Synthetic Point Cloud Pipeline for LiDAR Detection Blind Spots
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Solution Overview
Problem
Autonomous vehicle (AV) object detectors, such as LiDAR detectors, often fail to reliably detect rare or unknown objects, posing a safety risk due to potential detector failures in real-world scenarios.
Innovation Solution
Generate optimized objects with challenging geometries and poses that evade detection by the LiDAR detector, using a differentiable pipeline to iteratively perturb object shape and pose parameters until the detector confidence falls below a threshold, enabling proactive training to improve detector robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the LiDAR detector uses conventional detection methods, then the detection process is simple and fast, but the detector fails to reliably detect rare or unknown objects
Solution Approach 1:
The system performs preliminary action by generating optimized challenge objects in advance that are specifically designed to evade detection by the LiDAR detector. These pre-generated challenging objects are then used to train the detector before real-world deployment, improving its reliability for detecting rare or unknown objects without adding complexity to the actual detection process.
2Adaptability or versatility
If the detector is trained on more diverse object instances, then the detector robustness improves, but the training data requirements and processing time increase
Solution Approach 1:
Instead of collecting and training on large amounts of real-world diverse object data, the system creates synthetic copies of challenge objects through optimization. The differentiable pipeline generates optimized objects that replicate the challenging characteristics needed for robust training, significantly reducing the time and data requirements while maintaining or improving detector adaptability.
3Adaptability or versatility
If the object shape and pose parameters are highly variable, then the detector encounters more challenging cases, but the detection accuracy decreases
Solution Approach 1:
The system converts the harmful effect of highly variable object parameters (which cause detection failures) into a beneficial training resource. By optimizing objects with extreme shape and pose variations that deliberately evade detection, the system creates challenging training samples that, when used to train the detector, ultimately improve its accuracy and ability to handle variable objects in real scenarios.
Data Source
AI summary
The present disclosure provides solutions for generating synthetic three-dimensional (3D) objects for testing autonomous vehicle (AV) object detectors, such as AV Light Detection and Ranging (LiDAR) detectors. A process of the disclosed technology can include steps for receiving a first set of object shape data corresponding to a baseline object, permuting the first set of object shape data to generate a second set of object shape data, and providing the second set of object shape data to a differentiable pipeline to generate an output scene, wherein the output scene contains an optimized object that is based on the second set of object shape data. The process can further include steps for determining, using a detector, if the optimized object can be identified in the output scene. Systems and machine-readable media are also provided.