Evaluation for the Passenger Comfort in Automated Vehicles: Comparison
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With the development of intelligence and network connectivity, the development of the automotive industry is also moving toward intelligent systems. For passengers, the utility of intelligence is to achieve more convenience and comfort. The intelligent cockpit is the place where passengers directly interact with the car, which directly affects the experience of passengers in the car. For the intelligent cockpits that have emerged in recent years, it could be used for the evaluation of passenger comfort.

  • intelligent vehicle cockpit
  • human comfort
  • passenger experience
  • comprehensive evaluation model

1. Background

With the gradual maturing of automated driving technology, various types of autonomous vehicles are being used more and more. The original intention of intelligent driving technology was to improve traffic efficiency and reduce accidents [1]. In recent years, the comfort of intelligent vehicles has become a standard for evaluating their quality and has been paid more and more attention [2]. When choosing an intelligent vehicle, safety and comfort are the first factors to be considered which directly affect the acceptance and purchase degree of consumers [3,4,5][3][4][5]. There is a close relationship between the comfort of intelligent vehicles and passengers’ trust and acceptance of them [6,7][6][7]. In other words, improving comfort contributes to the popularity of intelligent vehicles. The comfort of the vehicle includes driving comfort and riding comfort. Driving comfort mainly exists in manual driving scenarios. As the level of automated driving is further improved from the L3 level, the vehicle cockpit will become more intelligent [8]. The driving task will be automatically taken over by the vehicle, and the driver’s driving status will be transformed into that of a normal passenger. Therefore, the vehicle cockpit will become the third space for work, play, and social interaction [9]. The comfort evaluation of the vehicle’s intelligent cockpit will also be transformed into the comfort evaluation of passengers. With the application of intelligent driving technology and the transformation of the driver’s identity, the interior of the vehicle cockpit will be redesigned, and comfort research based on the passengers’ riding experience will be particularly important [10]. Therefore, it is necessary to establish intelligent vehicle comfort evaluation standards and models.

2. Passenger Comfort

The research on vehicle comfort has a long history, but there is no unified and clear definition of comfort in academia. Comfort is considered a state of relaxation, pleasure, and subjective feeling, according to The Merriam-Webster Dictionary. Richards pointed out that comfort is a subjective state in which people respond to the environment or a situation [11]. Slater defines comfort as a state of physical, psychological, and physiological harmony between a person and the environment [12].
The debate around comfort has largely centered on the understanding of the difference between comfort and discomfort. Overall, there are two interpretations of comfort. One view holds that comfort is two discrete states: comfort and discomfort. Typically, comfort is defined as the absence of discomfort [13]. Branton also considered comfort to be a state of lack of negativity that does not necessarily indicate positivity [14]. Summala also noted that comfort is pleasant and not experienced in the face of high arousal [15]. From this point of view, as long as the passengers in the vehicle do not feel uncomfortable, it can be considered comfortable. Therefore, passengers in a comfortable state may ignore the fact that they are in a car [16]. Another view of comfort argues that comfort and discomfort are not one-dimensional assumptions on the same continuous scale. Multiple studiones have shown that comfort and discomfort are affected by different variables; therefore, Zhang et al. pointed out that the main goal of studyingfocus on comfort is to distinguish variables related to comfort and discomfort [17,18][17][18]. Although there is no agreement on a definition of comfort, scholars generally agree that most definitions have in common: (1) comfort is subjective; (2) comfort is influenced by both internal and external factors; (3) comfort is a feeling for something or reaction in the environment [19].
In summary, although scholars have different views on the definition of comfort, they all agree that comfort is generally associated with positivity, relaxation, and pleasure. At the same time, comfort is also associated with the absence of discomfort and restlessness, according to perceptions of comfort. Therefore, passenger comfort in an automated vehicle can be considered as the passenger not feeling discomfort in the vehicle cockpit or is being a state of physical and psychological relaxation and pleasure.

