NHAI to Deploy AI Dashcam Monitoring for National Highways

23 Mar 2026 CW Team

The RPVs will conduct comprehensive weekly surveys on all stretches and AI models will automatically identify over 30 types of defects and anomalies. The major focus will be on pavement condition including detection of potholes, rutting and severe cracking, while road furniture monitoring will detect damaged or faded lane markings, compromised crash barriers and non-functional street lighting. The system is also intended to monitor water stagnation, missing drainage covers, vegetation growth and the condition of bus bays for maintenance prioritisation.

Safety monitoring will cover unauthorised median openings, unauthorised signboards and illegal parking or encroachments to support timely enforcement and remediation. To further enhance evaluation of lighting and markings, at least one weekly survey will be carried out at night each month to assess road signages, pavement markings, road studs and highway lighting performance. The objective is to provide continuous condition assessment that informs targeted repairs and reduces safety risks for road users.

The authority has created five zones to ensure systematic monitoring and will develop a specialised IT platform with modules for data management, AI analytics and interactive visualisation dashboards. The platform will enable side-by-side comparisons of road condition over time and integration of AI generated results into the central NHAI Data Lake for seamless monitoring and timely rectification of defects. The move is expected to enable more efficient resource allocation, improve road safety and enhance user experience across the national highway network.

The National Highways Authority of India (NHAI) will deploy advanced Artificial Intelligence (AI) powered Dashcam Analytics Services (DAS) across around 40,000 km of the national highway network to modernise operations and maintenance. The initiative will use Machine Learning (ML) to process high-resolution imagery and video gathered from specialised dashboard cameras mounted on Route Patrol Vehicles (RPVs). It will aim to move operations and maintenance towards data-driven workflows and remote monitoring to improve efficiency and responsiveness. The RPVs will conduct comprehensive weekly surveys on all stretches and AI models will automatically identify over 30 types of defects and anomalies. The major focus will be on pavement condition including detection of potholes, rutting and severe cracking, while road furniture monitoring will detect damaged or faded lane markings, compromised crash barriers and non-functional street lighting. The system is also intended to monitor water stagnation, missing drainage covers, vegetation growth and the condition of bus bays for maintenance prioritisation. Safety monitoring will cover unauthorised median openings, unauthorised signboards and illegal parking or encroachments to support timely enforcement and remediation. To further enhance evaluation of lighting and markings, at least one weekly survey will be carried out at night each month to assess road signages, pavement markings, road studs and highway lighting performance. The objective is to provide continuous condition assessment that informs targeted repairs and reduces safety risks for road users. The authority has created five zones to ensure systematic monitoring and will develop a specialised IT platform with modules for data management, AI analytics and interactive visualisation dashboards. The platform will enable side-by-side comparisons of road condition over time and integration of AI generated results into the central NHAI Data Lake for seamless monitoring and timely rectification of defects. The move is expected to enable more efficient resource allocation, improve road safety and enhance user experience across the national highway network.

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