Predictive Safeguarding and Performance Monitoring for Learning-based Vehicle Motion Controllers

A major issue with learning-based control methods is their lack of generalization in unseen parts of the state space. They tend to exhibit inefficient control performances or even complete failure in situations that were not encountered during the training process. This limitation is highly problematic in safety-critical applications, such as autonomous driving, where reliability and safety are essential. To address this issue, we propose a prediction-based safeguarding and performance monitoring framework for learning-based vehicle motion controllers. The proposed framework ensures a fail-safe execution during runtime and additionally intervenes if the predicted performance of the controller is safe but inefficient due to poor generalization capabilities. More specifically, in our framework, the behavior of the learning-based controller is predicted for a finite time horizon. If predictions are unsafe, a fallback controller is activated that ensures vehicle safety. If predictions are safe, the fallback controller’s behavior is also predicted and compared to that of the learning-based controller. Afterwards, the more efficient controller is applied to the vehicle, which prevents the learning-based controller from executing safe but inefficient control behaviors. Simulation results for path following control applications demonstrate that the proposed framework is able to ensure vehicle safety at all times and that it is more efficient compared to the sole use of the fallback controller.

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