Z Axis Angular Velocity Analysis for Frequent Lane Change Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack effective active monitoring for vehicle traveling statuses, particularly for frequent lane changes in public transportation vehicles, which can lead to unsafe driving behaviors and traffic accidents.
Innovation Solution
A method and system that extract Z axis angular velocities from triaxial sensor data to detect frequent lane changes by storing angular velocities during specific time intervals where adjacent products are negative and analyzing the number of zero values exceeding a threshold, using a vehicular six-axes MENS sensor to collect real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If active monitoring for vehicle travelling statuses is implemented, then driving behaviors can be normalized and traffic accidents can be reduced, but the prior art lacks the capability to effectively detect frequent lane changes
Solution Approach 1:
The patent replaces complex mechanical monitoring systems with a simplified sensor-based detection system. By using a single six-axes MENS sensor to capture angular velocity data and applying algorithmic processing to detect lane changes, the system achieves effective monitoring without requiring complex mechanical structures or multiple sensors.
2Measurement precision
If Z axis angular velocities are extracted and analyzed in real-time, then frequent lane changes can be accurately detected, but computational resources and processing time are consumed
Solution Approach 1:
The patent performs preliminary data processing by pre-defining the detection threshold (threshold value greater than 2) and pre-structuring the analysis methodology. The system continuously monitors angular velocity data and immediately compares it against predetermined criteria, enabling rapid detection without requiring complex real-time computations during the actual detection process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables active monitoring of driving behaviors, normalizing lane changes and reducing traffic accidents by accurately identifying frequent lane changes through real-time data analysis.
Implementation Method 1
extracting Z axis angular velocities, the Z axis angular velocities are those Z axis angular velocities in triaxial angular velocities obtained by a sensor of a vehicle
Data Source
AI summary
A method and system for detecting frequent lane changes of moving vehicles are disclosed. A method for detecting frequent lane changes of moving vehicles including extracting the Z axis angular velocity values, and storing those Z axis angular velocities, from the Z axis angular velocity of which numerical product is negative, analyzing the Z axis angular velocities stored in the specific time period, and judging the number of the Z axis angular velocities with the numeral number 0. It is determined whether the vehicle has made frequent lane changes or not in its moving process, so that an active monitoring for vehicle travelling statuses is implemented, thereby normalizing driving behaviors, and reducing traffic accidents.