The ability to engage in other activities during the ride is considered by consumers as one of the key reasons for the adoption of automated vehicles. However, engagement in non-driving activities will provoke occupants' motion sickness, deteriorating their overall comfort and thereby risking acceptance of automated driving. Therefore, it is critical to extend our understanding of motion sickness and unravel the modulating factors that affect it through experiments with participants. Currently, most experiments are conducted on public roads (realistic but not reproducible) or test tracks (feasible with prototype automated vehicles). This research study develops a method to design an optimal path and speed reference to efficiently replicate on-road motion sickness exposure on a small test track. The method uses model predictive control to replicate the longitudinal and lateral accelerations collected from on-road drives on a test track of 70 m by 175 m. A within-subject experiment (47 participants) was conducted comparing the occupants' motion sickness occurrence in test-track and on-road conditions, with the conditions being cross-randomized. The results illustrate no difference and no effect of the condition on the occurrence of the average motion sickness across the participants. Meanwhile, there is an overall correspondence of individual sickness levels between on-road and test-track. This paves the path for the employment of our method for a simpler, safer and more replicable assessment of motion sickness.

翻译：乘客在乘车过程中从事其他活动的能力被消费者视为采用自动驾驶车辆的关键原因之一。然而，从事非驾驶活动会引发乘员的运动病，降低其整体舒适度，从而危及自动驾驶的接受度。因此，通过参与者实验来扩展我们对运动病的理解并揭示影响其调节因素至关重要。目前，大多数实验在公共道路（真实但不可复现）或测试轨道（适用于原型自动驾驶车辆）上进行。本研究开发了一种设计最优路径与速度参考的方法，可在小型测试轨道上高效复现道路驾驶中的运动病暴露。该方法采用模型预测控制，在70米×175米的测试轨道上复现从道路驾驶中采集的纵向与横向加速度数据。我们开展了受试者内实验（47名参与者），通过交叉随机化设计对比测试轨道与道路条件下乘员的运动病发生率。结果表明，两种条件下参与者的平均运动病发生率无差异且无条件效应。同时，个体在道路与测试轨道上的不适程度呈现整体对应性。这为采用本方法进行更简单、更安全且更具可复现性的运动病评估铺平了道路。