Inverse Reinforcement Learning for Minimum-Exposure Paths in Spatiotemporally Varying Scalar Fields
This addresses data synthesis for autonomous vehicle reliability analysis, but it is incremental as it applies an existing IRL method to a specific domain problem.
The paper tackles the problem of synthesizing datasets of minimum-exposure paths for autonomous vehicles in spatiotemporally varying threat fields, using an inverse reinforcement learning model. The result shows excellent performance in synthesizing paths for unseen initial conditions and low error on unseen threat fields, with the model able to generate distinct datasets from different training sets.
Performance and reliability analyses of autonomous vehicles (AVs) can benefit from tools that ``amplify'' small datasets to synthesize larger volumes of plausible samples of the AV's behavior. We consider a specific instance of this data synthesis problem that addresses minimizing the AV's exposure to adverse environmental conditions during travel to a fixed goal location. The environment is characterized by a threat field, which is a strictly positive scalar field with higher intensities corresponding to hazardous and unfavorable conditions for the AV. We address the problem of synthesizing datasets of minimum exposure paths that resemble a training dataset of such paths. The main contribution of this paper is an inverse reinforcement learning (IRL) model to solve this problem. We consider time-invariant (static) as well as time-varying (dynamic) threat fields. We find that the proposed IRL model provides excellent performance in synthesizing paths from initial conditions not seen in the training dataset, when the threat field is the same as that used for training. Furthermore, we evaluate model performance on unseen threat fields and find low error in that case as well. Finally, we demonstrate the model's ability to synthesize distinct datasets when trained on different datasets with distinct characteristics.