Every vehicle · Every road · Even where nobody is watching

The road watches
back now.

Roadside rescue in milliseconds, autonomous road-damage detection with a genuinely trained model, and a corridor that keeps reporting even when the network dies. All of it is running behind this page — the numbers below are live.

Illustration drawn for this project, not a photograph
–Detections logged
–Open findings
–Edge devices
–Corridor health
0.586YOLO11n mAP50 (held-out val)
–DB latency ms

● LIVE — pulled from the running platform every 8 seconds · device data on this build is SIMULATED

The working of it — in 3D

Move your pointer over the stack. Each slab lights up as the event travels the real pipeline.

01 · EDGECamera frame → YOLO11non-device inference, offline-first~72 ms CPU
02 · QUEUELocal store & offline queuesurvives restarts, zero network neededop_id
03 · SYNCIdempotent batch syncreplay-safe: applied / duplicate≤200
04 · GISPostGIS attachnearest segment within 250 mGiST
05 · SCORERoad Health 0–100transparent factors, never a black boxrule-v1
06 · HUMANAuthority verify → closea model raises signals, never dispatchesADR-05

Every number on this page is measured, not asserted — the same pipeline passes 378/378 end-to-end assertions in CI.

Every layer, live in 3D

The same stack as one model, running here. It turns on its own; open a layer, or press play to watch a real request travel through every one.

The layers of RoadAssist Bharat stacked as one model, rendered in Blender
Layers in 3D · the same model, rendered in Blender, until the live scene has loaded Open full screen

Through the device's eyes

Real Indian road frames, boxed live by the trained model — nothing hand-drawn. Move your pointer to tilt.

Road frame with model detection boxes

Frames: RDD2022 India subset — Arya et al., CC BY-SA 4.0 · detection boxes are the model's own output

Ninety seconds, no faking

The dashboard catching a live patrol, surviving a network cut, and the app booking a real rescue.

RECORDED FROM THE LIVE PLATFORM