MUMBAI, India, Jan. 23 -- Intellectual Property India has published a patent application (202541115042 A) filed by Dr. M. K. Vidhyalakshmi; Dr. R. Geetha; Dr. Aswathy K Cherian; Ramya Reddy N; Geetika Reddy Kvsl; Vaishnavi Jauhari; Smit Desai; and Pratik Vatsh, Chennai, Tamil Nadu, on Nov. 21, 2025, for 'advanced collision avoidance system for two-wheeled and four-wheeled vehicles using sensor fusion and machine learning algorithms.'
Inventor(s) include Dr. M. K. Vidhyalakshmi; Dr. R. Geetha; Dr. Aswathy K Cherian; Ramya Reddy N; Geetika Reddy Kvsl; Vaishnavi Jauhari; Smit Desai; and Pratik Vatsh.
The application for the patent was published on Jan. 23, under issue no. 04/2026.
According to the abstract released by the Intellectual Property India: "Advanced Collision Avoidance System is an innovative safety invention that applies to a two-wheeled and four-wheeled vehicle. It functions by initially collecting environmental information in detail by different types o f sensors such as cameras, radar and lidar. This crude data is then subjected to thorough filtering, and alignment to get accuracy, before being synthesized through sensor fusion resulting in a single and very dependable image of the environment that is beyond the reach of any single sensor. Based oh this, combined information, machine learning models classify and label all the objects in the surroundings of the vehicle and differentiate cars, motorcycles, pedestrians, and fixed objects correctly. The. movement of these, objects that are. detected is Continuously monitored by the system and predicted in terms of where they are likely to head in the future based on their speed and direction. Upon these predictions, the heart of the system .is created, that, conducts a risk evaluation to know the chance and immediate attention of a possible collision. The core component of the system independently decides the most relevant evasive maneuver to reduce the risk; this may be the issue of providing an immediate warning to the driver (e.g., haptic or auditory) or the actual management of the vehicle actuators, such as the brakes, steering. Importantly, the system is made in a permanent .monitoring and learning mode whereby as more real-world data is received the machine, learning models are refined and its overall performance and reliability to various and unpredictable driving situations is enhanced with time."
Disclaimer: Curated by HT Syndication.
