Road-User Importance Estimation Using Local and Global Context
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Solution Overview
Problem
Current systems lack the ability to effectively discern and prioritize road users in the vicinity of an ego vehicle, impacting safe navigation and trust in human and autonomous driving systems.
Innovation Solution
A computer-implemented method and system that analyzes images to determine local and global contexts, selecting potentially important road users based on their paths and the ego vehicle's predicted future path, and fuses this information to classify highly important road users for safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system monitors all road users in the vicinity of the ego vehicle, then the comprehensive safety coverage is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments road users into different importance levels (highly important, potentially important, and less important) based on their spatial relationship with the ego vehicle and predicted paths. This segmentation allows the system to focus computational resources on highly important road users while maintaining comprehensive safety coverage through the multi-level classification structure.
Solution Approach 2:
The system applies different levels of analysis and processing quality to different road users based on their local context and importance. Highly important road users receive detailed analysis including path prediction and context fusion, while less important road users receive basic detection only, optimizing the balance between safety coverage and computational complexity.
2Measurement precision
If the system classifies highly important road users using both local and global context, then the accuracy of road user prioritization is improved, but the processing time increases
Solution Approach 1:
The system performs preliminary classification of road users into importance levels using local context before applying the more computationally intensive global context analysis. This preliminary action filters the set of road users that require full context fusion, reducing overall processing time while maintaining classification accuracy for critical cases.
Solution Approach 2:
The system applies full context fusion (local + global context) only to road users who are identified as potentially important through initial local context analysis. For road users determined to be less important, the system uses partial action by relying solely on local context, thereby reducing processing time while maintaining sufficient accuracy for the overall safety system.
3Reliability
If the system accounts for all road users in decision-making, then the safety and trustworthiness of the driving system is improved, but the decision-making efficiency decreases
Solution Approach 1:
The system segments road users into different importance categories and processes them through different decision-making pathways. Highly important road users are incorporated into detailed decision-making with full context analysis, while less important road users are handled through streamlined processes, maintaining trustworthiness through comprehensive consideration while improving decision-making efficiency through differentiated processing.
Solution Approach 2:
The system applies different levels of decision-making quality and detail to different road users based on their importance classification. Critical decisions with full context fusion are applied to highly important road users, while simplified decision rules are applied to less important road users, optimizing the balance between safety/trustworthiness and decision-making efficiency.
Data Source
AI summary
A system and method for providing context aware road user importance estimation that include receiving at least one image of a vicinity of an ego vehicle. The system and method also include analyzing the at least one image to determine a local context associated with at least one road user located within the vicinity of the ego vehicle. The system and method additionally include determining a global context associated with the ego vehicle. The system and method further include fusing the local context and the global context to classify at least one highly important road user that is to be accounted for with respect to operating the ego vehicle.