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NHAI to Deploy AI-Powered Dashcam Monitoring System
The National Highways Authority of India (NHAI), vide its Press Release dated March 20, 2026, has announced the deployment of an AI-powered Dashcam Analytics System (DAS) for strengthening the operations and maintenance (O&M) of national highways. The initiative marks a structural shift in the approach O&M of highway assets by integrating artificial intelligence (AI) based monitoring with centralised data analytics systems for real-time assessment of road conditions and safety infrastructure. The system is proposed to be implemented across approximately 40,000 km of the national highway network, with a focus on enabling data-driven monitoring and maintenance.
Traditionally, monitoring of highway conditions has relied heavily on physical inspections conducted by field teams and periodic reporting mechanisms. While effective in identifying visible defects, such approaches often resulted in delayed detection of issues and limited visibility across geographically dispersed highway stretches.
The introduction of AI-enabled dashcam monitoring represents a decisive move towards automated condition assessment and predictive maintenance planning, enabling highway authorities to identify structural deficiencies, safety risks and operational anomalies through high-resolution imagery and machine-learning (“ML”) based analytics.
Under the proposed framework, specialised dashboard cameras will be mounted on Route Patrol Vehicles (RPVs) deployed across highway stretches to conduct weekly surveys of highway stretches.
The captured data will be processed using AI/ML models to enable automated detection of more than 30 categories of defects and anomalies, thereby improving consistency and frequency of monitoring as part of routine highway operations.
The AI-enabled system is designed to detect over 30 categories of defects, including:
- Pavement-related issues, such as potholes, rutting and surface cracking;
- Road furniture deficiencies, including faded lane markings, damaged crash barriers and non-functional streetlights;
- Safety and encroachment-related concerns, such as illegal median openings, unauthorised signage and roadside encroachments; and
- Maintenance-related issues such as water stagnation, vegetation growth, drainage deficiencies and the condition of bus bays.
The framework provides for weekly night-time surveys, at least once a month, to assess the performance of road signage, pavement markings, road studs and highway lighting.
For effective monitoring, the network has been divided into five zones, supported by a dedicated IT platform comprising modules for data management, AI analytics and visualisation dashboards.
The system enables comparison of road conditions over time, facilitating tracking of maintenance progress. The AI-generated outputs are integrated with the NHAI Data Lake, enabling centralised monitoring and timely rectification of defects. This integration is expected to enable faster identification of recurring maintenance risks and improve long-term infrastructure planning outcomes.
