Standards for passenger comfort in automated vehicles: Acceleration and jerk

Winkel, Ksander N. de; Irmak, Tugrul; Happee, Riender; Shyrokau, Barys

doi:10.1016/j.apergo.2022.103881

Cited by 62 publications

(20 citation statements)

References 22 publications

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“…In particular, no effect of longitudinal acceleration was seen on subjective evaluations. This lack of an effect of longitudinal acceleration on evaluation of comfort is in contrast to previous studies (Bae et al, 2019;de Winkel et al, 2023). This may be explained by the geometry of the simulated road used in this study, with the curved road sections necessitating many lateral and rotational manoeuvres.…”

Section: Discussioncontrasting

confidence: 79%

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User comfort and naturalness of automated driving: The effect of vehicle kinematics and proxemics on subjective response

Peng,

Wei,

Solernou

et al. 2023

Preprint

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Higher-level Automated Vehicles (AVs, SAE Level 4+) need to provide a comfortable user experience to enhance public acceptance. AV driving styles, characterised by vehicle kinematics and proxemics, affect user comfort, with “human-like” driving styles expected to provide natural feelings to further improve user comfort. This study investigated how the kinematic and proxemic characteristics of an AV’s driving style affect user comfort and naturalness of a ride. The similarities in automated and users’ own manual driving style, and how these similarities affect evaluations, was also investigated. Using a motion-based driving simulator, participants experienced three Level 4 automated driving styles (Defensive, Aggressive, and Machine-Learning based), and a manual drive. Participants provided ratings (separately) for comfort and naturalness of each automated controller, as it negotiated twenty-four UK road sections, with varying geometric and roadside features. Linear mixed-effects models were used to examine the effect of kinematics and proxemics of the AV’s driving style, on subjective evaluation of comfort and naturalness of the ride, and how similarities between users’ own driving style and that of the AV affected riders’ evaluation. Results showed that the AV controllers’ lateral and rotational kinematics significantly influenced both comfort and naturalness, while longitudinal jerk only affected comfort. The Euclidean distance in a range of kinematics, characterising similarities between manual and automated driving styles, had varied effects on subjective evaluations. This research facilitates understanding how control features of AVs affect user experience, which will contribute to designing more user-centred controllers, leading to better acceptance of higher-level AVs.

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Section: Discussioncontrasting

confidence: 79%

“…Furthermore, the association between longitudinal jerk and comfort was found to be positive. This contrasts with the findings of Bellem et al (2018), who suggested minimising jerk for acceleration and deceleration manoeuvres, but aligns with the results of de Winkel et al (2023). The latter found a similarly…”

Section: Discussioncontrasting

confidence: 47%

User comfort and naturalness of automated driving: The effect of vehicle kinematics and proxemics on subjective response

Peng,

Wei,

Solernou

et al. 2023

Preprint

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“…An accurate description of this indicator can be obtained through the use of dynamic driving simulators from empirical data. Authors in [56] proposed a study to obtain a clear parametrisation of expected discomfort given acceleration pulses. The thresholds found on accelerations and jerks, based on their direction, are crucial to develop motion planning algorithms.…”

Section: Roundabout Configuration Influenced By Cavsmentioning

confidence: 99%

Roundabouts: Traffic Simulations of Connected and Automated Vehicles—A State of the Art

Campi,

Mastinu,

Previati

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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The paper deals with traffic simulation within roundabouts when both "connected and automated vehicles" (CAVs) and human-driven cars are present. The aim is to present the past, current and future research on CAVs running into roundabouts within the Cooperative, Connected and Automated Mobility (CCAM) framework. Both microscopic traffic simulations and virtual reality simulations by dynamic driving simulators will be considered. The paper is divided into five parts. At first, the literature is analysed using the Systematic Literature Review (SLR) methodology based on Scopus database. Secondly, the influence of CAVs on roundabout-specific design features and configuration is analysed. Gap-acceptance models used to define the capacity of the roundabout, one of its most important key performance indicators, are also presented. Third, the most common simulation software are described and analysed in terms of traffic demand implementation. Then the communication approaches and path management algorithms are studied. An example is proposed on the integration of microscopic traffic simulations and dynamic driving simulators virtual reality simulations. Finally, car following models suitable for roundabout traffic are discussed. There is still a gap between simulations and actual experience. There are reasonable doubts on how modelling and optimizing CAVs' behaviour into roundabouts in view of CCAM. It seems that Cooperative, Connected and Automated Vehicles (CCAVs), more than simply Connected and Automated Vehicles (CAVs), could optimise traffic flow, safety and driving comfort within the roundabout. A very promising technology for traffic simulation within the roundabout seems the one based on dynamic driving simulators.

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“…Passenger comfort has been studied thoroughly, especially for automated vehicles, as it can affect the adoption of autonomous vehicles. Repetitive exposure to low-frequency motions can develop motion sickness [ 58 ], and regular exposure to high-frequency motions can lead to lower back pain [ 59 , 60 ]. The jerk can be used to sense these discomforts and sudden acceleration changes and ultimately optimize the autonomous vehicle’s behavior to ensure comfortable driving.…”

Section: Rl-based Merging Strategymentioning

confidence: 99%

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning

Irshayyid

Chen

2023

Sensors

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The time that a vehicle merges in a lane reduction can significantly affect passengers’ safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology.

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Standards for passenger comfort in automated vehicles: Acceleration and jerk

Cited by 62 publications

References 22 publications

User comfort and naturalness of automated driving: The effect of vehicle kinematics and proxemics on subjective response

User comfort and naturalness of automated driving: The effect of vehicle kinematics and proxemics on subjective response

Roundabouts: Traffic Simulations of Connected and Automated Vehicles—A State of the Art

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning

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