Sensor Fusion Motion Detection for Tangential Object Velocity
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting the position and velocity of objects in their environment due to limitations in existing sensor data processing, particularly with range data being sparse and unable to detect objects at large distances, and image data being insufficient for tangential motion detection.
Innovation Solution
A computer-implemented method that combines image data and range data to accurately determine the position and velocity of objects by identifying corresponding image features, synchronizing sensor data, and using a state manager to generate state data, flow data, and velocity calculations, enabling the detection of both tangential and radial motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If range data is used for object detection, then distance measurement capability is improved, but detection reliability deteriorates due to sparsity and inability to detect objects at large distances
Solution Approach 1:
The patent combines image data from cameras with range data from sensors to create a hybrid detection system. Image features provide dense coverage for detecting objects at large distances, while range data provides accurate distance measurement. By merging these complementary data sources, the system achieves both extended detection distance and maintained reliability.
Solution Approach 2:
The patent introduces an intermediary process that correlates image features with range data measurements. This intermediary layer matches visual features from images with corresponding range measurements, allowing the system to leverage the strengths of both data types while compensating for their individual weaknesses.
2Area of stationary object
If image data is used for object detection, then detection coverage is improved, but velocity detection capability deteriorates due to insufficiency in tangential motion detection
Solution Approach 1:
The patent merges image data processing with range data processing to enable accurate velocity detection. Image data provides comprehensive detection coverage and identifies object positions, while range data contributes to precise velocity measurements by providing distance information that, when combined with temporal changes, enables accurate tangential motion detection.
Solution Approach 2:
The patent transitions from two-dimensional image data to three-dimensional velocity detection by incorporating range data. This dimensional enhancement allows the system to compute velocity vectors with both tangential and radial components, significantly improving velocity measurement precision while maintaining the broad coverage provided by image data.
3Device complexity
If only image data is processed, then system complexity is reduced, but velocity determination accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary state manager that coordinates between image data processing and range data processing. This state manager correlates features from images with corresponding range measurements, enabling accurate velocity determination through a structured intermediate processing layer that manages the complexity of fusing multiple data sources.
4Measurement precision
If range data alone is used, then distance measurement accuracy is improved, but object detection capability deteriorates due to sparsity
Solution Approach 1:
The patent combines the sparse but accurate range data with the dense image data to achieve both accurate distance measurement and comprehensive object detection. Image features provide abundant detection points across the scene, while range data provides precise distance measurements for correlated features, resulting in a system that maintains high detection quantity and measurement precision.
Data Source
AI summary
Systems and methods for movement detection are provided. In one example embodiment, a computer-implemented method includes obtaining image data and range data representing a scene external to an autonomous vehicle, the image data including at least a first image and a second image that depict the scene. The method includes identifying a set of corresponding image features from the image data, the set of corresponding image features including a first feature in the first image having a correspondence with a second feature in the second image. The method includes determining a respective distance for each of the first feature and the second feature based at least in part on the range data. The method includes determining a velocity associated with a portion of a scene represented by the set of corresponding image features based at least in part on the respective distance for the first feature and the second feature.