Driver Distraction Analysis Using Inertial Sensor Filtering
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Solution Overview
Problem
Current systems face challenges in accurately determining driver distraction due to noisy data and lack of context, making it difficult to distinguish between purposeful and reactive steering, and road-induced noise, which limits the effectiveness of risk quantification and general applicability.
Innovation Solution
A driver distraction determination system that samples inertial measurements during a driving session, determines a steering activity metric, and assesses driver behavior based on this metric, incorporating both inertial and auxiliary data to quantify distraction levels and perform corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inertial data is used to determine driver distraction, then driver behavior can be quantified, but the data becomes noisy and difficult to interpret due to road-induced noise and inability to distinguish purposeful vs. reactive steering
Solution Approach 1:
The patent introduces an intermediary processing system that mediates between raw inertial data and driver distraction determination. This system applies signal processing techniques and contextual analysis to filter out road-induced noise and distinguish between purposeful and reactive steering behaviors, thereby resolving the contradiction between utilizing inertial data for distraction detection and managing its inherent noise
Solution Approach 2:
The patent transforms the inertial data by applying parameter changes through filtering operations and contextual normalization. By modifying the data parameters (removing noise components, adjusting for driving conditions), the system maintains the utility of inertial measurements while eliminating harmful noise effects
2Measurement precision
If contextual data is collected to improve driver behavior analysis, then accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent implements a multi-functional analysis system that processes multiple types of data (inertial measurements, driving context, environmental factors) through a unified framework. This universal approach allows the same system architecture to handle various data types and analysis requirements, improving accuracy while avoiding the need for separate complex subsystems for each data type
3Measurement precision
If individual driver models are used to account for user differences, then measurement accuracy improves for specific drivers, but generalizability across the population decreases
Solution Approach 1:
The patent implements a dynamic modeling approach where driver profiles are not fixed but adapt over time. The system maintains individualized models that evolve with each driving session, allowing it to capture individual driver characteristics while progressively improving generalizability through population-level pattern recognition. This dynamic adaptation resolves the contradiction between individual precision and population versatility
Data Source
AI summary
The disclosed embodiments include a onboard driver distraction determination system. The determination system includes a onboard sensing and computing system(s), which includes inertial sensor(s), internal sensor(s), and external sensor(s). The onboard system samples data from the sensor(s) during a driving session to determine steering activity metrics and driver behavior. A steering activity metric is a representation of the steering inputs by the driver during the driving session. Driver behavior is a representation of how distracted the driver is during the driving session. By performing the above mentioned steps, the system can provide an analysis of driver distraction and optionally, take control of the vehicle to avoid aberrant behavior.