Problem Statement

Despite ongoing advancements in automotive and mobility technologies, accurate and reliable positioning remains a fundamental limitation across many real-world environments.

Conventional precision positioning solutions are often:

• Cost-intensive, requiring specialized hardware or vehicle-specific configurations

• Limited in scalability, making broad adoption across mass-produced vehicles impractical

• Fragmented, with mobility data siloed across platforms and stakeholders

As a result, most vehicles, pedestrians, and cyclists operate without access to high-quality positioning intelligence, particularly in dense urban areas where accuracy is most critical.

At the same time, vast amounts of real-world mobility data are generated daily through driving, walking, and cycling. However, there is no effective mechanism to incentivize participation, standardize data collection, or transform this activity into usable AI intelligence. Without proper incentives, data quality remains inconsistent, and contributors receive no direct benefit from their participation.

This lack of scalable, cost-efficient precision positioning and the absence of a sustainable data participation model create a structural gap—one that limits both present-day mobility services and the data foundation required for the autonomous driving era.