The impact of AI on our daily lives is manifold. Technological innovations are disrupting various industries too, from education and healthcare to manufacturing and transportation, to name a few. In the field of transportation, self-driving cars have particularly gripped everyone’s imagination. But the mobility ecosystem has expanded over the last few years to go beyond autonomous navigation. The i-Hub-Data centre located on IIITH campus has been actively involved in various applications that are multi-disciplinary in nature for mobility-based problems.
One of them concerns detecting abnormal traffic patterns and rule violations on Indian roads. These include traffic violations by two wheelers such as riding without a helmet, triple riding, wrong side driving, and others. The traditional approach for tackling these problems has been to use CCTV cameras that are mounted on traffic lights and at intersections. The cameras capture videos which are then analyzed to detect violations. The data of the violation is transferred to a centralised server at the Traffic Control Room where a challan is generated. This system however has certain limitations; the chief being that the violators know exactly where the cameras are since the cameras are fixed. The element of surprise is missing here. In order for deterrence to be a key strategy that leads to a better adherence to traffic rules, then it is important that there be an element of surprise.
Leveraging Bodhyaan
A decision was taken to leverage Bodhyaan – an advanced multimodal research vehicle which is equipped with multiple sensors and helps in capturing traffic data. With this data, Bodhyaan enables research from academia and the Indian startup industry to work on specific problems pertaining to Indian traffic and roads. In order to overcome the limitations of static CCTV cameras, they developed a complete pipeline that captures videos of traffic violations via Bodhyaan and tags them under various violation categories. With these video clippings available at the police control room, it is easy to draw up challans.
Data Collection at Scale
Data of traffic violations by two wheelers was captured at a fleet scale rather than via a single platform. We used video recordings captured by dashcams mounted on cabs and buses that criss-cross the city roads. The videos are processed by our system and challans generated accordingly. In this way, there is not only an element of surprise but also a distribution in the capturing of data throughout the city.
Two-Wheeler Platform
There is now a two-wheeler technology platform that aims to collect data encompassing every facet of two wheeler safety such as technologies that detect falls, rider alert and assistance, as well as other technologies related to accessories such as smart helmets. The intent in this case is also to measure riding behaviour and come up with riding scores. The reasoning behind this is to potentially use these scores to rank people as safe drivers. It could be used by either insurance companies or automobile dealers to incentivise safe driving practices by changing premium amounts or offering discounts respectively.