Monocular Risk Object Identification With Counterfactual Driver Intent
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately identifying risk objects and determining driver intentions in complex environments, such as intersections, which hinders the implementation of realistic human-level driving behaviors and effective navigation.
Innovation Solution
A computer-implemented method and system that receives and analyzes monocular image data to perform semantic waypoint labeling and counterfactual scenario augmentation, determining driver intentions and responses by augmenting objects in the vehicle's surroundings, using a neural network to model road topology and predict vehicle paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic waypoint labeling and counterfactual scenario augmentation are performed to improve risk object identification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs semantic waypoint labeling and counterfactual scenario augmentation in advance before actual driving decisions are required. By pre-processing the environment understanding and generating potential risk scenarios beforehand, the system improves measurement precision for risk identification while managing complexity through staged processing rather than real-time computation of all possibilities.
Solution Approach 2:
The patent introduces intermediate processing layers including semantic labeling modules and scenario augmentation components that act as mediators between raw sensor data and final risk assessment. These intermediary components break down the complex task of risk identification into manageable stages, improving overall measurement precision while organizing system complexity into modular functional blocks.
2Reliability
If counterfactual scenario augmentation is applied to determine driver intentions, then reliability is improved, but loss of time increases
Solution Approach 1:
The system applies counterfactual scenario augmentation selectively rather than exhaustively, focusing on the most relevant scenarios for determining driver intentions in complex situations. By applying partial augmentation only where needed rather than to all possible scenarios, the system improves reliability for critical decisions while minimizing unnecessary processing time for routine situations.
3Adaptability or versatility
If monocular image data is analyzed to enable autonomous navigation, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent introduces semantic waypoint labeling as an intermediary processing layer that enhances monocular image analysis. By inserting this intermediate semantic understanding stage, the system improves adaptability to various environments while compensating for the inherent depth perception limitations of monocular cameras through learned semantic relationships and contextual cues.
Data Source
AI summary
A system and method for completing risk object identification that include receiving image data associated with a monocular image of a surrounding environment of an ego vehicle and analyzing the image data and completing semantic waypoint labeling of at least one region of the surrounding environment of the ego vehicle. The system and method also include completing counterfactual scenario augmentation with respect to the at least one region. The system and method further include determining at least one driver intention and at least one driver response associated with the at least one region.