Research Report on AI Foundation Models and Their Applications in Automotive Field, 2024-2025
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.
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