The knowledge of the states of a vehicle is a necessity to perform proper planning and control. These quantities are usually accessible through measurements. Control theory brings extremely useful methods -- observers -- to deal with quantities that cannot be directly measured or with noisy measurements. Classical observers are mathematically derived from models. In spite of their success, such as the Kalman filter, they show their limits when systems display high non-linearities, modeling errors, high uncertainties or difficult interactions with the environment (e.g. road contact). In this work, we present a method to build a learning-based observer able to outperform classical observing methods. We compare several neural network architectures and define the data generation procedure used to train them. The method is evaluated on a kinematic bicycle model which allows to easily generate data for training and testing. This model is also used in an Extended Kalman Filter (EKF) for comparison of the learning-based observer with a state of the art model-based observer. The results prove the interest of our approach and pave the way for future improvements of the technique.

翻译：车辆状态的准确认知是进行合理规划与控制的基础，这些量通常可通过测量获得。控制理论提供了极为有效的方法——观测器——来处理无法直接测量的量或受噪声干扰的测量值。经典观测器从数学模型推导而来。尽管卡尔曼滤波等观测器取得了成功，但当系统呈现高度非线性、建模误差、高不确定性或与环境（如路面接触）存在复杂交互时，其性能将受到限制。本文提出了一种构建基于学习的观测器的方法，该观测器能够超越经典观测方法。我们比较了多种神经网络架构，并定义了用于训练这些网络的数据生成流程。该方法在运动学自行车模型上进行了评估，该模型便于生成训练与测试数据。同时，在扩展卡尔曼滤波（EKF）中使用该模型，将基于学习的观测器与当前最先进的基于模型的观测器进行对比。实验结果证明了我们方法的有效性，并为该技术的未来改进铺平了道路。