Automotive AI Large Model Research: Competition Shifts from "Who Has the Stronger Model" to "Who Boasts Higher Link Efficiency"

ResearchInChina released the Automotive AI Large Model Technology Research Report, 2026. This report sorts out mainstream AI large model technologies in the automotive industry in 2026, and provides insights into shifts in technical focus of automotive AI and its trend evolution. 

In 2026, automotive AI large model technologies are mainly deployed in the following fields:

Overall, compared with the race for parameter scale in large model development over the past two years, large model technologies in 2026 have grown more pragmatic. They focus vertically on the technical routes required by service scenarios for continuous optimization, while giving due consideration to innovation. Meanwhile, technologies tailored for specific scenarios, such as trajectory planning and distillation-based deployment, have begun to show signs of convergence. Nevertheless, distinctive solutions and innovations still emerge in the details of engineering implementation. In other words, the large model competition in 2026 is shifting from "Who has the stronger model" to "Who boasts higher link level efficiency".      

Furthermore, these technologies are rarely deployed independently in application scenarios. They are frequently combined to build solutions. Typical combinations include multimodal + knowledge graph, reinforcement learning + diffusion model + feature representation & modeling optimization, multi agent system + AI orchestration, and training + deployment. 

Several selected technical domains are elaborated below:

Three Technical Paths for Reducing Computational Overhead

Although in-vehicle chip computing power keeps growing, parallel execution of complex tasks in the cockpit and intelligent driving domains may still make computing power stretched. To address this problem, on the premise of unchanged resources, three technical paths exist for cutting computational overhead:

Model side: parameter scale adjustment (e.g., distillation, pruning, parameter sharing, low?rank decomposition), model precision adjustment (e.g., quantization), and model architecture iteration (e.g., attention mechanism variants, MoE).
Reasoning framework side: speculative decoding, KV cache optimization, cache reuse, early exiting, etc.
Operator and compiler side: operator fusion, quantized kernel, explicit orchestration, etc.

Adjusting model parameters and precision represents the most common overhead optimization route. Compared with prior two years, OEMs have richer technologies to develop in the model architecture and reasoning framework routes in 2026, including MoE and KV cache optimization.   

1.Attention Mechanism Variants

In 2026 attention mechanism variants target sore points of constrained computing power on vehicle / edge devices. They tackle quadratic growth in computation and video memory overhead incurred by standard softmax attention as sequence length increases. Architecture design adopts a combination of “lightweight attention variants + standard attention”.  

In the case of reasoning optimization for Xiaomi’s MiMo V2.5 series, Hybrid SWA (Sliding Window Attention) cuts KVCache storage to approximately 1/7 of that under Full Attention. MoE leverages sparse activation to hold model capacity while lowering per token computational cost. Reduced KV Cache not only cuts computation overhead but also makes use of video memory headroom from SWA optimization, trimming Expert Parallelism to 1/2 of its original scale, decreasing cross machine communication, lifting end to end Prefill performance by roughly 40%, and boosting training GPU utilization.   

2.MoE Architecture

MoE architectures retain full scale parameters and control reasoning phase overhead via sparsification. In 2026, OEMs’ MoE technologies focus on load balancing, training reasoning routing discrepancies, and visual token redundancy.

Even with the same MoE architecture, routers are handled differently for distinct scenarios:

Input-oriented activation (traditional MoE): Conventional MoE adopts "input-oriented activation", meaning activated experts are determined directly by current input tokens. This approach excels in multi task or multimodal input yet is suboptimal for complex multi-step reasoning (GUI Agents).
Output-oriented activation (CoME): Xiaomi’s CoME architecture implements "output-oriented activation", aligning expert activation with specific reasoning phases (e.g., screen summarization, subtask planning, action decision making).
Scenario feature routing (EMoE): BYD’s intelligent driving solution uses EMoE. Its router incorporates an independent scenario router that directly ingests scenario features (e.g., left turn, right turn, going straight), maintaining consistent routing selections across all layers to accelerate model convergence.
Task/modality aware routing: In Geely’s solution, the MoE expert scheduling unit allocates weights via gating networks and selects leading experts with a Top 1 routing strategy, based on current task types (e.g., memory storage, memory retrieval) and modal feature weights.

3.Pruning & Quantization

Pruning and quantization are common techniques for model deployment under conditions of constrained computing power and increased latency, especially for on?device model deployment.

The potential of NAR begins to emerge.

At present, autoregressive (AR) architecture models, by virtue of mature ecosystems and explicit sequence supervision capabilities, are widely applied in voice dialogue, autonomous driving planning, cockpit agents and other scenarios (e.g., MoonCast, Transfuser and numerous AR based world models). Meanwhile, due to token by token/frame by frame sequential generation, AR models generally suffer from high reasoning latency and stability risks (e.g., word skipping, unintelligible long segments, and exposure bias). 

