Autonomous Vehicle World Model with Hypothetical Dark Objects
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Solution Overview
Problem
Autonomous vehicle systems face challenges in making safe driving decisions due to the inability to perceive and account for unknown objects in their environment, which can lead to potential collisions and unsafe scenarios.
Innovation Solution
The system generates a world model that includes hypothetical 'dark objects' in unperceived areas by determining boundaries between perceived and unperceived worlds, using data from sensors and road network databases to infer the presence and attributes of these objects, such as vehicles, pedestrians, and animals, and updates vehicle trajectories accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AV system relies only on sensor-perceived objects for driving decisions, then the system complexity remains low and processing is fast, but the safety and reliability deteriorate due to unknown objects in unperceived areas
Solution Approach 1:
The system performs preliminary actions by proactively generating hypothetical dark objects in unperceived areas before actual collisions or unsafe scenarios occur. Instead of reacting to perceived threats only, the system anticipates potential risks by populating the world model with hypothetical objects in sensor blind spots, allowing safer trajectory planning in advance.
Solution Approach 2:
The world model serves as an intermediary between sensor data and driving decisions. It mediates by integrating both perceived objects from sensors and hypothetical dark objects generated through probabilistic reasoning, creating a comprehensive representation that bridges the gap between limited sensor perception and the need for holistic environmental awareness.
2Reliability
If the AV system generates and processes hypothetical dark objects in unperceived areas, then the safety and reliability improve, but the computational complexity and processing time increase
Solution Approach 1:
The system applies partial action by generating dark objects selectively rather than uniformly across all unperceived areas. It focuses computational resources on regions where dark objects are most likely to exist based on probabilistic assessments, traffic patterns, and environmental context, rather than exhaustively processing every possible unperceived location.
Solution Approach 2:
The system changes parameters dynamically by adjusting the density and distribution of hypothetical dark objects based on situational context. In high-risk areas or conditions, more hypothetical objects are generated; in low-risk areas, fewer are generated, optimizing the balance between safety and computational efficiency.
3Reliability
If the AV system maintains a comprehensive world model including hypothetical objects, then the driving decisions become safer and more robust, but the data processing and model maintenance become more complex
Solution Approach 1:
The system segments the environmental model into perceived and unperceived regions, with further subdivision into different object categories and probability levels. This segmentation allows selective processing and updating of world model components based on their relevance and uncertainty, reducing overall processing complexity while maintaining comprehensive coverage.
Solution Approach 2:
The world model is maintained dynamically rather than statically. Hypothetical dark objects are continuously created, updated, and removed based on changing sensor data, vehicle trajectory, and environmental context. This dynamic maintenance allows the system to adapt to new information and reduce computational burden by eliminating obsolete hypothetical objects.
Data Source
AI summary
Among other things, a world model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the world model.