In-Vehicle Terminal Driving Skill Assessment and Assistance
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Solution Overview
Problem
Existing driving assistance systems fail to provide tailored information to drivers unfamiliar with driving, as they do not consider the driver's skill level when offering assistance.
Innovation Solution
An information system comprising an in-vehicle terminal and a server that records and analyzes a driver's running history to determine their skill level for each driving skill element, allowing for personalized driving assistance information to be distributed based on predefined difficulty levels, including practice routes and attention points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If driving assistance information is provided without considering driver skill level, then all drivers receive generic assistance, but drivers unfamiliar with driving cannot receive tailored guidance to reduce anxiety and improve skills
Solution Approach 1:
The system segments driving assistance information by dividing driving skills into multiple elements (e.g., lane changing, intersection negotiation, highway merging) and assesses each element separately. This allows tailored guidance for specific skill deficiencies rather than treating all drivers uniformly, resolving the contradiction between adaptability and complexity by making the assessment modular and manageable.
Solution Approach 2:
The system performs preliminary assessment of driver skill level by analyzing running history data before providing driving assistance information. By pre-evaluating driving behavior patterns and storing skill level assessments in a database, the system prepares personalized assistance content in advance, eliminating the need for complex real-time assessments and reducing system complexity while maintaining high adaptability.
2Measurement precision
If running history data is collected and analyzed to determine driver skill level, then personalized driving assistance can be provided, but data processing complexity and storage requirements increase
Solution Approach 1:
The system extracts only the essential features from running history data that are relevant to driving skill assessment, such as frequency of specific maneuvers, response times, and error patterns. By filtering and storing only these critical parameters rather than complete raw data, the system achieves precise skill measurement while minimizing data storage requirements.
Solution Approach 2:
The system transforms raw running history data into standardized skill level parameters by defining specific metrics (e.g., number of lane changes per hour, average distance from lane center). This parameter transformation converts large volumes of raw data into compact, meaningful indicators that enable precise skill assessment with minimal storage requirements.
Data Source
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AI summary
An information system includes an in-vehicle terminal 200 that is mounted on a host vehicle and a server 100 that communicates with the in-vehicle terminal 200. The server 100 includes a running history DB 116 in which a running history of the host vehicle is stored for each driver, a driving skill-level determination unit 115, a driving skill-level DB 116 in which a driving skill-level determined by the driving skill-level determination unit 115 is stored, and a distribution information control unit 123 that distributes driving assistance information based on the driving skill-level stored in the driving skill-level DB 119 to the in-vehicle terminal 200. The driving skill-level determination unit 115 determines a driving skill-level of a driver based on the running history stored in the running history DB 116 and a driving skill element definition list representing a difficulty level of driving predefined for each driving skill element.