The Indian Institute of Technology in Mandi (IIT-Mandi) recently developed a smart road monitoring system that aims to minimise the risk of road accidents. Students and faculty innovators at IIT-Mandi developed the system to prevent accidents caused by sharp or blind turns which lead to many fatalities, especially in hilly areas.
According to a news report, previously deployed techniques like installing convex mirrors helped but were not very effective. The system invented by the IIT-Mandi team works through sensors that detect the speed, direction, gradient of the slope, and type of vehicle and signals the driver about the oncoming turn. Speaking about the system, the Assistant Professor in the School of Engineering at IIT Mandi explained that the technique will reduce accident risks on roads with sharp curves and human intervention on traffic counts and management. It will also enable better decision-making.
IIT-Mandi said in a statement that at a prototype development stage, the system costs less than IN20,000 (US$269) excluding the alerting units per curve. However, the innovators are currently working on the commercial aspects and trying to bring down the overall product cost, by lowering operating and maintenance costs, and utilising alternative energy sources to make the system self-sustainable using solar energy.
Indian research organisations have developed several technology-driven tools to prevent and reduce road accidents. Earlier this year, the Indian Institute of Technology in Ropar (IIT-Ropar) developed an algorithm for driver drowsiness detection using machine learning and computer vision. The researchers used computer vision algorithms to extract facial features such as eye closure and yawning. They deployed machine learning techniques to effectively detect driver’s alertness.
More recently, OpenGov Asia reported that the Karnataka State Road Transport Corporation (KSRTC) plans to use artificial intelligence (AI)-based technologies to limit road accidents and improve passenger safety in bus transportation. The corporation recently floated a tender to implement an AI-powered Collision Warning System (CWS) and Driver Drowsiness System (DDS) for 1,044 buses. CWS will provide features like forward-looking collision warnings (FLCW), lane departure warnings (LDW), and virtual bumper. It will also generate real-time alerts. This is one of the first times a state-run bus corporation is using technology on a large scale to reduce accidents. Other state-run bus corporations in the country are waiting to adopt this system.
KSRTC officials said the FLCW system will identify an impending collision and inform the driver that they have entered an unsafe distance zone. An official noted that this would help the driver prepare to take the necessary action to avoid a collision. The system will provide real-time alerts to warn the driver against impending collisions. AI-based camera sensors will provide the detection of a vehicle from a sufficient range of at least 150m at any speed so that it can effectively warn the driver.
When minimum safe distance is not maintained, an alert will be generated. This minimum safe distance is based on a calculation of the time-to-collision (TTC) with the vehicle ahead including 2/3 wheelers, pedestrians, and cyclists. The officials added that the alarm will be initiated at a TTC of up to 2.5 to 3 seconds, be operational at a vehicle speed range of up to at least 120kmph, and generate both visual and audible alarms. It will also notify the driver when lane marks are not available. The DDS will check its drivers from dozing off at the wheel. It will monitor the driver’s eye movements and sound a warning alarm in case they appear sleepy. AI-based CCTVs will monitor the facial behaviour of the driver. It will also alert the KSRTC central control room if the driver ignored the alert.
