Established in 1995, the National Highways Authority of
India (NHAI) is an autonomous agency under the
Government of India. It is tasked with the management of
over 50,000 km of National Highways, which forms a part
of India’s total road network of 1,32,499 km. NHAI set a
goal to reduce road fatalities and enhance highway
operations. To achieve this, they proposed the
development of a system that can utilize computer vision,
Artificial Intelligence and machine learning to continuously
monitor and manage all activities on the highways.

Given the extensive length of expressways, monitoring various aspects such
as traffic congestion, passenger safety, traffic offenders, patrolling vehicles,
and sign boards posed a significant challenge for NHAI. Some of the challenges Faced
by NHAI were:
● Continuous Monitoring and Alert System- In response to escalating
highway rule violations and accidents.
● Data Integration from Diverse Sources- Considering the vast length
of expressways and highways, a system was needed that can efficiently
gather and process data from a variety of sources such as surveillance
cameras, sensors, , and traffic management systems.
● Expressway Safety Event Detection- With the numerous types of rule
violations occurring on highways, NHAI required a robust system
capable of identifying unsafe incidents on expressways, such as
high-speed vehicles, wrong-way traffic, congestion, poor visibility, and
stationary traffic.
● Minimize Response Time to Emergency Events- The NHAI required
a system to reduce the response time to accidents, casualties,
instances of high-speed vehicles, wrong-way driving, and congestion.

NHAI wanted to create an AI-driven system capable of processing extensive data from various sources such as CCTV cameras,
Radars, traffic sensors, and IoT devices. To accomplish this, We have developed and deployed 13 distinct modules on the
expressways, each addressing a specific issue. The
modules that are developed are as follows:
1. Video Incident Detection System- Vehicles moving in the wrong direction, visibility/congestion on the road, roadblocks, and
sudden vehicle stops are detected by the VIDS. The tracking of vehicles in real-time is facilitated by the use of a Local Processing
Unit (LPU), AI algorithms, and computer vision.
2. Vehicle Speed Detection System- Various aspects of traffic, including vehicle over speeding, vehicles moving in the wrong lane,
hour-wise traffic trends, and vehicle number detection, are monitored by the system. Road data and statistics are gathered using
tools such as a Radar Speed Sensor, ANPR Camera, Vehicle Actuated Speed Display (VASD), and a Road Condition Monitoring
module.
3. Traffic Monitor Camera System- The TMCS is used by Command Center executives to simultaneously view multiple camera
feeds based on locations in a grid. These feeds can also be filtered by package, and camera functions such as zoom, pan, and rotate
can be controlled. Various types of cameras were installed to meet the requirements for surveillance of traffic and vehicles
on the highway.
4. Highway Traffic Monitoring System- A comprehensive bird’s-eye view of the entire highway system is provided to the command
centre executive through a marker-based representation featured by the system, which represents devices and events on the map.
Event markers indicate incidentssuch as accidents or road closures, and device status indications provide alerts if a system is not
functioning properly or is down.
5. Variable Messaging System- A VMS dashboard was developed, enabling command center personnel to view all the signboards
on the road and their locations on a map. This tool also allows them to communicate important information, such as traffic alerts,
construction updates, and weather warnings, to road-users by sending messages to the signboards. The messages are delivered by
connecting to each signboard’s IP address.
6. Facility Management System- This system was developed to streamline essential operations related to various devices,
including cameras, VIDS devices, and radar systems. With the implementation of this system, the ability to seamlessly access a
real-time view of available devices, monitor their health status, and execute commands across all integrated devices within the
system is granted to personnel stationed at a command center.

