Object Detection Tracking Motion Prediction Temporal Spatial Input
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Solution Overview
Problem
Current computing systems face challenges in effectively detecting, tracking, and predicting the motion of objects in diverse environments, particularly in autonomous vehicles and robotic systems, due to the complexity of processing sensor data over time and varying operational demands.
Innovation Solution
A computer-implemented method that utilizes sensor data to generate an input representation with temporal and spatial dimensions, which is then processed by a machine-learned model to detect object classes, locations, and predicted paths, allowing for simultaneous object detection, tracking, and motion prediction, reducing errors and improving operational safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is processed using traditional methods, then object detection and tracking can be performed, but computational resources are excessive and processing speed is insufficient for real-time applications
Solution Approach 1:
The patent segments the object detection and tracking process into distinct modules: sensor data reception, input representation generation with temporal and spatial dimensions, machine-learned model processing for classification and localization, and bounding shape generation. This segmentation enables optimized processing at each stage, improving overall productivity while reducing computational overhead through specialized handling of different data aspects.
Solution Approach 2:
The patent introduces temporal and spatial dimensions into the input representation of sensor data, transforming traditional 2D image data into multi-dimensional tensors that incorporate time evolution and spatial relationships. This dimensional enhancement enables more efficient processing of motion patterns and object trajectories, achieving real-time performance with reduced computational resources.
2Measurement precision
If traditional object detection methods are used, then basic detection can be achieved, but accuracy and precision in motion prediction are insufficient
Solution Approach 1:
The patent applies preliminary action by generating structured input representations with temporal and spatial dimensions before feeding data to the machine-learned model. This preprocessing step organizes sensor data in a way that anticipates the model's processing requirements, enhancing detection accuracy and motion prediction precision while managing system complexity through systematic data preparation.
Solution Approach 2:
The system implements feedback mechanisms where the machine-learned model processes temporal sequences of sensor data and refines object classification, localization, and trajectory prediction based on historical patterns. This feedback loop continues across multiple time steps, progressively improving measurement precision for object detection and motion prediction while the system adapts to environmental variations.
Data Source
AI summary
Systems, methods, tangible non-transitory computer-readable media, and devices for object detection, tracking, and motion prediction are provided. For example, the disclosed technology can include receiving sensor data including information based on sensor outputs associated with detection of objects in an environment over one or more time intervals by one or more sensors. The operations can include generating, based on the sensor data, an input representation of the objects. The input representation can include a temporal dimension and spatial dimensions. The operations can include determining, based on the input representation and a machine-learned model, detected object classes of the objects, locations of the objects over the one or more time intervals, or predicted paths of the objects. Furthermore, the operations can include generating, based on the input representation and the machine-learned model, an output including bounding shapes corresponding to the objects.