Accurate velocity estimation is key to vehicle control. While the literature describes how model-based and learning-based observers are able to estimate a vehicle's velocity in normal driving conditions, the challenge remains to estimate the velocity in near-limits maneuvers while using only conventional in-car sensors. In this paper, we introduce a novel neural network architecture based on Long Short-Term Memory (LSTM) networks to accurately estimate the vehicle's velocity in different driving conditions, including maneuvers at the limits of handling. The approach has been tested on real vehicle data and it provides more accurate estimations than state-of-the-art model-based and learning-based methods, for both regular and near-limits driving scenarios. Our approach is robust since the performance of the state-of-the-art observers deteriorates with higher dynamics, while our method adapts to different maneuvers, providing accurate estimations even at the vehicle's limits of handling.

翻译：准确的车辆速度估计是车辆控制的关键。现有文献描述了基于模型和基于学习的观测器如何在正常行驶条件下估计车辆速度，但如何仅使用传统车载传感器在接近极限的操控工况下估计速度仍是一个挑战。本文提出了一种基于长短期记忆网络的新型神经网络架构，能够在不同驾驶条件下（包括极限操控工况）准确估计车辆速度。该方法已在真实车辆数据上进行了测试，在常规和极限驾驶场景下均能提供比现有基于模型和基于学习的方法更精确的估计。我们的方法具有鲁棒性——现有最先进观测器的性能会随着动力学复杂度升高而退化，而我们的方法能适应不同操控模式，即使在车辆操控极限工况下也能提供准确估计。