Breaking the Scale Curse in Autonomous Driving! VLM Large Model + Cloud-Edge Collaboration Achieves 1:100 Extreme Human-to-Vehicle Ratio Operation and Maintenance

2026-06-16

The autonomous driving industry has long faced an unsolvable vicious cycle: the larger the vehicle

fleet, the higher the operation and maintenance costs, and the more difficult it becomes to achieve commercial profitability. In recent years, L4 autonomous driving technology has continuously evolved in single-vehicle intelligence aspects such as perception, decision-making, and chassis control, with deployment scenarios expanding. However, behind large-scale commercialization, a safer and more efficient remote operation and maintenance system is becoming an essential requirement.

Traditional autonomous driving operation and maintenance heavily relies on manual monitoring, with the

industry's mainstream human-to-vehicle ratio stagnating at 1:20-1:30. This means that for every 20-30 autonomous vehicles, one

professional operator is needed for real-time monitoring and exception handling, with labor costs accounting for up to 40% of total

operational expenses. As the number of vehicles increases, the investment in operation and maintenance personnel grows, marginal benefits continue to decline, ultimately leading to the industry dilemma of

"expansion equals greater losses."

Simultaneously, the human-led

operation and maintenance model has inherent weaknesses: inconsistent

standards, high decision-making delays, and operational errors. These

shortcomings create safety hazards for large-scale autonomous driving

operations when dealing with long-tail scenarios such as road

construction, extreme weather, and foreign object intrusion.

To break the curse of large-scale

commercialization in autonomous driving, remote operation and

maintenance must be fundamentally upgraded from a "human-driven"

passive monitoring model to a "technology-driven" proactive

maintenance system. By enhancing single-vehicle intelligence and large model capabilities, dependence on operation and

maintenance personnel can be fundamentally reduced. UISEE,

based on its full-stack self-developed high-safety

single-vehicle intelligence technology stack combined with locally deployed VLM

(Vision-Language Model) large model technology under compliance

supervision, has achieved an industry-leading operation and maintenance

human-to-vehicle ratio of 1:100. Coupled with 99.8% ultra-high system availability and verification from over 9.2 million

kilometers of real-world operation, this breakthrough paves the technological path for the autonomous driving industry to transition from "operational pilots" to "large-scale commercialization."

<h1>Full-Stack Self-Developed Core Technology</h1>

Fortifying Operation and Maintenance Safety Foundation

Through extensive practical business implementation, UISEE has concluded that the upper limit of remote

operation and maintenance efficiency is essentially determined by the lower limit of single-vehicle intelligence. Only when

autonomous vehicles possess sufficiently strong autonomous processing capabilities can the need for human intervention be reduced at the source. By

building a full-stack self-developed perception-decision-control integrated single-vehicle intelligence technology stack, UISEE enables vehicles to handle over 99% of conventional scenarios autonomously.

Through proactive identification and downgrade processing of high-risk long-tail

scenarios such as sensor failures, temporary road construction, extreme weather, and foreign object intrusion, autonomous vehicles can

autonomously complete safety operations such as deceleration, pulling over, and risk avoidance before cloud operation and maintenance intervention, ensuring

vehicles remain safe and controllable, providing a safety net for cloud-based remote operation and maintenance.

Based on this powerful single-vehicle

intelligence technology stack, UISEE has developed a Cloud AI

Brain intelligent remote operation and maintenance platform to address

pain points in autonomous driving operation and maintenance, including

real-time monitoring of batch vehicles, remote operation risks, technical

complexity, and personnel training.

The platform delivers

comprehensive capabilities through:

<ul>

<li>Global visual monitoring for fleet-wide situational awareness</li>

<li>VLM large model intelligent analysis for automated decision support</li>

<li>Intelligent alert systems that prioritize critical incidents</li>

<li>Eagle-eye monitoring rooms providing centralized oversight</li>

<li>Single-vehicle multi-view tracking for detailed inspection</li>

<li>Customized operation and maintenance methods tailored to specific scenarios</li>

<li>Agile fault flow for rapid issue resolution</li>

<li>Simple deployment requiring minimal configuration</li>

<li>Automated data aggregation for comprehensive analytics</li>

</ul>

This integrated approach helps

operation and maintenance personnel achieve real-time monitoring and operation

of large vehicle fleets while reducing on-site operation risks. Simultaneously,

the platform emphasizes security assurance and privacy protection, ensuring the

safety of vehicles and data.

