Driving Assistance Device Using Biometric Prediction
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Solution Overview
Problem
Existing driving assistance systems may inconvenience drivers by providing unnecessary alerts, as they do not accurately predict the driver's state based on future environmental conditions, leading to inappropriate assistance.
Innovation Solution
A driving assistance system that uses biometric information to estimate the driver's state at a future time and compares it with predetermined conditions associated with the vehicle's predicted route or environment, adjusting assistance accordingly to minimize inconvenience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If driving assistance is provided based on current driver state only, then the system is simple to operate, but the assistance may be inappropriate for future driving conditions
Solution Approach 1:
The system performs preliminary estimation of the driver's future state by analyzing current biometric information and predicting how it will change during the remaining driving period. This allows the system to proactively determine appropriate assistance measures before the actual driving conditions arise, rather than reacting after the fact.
Solution Approach 2:
The system dynamically adjusts the driving assistance based on the estimated future driver state. Instead of using a fixed assistance strategy, the control unit modifies the assistance level according to the predicted concentration level at the time of arrival, making the system adaptive to varying future conditions without requiring complex manual reconfiguration.
2Reliability
If driving assistance is provided based on predicted future driver state, then the assistance becomes more appropriate, but the system complexity increases
Solution Approach 1:
The system uses feedback from current biometric measurements to continuously refine the prediction of future driver state. The biometric information obtaining unit monitors the driver's physiological parameters in real-time, and this feedback is fed into the estimation unit to update the predicted concentration level at arrival time, improving reliability through continuous monitoring and adjustment.
Solution Approach 2:
The system replaces complex mechanical or manual assessment methods with automated biometric sensing and computational estimation. Instead of requiring manual evaluation of driver state or complex mechanical sensors, the system uses electronic biometric sensors and algorithmic prediction to accurately assess future driver condition with simpler overall system architecture.
3Reliability
If driving assistance is provided too frequently, then driver safety is improved, but the driver feels inconvenienced
Solution Approach 1:
The system applies localized assistance only when and where it is truly needed, based on the specific predicted driver state at the time of arrival. Rather than providing uniform assistance throughout the journey, the control unit tailors the assistance level to the specific condition predicted for the arrival time, providing help only in situations where the driver's concentration is actually insufficient.
Solution Approach 2:
The system changes the parameter of assistance intensity based on the predicted future driver state. By adjusting the assistance level according to the estimated concentration level at arrival time, the system maintains driver safety through targeted assistance while avoiding excessive or unnecessary interventions that would inconvenience the driver during periods when their attention is adequate.
Data Source
AI summary
A heartbeat sensor obtains a heartbeat of a driver. A concentration level computing unit estimates a concentration level of the driver at a time later than a timing of obtaining the heartbeat by the heartbeat sensor, on the basis of the heartbeat obtained by the heartbeat sensor. A state determining unit controls driving assistance for a vehicle on the basis of a comparison of the concentration level of the driver estimated by the concentration level computing unit with a target concentration level of the driver that is set in association with a position or an environment in which the vehicle driven by the driver is predicted to travel.