Pre-NMS Ensemble Object Uncertainty Estimation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and classifying unknown objects due to background noise and uncertainties, leading to poor track heading and prediction, as existing systems like PointPillars lack confidence estimation and dynamic association cost adjustment.
Innovation Solution
The implementation of a pre-non-maximum suppression (NMS) ensemble process that estimates object uncertainties, including overall probability of existence and covariance for bounding boxes, to improve object detection accuracy by propagating uncertainty information downstream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection systems (e.g., PointPillars) are used, then detection speed is maintained, but detection accuracy and confidence estimation deteriorate due to lack of uncertainty modeling
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate bounding boxes (pre-NMS boxes) before non-maximum suppression, and calculates uncertainty estimates (covariances) for each candidate in advance. This allows the system to propagate uncertainty information through the detection pipeline, improving final detection accuracy without requiring complex real-time uncertainty modeling during inference.
Solution Approach 2:
The detection process is segmented into distinct stages: generating multiple pre-NMS candidate boxes, calculating individual uncertainty estimates for each candidate, applying NMS to select final boxes, and propagating uncertainty information downstream. This segmentation allows uncertainty modeling to be integrated systematically without overwhelming system complexity.
2Measurement precision
If multiple pre-NMS bounding boxes are generated and processed, then confidence score accuracy improves, but computational time increases
Solution Approach 1:
Uncertainty estimates (covariances) are calculated preliminarily for all pre-NMS candidate boxes before the NMS filtering step. This preliminary calculation allows efficient batch processing of uncertainty information without requiring iterative or complex computations during the time-critical NMS and tracking stages, thus minimizing processing time overhead.
Solution Approach 2:
The system generates multiple candidate bounding boxes (copies of the detection process with different parameters) and calculates uncertainty for each copy independently. These candidate boxes are then filtered through NMS, keeping only the most confident selections. This copying approach allows parallel uncertainty calculation that can be efficiently optimized, improving confidence accuracy without linearly increasing processing time.
3Reliability
If uncertainty estimation is added to the detection system, then tracking and classification performance improves, but algorithm complexity increases
Solution Approach 1:
Uncertainty estimates (covariances) serve as an intermediary information carrier between the detection stage and downstream tracking/classification modules. Instead of fundamentally changing the core detection algorithm, the system adds uncertainty calculation as an intermediary layer that propagates confidence information to improve tracking and classification without requiring complex re-engineering of existing modules.
Solution Approach 2:
The uncertainty estimates provide feedback to downstream tracking and classification algorithms, allowing them to adjust their behavior based on detection confidence. High-uncertainty detections can trigger more conservative tracking or additional verification, while low-uncertainty detections can be processed more efficiently. This feedback mechanism improves reliability without requiring complex bidirectional communication between modules.
Data Source
AI summary
Methods and systems are provided for detecting objects by utilizing uncertainties. In some aspects, a process can include steps for receiving, by an autonomous vehicle system, a frame of a scene with a detected object; estimating, by the autonomous vehicle system, an overall probability of the detected object in the frame; estimating, by the autonomous vehicle system, covariances for each state of at least one bounding box; and balancing, by the autonomous vehicle system, confidence values of the at least one bounding box based on the overall probability of the detected object and the covariances of each state of the at least one bounding box.