AI基础模式与汽车领域的用途(2024年～2025年)

推理能力提升了底层模型的表现

自2024年下半年以来，国内外基础模型公司纷纷推出推理模型，并利用Chain-of-Thought（CoT）等推理框架，增强基础模型处理复杂任务和自主决策的能力。

这次重点发布推理模型，旨在强化底层模型处理复杂场景的能力，为Agent应用奠定基础。例如，这可能包括在复杂语义背景下提高驾驶舱助理的意图识别，或提高自动驾驶规划和决策中时空预测的准确性。

2024年，汽车搭载基础模型的主流推理技术主要围绕CoT及其变体，如思考树（ToT）、思考图（GoT）、思考森林（FoT），结合生成模型（如扩散模型）、知识图谱、因果推理模型、累积推理、不同情境下的多模态推理链。

例如，吉利提出的模组化思维语言模型（MeTHanol），使底层模型能够合成人类思维并监督LLM的隐藏层，产生类似人类的思维行为并适应日常对话和个性化提示，从而增强大规模语言模型的思考和推理能力，提高可解释性。

2025年，推理技术的重点将转向多模态推理。常见的训练技术包括指令微调、多模态情境学习和多模态 CoT（M-CoT），通常透过结合多模态融合对齐和 LLM 推理技术来实现。

可解释性建立了人工智慧和使用者之间的信任。

使用者需要信任AI，才能体验到它的 "用处" 。 2025年，人工智慧系统的可解释性将成为增加汽车人工智慧用户群的主要因素。可以透过展示较长的 CoT 来解决这个课题。

人工智慧系统的可解释性可以在三个层面实现：资料可解释性、模型可解释性和事后可解释性。

以理想汽车为例，其L3级自动驾驶采用“AI推理可视化技术”，直观呈现端到端+VLM模型的思维过程，涵盖从物理世界的感知输入，到基础模型输出的驾驶决策的全过程，增强用户对智能驾驶系统的信任。

在理想汽车的 "AI推理视觉化技术" 中

注意力系统显示车辆感知的交通和环境讯息，透过即时视讯串流评估交通参与者的行为，并以热力学图的方式展示评估目标。

端到端（E2E）模型展示了驾驶轨迹输出背后的思考过程。该模型考虑了各种驾驶轨迹，提出了10种可能的输出结果，最终采用最可能的输出结果作为驾驶轨迹。

视觉语言模型 (VLM) 提供了基于对话的感知、推理和决策视图。

各种推理模型的对话式介面同样采用长 CoT 来分解推理过程。例如在DeepSeek R1中，与使用者对话时，先使用CoT呈现每个节点所做的决策，然后用自然语言提供解释。

此外，大多数推理模型，例如智普的GLM-Zero-Preview、阿里巴巴的QwQ-32B-Preview和Skywork 4.0 o1，都支援长CoT推理过程的演示。

本报告提供中国的汽车产业的相关调查，提供AI基础模式概要，种类，通用技术，企业，汽车的应用案例等资讯。

目录

第1章 AI基础模式概要

• AI模式的简介
• 基础模式的简介

第2章 不同的类型的AI基础模式的分析

• 大规模语言模式(LLM)
• 多模态大规模语言模式(MLLM)
• 视觉语言模式(VLM)和视觉语言行动(VLA)模式
• 世界模式

第3章 AI基础模式的通用技术

• 基础模式的架构，相关演算法
• 视觉处理演算法
• 训练，微调整技术
• 强化学习
• 知识图表
• 推论技术
• supasu化
• 生成技术

第4章 AI基础模式企业

• OpenAI
• Google
• Meta
• Anthropic
• Mistral AI
• Amazon
• Stability AI
• xAI
• Abu Dhabi Technology Innovation Institute
• SenseTime
• Alibaba Cloud
• Baidu AI Cloud
• Tencent Cloud
• ByteDance & Volcano Engine
• Huawei
• Zhipu AI
• Flytek
• DeepSeek

第5章 汽车的AI基础模式的应用案例

• 驾驶座的案例
• 智慧驾驶的案例

第6章 AI基础模式的应用趋势

• 资料
• 演算法
• 运算电力
• 工程

Research on AI foundation models and automotive applications: reasoning, cost reduction, and explainability

Reasoning capabilities drive up the performance of foundation models.

