6.8 Summary of Fingerprint Recognition Suppliers
    • 2.6.9 Vein Recognition Development Route
    • 2.6.10 Application Cases of Vein Recognition in Vehicle Models
    • 2.6.11 Summary of Vein Recognition Suppliers
    • 2.6.12 Heart Rate Recognition Development Route
    • 2.6.13 Application Case of Heart Rate Recognition Vehicle Models
    • 2.6.14 Summary of Heart Rate Recognition Suppliers
    • 2.6.15 Electromyography Recognition Development Route
    • 2.6.16 Introduction to Electromyography Recognition Equipment
    • 2.6.17 Application of Electromyography Recognition Vehicle Models
    • 2.6.18 Summary of Electromyography Recognition Suppliers

3 Cockpit Multimodal Interaction Solutions of OEMs

  • 3.1 SAIC
    • 3.1.1 Z-ONE Galaxy Full-stack Solution
    • 3.1.2 Rising Intelligent Cockpit Solution
    • 3.1.3 IM Intelligent Cockpit Solution
    • 3.1.4 IM Generative Foundation Model
    • 3.1.5 Multimodal Interaction OTA Content Summary (1): Rising Auto
    • 3.1.5 Multimodal Interaction OTA Content Summary (2): IM Motors
  • 3.2 BYD
    • 3.2.1 Intelligent cockpit Solution
    • 3.2.2 In-cabin Unique Multimodal Interactive Applications
    • 3.2.3 Xuanji AI Foundation Model
    • 3.2.4 Multimodal Interaction OTA Content Summary (1): BYD Dynasty & Ocean
    • 3.2.4 Multimodal Interaction OTA Content Summary (2): Denza
    • 3.2.4 Multimodal Interaction OTA Content Summary (3): Fangchengbao & Yangwang
  • 3.3 Changan Automobile
    • 3.3.1 Changan Intelligent Cockpit Solution
    • 3.3.2 Nevo Intelligent Cockpit Solution
    • 3.3.3 Deepal Intelligent Cockpit Solution
    • 3.3.4 Avatr Intelligent Cockpit Solution
    • 3.3.5 Automotive Foundation Model: Xinghai Model
    • 3.3.6 Multimodal Interaction OTA Content Summary (1): Changan
    • 3.3.6 Multimodal Interaction OTA Content Summary (2): Avatr
    • 3.3.6 Multimodal Interaction OTA Content Summary (3): Deepal
  • 3.4 GAC
    • 3.4.1 Intelligent Cockpit Solution
    • 3.4.2 ADiGO SENSE AI Foundation Model
    • 3.4.3 Multimodal Interaction OTA Content Summary
  • 3.5 Geely
    • 3.5.1 Geely Intelligent Cockpit Solution
    • 3.5.2 Zeekr Intelligent Cockpit Solution
    • 3.5.3 Jiyue Intelligent Cockpit Solution
    • 3.5.4 Xingrui AI Foundation Model
    • 3.5.5 Kr AI Foundation Model
    • 3.5.6 Multimodal Interaction OTA Content Summary (1): Geely
    • 3.5.6 Multimodal Interaction OTA Content Summary (2): Zeekr
    • 3.5.6 Multimodal Interaction OTA Content Summary (3): Jiyue
  • 3.6 NIO
    • 3.6.1 Intelligent Cockpit Solution
    • 3.6.2 ONVO Intelligent Cockpit Solution
    • 3.6.3 In-cabin Unique Multimodal Interactive Applications
    • 3.6.4 Multimodal Perception Model: NOMI GPT
    • 3.6.5 Multimodal Interaction OTA Content Summary
  • 3.7 Xpeng Motors
    • 3.7.1 Intelligent Cockpit Solution
    • 3.7.2 In-cabin Unique Multimodal Interactive Applications
    • 3.7.3 Automotive Large Language Model: XGPT
    • 3.7.4 Multimodal Interaction OTA Content Summary
  • 3.8 Li Auto
    • 3.8.1 Intelligent cockpit Solution
    • 3.8.2 In-cabin Unique Multimodal Interactive Applications
    • 3.8.3 Intelligent Cockpit
    • 3.8.4 Multimodal Interaction OTA Content Summary
  • 3.9 Leapmotor
    • 3.9.1 Intelligent Cockpit Solution (1)
    • 3.9.1 Intelligent Cockpit Solution (2)
    • 3.9.2 Voice Foundation Model: Tongyi
    • 3.9.3 Multimodal Interaction OTA Content Summary
  • 3.10 Xiaomi Auto
    • 3.10.1 Intelligent Cockpit Solution
    • 3.10.2 Car-side Large Model: MiLM
    • 3.10.3 Sound Foundation Model is Installed in Cars
    • 3.10.4 Multimodal Interaction OTA Content Summary (1)
    • 3.10.4 Multimodal Interaction OTA Content Summary (2)
  • 3.11 BMW
    • 3.11.1 Intelligent Cockpit Solution (1)
    • 3.11.1 Intelligent Cockpit Solution (2)
    • 3.11.2 In-cabin Unique Multimodal Interactive Applications
  • 3.12 Mercedes-Benz
    • 3.12.1 Intelligent Cockpit Solution
    • 3.12.2 In-cabin Unique Multimodal Interactive Applications
    • 3.12.3 Cooperation Dynamics of Cockpit Foundation Model
  • 3.13 Volkswagen
    • 3.13.1 Intelligent Cockpit Solution
    • 3.13.2 Upgrade Trends of Haptic Interaction System
    • 3.13.3 Upgrade Trends of Voice Interaction System

