Automated accident detection and reporting system using vision language models

Abstract

Computer system for automated accident detection and reporting using vision language models (VLMs), consisting of a data acquisition module, a vision language processing unit and a communication module for identifying, analyzing and reporting traffic accidents in real time.

Description

Technical field

[0001] The present invention relates to an automated system for detecting and reporting traffic accidents using vision language models (VLMs). The system combines real-time object detection, natural language processing (NLP), and artificial intelligence (AI) to analyze, interpret, and report accidents to the relevant authorities or stakeholders in real time. The system is designed for use in vehicles, road infrastructure, or surveillance cameras. Background of the invention

[0002] In urban and rural traffic environments, rapid accident detection and response are critical to saving lives and maintaining efficient traffic flow. Conventional accident detection methods rely on manually triggered emergency calls or simple sensors, which are often unreliable. Modern vision language models such as Mistral enable the combination of visual, textual, and environmental data to accurately identify and classify accidents. Summary of the invention

[0003] The proposed system uses YOLO's real-time object detection model for accurate accident identification and VLMs such as Mistral to generate detailed textual accident reports. By integrating environmental data from sensors, the system ensures a more comprehensive analysis of accident causes, including weather conditions and road hazards. Once the system detects an anomaly or potential accident, it assesses the severity of the event and automatically sends a report to the relevant authorities. Technical aspects of AI-supported accident detection

[0004] Automated accident detection with artificial intelligence (AI) is based on a multi-level system for recording, analyzing, and reporting safety-relevant incidents. This system combines modern sensor technology, edge computing, and cloud technologies to detect and respond to accidents in real time. Short description of the figure Fig. 1 illustrates the operation of a computer system according to the invention. Detailed character description

[0005] With reference to the enclosed Fig. 1, properties of the invention are explained below and, in particular, the processes carried out by a computer system according to the invention are described. 1. System activation ◯ Device: Control unit or edge computer (e.g. Raspberry Pi) ◯ Function: The system is started and the connection to sensors and cameras is established. 2. Data module ◯ Device: IoT sensors, surveillance cameras (e.g. Bosch IP cameras) ◯ Function: Captures real-time video data and sensor readings such as temperature or motion. 3. AI analysis (YOLO + VLM) ◯ Device: GPU server or AI edge device (e.g. TPU Coral) ◯ Function: The collected data is analyzed using object recognition (YOLO) and visual language model processing (VLM). 4. Incident categorization ◯ Device: AI software or cloud platform (e.g. AWS) ◯ Function: Categorization of the incident as an accident or a normal situation. 5. Accident detected ◯ Device: Event detection system with deep learning (e.g. PyTorch) ◯ Function: Identifies an accident and issues a warning. 6. Report preparation ◯ Device: Automated reporting tool (e.g. Microsoft Power BI) ◯ Function: Creates a detailed report of the incident with timestamp and location information. 7. Emergency notification ◯ Device: Alarm system or cloud notification (e.g. Firebase) ◯ Function: Sends messages to authorities or security services. 8. No accident detected ◯ Device: Automatic logging system (e.g. Graylog) ◯ Function: Saves the analysis without warning. 9. End: Authorities informed ◯ Device: Police system, emergency services platform (e.g. 112 emergency call systems)

Claims

[1] Computer system for automated accident detection and reporting using vision language models (VLMs), consisting of a data acquisition module, a vision language processing unit and a communication module for identifying, analyzing and reporting traffic accidents in real time. [2] The computer system of claim 1, wherein the data acquisition module comprises cameras and sensors installed in vehicles or at fixed locations to continuously collect visual and sensory data from traffic environments, taking into account contextual factors such as weather conditions. [3] The computer system of claim 1, wherein the vision-language processing unit uses the YOLO real-time object detection model and transformer-based VLMs such as Mistral to detect accidents and generate textual accident reports. [4] A computer system according to claim 1, wherein the communication module automatically transmits accident reports including time, location, severity ratings and environmental conditions to emergency services and relevant authorities. [5] The computer system of claim 1, wherein multimodal AI techniques combine image, text, and environmental data to improve the accuracy of accident detection and classification.

Images