Vision-Based Object Path Prediction With Multi-Trajectory Confidence
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Solution Overview
Problem
Existing vehicle systems rely heavily on costly and error-prone physical sensors like radar and LIDAR, which are less effective in adverse weather conditions, increasing manufacturing and maintenance costs and detection errors.
Innovation Solution
Implementing vision-only systems with machine learned algorithms trained on labeled data to identify and predict paths of travel for dynamic objects using solely vision inputs, generating multiple potential paths with confidence values, and adjusting based on external objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine vision systems use multiple separate cameras and complex calibration procedures to achieve 3D spatial understanding, then measurement precision can be improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent combines multiple camera functions into a single imaging sensor that captures both 2D image data and depth information simultaneously. This merging eliminates the need for multiple separate cameras and their associated calibration systems, reducing device complexity while maintaining 3D spatial understanding capability through integrated depth mapping.
Solution Approach 2:
The imaging sensor is designed to perform multiple functions: capturing 2D images, measuring depth, and generating 3D spatial maps simultaneously. This multi-functional approach replaces traditional multi-camera systems, reducing system complexity while improving measurement precision through unified data capture.
2Measurement precision
If traditional systems perform extensive calibration procedures to improve measurement precision, then 3D spatial accuracy improves, but loss of time increases
Solution Approach 1:
The system performs preliminary calibration actions by capturing depth information and spatial relationships during normal operation rather than requiring separate calibration procedures. The depth mapping is continuously updated based on real-time sensor data, eliminating time-consuming pre-calibration steps while maintaining measurement precision.
Solution Approach 2:
The imaging sensor system performs self-calibration by using its own depth mapping capabilities to automatically establish spatial relationships between objects and the robot. The system continuously refines its spatial understanding through real-time depth data without requiring external calibration equipment or procedures, reducing calibration time while maintaining accuracy.
3Measurement precision
If vision-based systems lack depth mapping capability, then device complexity remains low, but measurement precision and ability to detect object positions deteriorate
Solution Approach 1:
The patent introduces depth mapping as an intermediary function that bridges 2D image data and 3D spatial understanding. The depth map acts as a mediator that translates 2D sensor inputs into accurate 3D position information, enabling precise object detection without requiring complex multi-camera systems. This intermediary layer adds measurement precision while keeping device complexity manageable through single-sensor integration.
Data Source
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AI summary
Aspects of the present application correspond to utilization of a set of inputs from vision systems to generate simulations or predicted paths of travel for dynamic objects detected from the vision systems. Illustratively, a service can process the set of inputs (e.g., the associated ground truth label data) collected from one or more vision systems (or additional services) to identify predicted paths of travel for any dynamic objects detected from the captured vision system information. Typically, a plurality of predicted paths of travels can be generated such that more than one path of travel may be considered to meet or exceed a minimal threshold. The resulting predicted paths of travel can be further associated with confidence values that characterize the likelihood that any one predicted path of travel for a detected dynamic object will occur. The generated and processed paths can be provided or used as inputs for additional systems, such as navigation systems/services, semi-automated or automated driving systems/services and the like.