Since the second half of 2024, foundation model companies inside and outside China have launched their reasoning models, and enhanced the ability of foundation models to handle complex tasks and make decisions independently by using reasoning frameworks like Chain-of-Thought (CoT).

The intensive releases of reasoning models aim to enhance the ability of foundation models to handle complex scenarios and lay the foundation for Agent application. In the automotive industry, improved reasoning capabilities of foundation models can address sore points in AI applications, for example, enhancing the intent recognition of cockpit assistants in complex semantics and improving the accuracy of spatiotemporal prediction in autonomous driving planning and decision.

In 2024, reasoning technologies of mainstream foundation models introduced in vehicles primarily revolved around CoT and its variants (e.g., Tree-of-Thought (ToT), Graph-of-Thought (GoT), Forest-of-Thought (FoT)), and combined with generative models (e.g., diffusion models), knowledge graphs, causal reasoning models, cumulative reasoning, and multimodal reasoning chains in different scenarios.

For example, the Modularized Thinking Language Model (MeTHanol) proposed by Geely allows foundation models to synthesize human thoughts to supervise the hidden layers of LLMs, and generates human-like thinking behaviors, enhances the thinking and reasoning capabilities of large language models, and improves explainability, by adapting to daily conversations and personalized prompts.

In 2025, the focus of reasoning technology will shift to multimodal reasoning. Common training technologies include instruction fine-tuning, multimodal context learning, and multimodal CoT (M-CoT), and are often enabled by combining multimodal fusion alignment and LLM reasoning technologies.

Explainability bridges trust between AI and users.

Before users experience the "usefulness" of AI, they need to trust it. In 2025, the explainability of AI systems therefore becomes a key factor in increasing the user base of automotive AI. This challenge can be addressed by demonstrating long CoT.

The explainability of AI systems can be achieved at three levels: data explainability, model explainability, and post-hoc explainability.

In Li Auto's case, its L3 autonomous driving uses "AI reasoning visualization technology" to intuitively present the thinking process of end-to-end + VLM models, covering the entire process from physical world perception input to driving decision outputted by the foundation model, enhancing users' trust in intelligent driving systems.

In Li Auto's "AI reasoning visualization technology":

Attention system displays traffic and environmental information perceived by the vehicle, evaluates the behavior of traffic participants in real-time video streams and uses heatmaps to display evaluated objects.

End-to-end (E2E) model displays the thinking process behind driving trajectory output. The model thinks about different driving trajectories, presents 10 candidate output results, and finally adopts the most likely output result as the driving path.

Vision language model (VLM) displays its perception, reasoning, and decision-making processes through dialogue.

Various reasoning models' dialogue interfaces also employ a long CoT to break down the reasoning process as well. Examples include DeepSeek R1 which during conversations with users, first presents the decision at each node through a CoT and then provides explanations in natural language.

Additionally, most reasoning models, including Zhipu's GLM-Zero-Preview, Alibaba's QwQ-32B-Preview, and Skywork 4.0 o1, support demonstration of the long CoT reasoning process.

DeepSeek lowers the barrier to introduction of foundation models in vehicles, enabling both performance improvement and cost reduction.

Does the improvement in reasoning capabilities and overall performance mean higher costs? Not necessarily, as seen with DeepSeek's popularity. In early 2025, OEMs have started connecting to DeepSeek, primarily to enhance the comprehensive capabilities of vehicle foundation models as seen in specific applications.

In fact, before DeepSeek models were launched, OEMs had already been developing and iterating their automotive AI foundation models. In the case of cockpit assistant, some of them had completed the initial construction of cockpit assistant solutions, and connected to cloud foundation model suppliers for trial operation or initially determined suppliers, including cloud service providers like Alibaba Cloud, Tencent Cloud, and Zhipu. They connected to DeepSeek in early 2025, valuing the following:

Strong reasoning performance: for example, the R1 reasoning model is comparable to OpenAI o1, and even excels in mathematical logic.

Lower costs: maintain performance while keeping training and reasoning costs at low levels in the industry.

