RoadVision AI conducts recurring, weekly AI-based road surveys across 9,571 km of National Highways for the National Highways Authority of India (NHAI), using dashcam imagery captured across NHAI's Regional Offices (ROs) and Project Implementation Units (PIUs). The platform delivers automated detection of road defects and assets, GIS-based analytics, chainage-wise reports, and week-on-week condition comparisons giving NHAI's maintenance teams a continuously refreshed, proactive view of highway condition rather than a periodic snapshot.
NHAI manages one of the largest national highway networks in the world, organised operationally into Regional Offices and Project Implementation Units across the country. Traditional highway condition assessment whether an instrumented network survey vehicle or a manual PIU-level inspection produces a snapshot: an accurate picture of condition on the day it was captured, and then silence until the next scheduled round, which for a network this size can be months apart.
That gap is where deterioration happens. A crack that would have been a low-cost sealing job in week one can become a pothole by the time the next scheduled survey reaches that stretch. Under modern highway maintenance contract structures OMT, EPC, and hybrid annuity models alike NHAI and its concessionaires also need dated, stretch-specific evidence of condition to support maintenance accountability, not an annual report. NHAI needed a way to monitor its full network at a cadence fast enough to catch deterioration early, organised the way its own Regional Offices and PIUs already operate.
9,571 km cannot realistically be re-driven by a specialised network survey vehicle every week; periodic surveys leave long gaps where deterioration goes unmeasured between visits.
A single condition reading shows what a stretch looks like today, not whether last month's patch repair held, or how quickly a section is deteriorating relative to its last inspection.
OMT, EPC, and hybrid annuity contract structures require ongoing, stretch-specific evidence of condition to support maintenance accountability — not a single annual assessment.
With the network organised into ROs and PIUs, condition data collected in different formats or cadences across units makes network-wide comparison and prioritisation difficult.
RoadVision AI runs a rotating weekly survey programme across NHAI's 9,571 km network, organised by Regional Office and Project Implementation Unit, with each weekly cycle covering a defined batch of roads for example, Weekly-Cycle-2 alone covered 91 roads in a single pass so that, cycle over cycle, the full network is kept under continuous, near-real-time observation.
The full 9,571 km network was scoped and organised by NHAI's own Regional Office and Project Implementation Unit structure, with a rotating weekly survey schedule defined to cycle through the network in manageable, trackable batches.
Survey vehicles fitted with the RoadVision AI dashcam application covered each week's assigned batch of roads at normal traffic speed, capturing continuous video and GPS logs without requiring lane closures or specialised survey equipment
Footage from each weekly cycle was processed through RoadVision AI's computer vision engine, automatically detecting and classifying pavement defects, roadside assets, and safety-relevant findings, each tagged by severity and precise chainage
Findings were rolled into GIS-based analytics and chainage-wise reports, with each new weekly cycle compared against prior cycles for the same roads — surfacing whether condition on a given stretch was stable, improving, or deteriorating
Results were delivered through a live dashboard filterable by RO, PIU, and Weekly Survey cycle, giving NHAI's regional and project-level teams a network view organised exactly the way their own maintenance accountability already operates
The screenshots below show the RoadVision AI platform as deployed for NHAI's East Package organised by Regional Office, Project Implementation Unit, and weekly survey cycle, down to a single, chainage-level inspection point on a specific highway stretch.
Weekly-Cycle-2 alone one rotation of the recurring survey programme, covering 91 roads surfaced 27,344 classified findings. Beyond the individual defect counts, the real value of this deployment is what a continuously repeating cycle like this one enables: comparison over time, not just a count at one moment.
RoadVision AI's platform delivers five structured outputs from the recurring survey programme each built around the fact that this is a continuous service, not a one-time assessment.
The operational cadence behind the whole deployment
Roadway Surface, Asset Management & Road Safety, every cycle
Network-level view down to a single stretch
The capability a one-time survey cannot offer
Reporting that matches how NHAI already works
The shift from periodic, instrumented surveys to a continuous weekly AI-based programme changes what NHAI's maintenance teams can actually do with the data not just what they know, but when they know it.
Instead of waiting months between surveys, NHAI now has a rolling, cycle-by-cycle view of condition across its full 9,571 km network
Week-on-week comparison surfaces stretches deteriorating faster than expected, supporting intervention while the fix is still a low-cost one.
Every finding carries a date, a location, and a severity tier giving NHAI defensible evidence to support maintenance contract review
RO- and PIU-level filtering means the platform slots directly into NHAI's existing operational structure rather than requiring a new way of working
For national and state highway authorities, OMT/EPC concessionaires, and maintenance contract teams evaluating recurring, AI-based highway condition monitoring.
RoadVision AI conducts recurring, weekly AI-based road surveys across 9,571 km of National Highways for NHAI, using dashcam imagery organised around NHAI's own Regional Office (RO) and Project Implementation Unit (PIU) structure. The platform automatically detects road defects and assets, delivers GIS-based analytics and chainage-wise reports, and compares each weekly cycle against prior cycles to support proactive rather than reactive highway maintenance.
Rather than attempting to survey the entire network every week, the programme runs on a rotating weekly schedule: each cycle covers a defined batch of roads (Weekly-Cycle-2, for example, covered 91 roads), cycling through the full network over successive weeks. This keeps the whole 9,571 km network under continuous, recurring observation without requiring an unrealistic amount of survey capacity in any single week.
A single survey tells you what a road looks like on the day it was captured. Comparing successive weekly cycles for the same roads tells you whether that condition is stable, improving, or deteriorating and at what rate. This lets NHAI verify whether a completed repair has actually held, and flags stretches deteriorating faster than expected early enough to intervene before a crack becomes a pothole.
Every finding, and the dashboard itself, is filterable by RO, PIU, and Weekly Survey cycle the same organisational hierarchy NHAI already uses to manage the network operationally. This means regional and project-level teams can review condition and defect data at exactly the level they are already accountable for, without needing to translate the platform's output into their own structure.
The Autonomous Road Engineer
AI-powered road condition monitoring, asset management, & infrastructure intelligence helping agencies move from reactive maintenance to predictive road management.
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