基于车辆动力学的弯道安全控制驾驶员综合性能指标研究

Gaetano Bosurgi, Orazio Pellegrino, Alessia Ruggeri, Giuseppe Sollazzo
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摘要

道路线形设计依赖于车辆动力学变量的知识。然而,它假设驾驶员在直道和弯道上忠实地遵循车道轴。偏离这一假设将导致意想不到的结果,并可能严重影响用户的安全。在这种情况下,车速和纵向加速度作为国际标准的关键参考,发挥着至关重要的作用。它们提供了对关键驱动方面的见解;因此,有必要彻底分析他们的真实趋势。广泛的数据收集运动应得出综合指标,以便突出理想动态与实际动态之间的最终重大偏差。为了实现这一目标,作者提出了通过AVSimulation在Sim-Easy驾驶模拟器上进行实验研究得出的一些指标。重要的是,这些指标可以不受限制地自由应用于实际驾驶场景。在四条不同的水平曲线上对这些指标进行了测试,结果证明这些指标能够有效识别与纵向加速度和速度相关的相关特征。展望未来,通过分析真实道路上众多驾驶环境的类似数据,基础设施管理人员可以使用这种方法来识别那些对用户安全更容易受到攻击的路段。此外,从传感器收集的数据,使用这些指标进行处理,可以在特定道路上行驶时(通过ADAS工具)过滤并传输给用户,以及时提供潜在困难的警告。这些指标根据标准的规定来控制某一几何元素上的物理变量(加速度或速度)。例如,加速度指标相对于阈值进行归一化,而速度指标的结果取决于几何元素的末端控制点之间的差异。在这两种情况下,国际规则报告规定的或推荐的参考值,因此分析人员立即意识到操作中的任何关键问题。
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Synthetic Drivers’ Performance Measures Related to Vehicle Dynamics to Control Road Safety in Curves
The road alignment design relies on the knowledge of vehicle dynamics variables. However, it assumes that drivers faithfully follow the lane axis on straights and curves. Deviating from this assumption leads to unexpected outcomes and can significantly impact users’ safety. In this context, vehicle speed and longitudinal acceleration play a crucial role as key references in the international standards. They provide insights into critical driving aspects; therefore, it is essential to thoroughly analyze their real trends. Broad data collection campaigns should derive synthetic indicators in order to highlight eventual significant deviations between the ideal and real dynamics. To achieve this objective, the authors propose some indexes deduced during an experimental study with a Sim-Easy driving simulator, by AVSimulation. Importantly, these indicators can be freely applied in real driving scenarios without limitations. These indexes were tested on four different horizontal curves and proved effective in identifying relevant characteristics related to longitudinal acceleration and speed. Looking ahead, by analyzing similar data for numerous driving contexts on real roads, infrastructure managers could use this methodology to identify those sections with increased vulnerability for users’ safety. Moreover, the collected data from sensors, processed using these indicators, can be filtered and transmitted to users (via ADAS tools) while driving on a specific road to provide timely warnings about potential difficulties. The indicators control the physical variable (acceleration or speed) on a certain geometric element with reference to what is prescribed by the standard. For example, the acceleration indicators are normalized with respect to a threshold value while for speed indexes, the result depends on the difference between the end control points of the geometrical element. In both cases, international regulations report prescribed or recommended reference values, so the analyst is immediately aware of any critical issues in the maneuver.
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