Driver Distraction Estimation Using Weighted Input Events
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Solution Overview
Problem
Existing systems for determining driver distraction are imprecise, complex, costly, or require stable data connections, failing to provide a reliable and accurate assessment of a driver's level of distraction, which increases the risk of accidents.
Innovation Solution
A device and method that uses an input interface to receive driver data, an analysis unit to determine distraction levels based on predefined behaviors, and an output interface to issue warnings when distraction exceeds a threshold, utilizing existing vehicle sensors and units to provide precise and cost-effective distraction estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to detect driver distraction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (accelerometer, gyroscope, magnetometer, barometer, proximity sensor, light sensor, camera) into a single integrated mobile device platform. The sensor module integrates these diverse sensors with shared processing resources, reducing overall system complexity while maintaining the ability to detect multiple distraction indicators simultaneously
Solution Approach 2:
The mobile device serves multiple functions: it acts as both the distraction detection system and the communication device. The same sensors and processing unit used for distraction detection also support navigation, media playback, and communication functions, eliminating the need for separate dedicated hardware and reducing device complexity
2Reliability
If real-time driver distraction detection is implemented, then safety is improved, but energy consumption increases
Solution Approach 1:
The system performs distraction detection at periodic intervals rather than continuously. The processor evaluates sensor data at scheduled times to determine distraction indicators, reducing energy consumption while maintaining safety monitoring. The system can adjust the frequency of detection based on driving conditions and battery status
Solution Approach 2:
The system uses the mobile device's existing sensors and processing capabilities that are already powered on for normal device operation. Rather than adding dedicated always-on sensors, the system leverages the device's existing power state and computational resources to perform distraction detection with minimal additional energy consumption
3Measurement precision
If multiple distraction indicators are monitored, then measurement precision is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system segments distraction detection into distinct indicator types: visual distraction (head position, gaze direction), manual distraction (steering input patterns), and verbal distraction (conversation detection). Each indicator is detected by specific sensor combinations and processed through dedicated analysis routines, making the overall complex task more manageable and accurate
Solution Approach 2:
The system uses intermediate processing layers that translate raw sensor data into meaningful distraction indicators. The processor applies algorithms that convert accelerometer data into head position estimates, gyroscope data into orientation changes, and audio data into conversation detection, simplifying the measurement and analysis process
Data Source
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AI summary
The invention relates to a device (10) for estimating a degree of distraction (62) of a driver during a journey in a motor vehicle (30), and to a corresponding system (18), method, and computer program. An event is identified by an operational input from the driver and is weighted in respect of the expected driver distraction (62). Each event can be assigned a distraction potential, in a parameterisable manner, ranging from not distracting, through neutral, to very distracting. In addition, a time duration, the hold time (58), is determined and corresponds to the expected distraction duration of the specific event for the driver. Should the driver make a number of consistent operational inputs within a short space of time, these are filtered out within a defined time period, the dead time (54), since it is assumed that the driver is not distracted to a greater extent, but only for longer. An index (62) is determined from the events. This index can increase, which means that the driver is currently distracted to an increasing extent, and the index (62) can decrease by defined operational actions, for example heavy acceleration/braking, heavy steering, as a result of which it is identified that the driver is paying attention again to the guidance of the vehicle. If no further events are identified over a parameterisable time, the index (62) reduces to a neutral value (66). If the index (62) exceeds a predefined threshold (64), a distraction message (68) is issued.