Autonomous Vehicle Object Validation Using Dual Coordinate Virtual Boxes
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Solution Overview
Problem
Existing vehicle LiDAR systems often incorrectly identify external objects, leading to potential safety issues in autonomous driving scenarios.
Innovation Solution
A vehicle control apparatus and method that utilizes a sensor to generate virtual boxes for external objects, determines an interest virtual box based on vehicle state and object characteristics, and validates the box using multiple coordinate systems and hysteresis to ensure accurate tracking and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If LiDAR is used to identify external objects, then object detection capability is improved, but incorrect identification occurs leading to reduced reliability
Solution Approach 1:
The system validates virtual boxes by checking consistency across multiple frames and coordinate systems. The validation process uses feedback from heading direction changes, location variations, and contour point distributions to confirm or reject object identifications, thereby improving reliability while maintaining detection capability
Solution Approach 2:
The patent introduces virtual boxes as intermediary representations between raw LiDAR data and object identification decisions. These virtual boxes serve as a mediating structure that can be validated through multiple criteria before final object identification, reducing incorrect identifications while preserving detection accuracy
2Measurement precision
If multiple validation criteria are applied to virtual boxes, then identification accuracy is improved, but processing complexity increases
Solution Approach 1:
The validation process is segmented into distinct checks: heading direction validation, location validation, and contour point distribution validation. Each aspect is evaluated separately using dedicated algorithms, which improves identification accuracy through comprehensive validation while managing complexity through modular processing
3Measurement precision
If coordinate system transformations are performed for validation, then tracking precision is improved, but computational load increases
Solution Approach 1:
The system performs coordinate transformations only for virtual boxes that require validation, not for all detected objects. By applying transformations selectively to candidates that need verification, the system improves tracking precision for critical objects while reducing overall computational load
Data Source
AI summary
An apparatus for controlling autonomous driving of a vehicle may comprise a sensor that obtains virtual boxes corresponding to a plurality of external objects, and a processor. The processor may identify an interest virtual box from these virtual boxes based on factors such as the vehicle's operating state, the external object's location, or the size of the corresponding virtual box. The processor may determine a first distribution of contour points, centered on the interest virtual box, in a first coordinate system and a second distribution of the contour points in a second coordinate system, centered on the contour points. The interest virtual box may be validated based on criteria such as its heading direction, location, and the distributions of contour points. Based on this validation, the processor may determine whether to output the interest virtual box, generates a signal, and controls autonomous driving based on the signal.