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.

翻译：自动驾驶车辆的安全部署依赖于其有效应对环境变化的能力。这可能需要在不同路面上进行操控，尤其是在湿滑地形上仍是一个难题。为解决此问题，我们提出了一种新方法，通过将动力学模型以潜变量向量为条件进行训练，该向量存储当前位置的表面信息。我们训练一个潜变量映射器，在推理过程中利用多模态信息每次经过对应位置时更新这些潜变量，并将其存储在地图中。通过以动力学模型的损失函数进行端到端训练，我们强制潜变量映射器学习一种潜变量地图的更新规则，该规则对后续的动力学模型有效。我们在真实的微型电动汽车上实现并评估了该方法。结果表明，与未使用该信息的模型相比，潜变量地图的更新使动力学模型的预测更加准确。我们进一步证明，利用该模型可以在多变且具有挑战性的路面上提升驾驶性能。