Abstract
Motion planning is a fundamental component in automated vehicles. It influences the comfort and time efficiency of the ride. Despite a vast collection of studies working towards improving motion comfort in self-driving cars, little attention has been paid to the performance of human drivers as a baseline. In this paper, we present an experimental study conducted on a public road using an instrumented vehicle to investigate how human drivers balance comfort and time efficiency. The human driving data is compared with two optimization-based motion planners that we developed in the past. In situations when there is no difference in travel times, human drivers incurred an average of 23.5% more energy in the longitudinal and lateral acceleration signals than the motion planner that minimizes accelerations. In terms of frequency-weighted acceleration energy, an indicator correlated with the incidence of motion sickness, the average performance deficiency rises to 70.2%. Frequency-domain analysis reveals that human drivers exhibit more longitudinal oscillations in the frequency range of 0.2-1 Hz and more lateral oscillations in the frequency range of up to 0.2 Hz. This is reflected in time-domain data features such as less smooth speed profiles and higher velocities for long turns. The performance difference also partly results from several practical matters and additional factors considered by human drivers when planning and controlling vehicle motion. The driving data collected in this study provides a performance baseline for motion planning algorithms to compare with and can be further exploited to deepen the understanding of human drivers.
Abstract (translated)
运动规划是自动汽车中的基本组件,它会影响驾驶的舒适性和效率。尽管研究致力于改善自动驾驶汽车的驾驶舒适性,但对人类驾驶员的表现的关注程度却非常少。在本文中,我们介绍了一项在公路中使用仪器车辆进行的实验研究,以研究人类驾驶员如何平衡舒适性和效率。对人类驾驶数据与过去开发的两个基于优化的运动规划进行比较。在旅行时间无差异的情况下,人类驾驶员在 longitudinal 和横向加速度信号中平均产生了 23.5% 的能量增量,比最小化加速度的运动规划算法多。在频率加权加速度能量方面,与晕车率相关的指标显示,平均表现缺陷增加到 70.2%。频域分析表明,人类驾驶员在 0.2-1 赫兹频率范围内表现出更多的纵向振荡,在 0.2 赫兹频率范围内表现出更多的横向振荡。这反映在时间域数据特征,例如更平滑的速度曲线和长时间的转向中更高的速度。表现差异部分源于几个实际问题以及人类驾驶员在规划和控制车辆运动时考虑的额外因素。本研究中收集的驾驶数据提供了运动规划算法的比较基准,可以进一步利用以加深人类驾驶员的理解。
URL
https://arxiv.org/abs/2301.10538
