Description:Title:

ANALYSIS OF A VEHICLE DRIVER FOR ACCESSING THEIR DRIVING PERFORMANCE AND ENHANCING ROAD SAFETY

Field of the Invention

[0001] The present invention is related to the computer science domain and e-learning field.
[0002] The use of machine learning algorithms and other advanced analytics techniques to generate insights and feedback on driving habits.

Background

[0003] This idea of developing a comprehensive system that is based on continuous analysis of driver’s driving behavior to analyze both his safety skills as well as driving skills was originated after reading a research paper based on the asymmetric relationship between driving and safety skills which concluded that these are two distinct attributes contributing to road accidents and drivers with high levels of driving skills but low levels of safety skills are more prone to accidents than drivers with low level of both driving and safety skills.
[0004] Existing driver monitoring systems often focus on basic parameters such as speed and location, providing limited insights into the actual driving behavior. The proposed system builds upon the advancements in telematics technology, data analytics, and machine learning algorithms to provide a more comprehensive and accurate assessment of driver behavior.
[0005] The background of the project recognizes the need for an intelligent driver behavioural analysis system that goes beyond traditional monitoring solutions. By leveraging cutting-edge technology and analytics, the system aims to revolutionize road safety, mitigate accidents caused by reckless driving, and promote a culture of responsible and safe driving practices.

Objects of the Invention

[0006] Following are the objectives of the present disclosure:
• Enhancing Road Safety: By analyzing and promoting safer driving practices, the system aims to reduce the number of accidents caused by reckless behavior and improve overall road safety.
• Improving Driver Awareness: Through real-time feedback and personalized coaching, drivers will become more conscious of their driving habits, fostering a proactive approach to road safety.
• Fleet Management Optimization: The system's insights will empower fleet managers to make informed decisions regarding driver training, route planning, and vehicle maintenance, leading to cost savings and enhanced efficiency.
• Wide Adoption: Designed to be adaptable to various vehicle types and easily integrated into existing fleet management systems, the project seeks to facilitate global adoption across diverse transportation sectors.

Summary

[0007] The present invention majorly focus on behavior analysis and optimization, specifically related to the use of telematics technology to collect and analyze data on driver behaviour.
[0008] The present invention also focus on user security with applications in road safety.

Drawings

Figure 1: Algorithm Process Flowchart

Brief Description of the Drawing

[0009] The figure 1 represents Algorithm Process Flowchart of the working model in the present invention.

Figure 2: Process Flow-graph

Brief Description of the Drawing

[0010] The figure 2 represents Process Flow-graph of the working model in the present invention.

Detailed Description

[0011] The system utilizes telematics technology, onboard sensors, and advanced analytics. Real-time data on driving parameters such as acceleration, speed, and steering behavior is collected. Machine learning algorithms analyze the data to evaluate driver behavior and identify risky driving patterns.
[0012] Personalized feedback and recommendations are provided to drivers for improvement. The system benefits individual drivers, fleet managers, transportation companies, and road safety authorities. Advantages include enhanced road safety, improved driver performance, cost reduction, and positive social impact. The system is customizable and adaptable to different industries and driving contexts. The project aims to promote responsible driving, reduce accidents, and create a safer road environment.
[0013] The Technology used in the invention is as follows:
• Hardware Components:
? Sensors
? Camera
• Software Components:
? Python (Programming Language)
? SVM / Random Forest(Machine learning algorithm)
? Data Analytics Tools
? Database System
[0014] Following are the end users of the invention:
• Drivers
• Government Agencies and Road Safety Authorities
• Fleet Managers and Commercial Vehicle Operators
• Insurance Providers

Advantages of the Invention

[0015] Following are the advantages of the invention:
• The invention will reduce road accidents and enhance road safety remarkably.
• It will analyze enormous amounts of data using efficient machine learning algorithm.
• It will help government agencies and road safety authorities to keep a check on the drivers. , Claims:Following are the claims of the invention:

1. A system for intelligent driver behavioral analysis comprising:
• Onboard sensors for capturing real-time data on acceleration, speed, steering behavior, and rotational movements of a vehicle.
• Data collection devices for collecting and storing the captured data.
• Analytics module for processing the captured data using machine learning algorithms.
• Feedback generation module for providing personalized feedback and recommendations to drivers based on analyzed data.
2. The system of claim 1, wherein the machine learning algorithms analyze the captured data to identify patterns and indicators of risky driving behavior, including aggressive acceleration, harsh braking, and improper lane changes.
3. The system of claim 1, wherein the feedback generation module provides real-time feedback to drivers through visual or audio interfaces within the vehicle.
4. A method for enhancing road safety and driver performance using the system of claim 1, comprising:
• Capturing real-time data on driving parameters from the onboard sensors.
• Processing the captured data using machine learning algorithms to evaluate driver behavior.
• Generating personalized feedback and recommendations based on the evaluated driver behavior.
• Providing the feedback and recommendations to drivers to improve their driving skills and promote safer practices.
5. The method of claim 4, further comprising providing insights and analytics to fleet managers for optimizing fleet operations and implementing targeted driver training programs based on the evaluated driver behavior.