Vehicle Driver Emotion Detection via Situation Factor Segmentation
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Solution Overview
Problem
Conventional vehicle technologies fail to accurately detect a driver's emotion as they do not consider specific situations associated with vehicle operation, relying solely on biological signals without accounting for external factors.
Innovation Solution
A vehicle system that collects biological signals and driving information, communicates with an external server, and uses a controller to extract emotion information, identify primary situation factors influencing the driver's emotion, and generate a driving route to adjust the driver's emotional state based on these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only biological signals are used to detect driver emotion, then the detection system is simple, but the accuracy of emotion detection is low because it cannot distinguish emotions caused by vehicle operation from other factors
Solution Approach 1:
The system segments emotion detection into two independent parts: biological signal detection (emotion detection unit) and situation factor detection (situation detection unit). By dividing the detection system, each unit can focus on specific aspects without increasing overall complexity, thereby improving emotion detection accuracy through multi-dimensional analysis.
Solution Approach 2:
The controller acts as an intermediary that integrates biological signals and situation factors. It compares detected emotions with situation information to determine whether emotions are caused by vehicle operation or other factors, thereby improving detection accuracy without requiring direct complex interaction between sensors.
2Measurement precision
If situation factors are considered in emotion detection, then the accuracy of emotion detection is improved, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The controller performs multiple functions: it processes biological signals, acquires situation information, compares emotions with situations, and generates driving routes. By making the controller multi-functional, the system avoids adding separate dedicated devices for each function, thereby improving emotion detection accuracy while limiting increases in overall system complexity.
Solution Approach 2:
The system merges emotion detection and situation detection into a unified processing framework where the controller integrates both types of information. This combination allows the system to improve emotion detection accuracy through comprehensive analysis while avoiding the complexity of completely separate detection and processing systems.
3Reliability
If emotion tagged data from multiple drivers is collected and analyzed, then the reliability of emotion detection is improved, but the loss of information increases due to the need to manage and process large amounts of data
Solution Approach 1:
The system performs preliminary actions by collecting and storing emotion tagged data from multiple drivers in advance. This pre-collected data serves as a reference database that can be quickly compared against current detections, improving reliability while reducing real-time processing overhead and information loss.
Solution Approach 2:
The system uses feedback by comparing current driver emotions and situations with previously collected emotion tagged data. This feedback mechanism allows the system to improve detection reliability through pattern recognition while managing information efficiently by only processing relevant comparisons rather than all raw data.
Data Source
AI summary
A vehicle may include a detector configured to collect a biological signal of a driver and driving information of the vehicle; a communication device configured to communicate with an external server; a storage configured to store situation information and emotion tagged data received through the communication device and the biological signal of the driver; and a controller configured to acquire information about current emotion of the driver based on the biological signal of the driver, acquire information about inclination of the driver based on the driving information of the vehicle, extract emotion information corresponding to a current situation of the driver and the inclination of the driver from the emotion tagged data, compare the extracted emotion information with the current emotion information of the driver, and extract a primary situation factor having an influence on the emotion of the driver based on the comparison result.