The AI-Based Accident-Prevention System in Indian Railways is now being tested on some of the country’s busiest and most crucial routes. This major push toward rail safety aims to reduce human error, identify signalling or equipment faults in real time, and prevent collisions or derailments before they occur. As India’s rail network carries millions daily, these AI-driven upgrades could usher in a new era of safer and more reliable travel.
Why AI for Railway Safety?
With the scale and complexity of India’s railway network, traditional safety systems — largely dependent on manual inspection, human vigilance and legacy signalling — often fall short. Human error, mechanical faults, and delayed detection of anomalies have long contributed to serious accidents.
By integrating Artificial Intelligence (AI) and Machine Learning (ML), rail safety measures can become more proactive, consistent, and finely tuned to detect risks before they escalate.
What Are the New AI-Based Systems
Machine Vision Based Inspection System (MVIS)
In July 2025, the Indian Railways signed a MoU with Dedicated Freight Corridor Corporation of India Limited (DFCCIL) to deploy a new AI/ML-driven Machine Vision Based Inspection System (MVIS). This system monitors trains in real time, capturing high-resolution images of the under-carriage of moving trains and using AI to detect any loose, hanging, or missing parts.
On spotting anomalies, MVIS triggers instant alerts for maintenance — thereby preventing breakdowns and derailments.
AI-based Freight-Wagon Door Monitoring — ‘DRISHTI’
In November 2025, Indian Railways announced an AI-based locking and door-monitoring system, called DRISHTI, aimed at freight trains. This system uses cameras and sensors on selected wagons to detect unlocked or tampered doors during transit — a safety and security concern especially for long-distance freight. The prototype has already undergone successful trials, raising hopes for large-scale rollout.
Upgraded Signalling & Automatic Protection Systems — Kavach 4.0 and New Interlocking Technologies
Another pillar of the AI safety push is the enhancement of train signalling and collision-prevention systems. The indigenous anti-collision system Kavach, earlier proven effective in preventing rear-end and head-on collisions, is being rolled out faster. New upgrades (Kavach 4.0) are being integrated in multiple routes.
In parallel, a new signalling technology — Direct Drive Electronic Interlocking (DDEI) — has completed pilot tests at three stations. DDEI reduces human intervention by electronically controlling switches and signals, improving accuracy and lowering risk of human-error accidents.
Preventive Maintenance & Real-Time Monitoring
Besides safety-critical systems like collision-avoidance and signalling, AI also supports proactive maintenance. By automatically inspecting coaches, wheels, brakes and other rolling-stock components, AI can flag potential hazards before they become dangerous or cause delays. This continuous digital monitoring leads to more reliable and safer services.
What This Means for Passengers
Fewer Accidents
With systems like Kavach and DRISHTI, collision risk and derailment due to mechanical failures or freight issues will drop.
Timely Alerts & Corrections
Real-time detection means railway authorities can act before a fault triggers an accident — whether it’s a dangling part, a faulty wagon door, or a signal error.
Better Maintenance, Less Disruption
Automated inspections reduce reliance on manual checks, cutting down maintenance delays and cancellations.
Enhanced Safety Across Routes
As rollout expands from pilot routes to pan-India coverage, even remote routes will benefit from modern safety infrastructure.
Also Read: RailOne App Is Here : The Future of Railway Travel in India
Challenges & What to Watch
Despite these promising developments, achieving full coverage will take time:
- Deployment of AI-based systems across thousands of kilometers will require infrastructure upgrades (optical fibre, sensors, cameras, data centers).
- Continued reliability of AI detection will depend on maintenance, calibration, and integration with human oversight.
- Training staff and ensuring compliance across zones — with diverse geographies and environments — remains a challenge.
- For older routes & remote areas, legacy infrastructure might pose hurdles to swift modernization.
Nevertheless, Indian Railways seems committed to a phased but steady rollout, aiming for long-term safety gains.
The Spiritual Message Behind India’s Push for Railway Safety
From the perspective of the teachings of Sant Rampal Ji Maharaj, safety — physical, moral or spiritual — stems from foresight, awareness, and preventive effort rather than waiting for crises to strike. Just as railways are using AI to foresee and prevent accidents before they occur, Sant Rampal Ji Maharaj’s guidance emphasizes wisdom and righteous living to avoid future suffering.
He teaches that human life is precious and that one must act with responsibility and wisdom to safeguard it. In the world of railways, AI systems like Kavach, DRISHTI, and MVIS reflect this same principle: using intelligence and foresight to protect human lives. When institutions adopt such preventive measures, they mirror the spiritual value of acting with care, responsibility, and devotion — ensuring the wellbeing of many.
Thus, the modernization of Indian Railways through AI isn’t just about technology — it also resonates with the spiritual duty of protecting life, valuing human dignity and reducing suffering.
FAQ: AI-Based Accident-Prevention System
Q1: What routes are included in the AI-based accident-prevention trials?
As of 2025, trials have begun on select busy and high-risk routes; freight wagons and certain passenger corridors are under pilot deployment. Gradual expansion is planned across more tracks.
Q2: What is DRISHTI and how does it improve safety?
DRISHTI is an AI-based locking and door-monitoring system for freight wagons. It uses sensors and cameras to detect unlocked or tampered doors in real time, reducing risks of theft, spillage, derailment, or accidents due to compromised wagons.
Q3: How does the Machine Vision Based Inspection System (MVIS) work?
MVIS captures high-resolution images of the under-carriage of moving trains. AI algorithms then analyze those images to detect loose, hanging or missing components, triggering alerts if abnormality is found — preventing mechanical failures or derailments.
Q4: Is the anti-collision system Kavach also part of the AI rollout?
Yes — the enhanced version, Kavach 4.0, is being deployed alongside AI/ML-based monitoring systems. It automatically controls train speed and can apply brakes to avoid collisions if signals are missed or human error occurs.
Q5: When will all trains become safe under these systems?
Full coverage will be gradual. With infrastructure upgrades, installation of AI systems, and training, Indian Railways aims to extend these technologies across its network in phases over the coming years — making rail travel progressively safer for everyone.
