> For the complete documentation index, see [llms.txt](https://gotcar.gitbook.io/gotcar-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gotcar.gitbook.io/gotcar-docs/the-problem.md).

# The Problem

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:

<figure><img src="https://1285392671-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FACWOh8nwilfsUccunUDz%2Fuploads%2Fj1QIqjulMLTdP983Wksi%2FHRSNS1VaMAAf9Ma.jpeg?alt=media&amp;token=ac9feee3-71ad-4a1f-a8cf-47127d3aa179" alt=""><figcaption></figcaption></figure>

* **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.