How AI Enhances Road Safety Audits Conducted Under IRC-SP:72

India's road infrastructure is expanding faster than ever. From national highways and expressways to urban corridors, traffic volumes and road user diversity are increasing every year.

With this growth comes a critical responsibility: ensuring that roads are not only efficient, but also safe for everyone — including pedestrians, cyclists, and vulnerable road users.

That is why Road Safety Audits (RSAs) have become an essential requirement across highway development projects. In India, the Indian Roads Congress (IRC) provides structured guidance through IRC-SP:72, which defines how audits should be conducted at every stage of a road project.

However, as projects become larger and more complex, traditional audit methods are no longer enough on their own. This is where AI-enabled road safety audits are emerging as a powerful support system — enhancing audits without replacing engineering judgement.

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1. Why Road Safety Audits Matter More Than Ever

Road crashes remain a major concern in India, especially on high-speed corridors and mixed-traffic highways.

Many safety risks are not caused by driver behaviour alone, but by infrastructure gaps such as:

• Missing or unclear signage causing driver confusion
• Unsafe junction layouts creating conflict points
• Inadequate pedestrian crossings endangering vulnerable users
• Poor shoulder conditions reducing recovery space
• Roadside hazards (fixed objects, drop-offs) increasing crash severity
• Inadequate lighting affecting night visibility

Road Safety Audits help identify these issues early, before they lead to crashes.

But the challenge is scale. Auditing long highway corridors manually is time-consuming, inconsistent, and often reactive.

2. Understanding IRC-SP:72

2.1 What is IRC-SP:72?

IRC-SP:72 is the Indian Roads Congress code of practice for Road Safety Audits, providing a structured framework for evaluating road projects at all stages to identify safety concerns for all road users.

2.2 Audit Stages Under IRC-SP:72

• Feasibility stage: Alignment options, land use, major junctions
• Preliminary design stage: Cross-sections, intersection layouts, pedestrian facilities
• Detailed design stage: Geometric compliance, signage, barriers, lighting
• Construction stage: Work zone safety, temporary traffic management
• Pre-opening stage: Final safety checks before traffic opening
• Operational stage: In-service safety performance monitoring

2.3 Key Audit Elements

• Road alignment and cross-section
• Junctions and access points
• Signs, markings, and delineation
• Roadside safety features (barriers, medians, clear zones)
• Pedestrian and cyclist facilities
• Construction stage traffic management
• Operational stage safety performance

3. What IRC-SP:72 Requires from a Road Safety Audit

IRC-SP:72 defines a Road Safety Audit as a formal and independent examination of a road project to identify potential safety concerns for all categories of road users.

The code emphasises reviewing:

• Road alignment and cross-section for design consistency
• Junctions and access points for conflict reduction
• Signs, markings, and delineation for driver guidance
• Roadside safety features (barriers, medians, clear zones)
• Pedestrian and cyclist facilities for vulnerable users
• Construction stage traffic management for work zones
• Operational stage safety performance for in-service roads

Importantly, IRC-SP:72 provides a strong checklist framework, but it does not prescribe how large-scale data should be collected efficiently.

That gap is where digital and AI-based tools through the Road Safety Audit Agent are proving highly valuable.

4. Limitations of Conventional Road Safety Audit Practices

Traditional audits depend heavily on site visits, drawings, and manual observation. While effective, they face practical constraints in modern highway projects:

• Long corridors require repeated site inspections with limited resources
• Human observations may vary between audit teams
• Temporary risks can be missed between visits
• Manual documentation slows down reporting and action
• Consistency across audit stages becomes difficult
• Night-time and adverse weather conditions are rarely observed
• Pedestrian and cyclist behaviour is hard to capture

As road networks grow, these limitations directly affect the effectiveness of IRC-SP:72 implementation.

5. How AI Enhances Road Safety Audits Under IRC-SP:72

AI through the Road Safety Audit Agent does not replace auditors — instead, it strengthens their ability to detect, measure, and prioritise safety risks with better accuracy and coverage.

5.1 Continuous and Objective Corridor Data Collection

With vehicle-mounted cameras and sensors, AI platforms can capture continuous visual data across the entire road length.

AI automatically identifies safety-relevant elements such as:

• Lane and shoulder widths for compliance
• Sight distance constraints at curves and crests
• Roadside hazards (barriers, fixed objects, drop-offs)
• Footpaths and pedestrian facilities for accessibility
• Median openings and access points for conflict analysis
• Signage presence and condition for driver guidance

This directly supports IRC-SP:72 checklist requirements while ensuring no segment is overlooked.

5.2 Faster Detection of Safety Hazards

AI-based hazard detection through the Road Safety Audit Agent can flag common issues such as:

• Missing or damaged signs affecting driver information
• Faded road markings reducing night visibility
• Unsafe barrier terminations creating hazards
• Encroachments on shoulders limiting recovery space
• Improper junction channelisation causing confusion
• Vegetation overgrowth obscuring sight lines

This allows audit teams to focus on engineering interpretation and corrective recommendations rather than manual spotting alone.