By connecting to DeepSeek, OEMs can really reduce the costs of hardware procurement, model training, and maintenance, and also maintain performance, when deploying intelligent driving and cockpit assistants:

Low computing overhead technologies facilitate high-level autonomous driving and technological equality, which means high performance models can be deployed on low-compute automotive chips (e.g., edge computing unit), reducing reliance on expensive GPUs. Combined with DualPipe algorithm and FP8 mixed precision training, these technologies optimize computing power utilization, allowing mid- and low-end vehicles to deploy high-level cockpit and autonomous driving features, accelerating the popularization of intelligent cockpits.

Enhance real-time performance. In driving environments, autonomous driving systems need to process large amounts of sensor data in real time, and cockpit assistants need to respond quickly to user commands, while vehicle computing resources are limited. With lower computing overhead, DeepSeek enables faster processing of sensor data, more efficient use of computing power of intelligent driving chips (DeepSeek realizes 90% utilization of NVIDIA A100 chips during server-side training), and lower latency (e.g., on the Qualcomm 8650 platform, with computing power of 100TOPS, DeepSeek reduces the inference response time from 20 milliseconds to 9-10 milliseconds). In intelligent driving systems, it can ensure that driving decisions are timely and accurate, improving driving safety and user experience. In cockpit systems, it helps cockpit assistants to quickly respond to user voice commands, achieving smooth human-computer interaction.

Table of Contents

Definitions

1 Overview of AI Foundation Models

• 1.1 Introduction to AI Models
• Definition and Features of AI Models
• Classification of AI Models by Architecture
• Classification of AI Models by Task Type/Training Method
• Classification of AI Models by Supervision Mode
• Classification of AI Models by Modality
• Application Process of AI Models
• 1.2 Introduction to Foundation Models
• Classification of Foundation Models
• Current Development of Foundation Models in Automotive Industry
• Application Scenarios of Foundation Models in Automotive Industry
• Application Case 1: Application of LLM in Autonomous Driving
• Application Case 2: Application of VFM in Autonomous Driving
• Application Case 3: Application of MFM in Autonomous Driving

2 Analysis of AI Foundation Models of Differing Types

• 2.1 Large Language Models (LLM)
• Development History of LLM
• Key Capabilities of LLM
• Cases of Integration with Other Models
• 2.2 Multimodal Large Language Models (MLLM)
• Development and Overview of Large Multimodal Models
• Large Multimodal Models VS. Large Single-modal Models (1)
• Large Multimodal Models VS. Large Single-modal Models (2)
• Technology Panorama of Large Multimodal Models
• Multimodal Information Representation
• Multimodal Large Language Models (MLLM)
• Architecture and Core Components of MLLM
• Status Quo of MLLM
• Dataset Evaluation by Different MLLM Representatives
• Reasoning Capabilities of MLLM
• Synergy between MLLM and Agent
• Application Case 1: Application of MLLM in VQA
• Application Case 2: Application of MLLM in Autonomous Driving
• 2.3 Vision-Language Models (VLM) and Vision-Language-Action (VLA) Models
• Development History of VLM
• Application of VLM
• Architecture of VLM
• Evolution of VLM in Intelligent Driving
• Application Scenarios of VLM: End-to-end Autonomous Driving
• Application Scenarios of VLM: Combination with Gaussian Framework
• VLM->VLA
• VLA Models
• Principles of VLA
• Classification of VLA Models
• Application Cases of VLA (1)
• Application Cases of VLA (2)
• Application Cases of VLA (3)
• Application Cases of VLA (4)
• Case 1: Core Functions of End-to-End Multimodal Model for Autonomous Driving (EMMA)
• Case 2: World Model Construction
• Case 3: Improve Vision-Language Navigation Capabilities
• Case 4: VLA Generalization Enhancement
• Case 5: Computing Overhead of VLA
• 2.4 World Models
• Key Definitions of World Models and Application Development
• Basic Architecture of World Models
• Framework Setup and Implementation Challenges of World Models
• Video Generation Methods Based on Transformer and Diffusion Models
• Technical Principle and Path of WorldDreamer
• World Models and End-to-end Intelligent Driving
• World Models and End-to-end Intelligent Driving: Data Generation
• Case 1: Tesla World Model
• Case 2: NVIDIA
• Case 3: InfinityDrive
• Case 4: Worlds Labs Spatial Intelligence
• Case 5: NIO
• Case 6: 1X's "World Model"

