Vehicle Navigation System Using Cloud-Aggregated Traffic Light Data
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Solution Overview
Problem
Current vehicle navigation systems lack the ability to consider real-time information beyond traffic and delays, failing to account for factors that modify the driving context, such as traffic light patterns, occupant numbers, and lane-occupancy regulations, which limits their accuracy in estimating arrival times and providing optimal routes.
Innovation Solution
A vehicle system equipped with a sensor suite that observes and records traffic light information, occupant numbers, and lane-occupancy regulations, uploading this data to the cloud to learn and improve navigation by calculating optimal routes and avoiding traffic through the use of HOV lanes, based on historical data and real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigation systems only consider basic traffic information, then the system complexity remains low, but the navigation accuracy and route optimization capability deteriorates
Solution Approach 1:
The navigation system is segmented into multiple functional modules: data collection module (gathering traffic light patterns, HOV lane regulations, accident information), data processing module (analyzing and storing collected data), and route planning module (generating optimized routes based on processed data). This segmentation allows the system to handle complex navigation tasks through coordinated simple modules, improving navigation accuracy while managing system complexity.
Solution Approach 2:
The system performs preliminary data collection and storage of traffic light patterns, HOV lane regulations, and accident information before route planning is needed. Historical data is pre-processed and stored in databases, allowing the route planning module to quickly access and utilize this information when generating routes, thereby improving navigation accuracy without adding significant computational complexity during real-time route planning.
2Productivity
If the system collects and processes multiple types of location data (traffic lights, occupants, HOV regulations), then route optimization improves, but information processing time and computational load increases
Solution Approach 1:
The system collects and processes location data (traffic light patterns, HOV lane regulations, accident information) in advance and stores it in databases before it is needed for route planning. This preliminary data preparation reduces the computational load during real-time route optimization, allowing the system to quickly generate optimized routes without excessive processing delays.
Solution Approach 2:
The navigation system utilizes freely available public data sources such as traffic light timing information, HOV lane regulations, and accident reports from public databases. By leveraging these existing data sources, the system avoids the need for expensive and time-consuming proprietary data collection infrastructure, improving route optimization efficiency while minimizing data acquisition time.
3Measurement precision
If the system uses historical data and real-time conditions for route calculation, then arrival time estimation accuracy improves, but the computational complexity of route planning increases
Solution Approach 1:
The route planning process is divided into separate functional stages: historical data retrieval (accessing pre-stored traffic patterns and regulations), real-time condition assessment (current traffic and environmental conditions), and route generation (combining historical and real-time data to create optimized routes). This segmentation allows complex route planning to be handled through coordinated simple operations, improving arrival time estimation while managing computational complexity.
Solution Approach 2:
The system leverages freely available public data sources including traffic light timing information, HOV lane regulations, and accident reports from public databases. By utilizing these existing data resources, the system achieves accurate arrival time estimation without requiring complex proprietary data collection and processing infrastructure.
Data Source
AI summary
The systems and methods described herein can be applied to a vehicle equipped with a sensor suite that can observe information about a location, for example, a traffic light duration. The system can record information, e.g., the traffic light colors, duration of color changes, and location of the traffic lights and can upload this information to the cloud. Then, the system can augment or learn about the location, e.g., learning of traffic patterns, and store the augmented data as database-based information, where available. The learned information can help a requesting vehicle to better estimate an estimated time of arrival (ETA) for common routes taken, provide more accurate ETAs based on historical knowledge, and/or calculate or provide alternative route information.