5.3 Improved Focus on Vulnerable Road Users

IRC-SP:72 places strong emphasis on safety for:

• Pedestrians at crossings and along footpaths
• Cyclists on roads and dedicated facilities
• Two-wheelers in mixed traffic conditions
• Non-motorised traffic in urban areas

AI systems through the Traffic Analysis Agent can detect crossings, bus stops, roadside activity, and pedestrian conflict zones, helping auditors assess safety in real operating conditions — not just on drawings.

5.4 Behavioural Safety Analysis

AI captures:

• Vehicle speeds approaching critical points
• Pedestrian crossing behaviour at designated and informal locations
• Driver compliance with signage and markings
• Near-miss events invisible in crash records
• Queue formation at intersections and work zones

6. Key Safety Elements Detected by AI

6.1 Geometric Elements

ElementAI DetectionSafety ImpactCurve radiusAutomatic measurementLoss of control riskSight distance3D modellingRear-end and head-on crashesLane widthContinuous measurementLane disciplineShoulder conditionEdge detectionRecovery space

6.2 Traffic Control Elements

ElementAI DetectionSafety ImpactSign presenceObject detectionDriver informationSign retroreflectivityVisibility assessmentNight safetyMarking conditionTexture analysisLane guidanceSignal visibilitySight line analysisIntersection safety

6.3 Roadside Elements

ElementAI DetectionSafety ImpactGuardrailsPresence and conditionCrash severityClear zoneDistance measurementRecovery areaFixed objectsHazard identificationImpact riskVegetationEncroachment detectionSight distance

7. Best Practices: How RoadVision AI Supports IRC-SP:72 Audits

RoadVision AI enables scalable, repeatable, and data-rich safety audits aligned with Indian standards through its integrated suite of AI agents.

Its AI-driven platform helps consultants and authorities by providing:

• Digital corridor inspections with continuous coverage
• Automated detection of road safety hazards through the Road Safety Audit Agent
• Structured outputs mapped to audit checklist elements
• Integration with road inventory and asset condition data from the Roadside Assets Inventory Agent
• Documentation support for compliance and tracking
• Before-and-after analysis for intervention evaluation

By combining computer vision with digital twin technology, RoadVision AI ensures audits are not only compliant but also actionable.

8. Integration with Road Asset Management Workflows

AI-driven audits become even more effective when connected with broader infrastructure data.

For example:

• Guardrail and signage condition from road inventory surveys
• Pavement distress risks validated through pavement condition surveys from the Pavement Condition Intelligence Agent
• Traffic exposure data from the Traffic Analysis Agent
• Safety improvements prioritised alongside maintenance planning

This integrated approach strengthens decision-making under road asset management in India, ensuring safety upgrades are planned systematically.

9. Challenges and Practical Considerations

While AI offers strong advantages, adoption must be done responsibly. Key considerations include:

• AI outputs require auditor validation for engineering judgement
• Local IRC compliance must remain engineer-led
• Quality depends on accurate field data capture
• Authorities need structured workflows for implementation
• Training and upskilling for interpretation of AI insights
• Integration with existing audit processes for adoption

The best results come from combining AI efficiency through RoadVision AI with professional road safety expertise.

10. Final Thought

IRC-SP:72 provides India with a robust framework for road safety audits. By integrating AI-enabled digital audit tools into this framework, authorities can significantly enhance:

• Accuracy of hazard detection with objective measurements
• Coverage across long corridors without gaps
• Consistency across audit stages from design to operation
• Documentation for corrective action tracking with geo-tagged evidence
• Focus on vulnerable road users with behavioural analysis

The platform's ability to:

• Capture corridor data continuously at traffic speeds
• Detect safety hazards automatically with computer vision
• Integrate all data sources for unified audit workflows
• Support IRC-SP:72 compliance with automated reporting
• Scale from urban to rural roads efficiently

transforms how road safety audits are conducted across India.

RoadVision AI is helping modernise road safety audits in India through intelligent analytics, digital twin inspection, and IRC-aligned workflows.

If you are looking to upgrade your road safety audit process while staying fully aligned with Indian standards, book a demo with RoadVision AI today and explore how AI can support safer, smarter roads.

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FAQs

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Q1. Does AI replace road safety auditors under IRC-SP:72?
No. AI supports auditors by improving data collection and hazard detection while decisions remain engineer led.

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Q2. Can AI based audits be used for existing highways?
Yes. AI is particularly effective for operational stage audits where continuous monitoring is valuable.

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Q3. How does AI improve compliance with IRC-SP:72?
AI ensures consistent coverage of safety elements and helps auditors document risks more systematically.

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