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Comfort-oriented driving: performance comparison between human drivers and motion planners
arXiv - EE - Systems and Control Pub Date : 2023-01-25 , DOI: arxiv-2301.10538
Yanggu Zheng, Barys Shyrokau, Tamas Keviczky

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.

中文翻译:

以舒适为导向的驾驶:人类驾驶员和运动规划者之间的性能比较

运动规划是自动驾驶车辆的基本组成部分。它影响乘坐的舒适性和时间效率。尽管有大量研究致力于提高自动驾驶汽车的运动舒适度,但很少有人关注将人类驾驶员的表现作为基准。在本文中,我们提出了一项使用仪表车辆在公共道路上进行的实验研究,以研究人类驾驶员如何平衡舒适性和时间效率。将人类驾驶数据与我们过去开发的两个基于优化的运动规划器进行比较。在行程时间没有差异的情况下,与最小化加速度的运动规划器相比,人类驾驶员在纵向和横向加速度信号中产生的能量平均多 23.5%。就与晕动病发生率相关的频率加权加速能量而言,平均性能缺陷上升至 70.2%。频域分析表明,人类驾驶员在 0.2-1 Hz 的频率范围内表现出更多的纵向振荡,在高达 0.2 Hz 的频率范围内表现出更多的横向振荡。这反映在时域数据特征中,例如不太平滑的速度曲线和长转弯的较高速度。性能差异的部分原因还在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其他因素。本研究中收集的驾驶数据为运动规划算法提供了一个性能基准,可以与之进行比较,并可以进一步利用它来加深对人类驾驶员的理解。一项与晕动病发病率相关的指标,平均表现不足上升至 70.2%。频域分析表明,人类驾驶员在 0.2-1 Hz 的频率范围内表现出更多的纵向振荡,在高达 0.2 Hz 的频率范围内表现出更多的横向振荡。这反映在时域数据特征中,例如不太平滑的速度曲线和长转弯的较高速度。性能差异的部分原因还在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其他因素。本研究中收集的驾驶数据为运动规划算法提供了一个性能基准,可以与之进行比较,并可以进一步利用它来加深对人类驾驶员的理解。一项与晕动病发病率相关的指标,平均表现不足上升至 70.2%。频域分析表明,人类驾驶员在 0.2-1 Hz 的频率范围内表现出更多的纵向振荡,在高达 0.2 Hz 的频率范围内表现出更多的横向振荡。这反映在时域数据特征中,例如不太平滑的速度曲线和长转弯的较高速度。性能差异的部分原因还在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其他因素。本研究中收集的驾驶数据为运动规划算法提供了一个性能基准,可以与之进行比较,并可以进一步利用它来加深对人类驾驶员的理解。
更新日期:2023-01-26
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