Key Takeaways & Executive Findings
- •• Traditional metrics like maximum acceleration and jerk are insufficient to accurately evaluate passenger comfort in autonomous vehicles. • Motion complexity features significantly improve the explanation of passenger comfort compared to simple motion characteristics. • A real-time passenger comfort measurement using EMG and stepwise regression enables seamless data collection in naturalistic driving studies. • A machine learning-based method using only vehicle motion information can accurately estimate passenger comfort in real-time, highlighting the importance of motion complexity for future AV design.
Abstract
Traditionally, passenger comfort in vehicles is perceived as being most influenced by acceleration and jerk. Consequently, the current research primarily focuses on developing control algorithms to limit the maximum acceleration and jerk of the vehicle in order to improve passenger comfort. However, naturalistic driving studies demonstrate that such simple characteristics are insufficient for accurately evaluating passenger comfort. This study identifies motion complexity as a key factor of passenger comfort. A series of naturalistic driving studies are conducted, during which passenger comfort is assessed using a 5-point Likert scale. Moreover, a real-time passenger comfort measurement based on electromyography (EMG) and stepwise regression is proposed to facilitate seamless data collection. Time-series features representing motion complexity are then introduced to better describe passenger comfort. Hierarchical regression confirms that simple characteristics of motion are insufficient to explain passenger comfort, and shows that the proposed motion complexity features have a substantial effect on passenger comfort. Finally, a machine learning-based real-time passenger comfort estimation method is developed according to the foregoing findings. Experimental results show that the proposed method can accurately estimate passenger comfort in real-time using only vehicle motion information. The findings of this study suggest that vehicle motion complexity should be considered in future passenger comfort studies.
1. Introduction
In recent years, autonomous vehicles (AVs) have emerged as the new generation of transportation. Researchers and engineers from various fields are devoting their efforts to the development of AVs. Consequently, an increasing number of cutting-edge technologies have been applied to AVs. However, despite the rapid advancements in autonomous driving technology, the social acceptance and market share of AVs are not making significant progress, indicating that the performance of AVs does not meet the general public’s expectations in many aspects [1, 2]. In particular, the comfort level of AVs often falls short of consumers’ demands during the transition from conventional vehicles to autonomous vehicles [3]. Uncomfortable experiences can significantly diminish users’ trust and increase their perceived risk of AVs, hindering people from embracing this new technology. Therefore, there is an urgent need to improve passenger comfort for AVs.
Before the emergence of autonomous driving, studies on passenger comfort mainly focused on ride comfort, which is associated with the vibration and vertical motion of the chassis [4–6]. On the other hand, handling comfort, which is associated with the longitudinal and lateral motion of the vehicle, is seldom studied since it is mainly governed by the driver. In the era of autonomous driving, drivers are freed from the burden of handling the vehicle, which also means that AVs should be responsible for the handling comfort of passengers. Moreover, people are expecting improved passenger comfort in AVs compared to conventional vehicles due to the massive technologies implemented in AVs. This highlights a gap between the swift evolution of AVs and the scarcity of literature concerning the handling comfort of passengers.
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Titong Jiang, Jingyuan Li, Liang Ma, Xuewu Ji, Yahui Liu (2025). Passenger Comfort Assessment via Motion Complexity Analysis for Autonomous Vehicles. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01289-5
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Frequently Asked Questions
What is the main limitation of traditional passenger comfort metrics?
Traditional metrics like maximum acceleration and jerk are insufficient to accurately evaluate passenger comfort, as naturalistic driving studies show that simple characteristics do not capture the full complexity of comfort perception.
How is motion complexity defined in this study?
Motion complexity is identified as a key factor of passenger comfort, represented by time-series features that capture the intricate patterns of vehicle motion beyond simple acceleration and jerk.
What method is proposed for real-time passenger comfort measurement?
A real-time passenger comfort measurement based on electromyography (EMG) and stepwise regression is proposed to facilitate seamless data collection during naturalistic driving studies.
How does the proposed machine learning method estimate passenger comfort?
The machine learning-based method uses only vehicle motion information, including motion complexity features, to accurately estimate passenger comfort in real-time.
What are the implications of this study for autonomous vehicle design?
The findings suggest that vehicle motion complexity should be considered in future passenger comfort studies and in the development of control algorithms for autonomous vehicles to improve passenger comfort.
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