7. Event Management Module– Traffic jams are automatically detected by the system by comparing the real-time speed of
vehicles. Events are manually registered by operators after verification through various methods, which include the use of patrol
vehicles and camera detection from systems such as TMCS, VIDS, ANPR, VSDS, etc., as well as other sources of information.
8. Automatic Traffic Count & Classification- Vehicle counting and classification is automatically done by the ATCC module. All
vehicle types on highways are recorded for monitoring and data collection at the ATMS Control Centre using data from ANPR and
VIDS cameras.
9. Travel Time Measurement System- Travel times and speeds between major project sections are measured by the Travel Time
Measurement System (TTMS) using ANPR cameras at VSDS locations. Images of vehicles are captured, numbers are extracted, and
time and location stamps are used for speed calculations.
10. Report Generation Module– Key reports for traffic management, including the Alerts Report for incident tracking, the ATCC
Report for vehicle data, the Event Report for significant occurrences, the Device Malfunction Report for equipment performance,
and the IP Address Master Report for network device management, are generated by the system.
11. Advanced Driver Assistance System- To prevent deaths and injuries that occur in car accidents, this module has been
developed. It is installed on vehicle tabs and can detect nearby obstacles or driver errors. It offers multiple screens such as a
camera screen, map screen, vehicle details screen, call screen, alert and ticket screen.
12. Emergency Calling Box- Roadside assistance during emergencies is provided by the Emergency Calling Box (ECB). It is linked to
a control center that receives calls from the ECB, telecom providers, and the 1033 service. When a call is initiated, it is processed
by the server and handled by the SDK to ensure swift communication.

Assessment and Planning: The implementation of the VIDS (Video-based Intelligent Detection System) solution for the National Highways
Authority of India (NHAI) began with a comprehensive assessment of the toll plaza infrastructure and traffic patterns. This initial phase
involved identifying key areas for camera placement and determining the optimal configuration for real-time video streaming.
Hardware Deployment: Following the assessment phase, the necessary hardware components, including cameras and processing units,
were deployed at the toll plaza site. Approximately 50 cameras were strategically positioned to provide comprehensive coverage of the
highway lanes, enabling the system to capture real-time video streams effectively.
Integration with VIDS Platform: The real-time video streams captured by the cameras were seamlessly integrated with the VIDS platform,
which serves as the central hub for processing and analyzing the video data. This integration facilitated the efficient transmission of video
feeds to the VIDS hardware for immediate analysis and alert generation.
Alert Generation for Various Events: The VIDS solution was configured to detect and generate alerts for a wide range of events and
anomalies, including:
Wrong direction: Alerts triggered when vehicles are detected traveling in the wrong direction on the highway lanes.
Stopped vehicle: Alerts generated when vehicles come to a sudden halt or stop for an extended period, indicating potential traffic
congestion or safety hazards.
Intrusion on highway: Alerts issued when unauthorized individuals or objects are detected entering the highway lanes, posing a risk to
motorists.
Over speed and under speed vehicle: Alerts generated when vehicles exceed or fall below predefined speed thresholds, helping
enforce speed limits and ensure safe driving practices.
Fog and smoky climate: Alerts triggered during adverse weather conditions such as fog or smoke, enhancing visibility and warning
drivers of hazardous road conditions.
Automatic number plate recognition (ANPR): Alerts generated upon detecting vehicle license plates, with integration to the Vaahan
database of the Government of India for vehicle identification and tracking.

Increased Safety Measures:
Reduced the incidence of wrong-way driving by 30% through early detection and alerts.
Decreased instances of stopped vehicles on highways by 25%, minimizing traffic congestion and potential accidents.
Mitigated intrusions on highways by 40%, enhancing overall road safety for motorists.
Enhanced Traffic Management:
Improved average vehicle speed by 15% through monitoring and enforcement of speed limits, reducing the risk of accidents.
Reduced the number of vehicles operating below optimal speed by 20%, optimizing traffic flow and minimizing disruptions.
Enabled timely response to fog and smoky conditions, reducing accidents by 25% during adverse weather.
Efficient Law Enforcement:
Enhanced law enforcement capabilities through automatic number plate recognition (ANPR), resulting in a 50% increase in the
identification of vehicles involved in illegal activities.
Facilitated seamless integration with the Vaahan database, enabling swift identification and tracking of vehicles with outstanding
violations or warrants.
Cost Savings:
Saved an estimated 20% in operational costs through streamlined traffic management and reduced incidents of traffic violations.
Reduced maintenance expenses by 15% through proactive monitoring and preventive maintenance of highway infrastructure.
Improved Public Perception:
Boosted public confidence in highway safety measures, leading to a 25% increase in positive feedback from motorists and commuters.
Strengthened NHAI’s reputation as a leader in implementing advanced technologies for ensuring road safety and efficiency.