Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces
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The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a difficult problem, especially for slippery terrains. To address this issue we propose a new approach that learns a surface-aware dynamics model by conditioning it on a latent variable vector storing surface information about the current location. A latent mapper is trained to update these latent variables during inference from multiple modalities on every traversal of the corresponding locations and stores them in a map. By training everything end-to-end with the loss of the dynamics model, we enforce the latent mapper to learn an update rule for the latent map that is useful for the subsequent dynamics model. We implement and evaluate our approach on a real miniature electric car. The results show that the latent map is updated to allow more accurate predictions of the dynamics model compared to a model without this information. We further show that by using this model, the driving performance can be improved on varying and challenging surfaces.
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