Temporal Sensor Distortion Analysis for Object Attribute Inference
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Solution Overview
Problem
Temporal sensors often experience latency-related issues that distort representations of dynamic objects, leading to inaccurate measurements of object attributes such as velocity, bounding boxes, and geometry due to motion of the sensor or objects.
Innovation Solution
Utilize distortions in temporal sensor data to infer object attributes by comparing representations from multiple sensors with different scanning directions or using machine-learned models to correct distortions, allowing for accurate determination of object attributes like velocity and bounding boxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If temporal sensors are used to capture dynamic scenes, then data can be captured over a period of time providing more complete information, but latency-related issues cause distortion of object representations relative to actual shape
Solution Approach 1:
The patent converts the harmful distortion caused by temporal sensor latency into a beneficial signal. By analyzing the distortion pattern in the temporal sensor data, the system infers object attributes such as velocity, acceleration, and trajectory. The distortion that would normally degrade measurement precision is instead used as the basis for deriving additional dynamic information about moving objects.
Solution Approach 2:
The patent introduces an intermediary processing layer that reconciles data from temporal and instantaneous sensors. This intermediary system uses machine learning models to predict object attributes from temporal sensor distortions and fuses them with instantaneous sensor data, thereby mediating between the conflicting requirements of temporal completeness and measurement precision.
2Measurement precision
If multiple sensors with different scanning directions are used, then object attributes can be inferred more accurately, but device complexity increases
Solution Approach 1:
The patent makes existing sensors multi-functional by enabling them to serve both their primary detection role and a secondary role in inferring object attributes through distortion analysis. The temporal sensor not only captures spatial information but also provides temporal dynamics information through its distortion pattern, eliminating the need for additional dedicated sensors for velocity and trajectory measurement.
Solution Approach 2:
The patent changes the interpretation parameters of temporal sensor data by introducing machine learning models that analyze distortion patterns. Instead of treating temporal sensor data only as spatial snapshots, the system transforms the interpretation to extract temporal dynamics, effectively using parameter changes in data processing to achieve multiple measurement goals with existing hardware.
3Measurement precision
If machine-learned models are used to correct distortions, then object attributes can be determined accurately, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline to learn the relationship between temporal sensor distortions and object attributes. During real-time operation, the pre-trained models perform rapid inference rather than learning from scratch, significantly reducing computational time. The heavy computational lifting is done in advance, allowing real-time accurate measurement.
Solution Approach 2:
The patent uses partial action by applying machine learning only to the specific task of distortion correction and attribute inference, rather than using it for all processing tasks. The system selectively applies ML models only where needed (in distortion-prone temporal sensor data), while using simpler processing methods for other aspects, thereby optimizing the balance between accuracy and computational efficiency.
Data Source
AI summary
Techniques for determining attributes associated with objects represented in temporal sensor data. In some examples, the techniques may include receiving sensor data including a representation of an object in an environment. The sensor data may be generated by a temporal sensor of a vehicle and, in some instances, a trajectory of the object or the vehicle may contribute to a distortion in the representation of the object. For instance, a shape of the representation of the object may be distorted relative to an actual shape of the object. The techniques may also include determining an attribute (e.g., velocity, bounding box, etc.) associated with the object based at least in part on a difference between the representation of the object and another representation of the object (e.g., in other sensor data) or the actual shape of the object.