Vehicle User Identification via Sequential Motion Probability
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Solution Overview
Problem
Existing user identification systems for vehicles face challenges in accurately identifying users based on limited information acquired from specific scenes, such as approaching or boarding motions, which can lead to unsatisfactory accuracy in user recognition.
Innovation Solution
A user identification system that includes sensors to detect multiple motions, an acquisition unit to gather boarding information, a memory for association data, a probability calculator to assess user probabilities, and an identification unit to accurately identify users based on these probabilities, allowing for improved accuracy and convenience in controlling vehicle devices according to user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user identification is based on information acquired from a specific scene (approaching or boarding motion), then the system structure remains simple, but the identification accuracy deteriorates
Solution Approach 1:
The system performs preliminary actions by detecting multiple types of motions (approaching, opening door, sitting, closing door) before final user identification. Each detected motion provides additional information that is accumulated and used to calculate identification probability, thereby improving accuracy without requiring a completely complex system structure
Solution Approach 2:
The system dynamically updates the probability of each user being the current user based on sequentially detected motions. The identification accuracy improves as more motions are detected, allowing the system to adapt its confidence level dynamically rather than relying on a single static measurement
2Measurement precision
If multiple types of boarding information are collected and probability calculation is performed, then user identification accuracy improves, but the processing complexity increases
Solution Approach 1:
The system performs partial probability calculations for multiple users simultaneously, updating probabilities incrementally as each motion is detected. This allows accurate identification without requiring complete information about all users at once, reducing processing complexity while maintaining high accuracy
Solution Approach 2:
The system uses feedback mechanisms where each detected motion provides information that updates the probability calculation. The identification result feeds back into the system to confirm or adjust user identity, creating a self-correcting process that improves accuracy without proportionally increasing complexity
Data Source
AI summary
A user identification system includes: a detection unit that detects prescribed motions of a person when the person boards or alights from a vehicle; an acquisition unit that acquires types of boarding information indicating that one of users boards or is on board the vehicle when one of the motions is detected; a memory that stores association information in which identification information of the users is associated with the types of boarding information corresponding to the users; a probability calculating unit configured to calculate a probability that each of the users boards or is on boarded the vehicle based on the types of boarding information and the association information; and an identification unit that identifies a user who boards or is on boarded the vehicle among the users based on the probability calculated by the probability calculating unit.