3 Common Technologies in AI Foundation Models

• Common Foundation Model Algorithms and Architectures
• Comparison of Features and Application Scenarios between Foundation Model Algorithms
• 3.1 Foundation Model Architectures and Related Algorithms
• Transformer: Architecture and Features
• Transformer: Algorithm Mechanisms
• Transformer: Multi-head Attention Mechanisms and Their Variants
• KAN: Potential to Replace MLP
• KAN: Cases of Integration with Transformer Architecture
• MAMBA: Introduction
• MAMBA: Architectural Foundations
• MAMBA: Latest Developments
• MAMBA: Application Scenarios
• MAMBA: Cases of Integration with Transformer Architecture
• Applicability of CNN in the Era of Foundation Models
• Applicability of RNN Variants in the Era of Foundation Models
• 3.2 Visual Processing Algorithms
• Common Vision Algorithms
• ViT
• CLIP Scenarios and Features
• CLIP Workflow
• LLaVA Model
• 3.3 Training and Fine-Tuning Technologies
• Foundation Model Training Process
• Training Case: Geely's CPT Enhancement Solution
• Instruction Fine-tuning
• Training Case: Geely's Fine-tuning Framework for Multi-round Dialogues
• 3.4 Reinforcement Learning
• Introduction to Reinforcement Learning
• Reinforcement Learning Process
• Comparison between Some Reinforcement Learning Technology Routes
• Cases of Reinforcement Learning (1)-(3)
• 3.5 Knowledge Graphs
• Optimization Directions for Retrieval-Augmented Generation (RAG)
• Evolution Directions of RAG (1): KAG
• Evolution Directions of RAG (2): CAG
• Evolution Directions of RAG (3): GraphRAG
• RAG Application Case 1:
• RAG Application Case 2:
• RAG Application Case 3: Li Auto
• RAG Application Case 4: Geely
• Comparison between RAG Routes
• Function Call
• 3.6 Reasoning Technologies
• Reasoning Process of Transformer Models
• Evaluation of Reasoning Capabilities
• Three Optimization Directions for Foundation Model Reasoning
• Reasoning Task Types (1)
• Reasoning Task Types (2)
• Reasoning Task Types (3)
• Common Reasoning Algorithm 1: CoT
• Common Reasoning Algorithm 2: GoT/ToT
• Comparison between Common Reasoning Algorithms
• Common Reasoning Algorithm 3: PagedAttention
• Reasoning Case 1: Geely
• Reasoning Case 2: NVIDIA
• 3.7 Sparsification
• Characteristics of MoE Architecture
• Principles of MoE Architecture
• MoE Training Strategies
• Advantages and Challenges of MoE
• MoE Models from Different Foundation Model Companies
• Evolution Direction of MoE
• 3.8 Generation Technologies
• Introduction to Generative Models
• Comparison between Generation Technologies
• Case 1: Li Auto
• Case 2: XPeng
• Case 3: SAIC

