Page 152 - AI for Good-Innovate for Impact

AI for Good-Innovate for Impact

dynamic risk mapping. This data will guide infrastructure upgrades and policy decisions. After
successful implementation in 3 highway corridors in Telangana, iRASTE’s successful model is
set to expand to more cities in the next 3-5 years. The objective is to outfit 350 buses, setting
a standard for nationwide implementation and reducing India’s road fatality rates. Ultimately,
iRASTE represents a significant step towards a safer future, showcasing AI’s role in public safety
and the promise of scalable, life-saving innovations.

34�3� Use case requirements

• UC44-REQ01: It is critical that the transport vehicles are equipped with ADAS devices
which are network capable.
• UC44-REQ02: It is critical that inference from the model is mapped to the location data
such as maps to visualize the locations of the grey spots.
• UC44-REQ03: It is critical that historical data in form of black spots is available for a period
of study.
• UC44-REQ04: it is critical that the road owning agency consumes or considers the
inference from the models to take preventive actions to reduce accidents.
• UC44-REQ05: it is expected that the driver behavior assessment is done by correlating
the ADAS alerts per kilometer per driver.

34�4� Sequence diagram

34�5� References

[1] Project iRASTE Telangana

[2] Project iRaste Telangana Youtube:

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