India's national highways carry a disproportionate share of the country's road fatalities. The reasons are well understood — high speeds, heavy freight, mixed traffic, and infrastructure that deteriorates faster than it is maintained. But the specific hazards on any given stretch are rarely documented in a consistent, actionable form. Faded lane markings, damaged crash barriers, missing road studs, misplaced signage, and unauthorised road cuts accumulate quietly until they contribute to an accident.
SaveLIFE Foundation (SLF), India's leading road safety non-profit, identified a 115 kilometre stretch of NH-48 in Maharashtra as a priority for a comprehensive safety audit. The challenge was not just identifying hazards — it was doing so at scale, consistently, with enough precision to generate IRC-aligned intervention recommendations that highway authorities could actually implement.
Traditional safety audits are manual, slow, and produce reports that are difficult to compare across stretches or update over time. SLF needed an approach that could cover 230 kilometres of highway — both sides — with the speed, consistency, and evidential rigour that a genuinely effective safety intervention requires. AI-based road safety survey, automated road hazard detection, and AI-powered highway safety audits are enabling infrastructure stakeholders to move from reactive maintenance to proactive safety management.
RoadVision AI deployed its AI and Digital Twin platform to conduct a detailed technical road safety audit across both carriageways of NH-48 — covering 115 kilometres of highway in each direction for a total assessed length of 230 kilometres. The audit went significantly beyond standard pavement condition assessment, evaluating the full spectrum of road safety infrastructure against IRC compliance standards through AI-driven road infrastructure inspection.
Engineers used the RoadVision AI smartphone application to collect real-time imagery and GPS data across both sides of the highway. Vehicles drove at normal traffic speed in each direction, capturing continuous visual data that the AI platform processed to detect pavement distress, safety asset conditions, and compliance gaps through computer vision road safety analysis. The mobile setup required no specialist equipment, no lane closures, and no interruption to highway traffic.
The audit covered three distinct layers of highway safety infrastructure. First, geometric design analysis evaluated road alignments, cross-sections, and median barriers for design-level safety compliance. Second, traffic control device evaluation assessed the placement, condition, and compliance of road signs, signals, and lane markings. Third, roadside safety inspection identified issues with guardrails, pedestrian crossings, and unauthorised access points — the kind of hazards that rarely appear in routine maintenance logs but frequently appear in accident reports.
Every hazard identified was matched to a specific IRC standard and a concrete remediation action. Faded markings were tied to IRC: 35-2015. Missing road studs to IRC: SP: 84-2019. Damaged crash barriers to IRC: 119-2015. Signage gaps to IRC: 67-2022. This linkage between finding and standard transformed the audit from a problem list into a compliance-ready action plan that highway authorities could implement without additional interpretation.
The 230 kilometre audit of NH-48 revealed a highway carrying a significant and varied hazard load — with over 500 individual safety issues catalogued across five categories. Each finding was geotagged to a precise location, assigned a severity classification, and linked to a specific IRC-compliant intervention. For the first time, SLF had a structured, evidence-based picture of exactly what was wrong, where it was, and what the standard required to fix it.
The SaveLIFE Foundation collaboration produced something rare in Indian road safety work: a technically rigorous, GPS-tagged, IRC-aligned audit of a major national highway stretch that gives both the non-profit sector and highway authorities the evidence they need to act. The outcomes extend beyond the 230 kilometres surveyed.
India has over 140,000 kilometres of national highways. The idea of auditing them systematically — both carriageways, every hazard category, IRC-aligned recommendations — has always been theoretically desirable and practically impossible with manual methods. The NH-48 deployment changes that calculus.
RoadVision AI's smartphone-based platform covered 230 kilometres of one of Maharashtra's busiest highways with no specialist vehicles, no lane closures, and no weeks of mobilisation. The output was not a general report but a structured, geotagged, standards-referenced audit document that highway authorities, contractors, and safety advocates can all act on directly.
The SaveLIFE Foundation collaboration shows what is possible when India's road safety ambition is matched with technology that can actually deliver it at national scale. Every national highway corridor in India has a version of the hazard profile found on NH-48. Now there is a method to find them all — and fix them before they become statistics.
The Autonomous Road Engineer
AI that is transforming road infrastructure, from reactive to predictive.
