Use case · Automotive and ADAS
Navigation data that works on Indian roads
Global datasets are often wrong or empty for India: posted speed limits are absent or off, and locality-centric addresses break geocoding. SmartData ships the India layers automotive and ADAS systems actually need, field-surveyed and refreshed every year.
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The problem
Where global datasets fail India
A navigation or ADAS stack is only as safe as the road data underneath it. On Indian roads, global datasets routinely fall short in the exact places that matter: posted speed limits are absent or wrong, road geometry misses recent construction, and India addresses are locality-centric, so geocoding lands you on the wrong block.
For an automotive or ADAS program, those gaps are not cosmetic. A missing speed limit is a missing constraint. A stale road segment is a route that no longer exists. A geocode that drifts to a locality centroid is a pickup that never happens. The fix is not more inference over thin data. It is India data built on the ground.
What the road layers cover
The India attributes a stack needs
- 01
Posted speed limits
The constraint global sets miss, captured from the road itself rather than inferred, so the stack reasons against the limit that is actually signed.
- 02
A road network in 11 classes
Geometry and classification across the full network, with urban roads surveyed to 1:5,000, so routing distinguishes a highway from a service lane.
- 03
Real-time closures in CIFS
Live road closures published in CIFS, the closure format built to be ADAS-compatible, so a shut junction reaches the system before the vehicle does.
- 04
Accident zones
Known accident zones as a map layer, so a program can raise caution and alerts where the road history says it matters.
- 05
Building polygons with entry and exit
Building footprints carrying entry and exit, so the last leg resolves to the right gate, not a locality centroid.
The origin
Field-surveyed, not inferred
Every SmartData layer starts as primary survey: ground-surveyed, not algorithmically inferred. That is the whole point for a vehicle program. A speed limit you can stand next to, a closure logged from the field, an address geocoded to the building.
And it stays current. The portfolio refreshes annually, with the infrastructure layers rolling continuously, so the road network your stack reasons over keeps pace with the road network drivers actually meet.
The scale behind it
Built across the whole country
The road and address layers are not a sample. They span the country, so a vehicle program can ship one data dependency instead of stitching coverage city by city.
How it fits your stack
Formats your systems already read
The road data lands in the standards a vehicle program already runs. Closures ship in CIFS, the ADAS-compatible closure format, and the network arrives as boundary polygons and centroids alongside the geometry. There is nothing to reverse-engineer: license the layers you need, or build against the SmartData APIs for routing, geocoding and point-to-admin detection.
- 01
Speed reasoning that holds
The stack reasons against posted limits that are actually signed, not inferred guesses, on the roads where global sets go blank.
- 02
Routes that match the road
An 11-class network with real-time closures keeps routing aligned with the road network drivers meet, junction by junction.
- 03
Geocoding to the building
Building-level address points with entry and exit resolve the last leg to the gate, not a locality centroid.
Proof
The data platforms build on
The credibility of India road data is who relies on it. The world's leading mapping platforms license India data from Lepton, and the same survey discipline runs national-scale public infrastructure programs with Railtel and NIC.
Proof points: Railtel, NIC, primary survey, 11-class network, CIFS closures, annual refresh.
Put real India road data behind the wheel.
Bring a city, a corridor or a vehicle program. We will show you the road, speed-limit, closure and address layers SmartData holds for it.
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