MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202641062275 A) filed by B Jyothi; and Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, on May 16, for 'vehicle intrusion detection system.'
Inventor(s) include B. Jyothi; G. Sai Naga Sathvik; P Bhavana; Shaik. Rabbannie; P. Aasritha Sai; J. Mohammad Gouse; and R. Vishnu Shanthan.
The application for the patent was published on May 29, under issue no. 22/2026.
According to the abstract released by the Intellectual Property India: "The proposed system presents an Artificial Intelligence-based Vehicle Intrusion Detection System designed to enhance vehicle security and reduce theft and unauthorized access. In recent years, vehicle-related crimes have increased significantly, especially in urban areas, causing financial loss and safety concerns. Based on this growing issue, this system aims to minimize intrusion incidents and improve overall vehicle protection, thereby contributing to a safer society. The proposed system utilizes advanced technologies such as Artificial Intelligence (AI), Computer Vision, sensor modules, and intelligent alert mechanisms to detect suspicious activities around the vehicle. Deep learning methods like Convolutional Neural Networks (CNN) are used to analyze real-time visual data captured through camera modules installed in and around the vehicle. The system identifies human presence, unusual movements, and unauthorized access attempts by continuously monitoring the surroundings. The AI-based vision system processes the captured images and compares them with learned patterns of normal and suspicious behaviour to accurately detect intrusion attempts, even under varying lighting conditions such as daytime and night time. Additionally, the system incorporates motion sensors and optional audio detection to identify activities like forced entry, repeated tampering, or abnormal disturbances. When an intrusion is detected, the system immediately triggers alerts such as alarm sounds, mobile notifications, and voice warnings like "Unauthorized access detected." If the suspicious activity continues, the system can escalate the response by capturing visual evidence, sending real-time updates to the vehicle owner, and activating safety mechanisms such as automatic door locking or engine immobilization. Through this intelligent monitoring and rapid response approach, the system effectively reduces the risk of vehicle theft and enhances security. Keywords: Internet of Things (IoT), Local Outlier Factor, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM)."
Disclaimer: Curated by HT Syndication.
