Multi-resolution voxel segmentation for autonomous vehicle object detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurate object detection and segmentation due to the complexity of three-dimensional data processing, particularly in resource-constrained environments, where increasing voxel size and number lead to memory and processing demands that are difficult to manage.
Innovation Solution
The use of a multi-resolution voxel space, where data is represented in multiple voxel spaces with varying resolutions, allowing for efficient data management near the vehicle and reducing memory usage, combined with machine learning algorithms to output masks and segmentation data for object classification and trajectory planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the voxel space size and number of voxels are increased to improve object detection accuracy, then the measurement precision is improved, but the memory usage and processing requirements increase significantly
Solution Approach 1:
The patent divides the three-dimensional space into multiple voxel spaces with different resolutions. A first voxel space with higher resolution is used for near-field objects requiring precise detection, while a second voxel space with lower resolution is used for far-field objects. This segmentation allows the system to maintain high object detection accuracy for critical nearby objects while reducing the total number of voxels and memory usage by using coarser resolution for distant objects where high precision is less critical.
2Measurement precision
If the voxel space size and number of voxels are increased to improve object detection accuracy, then the measurement precision is improved, but the processing time increases
Solution Approach 1:
By segmenting the processing space into multiple resolution levels, the patent reduces the computational burden. The machine learning algorithm processes only relevant portions of the scene at high resolution, while other portions are processed at lower resolution. This significantly reduces the total number of operations required, thereby reducing processing time while maintaining accurate object detection for objects that require high precision.
3Measurement precision
If a single high-resolution voxel space is used to maintain accurate object detection at increased ranges, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent manages device complexity by segmenting the voxel space into multiple resolution levels, each optimized for specific detection ranges. This approach simplifies data management compared to using a single high-resolution voxel space for the entire scene, as the system only needs to maintain high resolution for specific regions where objects require accurate detection, rather than uniformly high resolution throughout the entire environment.
Data Source
AI summary
Techniques for segmenting sensor data are discussed herein. Data can be represented in individual levels in a multi-resolution voxel space. A first level can correspond to a first region of an environment and a second level can correspond to a second region of an environment that is a subset of the first region. In some examples, the levels can comprise a same number of voxels, such that the first level covers a large, low-resolution region, while the second level covers a smaller, higher-resolution region, though more levels are contemplated. Operations may include analyzing sensor data represented in the voxel space from a perspective, such as a top-down perspective. From this perspective, techniques may generate masks that represent objects in the voxel space. Additionally, techniques may generate segmentation data to verify and/or generate the masks, or otherwise cluster the sensor data.