Page 801 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

stationarity tests. Once the cluster is formed and the leader is elected, the engine
updates the network with this information. Since this process is ongoing, the clusters
are dynamic and adapt to changing network conditions.
• pQoS Engine: The pQoS Engine plays a critical role in predicting the QoS of the
network, which is essential for making informed task offloading decisions. It employs Transport 4.10: Intelligent
ML models to forecast network behaviour and performance in the future. The offloading
decision is based on an End-to-End (E2E) prediction of the vehicle-to-network link.
Predictive QoS not only enables the feasibility assessment of task offloading but also
ensures a seamless transition back to in-vehicle resources if the network conditions
degrade.
• Task Offloading Manager: Autonomous vehicles require substantial computational
power for tasks like critical-safety applications. Due to space, cost, and energy
constraints, partial or full offloading of these tasks to a cloud-based MEC environment
is a viable solution. The task offloading manager is responsible for executing these
tasks in the cloud and making decisions regarding task offloading. The task offloading
manager sends the response back to the leader vehicle, which then communicates the
decision to the other vehicles in the cluster, ensuring efficient resource management
across the system.

Impact on Intelligent Transport

The integration of AI into transport systems is transforming the future of mobility by directly
enhancing road safety, operational efficiency, and user experience. A primary impact is the
reduction of traffic-related deaths and injuries through intelligent systems that anticipate risks,
improve driver behavior, and enable faster emergency response.

AI-powered tools improve the accuracy of crash data collection and analysis, offering insights
that help prevent accidents and guide better infrastructure planning. These systems can support
dynamic traffic management and enable predictive interventions that reduce congestion and
increase overall transport system efficiency.

In the realm of intelligent transport, autonomous and semi-autonomous features such as lane-
keeping assistance, emergency braking, and automatic parking are already making vehicles
safer and easier to operate. These technologies also reduce driver workload and error, key
factors in many road incidents.

Moreover, intelligent transport benefits from AI-enhanced traffic coordination, enabling real-
time adjustments based on road conditions, vehicle density, and weather. This results in more
responsive and resilient urban mobility networks.

Emerging mobility solutions—such as connected vehicles, ride-sharing platforms, and integrated
infotainment and communication systems—further contribute to smarter transportation
ecosystems. These innovations help streamline vehicle coordination, improve route efficiency,
and create safer, more enjoyable travel experiences.

Ultimately, the adoption of AI in transport is not only about improving individual vehicle
performance but about building a connected, adaptive, and sustainable transport infrastructure
that enhances safety, accessibility, and urban quality of life on a system-wide level.

2�2 Benefits of use case

The integration of AI into transport systems is transforming the future of mobility by directly
enhancing road safety, operational efficiency, and user experience. A primary impact is the

765