Vehicle Detection System Azimuth Segmentation for Target Characterization
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Solution Overview
Problem
Vehicle detection systems face inaccuracies due to ego-motion and non-uniform error distribution across the field of view, leading to incomplete correction of errors, which affects target characterization.
Innovation Solution
A detection system that transmits signals across an azimuth and elevation range, assigns initial characterizations to bins based on return angles, calculates bin corrections and orientation corrections over time, and adjusts initial characterizations to account for errors specific to each segment of the azimuth range, ensuring accurate target characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform corrective calculations are applied across the entire field of view to account for vehicle motion, then certain known errors are remedied, but additional inaccuracies are introduced due to non-uniform error distribution across different azimuth segments
Solution Approach 1:
The field of view is divided into multiple azimuth segments (bins), with separate correction calculations performed for each segment. This segmentation allows the system to account for non-uniform error distribution across different angular ranges, applying appropriate corrections to each segment rather than using a single uniform correction factor.
Solution Approach 2:
Different correction strategies are applied to different azimuth segments based on their specific error characteristics. Left side bins and right side bins have separate correction calculations, allowing each region to be corrected according to its local error pattern rather than applying a global correction that may not be appropriate for all regions.
2Measurement precision
If corrective calculations are applied to account for vehicle motion effects, then some inaccuracies are corrected, but other inaccuracies remain uncorrected or are worsened due to the uniform adjustment approach
Solution Approach 1:
The correction system is segmented into multiple independent bin corrections, each handling a specific azimuth range. This modular approach manages complexity by breaking down the overall correction task into smaller, more manageable segments that can be processed independently.
Solution Approach 2:
The system applies corrections selectively to different azimuth segments rather than attempting to correct all errors uniformly across the entire field of view. This partial action approach focuses correction efforts where they are most needed while avoiding the introduction of errors in segments where uniform correction may be harmful.
3Measurement precision
If the detection system accounts for ego-motion to improve target characterization, then detection accuracy is enhanced, but the system complexity increases due to the need for multiple correction calculations
Solution Approach 1:
The processing complexity is managed by segmenting the correction calculations into discrete bins, where each bin handles a specific azimuth segment. This segmentation allows for systematic processing of corrections without requiring complex global optimization algorithms.
Solution Approach 2:
Correction factors are pre-calculated for each azimuth bin based on the vehicle's motion parameters. By performing these calculations in advance and organizing them by azimuth segment, the system reduces real-time processing complexity while maintaining high measurement precision.
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 target characterization by addressing non-uniform errors across the detection system's field of view, improving the precision of vehicle detection systems for applications like collision avoidance and self-driving.
Implementation Method 1
A plurality of signals are transmitted into the environment for reflecting off at least one target in the environment to create return signals
Implementation Method 2
The return signals are received... An initial characterization is determined for each of the at least one targets, the initial characterizations including a distance, a reference speed, and a return angle
Data Source
AI summary
A detection system and method for characterizing targets in an environment around vehicle. Signals are transmitted into the environment and return signals that have reflected off targets are received. The targets are initial characterized based on their return signals and the ego-motion of vehicle as determined from sensors on the vehicle. Corrections are determined based on the position of targets with respect to the azimuth range of the detection system and the side of the target with respect to a boresight. The initial characterizations are then adjusted to obtain a final target characterization.