Traffic Light Tracking Using GPS and ML Speed Control
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Solution Overview
Problem
Existing traffic management systems are limited by the high cost and maintenance requirements of smart traffic lights, and vehicle-to-vehicle communication may provide inaccurate or outdated information, leading to inefficiencies in reducing wait times and improving traffic flow.
Innovation Solution
A system using GPS location, real-time traffic light status data from sensors, and machine learning algorithms to calculate and provide a recommended cruising speed to vehicles, enabling adaptive control to optimize fuel efficiency and travel time without the need for constant driver intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If smart traffic lights with sensors are deployed to detect vehicles and adjust timing, then traffic flow is optimized and wait times are reduced, but installation and maintenance costs increase significantly
Solution Approach 1:
The patent introduces an intermediary system consisting of roadside sensors and a central server that mediates between vehicles and traffic lights. Instead of making traffic lights themselves smart and expensive, the system uses separate sensing infrastructure to detect vehicles and communicate with a central server, which then determines optimal speeds and communicates them to vehicles via V2V communication, thereby optimizing traffic flow without requiring expensive smart traffic light infrastructure
Solution Approach 2:
The patent replaces the mechanical/electrical system of smart traffic lights with sensors and actuators with an information-based system using GPS, roadside sensors, central server processing, and V2V communication. The physical modification of traffic lights is replaced by digital information exchange, where the central server computes optimal speeds and vehicles receive guidance through communication networks, eliminating the need for expensive hardware modifications to traffic lights
2Loss of time
If vehicle-to-vehicle communication is used to provide real-time traffic information, then wait times at traffic lights are reduced, but information accuracy and timeliness deteriorate
Solution Approach 1:
The patent merges multiple information sources including roadside sensor data, central server processing, GPS location data, and V2V communication to create a comprehensive and accurate traffic information system. By combining these sources, the system cross-validates information and ensures accuracy, with the central server acting as a hub that aggregates data from multiple sensors and vehicles to provide reliable real-time traffic light status information
Solution Approach 2:
The patent implements feedback mechanisms where vehicles receive speed recommendations from the central server based on real-time traffic light status, and the system continuously monitors vehicle positions and adjusts recommendations dynamically. The roadside sensors provide continuous feedback on vehicle presence and movement, allowing the central server to update traffic light status information and recompute optimal speeds in real-time, ensuring information remains accurate and timely
3Use of energy by moving object
If drivers manually monitor traffic lights and adjust speed, then fuel efficiency improves, but driver workload and complexity of operation increase
Solution Approach 1:
The patent enables the vehicle system to serve itself by automatically receiving speed recommendations from the central server and using this information to optimize its own fuel efficiency without requiring driver action. The system autonomously processes traffic light status information, calculates optimal speeds, and provides guidance to the driver or automatically controls vehicle speed, allowing the vehicle to improve its own energy efficiency independently of driver effort
Solution Approach 2:
The patent introduces an intermediary system between the driver and the traffic light environment, where the central server acts as a mediator that processes complex traffic information and provides simplified speed recommendations to the driver. This intermediary handles the complexity of monitoring multiple traffic lights, calculating optimal speeds, and timing adjustments, thereby improving fuel efficiency while keeping the driver's workload minimal through simple guidance instructions
Data Source
AI summary
The present invention relates to method and system for controlling speed of vehicle by using GPS location, real-time traffic light status and machine learning techniques to recommend cruising speed to vehicle without stopping at the traffic lights. The method includes determining status of traffic lights located on route of traffic junctions and associated time period for status of traffic lights based on data received from sensors. The method includes broadcasting status of traffic lights and associated time period. The method includes calculating in real-time a recommended cruising speed of the vehicle based on GPS location associated with vehicle, status of traffic lights and associated time period, and a time period required by vehicle to arrive at the traffic lights using machine learning techniques. The method includes providing in real-time the recommended cruising speed to vehicle. The method includes adaptively controlling in real-time speed of vehicle to recommended cruising speed.