Autonomous Vehicle Reachability Control for Safe State Transitions
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Solution Overview
Problem
As autonomous systems become increasingly complex and operate in unpredictable environments, current methods for ensuring correct operation become infeasible due to the need to analyze all possible states, which is impractical and inefficient.
Innovation Solution
A system and method that employs a neural network model for control signal generation, coupled with a reachability controller using an analytical model to rapidly determine safe or unsafe states, allowing for complete testing of all operating conditions and blocking unsafe transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network model is used for control signal generation in autonomous systems, then the system can handle complex and unpredictable environments, but the complexity of testing all possible states becomes infeasible
Solution Approach 1:
The system segments the control architecture into two distinct components: a neural network model for generating control signals and a reachability controller for safety verification. This segmentation allows the complex neural network to operate independently for adaptability while the separate reachability controller handles safety testing, thus resolving the contradiction between handling complex environments and managing testing complexity.
Solution Approach 2:
The reachability controller acts as an intermediary between the neural network model and the autonomous system. It receives control signals from the neural network, analyzes them using an analytical model, and determines safety before execution. This intermediary layer enables the system to leverage the neural network's adaptability while maintaining feasible safety testing through the analytical model.
2Reliability
If all possible states are analyzed to ensure correct operation, then system safety is improved, but the testing process becomes impractical and inefficient
Solution Approach 1:
The system creates a simplified analytical model as a copy or approximation of the complex neural network model. This analytical model is designed to be computationally tractable and allows for complete state space analysis. By using this analytical copy for safety verification while the neural network handles actual control, the system achieves both comprehensive safety analysis and testing efficiency.
3Speed
If a simpler analytical model is used for reachability analysis, then the calculation speed of states increases, but the model may not capture all system complexities
Solution Approach 1:
The system applies different levels of model fidelity to different functions: the neural network model provides high-fidelity representation for control signal generation, while the simpler analytical model provides sufficient fidelity for safety verification. This local differentiation of model quality allows fast calculation where simplicity suffices while maintaining accuracy where needed, resolving the contradiction between speed and precision.
Data Source
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AI summary
An autonomous system includes a vehicle operable to travel from a first point to a second point, a first actuator operable to adjust a speed of the vehicle, and a second actuator operable to adjust a direction of travel of the vehicle. A controller is operable to send control signals to the first actuator and the second actuator to facilitate the transition of the system from a first state to a second state during travel between the first point and the second point. A reachability controller is coupled to the controller to receive the first state and the control signals and to analyze the first state and the control signals to determine if the second state is a safe state.