Personalized ADAS Warnings Based on Driver Response Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automated driver assistance systems (ADAS) provide user-generic warnings that may not be appropriate for individual drivers, leading to potential unresponsiveness during critical situations.
Innovation Solution
An ADAS system that determines a driver's identity and adjusts warnings based on user-specific profiles, updating them based on response rates and other factors to enhance driver attentiveness and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional default or predetermined ADAS warnings are used, then the system is simple and easy to implement, but the warnings may not be appropriate for individual drivers, leading to potential unresponsiveness during critical situations
Solution Approach 1:
The system performs preliminary actions by determining driver identity before providing warnings, and by updating warning parameters based on historical response data. This allows the system to proactively adapt warnings to individual driver characteristics before critical situations arise, improving responsiveness without adding complex real-time decision-making requirements
Solution Approach 2:
The system dynamically adapts warning parameters (volume, frequency, type) based on driver identity and historical response patterns. This dynamic adjustment allows the same ADAS system to provide customized warnings for different drivers, enhancing reliability while maintaining a unified system architecture that manages complexity
2Reliability
If user-specific ADAS warnings are implemented, then driver responsiveness improves, but the system requires storing and managing multiple user profiles
Solution Approach 1:
The system applies local quality by storing only the specific warning parameters (volume, frequency, type preferences) relevant to each driver's responsiveness characteristics, rather than storing complete profile data. This targeted approach provides personalized warnings while minimizing data storage requirements to essential responsiveness metrics
3Adaptability or versatility
If the system updates warnings based on driver response rates, then the adaptability improves, but the calibration process becomes more complex
Solution Approach 1:
The system implements feedback by monitoring driver response rates to warnings and automatically adjusting warning parameters based on this feedback. This closed-loop approach enables the system to adapt to individual driver characteristics over time, improving versatility while using automated algorithms to manage calibration complexity
Solution Approach 2:
The system performs self-calibration by automatically updating warning parameters based on observed driver response patterns, eliminating the need for manual calibration processes. This self-service capability enhances adaptability while reducing the complexity burden on users or system configurators
Data Source
Figure 1
Figure 2
AI summary
Automated driver assistance systems (ADAS) and methods that provide user-specific ADAS warnings each involve determining an identify of the driver of the vehicle, accessing a memory configured to store a set of user profiles, each user profile defining a set of ADAS warnings, identifying a target user profile based on whether the driver identity corresponds to any of the set of user profiles, including accessing one of the set of stored user profiles or creating and storing a new user profile, and, during a period after identifying the target user profile, updating the set of ADAS warnings defined by the target user profile based on the vehicle's operation and providing ADAS warnings based on the set of ADAS warnings defined by the target user profile.