Autonomous Driving Relational Inference for Interpretable Decisions
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Solution Overview
Problem
Existing autonomous driving systems lack human-interpretability, making it difficult for humans to understand and anticipate the actions taken by these systems, which is essential for effective cooperation on the road.
Innovation Solution
A grounded interpretable relational inference (GIRI) model is introduced, which represents dynamic systems using graph-based representations. This model includes a reward decoder that evaluates reward functions defined based on human domain knowledge, allowing for interpretable autonomous driving decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learning-based methods are used for autonomous driving, then substantial progress is achieved in perception and control, but human-interpretability deteriorates making it difficult for humans to understand system actions
Solution Approach 1:
The patent introduces an intermediary representation layer between the learning-based perception/control modules and the human operator. This intermediary uses graph-based representations and relational inference to translate complex sensor data and system decisions into human-understandable formats, preserving interpretability while maintaining the performance benefits of deep learning
Solution Approach 2:
The autonomous driving system is segmented into distinct functional modules (perception, relational inference, decision-making, control) that can be independently understood and analyzed. Each module processes specific aspects of the driving task, allowing humans to trace decisions through discrete functional boundaries rather than treating the system as an opaque black box
2Adaptability or versatility
If complex machine learning models are deployed for autonomous operation, then system capabilities are enhanced, but ease of operation deteriorates as humans cannot anticipate system actions
Solution Approach 1:
The system employs visual metaphors and annotated representations that change appearance based on their significance or state, making abstract concepts tangible. For example, graph nodes representing detected objects may be highlighted or colored according to their relevance to upcoming decisions, allowing humans to quickly grasp system priorities and intentions
Solution Approach 2:
The system provides continuous feedback to human operators about its internal state, reasoning process, and anticipated actions. This feedback loop allows humans to understand system behavior in real-time, anticipate future actions, and intervene when necessary, thereby maintaining ease of operation despite enhanced autonomous capabilities
Data Source
AI summary
In some examples, a dynamic system, including a vehicle, may be represented using a graph-based representation. One or more nodes in the graph-based representation may correspond to one or more agents in the dynamic system, and one or more edges between the nodes in the graph-based representation may correspond to one or more interactions between the agents in the dynamic system. The interactions may be defined based on human domain knowledge of the dynamic system. The dynamic system may be modeled using a respective machine learning model that includes a reward decoder that operates on the graph-based representation and evaluates one or more reward functions for the dynamic system. The one or more reward functions may be defined based on the human domain knowledge of the dynamic system. Autonomous operation of the vehicle may be controlled based on the modeling of the dynamic system.