For the autonomous operation of articulated vehicles at distribution centers, accurate positioning of the vehicle is of the utmost importance. Automation of these vehicle poses several challenges, e.g. large swept path, asymmetric steering response, large slide slip angles of non-steered trailer axles and trailer instability while reversing. Therefore, a validated vehicle model is required that accurately and efficiently predicts the states of the vehicle. Unlike forward driving, open-loop validation methods can not be used for reverse driving of articulated vehicles due to their unstable dynamics. This paper proposes an approach to stabilize the unstable pole of the system and compares three vehicle models (kinematic, non-linear single track and multibody dynamics model) against real-world test data obtained from low-speed experiments at a distribution center. It is concluded that single track non-linear model has a better performance in comparison to other models for large articulation angles and reverse driving maneuvers.

翻译：针对配送中心铰接式车辆的自主运行，精确定位车辆至关重要。此类车辆自动化面临诸多挑战，例如大扫掠路径、非对称转向响应、非转向挂车车轴的大侧偏角以及倒车时的挂车不稳定性。因此，需要建立一个经过验证的车辆模型，以准确高效地预测车辆状态。与前进驱动不同，由于铰接式车辆倒车时存在不稳定动力学特性，开环验证方法无法适用。本文提出了一种稳定系统不稳定极点的方法，并对比了三种车辆模型（运动学模型、非线性单轨模型和多体动力学模型）与配送中心低速实验中获取的真实测试数据。结果表明，在大折角及倒车工况下，非线性单轨模型相较于其他模型具有更优的性能表现。