For autonomous driving or advanced driving assistance, it is key to monitor the vehicle dynamics behavior. Accurate models of this behavior include acceleration, but also the side-slip angle, that eventually results from the complex interaction between the tires and the road. Though it is an essential quantity (e.g. for stability assessment), as opposed to accelerations, it is not measurable through conventional off-the-shelf sensors. Therefore, accurate side-slip angle observers are necessary for the proper planning and control of vehicles. In this paper, we introduce a novel approach that combines model-based side-slip angle estimation with neural networks. We apply our approach to real vehicle data. We prove that the proposed method is able to outperform state-of-the-art methods for normal driving maneuvers, and for near-limits maneuvers where providing accurate estimations becomes challenging.

翻译：对于自动驾驶或高级驾驶辅助系统，监控车辆动力学行为至关重要。精确的动力学模型不仅包含加速度，还涉及最终由轮胎与路面复杂相互作用产生的侧偏角。尽管侧偏角是评估车辆稳定性等关键参数，但与加速度不同，该角度无法通过常规传感器直接测量。因此，针对侧偏角的精确估计算法对于车辆路径规划与控制至关重要。本文提出一种结合基于模型的侧偏角估计方法与神经网络的新型混合方案，并在实车数据上验证其有效性。实验证明，该方法在常规驾驶工况下能够超越现有最优方法，在估计精度极具挑战的近极限工况下也能保持优异性能。