<h1>VLM

Large Model Reconstruction</h1>

Enhancing Remote Operation and

Maintenance Efficiency

VLM (Vision-Language Model) is a multimodal large model that integrates computer

vision and natural language processing technologies. It can simultaneously

understand images, videos, and text information, achieving cross-modal

reasoning, understanding, and generation, possessing general

understanding capabilities for open scenarios. UISEE deeply integrates VLM

large model technology into the entire remote operation and

maintenance workflow, leveraging its powerful multimodal understanding and

reasoning capabilities to comprehensively reconstruct the remote operation and

maintenance platform, significantly improving efficiency.

Based on general VLM large

models, UISEE has fine-tuned the model using accumulated extensive full-scenario

edge scenario data, further enhancing VLM's scene understanding

capabilities, achieving both breadth and depth in understanding autonomous

driving scenarios.

By feeding various multimodal

data including operational data, vehicle fault/diagnosis information,

and other relevant data into the VLM large model for reasoning, the system

achieves accurate understanding of various autonomous driving edge scenarios.

It precisely identifies and distinguishes dozens of exception types including:

<ul>

<li>Various vehicle faults</li>

<li>Autonomous driving system faults</li>

<li>Abnormal obstacle blockages</li>

<li>Traffic violations</li>

<li>Dangerous behaviors of external traffic participants</li>

<li>Abnormal passenger behaviors inside vehicles</li>

</ul>

For each identified exception,

the system generates standardized response solutions, ensuring

consistent and optimal handling across all scenarios.

Taking the common foreign

object intrusion scenario as an example, autonomous vehicles in normal

operation need to promptly determine which situations allow continued passage

and which require avoidance or emergency stops. The "locally deployed VLM

large model technology under compliance supervision" can analyze data,

faults, and diagnostic information uploaded by vehicles in real-time, forming

complete scene understanding and identifying detailed classifications of

abnormal obstacles:

l Crushable obstacles (plastic bags, paper sheets, branches): Autonomous vehicle continues its

journey

l Non-passable obstacles (tires, parts, delivery boxes): Autonomous vehicle brakes and actively

changes lanes to avoid them

The VLM large model can also

automatically mine similar long-tail scenarios from massive

historical vehicle operation data, analyze each remote operation and

maintenance case, extract and collect high-value data, and iteratively update

the VLM large model to continuously enhance the automation capabilities of the

remote operation and maintenance platform, gradually reducing the need for

remote human intervention in similar scenarios.

<h1>Full-Scenario

Large-Scale Implementation</h1>

Real Data Validates

Technological Value

Ultimately, all technological

innovations must be implemented in real scenarios and validated through

commercialization. Currently, UISEE's remote operation and maintenance system, composed of high-safety single-vehicle intelligence technology stack combined with locally deployed VLM large model technology under compliance supervision, has been implemented in commercial

projects across over 249 leading clients in six

countries and regions worldwide. It covers diverse scenarios

including:

l Airport Autonomous Driving (airside operations)

l Smart Factory environments

l Smart Port logistics

l Urban Smart City applications

l Smart Mining operations

l Agricultural and ranch settings

The system adapts to various

complex environments including closed, semi-closed, and open roads,

demonstrating exceptional versatility across operational domains.

As new scenarios continue to

be implemented, real operational data resources continuously

accumulate and expand, consistently empowering algorithm iteration upgrades and

system generalization capability optimization, building a positive cycle

flywheel where "broader scenario coverage leads to more data accumulation,

which in turn creates higher technological barriers."

From a commercial perspective,

the breakthrough of 1:100 human-to-vehicle ratio fundamentally

changes the economic model of autonomous driving. Labor costs no longer increase linearly with vehicle fleet expansion, meaning significant

reductions in customer initial investment and long-term operational costs,

shortening return on investment cycles, making autonomous

driving technology a truly profitable, scalable productivity tool.