Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim^® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic^®. The use of both Trucksim^® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

中文翻译：

---

通过深度学习方法同时估算车辆侧倾角和侧滑角。

目前，自动驾驶汽车正在上升，并有望在未来几年内出现在道路上。从这个意义上说，有必要对其状态有足够的了解，以设计能够在所有驾驶情况下提供足够性能的控制器。侧滑角和侧倾角是车辆横向稳定性的关键参数。后者对重心较高的车辆（例如卡车，公共汽车和工业车辆等）具有很大的影响，因为它们容易发生侧翻。由于用于直接测量这些角度的电流传感器的成本很高，因此许多研究都集中在估计它们上。缺点之一是车辆是强大的非线性系统，需要能够解决此特征的特定方法。人工智能模型的发展，例如构成深度学习范例的复杂的人工神经网络架构，已显示出为复杂和非线性控制问题提供出色的性能。在本文中，作者提出了一种基于深度学习的廉价但功能强大的模型，用于同时估计量产车辆中的侧倾角和侧倾角。该模型使用可直接从车载传感器获取的输入信号，例如纵向和横向加速度，转向角以及侧倾和横摆率。使用Trucksim提供的数十万数据对模型进行了训练 作者提出了一种基于深度学习的廉价但功能强大的模型，用于同时估计量产车辆中的侧倾角和侧滑角。该模型使用可直接从车载传感器获取的输入信号，例如纵向和横向加速度，转向角以及侧倾和偏航率。使用Trucksim提供的数十万数据对模型进行了训练 作者提出了一种基于深度学习的廉价但功能强大的模型，用于同时估计量产车辆中的侧倾角和侧滑角。该模型使用可直接从车载传感器获取的输入信号，例如纵向和横向加速度，转向角以及侧倾和偏航率。使用Trucksim提供的数十万数据对模型进行了训练^®以及使用来自诸如VBOX3i双天线从Racelogic的GPS使用校准的地面实况设备真实的驾驶操纵捕获的数据验证^®。同时使用Trucksim的^®软件和VBOX测量设备被认可并广泛应用在汽车领域，本文中所示的研究提供了可靠的数据。