In contrast, non?autoregressive (NAR) models have demonstrated a range of merits in recent years across scenarios including monolingual TTS, voice dialogue and driving planning: improved stability upon adaptation, higher efficiency and better cost performance. Typical examples include Xiaomi ZipVoice?Dialog, OmniVoice, Changan FlowR2A, and Li Auto TrajHF. Although their engineering deployment for complex tasks is still constrained by fundamental supports such as data and deployment paradigms, resulting in a limited scope of application scenarios, NAR models leverage parallel generation or flow?matching/diffusion paradigms to outperform AR baselines in both speed and quality on multiple benchmarks, making them one of the promising technical routes. 
 
NAR models deliver advantages in generation speed, post adaptation stability and computing power friendliness, qualifying them as a new paradigm for AI generation and decision making. Restricted by native adaptation difficulty, data support and engineering path dependency, NAR and AR models are expected to co exist long term in technical evolution.

Multi-Agent Technology: Unified Scheduling Core + Diverse Information Sharing Mechanisms

During 2026 2027, model providers keep enhancing foundation model capabilities, while hardware vendors drag down token costs at an annual rate of 60-70%. How to fully leverage large model technologies to solve current challenges has become a top priority. On vehicles, one key embodiment is the "Always on Agent", whose mainstream construction modes falls under multi agent technology.   

In 2026, multi agent technologies witness converging technical routes alongside horizontally expanded application scenarios. They adopt the mainstream "hub + expert" paradigm, extending from isolated cockpit and intelligent driving scenarios to cross system, cross domain collaborative scenarios including operation & maintenance, marketing and manufacturing. 

Different OEMs implement distinct Agent collaboration mechanisms for multiagent systems. Centralized startopology scheduling sees the highest adoption frequency, yet alternative architectures yield superior outcomes for specific scenarios.

Multi-agent system in 2026:
Instead of creating agents with overlapping capabilities, dedicated sub?agents of function, role and cognition types (including planning, searching, execution, defense, analysis and learning agents) are defined to perform respective subtasks.
Agents rely on diverse collaboration mechanisms (standardized communication and state sharing paths such as A2A protocol, global state sharing, data interaction interfaces, inter?core instruction frames, Memory read?write operations, and streaming cache) to ensure efficient information flow and consistent states.
After each sub-agent completes its task, the central module aggregates all subtask outcomes, integrates and outputs the optimal solution, and feeds it back for execution. This enables parallel business processing to improve real-time performance, and enhances safety and robustness through task division and redundancy backup.

Take FAW’s Agentbased engine ECU as an example:

This solution deploys three physicallyisolated computing domains on the heterogeneous multi-core SoC to host three types of Agents: Reaction Agent, Planning Agent and Learning Agent.
The Reaction Agent runs on ASIL-D lock-step cores, collecting sensor signals with ≤1ms cycles, handling knock and other emergency operating conditions at millisecond-level latency and holding emergency takeover privileges.
The Planning Agent runs on ASIL?B high?performance cores. It invokes a local shared digital twin engine (combining physical mechanism models + neural networks) to simulate multiple candidate control strategies, generates optimal control parameters through multi-objective optimization, and delivers outputs via structured data frames carrying confidence levels, validity periods and CRC checksums.
The Learning Agent runs on QM coprocessors. It collects control deviation data in the background and only performs incremental updates for digital twin models.

These three agents communicate securely through MPU protected shared memory partitions and inter core interrupts, complemented by fault degradation and model verification update mechanisms. This setup delivers a full closed loop self-optimizing control workflow featuring "real time emergency response — forward looking planning — online correction" while complying with ISO?26262 functional safety requirements. 

Beyond multi-agent technologies, active service scenarios see accelerated implementation with "Claw"?based technologies.

Li Auto’s StreamingClaw serves as an example of an embodied agent:
It leverages incremental streaming reasoning plus master?slave proxies. Instead of processing video as offline files, vehicles perceive and update states in real time similar to human cognition.
StreamingReasoning handles real?time perception and planning; StreamingMemory stores multimodal experience through hierarchical memory evolution; StreamingProactivity continuously monitors risks and triggers proactive actions.
It enables functions such as proactive alerts when drivers yawn or operate mobile phones while driving, proactive greetings upon vehicle access, and real?time recognition of objects held by passengers.

From the vehicle perspective, agent competition evolves from "standalone feature battles" toward "fullscenario ecosystem experience battles". Agent services transform from discrete feature items on specification sheets into partners who proactively interact with users yet retain driving authority for human drivers.