RADAR Bearing Angle Correction via Statistical Feedback
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Solution Overview
Problem
Modern vehicle remote detection safety systems, such as vehicle lane change assist and rear cross traffic alert systems, face errors in perceived alignment and position of targets due to factors like vehicle fascia and environmental conditions, leading to inaccurate collision avoidance.
Innovation Solution
The system generates correction values for RADAR errors using stationary and moving targets, expressed as range and angle-dependent bias and variance estimates, which are applied to improve measurement accuracy over time by comparing estimated and observed bearing angles and applying these corrections through a filter model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RADAR systems are used for vehicle remote detection, then collision avoidance capability is improved, but measurement precision deteriorates due to errors in perceived alignment and position of targets
Solution Approach 1:
The system uses observed bearing angles of targets as feedback to correct estimated bearing angles. By continuously comparing estimated angles (from RADAR data processing) with observed angles (from actual sensor measurements), the system generates correction values that are applied to improve future measurements, thereby resolving the measurement precision issue while maintaining collision avoidance capability
Solution Approach 2:
The system changes the parameters used for bearing angle estimation by generating correction values expressed as range and angle-dependent bias and variance estimates. These correction parameters are continuously refined using target observation data, allowing the system to compensate for RADAR errors and improve measurement precision without sacrificing detection reliability
2Measurement precision
If correction values are continuously refined using target data, then measurement precision is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system performs self-correction by using its own target observation data to generate and refine correction values. The RADAR system automatically compares estimated bearing angles with observed bearing angles and updates its correction parameters without external intervention, improving measurement precision while managing complexity through autonomous operation
Solution Approach 2:
The system applies correction values selectively based on range and angle-dependent bias estimates. Rather than correcting all measurements uniformly, it focuses computational resources on generating correction data from representative targets and applying corrections where they are most needed, thereby improving precision without proportionally increasing overall system complexity
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 lane assignments and collision avoidance systems by continuously refining error corrections, even when targets are beyond a threshold distance, thereby improving system performance and reliability.
Implementation Method 1
Modern automobiles often employ various vehicle remote detection safety systems, such as vehicle lane change assist (LCA) systems, rear cross traffic alert (RCTA) systems, and the like. Such systems employ remote, environmental sensors, such as RADAR, LIDAR, cameras, etc.
Data Source
AI summary
Systems and methods for improving vehicle RADAR or other sensor performance by application of error statistics. In some implementations, statistical correction of vehicle tracking errors are achieved by generating a first data set representative of a historical map of a host vehicle trajectory, a second data set representative of one or more adjacent lanes to a current lane of the host vehicle, and a third data set comprising estimated error correction values of an estimated bearing angle while the moving target vehicle is beyond a threshold distance by comparing the estimated bearing angle of the moving target vehicle relative to the host vehicle with an observed bearing angle of the moving target vehicle relative to the host vehicle. A set of estimated lane assignments may be generated after the moving target vehicle has passed the threshold distance from the host vehicle, after which a lane assignment may be confirmed.