The rise of artificial intelligence (AI) is transforming infrastructure, notably India's extensive highway network. AI-powered cameras are increasingly deployed to enhance road safety and traffic management, capable of real-time accident detection, identification of traffic violations, and flagging potential hazards. However, this technological leap raises significant questions regarding data governance, privacy, and accountability.
The Evolution of Highway Monitoring
Traditionally, CCTV systems primarily recorded events for later human review. Modern AI video analytics, however, can interpret live feeds, identify patterns, and generate immediate alerts for anomalies like stalled vehicles, collisions, or overspeeding. This shift from passive recording to active, intelligent detection promises faster response times for authorities.
Anoop G Prabhu, Co-Founder and CTO of Vehant Technologies, highlighted the transformative potential. "The real potential of AI on highways lies not merely in monitoring what is happening, but in using real-time video intelligence to detect, interpret and respond to incidents as they unfold," Prabhu stated. He emphasized that AI analytics can continuously process feeds, classify incidents, and provide context, enabling a proactive approach to road safety.
Challenges of Automated Surveillance
While AI offers clear benefits, its deployment on a national scale—India's national highway network expanded to 146,572 km by March 2026—brings inherent challenges. Automated systems must address concerns about detection accuracy and the potential for mistaken identification. A system-flagged incident is not conclusive proof of a violation, necessitating clear procedures for human verification and avenues for citizens to dispute penalties. Maintaining public trust hinges on fair and transparent processes.
Data Access, Retention, and Accountability
Critical to the ethical implementation of AI surveillance are robust policies around data access and retention. Surveillance footage must only be accessible by authorized government entities, with strict protocols governing storage, permissions, and documentation of all access and usage.
The duration for which data is retained is also a key concern. Indefinite retention poses privacy and security risks, while premature deletion could hinder legitimate investigations. Retention periods should be clearly defined, balancing operational needs with legal and ethical considerations. As AI-driven surveillance integrates further into highway monitoring and enforcement, authorities must establish clear accountability for how these automated systems operate. This includes distinguishing between an automated alert and a verified violation, ensuring human review for disputed enforcement decisions, and providing transparent rules on data use, independent oversight, and accessible grievance mechanisms. The success of intelligent highways will ultimately depend on both the speed of incident detection and the accuracy, explainability, and accountability of the decisions made by these systems.