Driving Assistance Recommendations Using Fleet Route Use Frequency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing driving assistance systems fail to effectively suggest the use of driving assistance functions to users when they are most beneficial, leading to inefficient utilization and user convenience issues due to mixed environments with and without suitable conditions for assistance.
Innovation Solution
A system that aggregates driving assistance function use frequencies from multiple vehicles and suggests its use at positions where the frequency is high, using onboard and server components to provide tailored recommendations based on historical data and planned routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If driving assistance function use is suggested indiscriminately across all roads, then user awareness of the function is improved, but suggestion accuracy deteriorates due to mixed environments with and without suitable conditions
Solution Approach 1:
The system applies local quality by providing driving assistance suggestions based on the specific characteristics of each road section. It analyzes environmental data (curvature, gradient, visibility) for different locations and generates targeted suggestions only for sections where the driving assistance function would be beneficial, rather than uniformly suggesting across all roads.
Solution Approach 2:
The system incorporates feedback mechanisms by collecting actual driving data and user responses to suggestions. It uses this feedback to refine its recommendation algorithm, learning from patterns in when users accept or reject suggestions, thereby improving the accuracy of future suggestions while maintaining user convenience.
2Loss of information
If driving assistance suggestions are provided only in high-frequency use areas, then suggestion accuracy is improved, but coverage of useful suggestions is reduced
Solution Approach 1:
The system dynamically adjusts the threshold parameters for generating suggestions based on multiple factors including road conditions, vehicle type, time of day, and aggregated fleet data. This allows the system to expand or contract the coverage area for suggestions while maintaining reliability by adapting to changing conditions rather than using fixed geographic boundaries.
3Reliability
If use frequency threshold is set high, then suggestion reliability is improved, but number of suggestions provided is reduced
Solution Approach 1:
The system implements dynamic threshold adjustment where the use frequency threshold is not fixed but adapts based on real-time conditions, vehicle characteristics, and aggregated learning from the fleet. This allows the system to maintain high reliability by raising thresholds when appropriate while increasing suggestion volume when conditions warrant, optimizing both parameters simultaneously.
Data Source
AI summary
A driving assistance system includes a memory and a processor coupled to the memory. Based on use history data of a driving assistance function including positions at which the driving assistance function has been used at respective vehicles, which is collected from plural vehicles and stored in the memory, and based on position information indicating planned travel positions at which a specific vehicle is going to travel, the processor aggregates use frequencies of the driving assistance function at the planned travel positions of the plural vehicles, and, based on the aggregated use frequencies, the processor outputs information that suggests use of the driving assistance function at the specific vehicle.