Causal Inference for Risk Object Identification in Driving Scenes
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Solution Overview
Problem
Existing automated driving systems face challenges in efficiently identifying risk objects in driving scenes, requiring extensive processing power and manual labeling without providing explicit reasoning for risk object identification.
Innovation Solution
A computer-implemented method using causal inference to analyze driving scenes, where dynamic objects are masked and removed to assess their influence on driving behavior, assigning a causality score to identify risk objects based on their impact on driving actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of risk objects is performed in existing automated driving systems, then risk object identification accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent replaces manual labeling (mechanical human operation) with an automated causal inference system that uses counterfactual reasoning and object removal techniques to automatically identify risk objects, thereby eliminating the need for time-consuming manual annotation while maintaining identification accuracy
Solution Approach 2:
The patent creates synthetic training data by generating counterfactual images through object removal and inpainting, where original images are copied and modified to create augmented datasets that enable the system to learn risk object identification without requiring extensive manual labeling of real-world scenarios
2Reliability
If numerous inputs are processed in existing risk identification systems, then identification comprehensiveness is improved, but processing power requirements increase
Solution Approach 1:
The patent extracts and removes specific dynamic objects from driving scenes individually to assess their causal impact on driving behavior, focusing computational resources on evaluating the significance of each object rather than processing all objects uniformly, thereby reducing overall processing power requirements while maintaining comprehensive risk assessment
Solution Approach 2:
The patent changes the parameter of object significance by using causal inference metrics (counterfactual analysis) to dynamically prioritize which objects require detailed processing, allowing the system to adjust processing depth based on each object's identified risk level rather than applying uniform high-processing to all objects
3Productivity
If existing systems provide risk object identification without explicit reasoning, then processing speed is improved, but interpretability and trustworthiness deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the system generates counterfactual explanations by simulating what would happen if risk objects were removed from the scene, providing interpretable reasoning that connects identified risk objects to their causal impact on driving behavior, thereby maintaining processing speed while recovering lost reasoning information
Data Source
AI summary
A system and method for risk object identification via causal inference that includes receiving at least one image of a driving scene of an ego vehicle and analyzing the at least one image to detect and track dynamic objects within the driving scene of the ego vehicle. The system and method also include implementing a mask to remove each of the dynamic objects captured within the at least one image. The system and method further include analyzing a level of change associated with a driving behavior with respect to a removal of each of the dynamic objects. At least one dynamic object is identified as a risk object that has a highest level of influence with respect to the driving behavior.