4 AI Foundation Model Companies

• Development History of Mainstream Foundation Models
• Mainstream Foundation Models and Their Companies (Foreign)
• Mainstream Foundation Models and Their Companies (Chinese)
• Rankings of Evaluated Foundation Models
• 4.1 OpenAI
• Product Layout
• Product Iteration History
• GPT Series: Features
• GPT Series: Architecture
• From GPT-4V to 4o
• Reasoning Model OpenAI o1
• SORA: Features
• SORA: Performance Evaluation
• SORA: Advantages and Limitations
• 4.2 Google
• Development History of Foundation Models
• Typical Model BERT: Architecture
• Typical Model BERT: Variants
• Gemini Model
• Cases of Foundation Models in the Automotive Industry
• 4.3 Meta
• LLAMA3.3
• LLAMA Series: Evolution
• LLAMA Series: Features
• LLAMA Series: Training Methods
• LLAMA Series: Alpaca
• LLAMA Series: Vicuna
• 4.4 Anthropic
• Claude Performance Evaluation
• Claude-based PC-side Agent
• 4.5 Mistral AI
• Expert Model: Architecture
• Expert Model: Algorithm Features (1)
• Expert Model: Algorithm Features (2)
• Large Language Model: Mistral Large 2
• 4.6 Amazon
• Nova Product System
• Application Cases of Amazon AI Cloud in the Automotive Industry (1)-(3)
• 4.7 Stability AI
• Product System
• Stable Diffusion Architecture Based on Diffusion Models
• Comparison between Stable Diffusion Video Generation Technology with Competitors
• 4.8 xAI
• Product System
• Capabilities of xAI Models
• Capabilities of Grok-2
• Capabilities of Grok-0/1
• 4.9 Abu Dhabi Technology Innovation Institute
• Iteration History of Falcon Model Series
• Parameters of Falcon 3 Series
• Evaluation of Falcon 3 Series
• 4.10 SenseTime
• Major Foundation Model Product Systems
• Major Foundation Model Product Systems
• Foundation Model Training Facilities
• Functional Scenarios of Foundation Models
• Foundation Model Technologies
• 4.11 Alibaba Cloud
• Foundation Model Product System
• End-cloud Integration Solutions of Foundation Models
• 4.12 Baidu AI Cloud
• Foundation Model Product System
• 4.13 Tencent Cloud
• Foundation Model Product System
• Reasoning Service Solutions (1)-(3)
• Generation Scenario Solutions for Foundation Models
• Q&A Scenario Solutions for Foundation Models
• 4.14 ByteDance & Volcano Engine
• Doubao Model System
• Functional Highlights of Volcano Engine's Cockpit
• 4.15 Huawei
• Pangu Model Product System
• Application Cases of Pangu Models in Data Synthesis
• LLM Architecture of Pangu Models
• Capabilities of Pangu Models: Multimodal Technology
• Capabilities of Pangu Models: Thinking & Reasoning Technology
• AI Cloud Services of Pangu Models
• 4.16 Zhipu AI
• Product System
• Foundation Model Base in the Automotive Industry
• Technical Features
• 4.17 Flytek
• Product System
• Functional and Technical Highlights
• Cockpit AI System
• 4.18 DeepSeek
• Product System
• Technical Inspiration from DeepSeek V3
• Technical Highlights of DeepSeek R1
• Application Cases of DeepSeek (1)-(3)

5 Application Cases of AI Foundation Models in Automotive

• 5.1 Cockpit Cases
• Lenovo's AI Vehicle Computing Framework Used in Cockpits
• In-cabin Functions of Thundersoft's Rubik Foundation Model
• LLM Empowers Smart Eye's DMS/OMS Assistance System
• Application of DIT in Voice Processing Scenarios
• Application of Unisound's Shanhai Model in Cockpits
• Phoenix Auto Intelligence's Cockpit Smart Brain
• 5.2 Intelligent Driving Cases
• Li Auto: Multimodal Technology in Autonomous Driving (1)
• Li Auto: Multimodal Technology in Autonomous Driving (2)
• Li Auto: Multimodal Technology in Autonomous Driving (3): Overcoming 2D Limitations
• Li Auto: Data Generation Technology (1)
• Li Auto: Data Generation Technology (2)
• Li Auto: CoT Technology in DriveVLM
• Li Auto: Application of Visual Processing
• Li Auto: Data Selection
• Geely: Application of Visual Processing
• Geely: Multimodal Learning Framework
• Waymo: Generative World Model GAIA-1
• Tesla: Algorithm Architecture (Including NeRF)
• Tesla: Skeleton, Neck, and Head of Vision Algorithms
• Tesla: Core of Visual System - HydraNet
• Giga's World Model

6 Application Trends of AI Foundation Models

• 6.1 Data
• Trend 1:
• Trend 2:
• 6.2 Algorithm
• Trend 1:
• Trend 2:
• Trend 3
• Trend 4:
• 6.3 Computing Power
• Trend 1:
• Trend 2:
• 6.4 Engineering
• Trend 1
• Trend 2