In-Vehicle Navigation System for Goods-Vehicles Using Driver Feedback
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Solution Overview
Problem
Current navigation devices lack accurate and comprehensive goods-vehicle-specific information, relying on incomplete official data or laborious manual collection, which can lead to navigation issues for inexperienced drivers and congestion problems, as they fail to incorporate expert knowledge from experienced drivers about preferred and avoided routes.
Innovation Solution
A system that logs vehicle characteristics and route data from navigation devices, analyzes patterns to categorize roads as 'goods-vehicle-common' or 'goods-vehicle-wary', and updates digital maps with this expert knowledge, allowing route planning to avoid routes that experienced drivers would not take, thereby improving navigation for less experienced drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If official map data and manual collection methods are used to gather goods-vehicle route information, then data completeness is improved, but data accuracy and reliability deteriorate due to lack of expert driver knowledge
Solution Approach 1:
The system implements feedback by collecting actual route data from multiple goods-vehicles equipped with navigation devices, analyzing this feedback information to identify commonly used and avoided routes, and using this aggregated feedback to continuously improve the supplementary road information in digital maps. This feedback loop transforms raw navigation data into reliable expert knowledge about goods-vehicle routing preferences.
Solution Approach 2:
The system enables goods-vehicles and their drivers to self-contribute to the improvement of route information by automatically logging their navigation data through onboard devices. The drivers' actual routing choices serve as self-generated expert knowledge that automatically enriches the map database without requiring manual intervention, allowing the system to self-improve through accumulated real-world usage data.
2Reliability
If manual collection of goods-vehicle route preferences is performed, then data accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system replaces the mechanical process of manual data collection with an automated electronic system. Navigation devices automatically log route data, servers electronically process and analyze the information using algorithms, and digital maps are automatically updated. This substitution of manual mechanical collection with automated electronic processing dramatically reduces time consumption while maintaining or improving data accuracy through systematic analysis of large datasets.
Solution Approach 2:
The server acts as an intermediary between navigation devices and digital map databases. It receives raw navigation data from multiple vehicles, processes this information through analysis algorithms to extract meaningful route patterns, and translates this processed information into supplementary road information that updates the digital maps. This intermediary processing layer efficiently transforms individual vehicle data into collective expert knowledge without requiring manual intervention at any stage.
3Ease of operation
If navigation devices provide basic route guidance, then ease of operation is improved, but reliability for goods-vehicles deteriorates due to lack of vehicle-specific route information
Solution Approach 1:
The system applies local quality by providing vehicle-specific supplementary road information tailored to goods-vehicles. The digital maps include localized knowledge about which routes are suitable or unsuitable for goods-vehicles based on actual driver behavior patterns. This localized expertise is integrated into the general navigation system, allowing the device to provide both easy-to-use interface and reliable vehicle-specific guidance simultaneously.
Solution Approach 2:
The system changes the parameter of route information quality by incorporating supplementary road information that reflects actual goods-vehicle routing preferences. This transforms the navigation system from providing generic route guidance to providing optimized route recommendations specific to goods-vehicle characteristics and driver preferences, thereby improving reliability while maintaining ease of operation through the same user interface.
Data Source
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Figure 3
Figure 4a~4b
AI summary
The present invention provides a technique for enabling collection and/or refinement of digital map information for aiding navigation route planning for vehicles larger than ordinary cars, such as goods-vehicles, buses, and car+caravan combinations. A profile of a vehicle's characteristics, and one or more routes followed, are logged by a navigation device (200), and fed-back to a server (150) that supports the navigation device with map data updates. At the server, or an alternative processing centre, the fed-back data from plural navigation devices is analysed (400-404) to observe statistically the patterns of roads used by these vehicles, and to categorise these by the type of vehicle.