VEHICLE TEMPERATURE MONITORING AND ACCIDENT PREDICTION SYSTEM

Abstract

This invention presents a system that utilizes thermal cameras, machine learning algorithms, and real-time data analysis to monitor vehicle temperatures and predict accident possibilities. Designed for enhanced road safety, the system provides early alerts to authorities and drivers, enabling proactive intervention and timely emergency response to mitigate road accident risks.

Specification

Description:FIELD OF THE INVENTION
This invention relates to road safety and accident prevention technology, specifically focusing on a system that utilizes thermal cameras, machine learning algorithms, and real-time data analysis to monitor vehicle temperatures and predict accident possibilities. The system aims to enhance road safety by proactively assessing accident risks and alerting authorities for immediate response.
BACKGROUND OF THE INVENTION
Road accidents are a major cause of fatalities globally, with many incidents caused by issues such as overheating, tire blowouts, and inadequate vehicle maintenance. Traditional accident prevention measures primarily involve post-incident response, lacking a proactive approach to monitor and predict potential hazards in real time. Existing solutions often fail to incorporate continuous temperature monitoring and real-time prediction, which are critical for preemptive safety measures. This invention addresses these challenges by introducing a system that continuously monitors vehicle temperature using thermal cameras and predictive machine learning algorithms. By analyzing real-time data and comparing it to historical accident patterns, this invention provides early alerts to mitigate potential accidents and enables immediate emergency response.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention provides a Vehicle Temperature Monitoring and Accident Prediction System that utilizes thermal cameras, data sensors, and machine learning to monitor and analyze vehicle temperatures. The system processes data in real time, identifying accident risks based on temperature fluctuations and other vehicle parameters. Alerts are sent to the nearest police station, PCR units, and the vehicle driver through a wireless communication interface, enhancing road safety and emergency response.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SHOWS THE SYSTEM ARCHITECTURE, INCLUDING THERMAL CAMERA POSITIONING, DATA SENSORS, AND THE MACHINE LEARNING ALGORITHM SETUP.
FIGURE 2: ILLUSTRATES THE ACCIDENT PREDICTION MODULE, HIGHLIGHTING DATA COLLECTION, PREPROCESSING, AND THE PREDICTIVE ANALYTICS MODEL.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The Vehicle Temperature Monitoring and Accident Prediction System is designed to improve road safety through continuous monitoring and predictive analysis of vehicle conditions. This system employs thermal cameras strategically installed along the road infrastructure, capturing thermal images of vehicles as they pass through the detection zone. These thermal cameras monitor fluctuations in vehicle temperature, which serve as a crucial indicator of potential accidents. In addition to thermal imaging, the system incorporates data collection modules that monitor critical parameters such as vehicle speed, engine temperature, and tire pressure. Data from these sensors are continuously collected and sent to the Vehicle Data Analysis Module for preprocessing.
The Vehicle Data Analysis Module preprocesses the collected data to handle inconsistencies, missing values, and outliers. This preprocessing is essential for ensuring data accuracy and reliability, particularly in real-time applications. The processed data is then inputted into a machine learning model, specifically a Support Vector Machine (SVM) algorithm designed for predictive analytics. The algorithm analyzes the temperature and other vehicle parameters, correlating them with historical accident data to determine the probability of an accident occurring.
When the system detects a high probability of an accident, it triggers the Emergency Alert Mechanism, which sends real-time notifications to the nearest police station and PCR units, enabling rapid emergency response. The alert mechanism is integrated with a wireless communication system that transmits accident risk information to a central cloud database, accessible by authorized personnel. Furthermore, the system communicates with the vehicle driver through an auditory alert system, prompting them to take immediate precautions to prevent a potential accident.
All temperature data, accident risk predictions, and emergency alerts are centrally stored for further analysis and record-keeping, providing valuable insights into accident patterns and helping improve system accuracy. This data storage also supports adaptive learning, allowing the machine learning model to continuously improve its prediction capabilities based on new data. The combination of thermal imaging, real-time data analysis, and predictive machine learning makes this invention a proactive solution to enhance road safety and reduce accident rates.
, Claims:1. A Vehicle Temperature Monitoring and Accident Prediction System comprising thermal cameras, data sensors, and a machine learning model for real-time temperature monitoring and accident risk prediction.
2. The system as claimed in Claim 1, wherein thermal cameras capture vehicle temperature fluctuations, serving as indicators for accident prediction.
3. The system as claimed in Claim 1, wherein the data collection module monitors vehicle parameters such as speed, engine temperature, and tire pressure to enhance predictive accuracy.
4. The system as claimed in Claim 1, wherein the Vehicle Data Analysis Module preprocesses data to handle missing values, inconsistencies, and outliers for accurate analysis.
5. The system as claimed in Claim 1, wherein a Support Vector Machine (SVM) algorithm predicts accident risks based on correlations between vehicle temperature and historical accident data.
6. The system as claimed in Claim 1, wherein the Emergency Alert Mechanism notifies nearby police stations, PCR units, and the driver in real time through a wireless communication interface.
7. The system as claimed in Claim 1, wherein the cloud database stores all data, including temperature variations, accident predictions, and emergency alerts, for further analysis and system improvement.
8. A method for real-time vehicle accident prediction as claimed in Claim 1, involving data collection, preprocessing, predictive analysis, and emergency alerting for enhanced road safety.
9. The system as claimed in Claim 1, wherein it provides a proactive approach to road safety, combining temperature monitoring, predictive analytics, and emergency alerting to reduce accident rates.

Documents