Vision-Based Object Path Prediction With Confidence Scoring
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Solution Overview
Problem
Traditional vehicles relying on physical sensors for navigation and safety systems are costly to manufacture and maintain, and these systems can be less effective in adverse environmental conditions such as rain, fog, or snow, leading to detection errors.
Innovation Solution
Implementing vision-only systems with machine learned algorithms that process inputs from cameras to identify and characterize objects, generate predicted paths of travel for dynamic objects, and provide these paths with confidence values for use in navigation and safety systems, eliminating the need for additional detection systems like radar or LIDAR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical sensors (radar, LIDAR) are used for navigation and safety systems, then detection reliability in adverse environmental conditions is improved, but manufacturing and maintenance costs increase
Solution Approach 1:
The vision system is designed to perform multiple functions including object detection, path prediction, and environmental condition assessment using a single camera-based platform. This multi-functional approach eliminates the need for separate radar and LIDAR systems while maintaining detection capabilities across various environmental conditions through algorithmic adaptation.
Solution Approach 2:
The system dynamically adjusts processing parameters and algorithmic approaches based on environmental conditions detected by the vision system. By changing parameters such as image processing thresholds, prediction time horizons, and confidence value requirements, the system maintains reliable detection across varying conditions without requiring additional hardware.
2Ease of manufacture
If vision-only systems are used to reduce costs, then manufacturing and maintenance costs decrease, but detection accuracy in adverse environmental conditions worsens
Solution Approach 1:
The system performs preliminary processing of vision data including enhancement, filtering, and pre-analysis before final object detection and path prediction. This preliminary action allows the vision system to compensate for adverse environmental conditions through proactive image processing and feature extraction, maintaining detection accuracy without additional sensors.
Solution Approach 2:
Machine learned algorithms serve as intermediaries between the raw vision data and the navigation/safety decision-making processes. These algorithms process and interpret camera inputs, generating reliable object detection and path prediction outputs even in adverse environmental conditions where direct sensor-to-decision pathways would fail.
3Reliability
If machine learned algorithms are implemented for path prediction, then navigation and safety functionalities are enhanced, but system complexity increases
Solution Approach 1:
The machine learned algorithm system is divided into distinct functional modules including object detection, path prediction, confidence value generation, and integration with navigation/safety systems. This segmentation allows each module to be optimized independently and simplifies the overall system architecture, making complex functionalities manageable and maintainable.
Data Source
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.