Advanced driving assistance systems (ADAS) are primarily designed to increase driving safety and reduce traffic congestion without paying too much attention to passenger comfort or motion sickness. However, in view of autonomous cars, and taking into account that the lack of comfort and motion sickness increase in passengers, analysis from a comfort perspective is essential in the future car investigation. The aim of this work is to study in detail how passenger’s comfort evaluation parameters vary depending on the driving style, car or road. The database used has been developed by compiling the accelerations suffered by passengers when three drivers cruise two different vehicles on different types of routes. In order to evaluate both comfort and motion sickness, first, the numerical values of the main comfort evaluation variables reported in the literature have been analyzed. Moreover, a complementary statistical analysis of probability density and a power spectral analysis are performed. Finally, quantitative results are compared with passenger qualitative feedback. The results show the high dependence of comfort evaluation variables’ value with the road type. In addition, it has been demonstrated that the driving style and vehicle dynamics amplify or attenuate those values. Additionally, it has been demonstrated that contributions from longitudinal and lateral accelerations have a much greater effect in the lack of comfort than vertical ones. Finally, based on the concrete results obtained, a new experimental campaign is proposed.

Ride comfort improvement in driving scenarios is gaining traction as a research topic. This work presents a direct methodology that utilizes measured car signals and combines data processing techniques and machine learning algorithms in order to identify driver actions that negatively affect passenger motion sickness. The obtained clustering models identify distinct driving patterns and associate them with the motion sickness levels suffered by the passenger, allowing a comfort-based driving recommendation system that reduces it. The designed and validated methodology shows satisfactory results, achieving (from a real datasheet) trained models that identify diverse interpretable clusters, while also shedding light on driving pattern differences. Therefore, a recommendation system to improve passenger motion sickness is proposed.

Rapid development in technology has expanded research areas in the automotive industry, with ride comfort being one of them. The purpose of this work is to discern certain driving actions that affect motion sickness. Moreover, the effect of road types on motion sickness has been characterized and evaluated. With this goal, the work presents a generalization of a data processing and clustering methodology that uses car signals related to driver actions. This model is able to successfully achieve interpretable clusters even if different road types are considered. As a final result, we propose a recommendation system based on driving patterns that can be contextualized for different road types.

The passenger’s comfort is of vital importance in today’s car, even more so in the future autonomous car. Its thorough analysis requires the use of objective measures, but the subjectivity of the passenger must also be considered. The relationship between both aspects is an uncommon research avenue, and could greatly benefit from the use of a vehicle simulator. This study use a simulator to replicate a journey with diverse characteristics that numerous passengers may experience (including engaging in different activities). The appropriate variables for a proper comfort analysis are determined in this study, resulting in a comprehensive database for the same purpose. Additionally, a passenger comfort based questionnaire is developed and applied to obtain the subjective assessment of different passengers. The results of these questionnaires help not only in the identification and study of the self perception of comfort by the passengers but also in the use of the simulator for comfort experiments. The detailed comparison between both the objective approach of signal analysis and the subjective approach of questionnaires that is performed thanks to the simulator creates a foundation for future research and narrows the existing research gap.

Motion sickness poses a significant challenge in vehicle dynamics, impacting passenger comfort and usability. This study presents a robust framework for understanding motion sickness and its origins through advanced modeling and interpretability techniques. The proposed approach leverages both classical and modern algorithms, with CatBoost emerging as the top-performing model after optimization. To address the interpretability of these models, SHAP methods are employed to extract actionable insights, revealing key factors contributing to motion sickness. CatBoost also demonstrates superior interpretability by providing balanced and meaningful feature importance distributions. The combination of advanced performance and interpretability allowed the model to accurately detect diverse motion sickness-inducing scenarios and uncover critical risk patterns.

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