Careless Driving Detection Using Normal Pattern Baseline
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Solution Overview
Problem
Existing methods for detecting careless driving patterns in vehicles face challenges in differentiating between normal and careless driving, as they often require drivers to intentionally perform careless driving, making it difficult to collect reliable data, and struggle to set a clear boundary between the two.
Innovation Solution
An apparatus and method that utilize driving performance data to generate normal driving patterns at the start of a trip, detect careless driving patterns using an artificial neural network (ANN) technique, and determine the boundary between normal and careless driving, allowing for more reliable identification of careless driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If drivers are required to intentionally perform careless driving to collect data, then careless driving patterns can be learned, but it becomes difficult to allow the driver to intentionally perform the careless driving
Solution Approach 1:
Instead of requiring drivers to intentionally perform careless driving, the system inverts the approach by having drivers perform only normal driving tasks. The system then uses pattern recognition and boundary determination algorithms to automatically identify careless driving patterns from naturally occurring driving data, eliminating the need for intentional careless driving performance.
Solution Approach 2:
The system enables self-service by automatically learning normal driving patterns during an initial period and then autonomously detecting deviations that indicate careless driving. The boundary determination unit automatically establishes thresholds without requiring driver intervention or intentional careless driving demonstrations.
2Ease of manufacture
If only normal driving patterns are learned for the initial 10 to 15 minutes, then the system is simple to implement, but it becomes difficult to find a boundary which differentiates the normal driving and the careless driving
Solution Approach 1:
The system performs preliminary action by learning normal driving patterns during an initial period (10-15 minutes) before actual careless driving detection begins. This preliminary learning phase establishes a baseline that the boundary determination unit then uses to automatically differentiate careless driving patterns, maintaining both simplicity and precision.
Solution Approach 2:
The boundary determination unit acts as an intermediary that bridges the gap between simple normal driving pattern learning and precise careless driving differentiation. It automatically establishes boundary values and thresholds that enable accurate classification without requiring complex manual configuration or intentional careless driving data.
3Measurement precision
If both normal driving patterns and careless driving patterns are learned together, then the boundary between normal and careless driving is clearer, but the system complexity increases and requires intentional careless driving performance
Solution Approach 1:
The system segments the learning process into distinct phases: first learning only normal driving patterns during an initial period, then using the boundary determination unit to automatically establish differentiation criteria. This segmentation avoids the complexity of learning both patterns simultaneously while still achieving clear boundary definition through automated threshold establishment.
Data Source
AI summary
An apparatus and a method for determining careless driving are provided and determine more reliable careless driving by generating normal driving patterns using driving performance data for a reference time at the beginning of driving. In addition, careless driving patterns greater than a predetermined number are detected using the normal driving pattern and a boundary between the normal driving and the careless driving is determined using a supervised learning method. The careless driving of the driver is then determined based on the determined boundary.