Ergonomic Path Planning for Autonomous Vehicles-An Investigation on the Effect of Transition Curves on Motion Sickness

Siddiqi, Muhammad Rehan; Milani, Sina; Jazar, Reza N.; Marzbani, Hormoz

doi:10.1109/tits.2021.3067858

“…The distance is divided equally into N p intervals to find reference stations. The N p defined in (24) is further referred to as the planning horizon. Consequently, the nominal sampling time T s is T p /N p .…”

Section: Receding-horizon Approachmentioning

confidence: 99%

“…Most of the studies mentioned above do not explicitly consider motion sickness or accelerations in general and therefore have limited applicability in the dynamic driving scenarios that automated vehicles encounter. Although there has been an attempt to analyze the frequency components in the resulting acceleration patterns arising from the use of different transition curves [24].…”

Section: Introductionmentioning

confidence: 99%

Mitigating Motion Sickness with Optimization-based Motion Planning

Zheng¹,

Shyrokau²,

Keviczky³

2023

Preprint

0

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The acceptance of automated driving is under the potential threat of motion sickness. It hinders the passengers' willingness to perform secondary activities. In order to mitigate motion sickness in automated vehicles, we propose an optimization-based motion planning algorithm that minimizes the distribution of acceleration energy within the frequency range that is found to be the most nauseogenic. The algorithm is formulated into integral and receding-horizon variants and compared with a commonly used alternative approach aiming to minimize accelerations in general. The proposed approach can reduce frequency-weighted acceleration by up to 11.3% compared with not considering the frequency sensitivity for the price of reduced overall acceleration comfort. Our simulation studies also reveal a loss of performance by the recedinghorizon approach over the integral approach when varying the preview time and nominal sampling time. The computation time of the receding-horizon planner is around or below the realtime threshold when using a longer sampling time but without causing significant performance loss. We also present the results of experiments conducted to measure the performance of human drivers on a public road section that the simulated scenario is actually based on. The proposed method can achieve a 19% improvement in general acceleration comfort or a 32% reduction in squared motion sickness dose value over the best-performing participant. The results demonstrate considerable potential for improving motion comfort and mitigating motion sickness using our approach in automated vehicles.

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“…Most of the studies mentioned above do not explicitly consider motion sickness or accelerations in general and therefore have limited applicability in the dynamic driving scenarios that automated vehicles encounter. Although there has been an attempt to analyze the frequency components in the resulting acceleration patterns arising from the use of different transition curves [25].…”

Section: Introductionmentioning

confidence: 99%

Mitigating Motion Sickness With Optimization-Based Motion Planning

Zheng

¹

,

Shyrokau

²

,

Keviczky

³

2024

IEEE Trans. Intell. Veh.

0

View full text Add to dashboard Cite

The acceptance of automated driving is under the potential threat of motion sickness. It hinders the passengers' willingness to perform secondary activities. In order to mitigate motion sickness in automated vehicles, we propose an optimization-based motion planning algorithm that minimizes the distribution of acceleration energy within the frequency range that is found to be the most nauseogenic. The algorithm is formulated into integral and receding-horizon variants and compared with a commonly used alternative approach aiming to minimize accelerations in general. The proposed approach can reduce frequency-weighted acceleration by up to 11.3% compared with not considering the frequency sensitivity for the price of reduced overall acceleration comfort. Our simulation studies also reveal a loss of performance by the recedinghorizon approach over the integral approach when varying the preview time and nominal sampling time. The computation time of the receding-horizon planner is around or below the realtime threshold when using a longer sampling time but without causing significant performance loss. We also present the results of experiments conducted to measure the performance of human drivers on a public road section that the simulated scenario is actually based on. The proposed method can achieve a 19% improvement in general acceleration comfort or a 32% reduction in squared motion sickness dose value over the best-performing participant. The results demonstrate considerable potential for improving motion comfort and mitigating motion sickness using our approach in automated vehicles.

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“…On the other hand, the comfort of autonomous driving, being an advanced requirement, also merits attention [5] [6]. Several researchers have investigated the comfort of autonomous driving in local path planning and vehicle control [7][8][9][10][11][12][13]. This includes integrating acceleration or acceleration values as constraints during control.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Driving Comfort Prediction: Improve Comfort through Global Path Planning

Huang,

Chen,

Liu

et al. 2024

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0

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Comfort is a pivotal consideration in the realm of autonomous driving, prompting researchers to address methods of enhancing driving comfort for autonomous driving vehicles. A central focus of current research revolves around the judicious planning of driving trajectories with a primary emphasis on comfort. Given the transient nature of comfort, prevailing path planning strategies predominantly center on local considerations. Nevertheless, it is imperative to recognize that macroscopic factors, including traffic flow and road conditions, wield a substantial influence on comfort. For instance, complex traffic scenarios elevate the likelihood of emergency braking, thereby affecting comfort. Consequently, investigating the intricate interplay between comfort and global path planning becomes essential. In the context of autonomous driving, this paper introduces a methodology and framework for predicting driving comfort by leveraging road information. The study establishes a comprehensive road information-comfort dataset and devises predictive models for driving comfort, employing a multi-head attention approach. The ensuing discussion elucidates the practical application of the model in path selection and planning through illustrative examples. Finally, real-road vehicle tests were conducted. Following the utilization of the path optimized by the model, the autonomous driving car exhibited an approximate 17.3% reduction in the mean longitudinal jerk and a 9.7% reduction in the mean lateral jerk. These results serve to validate the effectiveness and significance of the proposed model. Notably, this research marks the pioneering integration of driving comfort with global path planning, which is of immense importance for autonomous driving navigation systems, offering an insightful and valuable reference for related study.

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Ergonomic Path Planning for Autonomous Vehicles-An Investigation on the Effect of Transition Curves on Motion Sickness

Cited by 11 publications

References 58 publications

Mitigating Motion Sickness with Optimization-based Motion Planning

Mitigating Motion Sickness with Optimization-based Motion Planning

Mitigating Motion Sickness With Optimization-Based Motion Planning

Autonomous Driving Comfort Prediction: Improve Comfort through Global Path Planning

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