4 Cockpit Multimodal Interaction Solutions of Suppliers

  • 4.1 Desay SV
    • 4.1.1 Profile
    • 4.1.2 Multimodal Interaction Solution (1)
    • 4.1.2 Multimodal Interaction Solution (2)
  • 4.2 Joyson Electronics
    • 4.2.1 Profile
    • 4.2.2 Evolution of Joynext Intelligent Cockpit
    • 4.2.3 Multimodal Interaction Layout
    • 4.2.4 Features of Joynext Intelligent Cockpit Interaction (1)
    • 4.2.4 Features of Joynext Intelligent Cockpit Interaction (2)
  • 4.3 SenseTime
    • 4.3.1 I Profile
    • 4.3.2 SenseAuto Intelligent Cockpit Product System
    • 4.3.3 SenseAuto Intelligent Cockpit Products
    • 4.3.4 SenseNova Model Empowers Cockpit Interaction
    • 4.3.5 SenseAuto Multimodal Interaction Application Case
  • 4.4 iFLYTEK
    • 4.4.1 Profile
    • 4.4.2 Full-Stack Intelligent Interaction Technology
    • 4.4.3 Features of Multimodal Perception System
    • 4.4.4 Spark Cognitive Foundation Model
    • 4.4.5 Spark Foundation Model Enables Cockpit Interaction
    • 4.4.6 Multimodal Interaction Becomes the Key Direction of iFlytek Super Brain 2030 Plan
  • 4.5 ThunderSoft
    • 4.5.1 Profile
    • 4.5.2 Cockpit Interaction Features
    • 4.5.3 Rubik Model Enables Cockpit Interaction
    • 4.5.4 Vehicle Operating System
  • 4.6 AISpeech
    • 4.6.1 Profile
    • 4.6.2 Features of Multimodal Interaction Solution
    • 4.6.3 Multimodal Interaction Products
    • 4.6.4 Language Foundation Model
  • 4.7 Huawei
    • 4.7.1 Profile
    • 4.7.2 Multimodal Interaction History
    • 4.7.3 Harmony OS 4.0 Intelligent cockpit
    • 4.7.4 New-generation HarmonySpace cockpit
    • 4.7.5 HarmonySpace Interaction Features (1)
    • 4.7.5 HarmonySpace Interaction Features (2)
    • 4.7.5 HarmonySpace Interaction Features (3)
    • 4.7.5 HarmonySpace Interaction Features (4)
    • 4.7.5 HarmonySpace Interaction Features (5)
    • 4.7.5 HarmonySpace Interaction Features (6)
    • 4.7.6 HarmonyOS NEXT Interaction Features
    • 4.7.7 Pangu Foundation Model
  • 4.8 Baidu
    • 4.8.1 Profile
    • 4.8.2 Interaction Features of AI Native Operating System
    • 4.8.3 ERNIE Bot Empowers Baidu Smart Cabin
    • 4.8.4 Interaction Features of Baidu Smart Cabin Model 2.0
  • 4.9 Tencent
    • 4.9.1 Profile
    • 4.9.2 Cockpit Interaction Features (1)
    • 4.9.2 Cockpit Interaction Features (2)
  • 4.10 NavInfo
    • 4.10.1 Profile
    • 4.10.2 Cockpit Interaction Features
    • 4.10.3 Introduction to AutoChips
    • 4.10.4 Intelligent Cockpit Domain Control SoC Chip of AutoChips
    • 4.10.5 Application of AutoChips In-cabin Monitoring Function
  • 4.11 Continental
    • 4.11.1 Profile
    • 4.11.2 Multimodal Product Layout
    • 4.11.3 Cockpit Interaction Features
    • 4.11.4 Multimodal Interaction Products (1)
    • 4.11.4 Multimodal Interaction Products (2)
  • 4.12 MediaTek
    • 4.12.1 Profile
    • 4.12.2 Cockpit Interaction Features

5 Application Cases of Multimodal Interaction Solutions in Benchmarking Vehicle Models

  • 5.1 Cases of Traditional Brands
    • 5.1.1 Yangwang U9
    • 5.1.2 IM L6
    • 5.1.3 Geely Galaxy E8
    • 5.1.4 Zeekr 7X
    • 5.1.5 Jiyue 07
    • 5.1.6 Changan UNI-Z
    • 5.1.7 Changan Deepal G318
    • 5.1.8 Avatr 07
    • 5.1.9 Dongfeng e-phi-007
    • 5.1.10 ARCFOX aS5
    • 5.1.11 Exeed Sterra ET
  • 5.2 Cases of Emerging Brands
    • 5.2.1 Xiaomi SU7
    • 5.2.2 Luxeed R7
    • 5.2.3 STELATO S9
    • 5.2.4 Li Auto MEGA Ultra
    • 5.2.5 Xpeng MONA 03
    • 5.2.6 ONVO L60
    • 5.2.7 Leapmotor C16
  • 5.3 Cases of Joint Venture Brands
    • 5.3.1 Volvo EX30
    • 5.3.2 Lotus EMEYA
    • 5.3.3 2024 Buick E5
    • 5.3.4 2025 BMW i4
    • 5.3.5 2025 Mercedes-Benz All-electric EQE
    • 5.3.6 2025 Genesis GV70

6 Summary and Development Trends of Multimodal Interaction

  • 6.1 Fusion Application of Multimodal Interaction in Intelligent Cockpits
  • 6.2 Trend 1
  • 6.3 Trend 2 (1): Cockpit Interaction Carriers Expand, and Interaction Range Extends outside the Vehicle
  • 6.3 Trend 2 (2)
  • 6.3 Trend 2 (3)
  • 6.4 Trend 3 (1)
  • 6.4 Trend 3 (2)
  • 6.4 Trend 3 (3)
  • 6.5 Trend 4