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 only considers known objects in the perceived world for driving decisions, then the decision-making process is simpler and faster, but the safety is compromised due to undetected unknown objects
Solution Approach 1:
The system performs preliminary actions by generating hypothetical dark objects in unperceived areas before making driving decisions. The world model proactively populates potential unknown objects based on environmental cues and sensor data, allowing the planning system to account for these possibilities in advance rather than reacting to actual detections only.
Solution Approach 2:
The world model acts as an intermediary between sensor perception and driving decision-making. It bridges the gap by incorporating both known observed objects and hypothetical dark objects into a unified representation, allowing the planning system to make decisions based on this comprehensive model without directly processing raw sensor data.
2Loss of information
If the AV system expands sensor coverage to detect more objects, then the awareness of unknown objects improves, but the computational load and system complexity increase
Solution Approach 1:
Instead of expanding physical sensor coverage, the system performs preliminary computational action by generating hypothetical dark objects in unperceived areas. The world model proactively infers potential objects based on environmental cues, road geometry, and sensor data, effectively extending awareness without additional hardware.
Solution Approach 2:
The system creates computational copies of potential objects (dark objects) in the world model based on observed patterns and environmental context. These hypothetical objects are generated as probabilistic representations that mirror real objects might appear, allowing the system to plan for their potential presence without physically detecting them.
3Reliability
If the AV system conservatively plans for all possible unknown objects, then the safety improves, but the productivity and driving efficiency decrease
Solution Approach 1:
The system applies local quality by generating dark objects selectively in specific unperceived areas rather than uniformly across all space. Dark objects are hypothesized in regions where environmental cues suggest their likelihood (e.g., behind occlusions, in blind spots) while leaving other areas without hypothetical objects, allowing efficient resource allocation.
Solution Approach 2:
The system performs partial action by generating only the necessary number of dark objects required for safe planning rather than considering all possible objects everywhere. The world model balances between being sufficiently conservative to ensure safety and avoiding excessive hypothetical objects that would unnecessarily constrain driving behavior.
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.