On-vehicle Preceding Vehicle Identification Using Dynamic Thresholds
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Solution Overview
Problem
Existing techniques for identifying preceding vehicles are prone to errors due to neglecting traffic density, inability to accurately detect vehicles without inter-vehicle communication devices, and reliance on self-indicating actions that may not be feasible in all situations.
Innovation Solution
An on-vehicle apparatus that compares GPS-based positional information with autonomous sensor data, adjusting the comparison threshold based on error levels to precisely identify the preceding vehicle, even in dense traffic or among multiple vehicles, using sensors like millimeter-wave radar or cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If GPS-based positional information is used to identify preceding vehicles, then the identification range is expanded, but the measurement precision deteriorates due to GPS errors
Solution Approach 1:
The patent combines GPS-based positional information with autonomous sensor data (radar, camera) to identify preceding vehicles. By merging these two information sources, the system expands the identification range while compensating for GPS errors through sensor data, thus resolving the contradiction between expanded range and maintained precision.
Solution Approach 2:
The patent dynamically adjusts the comparison threshold based on GPS error levels. When GPS error is high, the threshold is adjusted to account for larger positional deviations. This parameter change allows the system to maintain accurate identification despite varying GPS precision across different locations and conditions.
2Device complexity
If a fixed comparison threshold is used for identifying preceding vehicles, then the processing is simple, but the reliability deteriorates in dense traffic or high error conditions
Solution Approach 1:
The patent implements a dynamic comparison threshold that adjusts based on GPS error levels and traffic conditions. Instead of a fixed threshold, the system continuously adapts the threshold parameter to match current operational conditions, thereby maintaining high reliability in both dense traffic and open road scenarios without excessive complexity.
3Measurement precision
If inter-vehicle communication is used to obtain positional information, then the measurement precision is improved, but the adaptability worsens because vehicles without communication devices cannot be detected
Solution Approach 1:
The patent creates a universal preceding vehicle identification system that works with both communication-equipped and communication-free vehicles. By using autonomous sensors that can detect any vehicle regardless of its equipment, the system achieves broad adaptability while maintaining precision through the optional use of communication data when available.
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 enhances the accuracy of preceding vehicle identification by dynamically adjusting the comparison threshold according to error levels, reducing false positives and ensuring precise detection in various traffic conditions.
Implementation Method 1
using sensors like millimeter-wave radar or cameras
Implementation Method 2
using sensors like millimeter-wave radar or cameras
Implementation Method 3
comparing GPS-based positional information with autonomous sensor data
Data Source
AI summary
An ECU acquires relative position information related to a relative position between a host vehicle and another vehicle (A, B, C) traveling ahead of the host vehicle, and error information related to an error in the relative position, detects the position of a preceding vehicle (A) traveling in front of the host vehicle, identifies the position of the preceding vehicle (A) by comparing the relative position based on the acquired relative position information with the detected position, and if relative position information oh a plurality of other vehicles (A, B, C) is acquired, identifies the position of the preceding vehicle (A) by comparing the relative position based on the acquired relative position information with the detected position, by using a threshold that is varied in accordance with the error information.