AI Driver Upgraded! Resilient in Weak Networks, Flexible in Right-of-Way!

2026-07-31

As L4 autonomous driving achieves scaled operations

in airports, industrial parks, ports, and other scenarios, dozens or even

hundreds of autonomous vehicles begin to operate at high density within the

same site. How to avoid driving conflicts between autonomous vehicles, rapidly

adapt to site-specific rules, and coordinate efficiently has become a core

challenge for multi-vehicle coordination technology.

Benefiting from the technical accumulation of over

10 million kilometers of real-scenario autonomous driving operations, UISEE

adopts a technical architecture of vehicle-cloud

communication framework + universal multi-vehicle coordination foundation +

rapid adaptation of driving rules, and has delivered targeted optimization

upgrades for coordinated weak-network

operation and hierarchical

right-of-way based on real operational pain points, providing an

intelligent multi-vehicle coordination solution for the long-term, efficient,

and stable operation of autonomous vehicle fleets in closed scenarios.

Multi-Vehicle Coordination in Scaled L4 Operations

in Closed Scenarios

On open roads, traffic regulations are unified,

intersections are standardized, and the primary objects of interaction are

human drivers. Reasonable yielding strategies are already embedded in

intelligent driving algorithms, and single-vehicle intelligence can handle most

driving conflicts. However, in closed scenarios, scaled autonomous vehicle

operations face fundamentally different challenges.

Every airport, industrial park, and port is like a

self-contained "small city" with its own unique driving regulations.

Rules such as "through traffic yields to turning traffic" and

"vehicles with trailers yield to those without" — whether counterintuitive or customized requirements

are extremely common — follow no unified standard. The vast majority of

internal roads lack traffic lights and ground markings, and relevant areas generate a large number of

unprotected driving conflicts. In high-density autonomous vehicle

scenarios, the primary interaction partner for intelligent driving is

intelligent driving itself. This long-term, high-frequency self-coordination

has become a unique challenge in closed scenarios.

This means that relying solely on single-vehicle

decision-making algorithms cannot maintain the traffic efficiency and safety of

scaled fleets over the long term. A flexible, stable, and reliable

multi-vehicle coordination system is the key technical foundation for scaled L4

operations in closed scenarios to evolve from "functional" to

"effective."

UISEE's Solution: Balancing Efficiency and

Flexibility

To address the fragmentation of driving rules in

closed scenarios while ensuring delivery efficiency for commercial projects and

the stability of long-term autonomous vehicle operations, UISEE balances

efficiency and flexibility in the overall technical architecture of

multi-vehicle coordination, adopting an integrated solution of vehicle-cloud communication framework +

universal multi-vehicle coordination foundation + rapid adaptation of driving

rules.

Under the vehicle-cloud communication framework,

all autonomous vehicles uniformly connect to the cloud environment during

operation, uploading their own information in real time and receiving cloud

instructions to complete various tasks, providing the necessary communication

channel for multi-vehicle coordination.

To ensure conflict-free multi-vehicle driving,

UISEE, based on its self-developed

large-scale multi-vehicle simulation system, uses reinforcement learning to

train the core AI model within the universal multi-vehicle coordination

foundation, enabling it to predict all potential driving conflicts and make

command decisions. Meanwhile, the foundation incorporates a customized rule description system that

supports converting site-specific rules into decision constraints,

providing feasibility for the universal foundation to adapt to various driving

rules.

Finally, leveraging large model capabilities, rules described by customers in natural

language are rapidly converted into system-recognizable rule description files,

which are validated and iterated through the multi-vehicle simulation system,

significantly shortening the rule adaptation and delivery cycle for new

scenarios.

Core Strategy Upgrades Meet Stringent Operational

Requirements

Building on this multi-vehicle coordination

technical solution, UISEE has completed targeted special strategy upgrades and

optimizations for the two most commonly reported pain points from frontline

projects — coordinated weak-network

operation and hierarchical

right-of-way — enabling multi-vehicle coordination to truly help customers

reduce costs and increase efficiency, and proving effective in long-term

commercial operations.

Coordinated Weak-Network Operation: Smooth Traffic

Even with Poor Connectivity

In large sites such as airports and ports, network

dead zones such as tunnels, building-shielded areas, and no-signal zones are

inevitable. Traditional multi-vehicle coordination relies heavily on real-time

network connectivity; once disconnected, vehicles can only stop and wait,

disrupting operational rhythm.

To address this issue, UISEE has incorporated

simulation of weak-network conditions into large-scale simulations. Through

training and fine-tuning, the universal coordination foundation model has

learned command strategies under weak-network conditions. The

weak-network-capable multi-vehicle coordination solution can achieve:

• Prediction

before disconnection: The model continuously monitors network data. When

it predicts that weak-network conditions are imminent, it proactively pushes traffic strategies that account for potential

disconnection to the vehicle side, ensuring vehicles always have

instructions to follow.

• Autonomy

during disconnection: After completely losing cloud connection, vehicles

can still comprehensively assess safe

traffic conditions based on pre-delivered information and continue autonomous

driving, without freezing in the middle of the road and blocking other

vehicles.

• Alignment

after recovery: After network recovery, the vehicle side automatically synchronizes the latest status with the

cloud and seamlessly switches back to normal coordination mode, avoiding

operational deviations caused by using outdated information.

This solution has been deployed in multiple

projects, successfully resolving multi-vehicle coordination challenges in areas

with poor signal coverage.

Hierarchical Right-of-Way: Passenger Transport

Always Prioritized Over Cargo

In mixed-traffic scenarios with passenger buses and

cargo tractors, customers often require passenger vehicles to have higher

traffic priority — every extra minute a bus waits means degraded experience for

dozens of passengers. Traditional road-based right-of-way yielding rules cannot

meet such differentiated requirements.

Based on UISEE's customized coordination rule description system, hierarchical

right-of-way configurations can be completed rapidly. This solution allows

operators to flexibly set traffic

priorities by vehicle type, task type, and other dimensions. The system

automatically identifies each vehicle's identity and role, automatically following preset rule constraints to form command

decisions, meeting specific right-of-way requirements.

At Urumqi Tianshan International Airport, over 50

autonomous tractors and over 20 autonomous buses operate in mixed traffic.

After enabling hierarchical right-of-way, the number of bus stops and waits has

been significantly reduced, and passenger experience has notably improved.

Deeply rooted in diverse business scenarios, UISEE

continues to expand its technological boundaries. Every iteration is always

centered on the real needs of L4 commercialization. We must not only solve the

technical question of "whether it works," but also answer the

commercial propositions of "whether it works well" and "whether

it can scale."