3. Evaluation Criteria and Models for Passenger Comfort in Automated Vehicles

In an intelligent vehicle cockpit, the comfort of passengers will be affected by the internal and external environment. When evaluating passenger comfort in a vehicle cockpit, the occupant, vehicle, and cockpit should be considered as a system. Vibration and noise caused by the vehicle itself and the road, air movement, temperature in the cockpit, lighting conditions, seat ergonomics, and so etc.on, will affect the comfort of the passengers in the cockpit. In addition, individual passenger characteristics and sitting posture can also lead to differences in comfort or discomfort.
In traditional automobiles, the most common practice is to use the car seat as the object to measure the vehicle vibration, acceleration, and other indicators to determine comfort [20]. After the vertical vibration of the vehicle and the shifting shock are transmitted to the passengers, the discomfort felt by adults and children is also different [21,22][21][22]. Through dummy experiments and data statistics, the noise level in the vehicle can be obtained, which can then be used to establish the relationship between noise and comfort [23,24][23][24]. In addition, different road conditions and engine-induced noises have different effects on passenger comfort [25]. The vehicle acoustic comfort index can be used to build an optimization model. Therefore, the relationship between engine noise and vibration can be spaid attentudiedion, and the rules of acoustic comfort in passenger vehicle cockpits can also be obtained [26]. Cockpit temperature is also one of the main factors affecting passenger comfort. OneIt studywas found that the flow field and temperature field of the passenger compartment can affect the thermal comfort of the occupants [27]. Vehicles’ thermal comfort is affected by solar radiation, body heat insulation effects, average radiation temperature, and exposure time [28]. The infrared reflection treatment of vehicle glass can reduce the air temperature in the vehicle cockpit, which is also beneficial to improving passenger comfort and vehicle economy [29]. In addition, economical sensors can be used to monitor the temperature distribution in the cockpit, thereby improving thermal comfort [30,31][30][31]. At the same time, the lighting function in the cockpit cannot be ignored, which can improve the driver’s driving comfort and occupant reading [32]. The lighting in the cockpit is also affected by the instrumentation in the cockpit. If there is glare, and so etc.on, driving fatigue can easily result, which will lead to improper operation and traffic accidents [33]. With the development of automated driving technology, the ergonomic intelligent cockpit has been redesigned and rearranged with regard to ISO standards. There are more and more human–computer interaction functions in the intelligent cockpit. In addition to the traditional physical interface of the cockpit, the individual characteristics of the passengers, sitting posture, and so etc.on, all have an impact on comfort [34,35][34][35].
A combination of subjective and objective evaluation is generally used for vehicle comfort evaluation [19,36,37][19][36][37]. Using data measurement and passenger scoring, a passenger evaluation model for the ride comfort of a vehicle can be obtained. From the above literature review, it can be seen that there is a lack of models for comprehensive evaluation of the comfort of vehicle cockpits. With the development of intelligent vehicles, the intelligent cockpit has been redesigned and rearranged, and the passenger experience has been further improved, so it is more and more necessary to comprehensively evaluate the comfort of the intelligent cockpit.

References

  1. Vlakveld, W.; van Nes, N.; de Bruin, J.; Vissers, L.; van der Kroft, M. Situation awareness increases when drivers have more time to take over the wheel in a Level 3 automated car: A simulator study. Transp. Res. Part F Traffic Psychol. Behav. 2018, 58, 917–929.
  2. Du, Y.; Liu, C.; Li, Y. Velocity control strategies to improve automated vehicle driving comfort. IEEE Intell. Trans-Portation Syst. Mag. 2018, 10, 8–18.
  3. Paddeu, D.; Parkhurst, G.; Shergold, I. Passenger comfort and trust on first-time use of a shared autonomous shuttle vehicle. Transp. Res. Part C Emerg. Technol. 2020, 115, 102604.
  4. Terken, J.; Pfleging, B. Toward shared control between automated vehicles and users. Automot. Innov. 2020, 3, 53–61.
  5. Wang, C.; Zhao, X.; Fu, R.; Li, Z. Research on the comfort of vehicle passengers considering the vehicle motion state and passenger physiological characteristics: Improving the passenger comfort of autonomous vehicles. Int. J. Environ. Res. Public Health 2020, 17, 6821.
  6. Bellem, H.; Thiel, B.; Schrauf, M.; Krems, J.F. Comfort in automated driving: An analysis of preferences for different automated driving styles and their dependence on personality traits. Transp. Res. Part F Traffic Psychol. Behav. 2018, 55, 90–100.
  7. Siebert, F.W.; Oehl, M.; Höger, R.; Pfister, H.-R. Discomfort in automated driving–the disco-scale. In Proceedings of the International Conference on Human-Computer Interaction, Las Vegas, NV, USA, 21–26 July 2013; Springer: Berlin, Germany, 2013; pp. 337–341.
  8. Weigl, K.; Schartmüller, C.; Riener, A.; Steinhauser, M. Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): Data-driven models for Level 3 and Level 5 automated driving. Transp. Res. Part F Traffic Psychol. Behav. 2021, 83, 42–59.
  9. Tan, H.; Sun, J.H.; Guan, D.S.; Zhou, M.L.; Jian-Ping, Q.I.; Zhao, Y. Development Trend of Human-Computer Interaction in Intelligent Vehicles. Packag. Eng. 2019, 40, 32–42.
  10. Elbanhawi, M.; Simic, M.; Jazar, R. In the passenger seat: Investigating ride comfort measures in autonomous cars. IEEE Intell. Transp. Syst. Mag. 2015, 7, 4–17.
  11. Richards, L. On the Psychology of Passenger Comfort; Human Factors in Transport Research; Oborne, D.J., Levis, J., Eds.; Academic Press: New York, NY, USA, 1980; Volume 2.
  12. Slater, K. Human Comfort; CC Thomas: Springfield, IL, USA, 1985.
  13. Floyd, W.; Roberts, D. Anatomical and physiological principles in chair and table design. Ergonomics 1958, 2, 1–16.
  14. Braton, P. Behaviour, body mechanics and discomfort. Ergonomics 1969, 12, 316–327.
  15. Summala, H. Towards understanding motivational and emotional factors in driver behaviour: Comfort through satisficing. In Modelling Driver Behaviour in Automotive Environments; Springer: London, UK, 2007; pp. 189–207.
  16. Bishu, R.R.; Hallbeck, M.S.; Riley, M.W.; Stentz, T.L. Seating comfort and its relationship to spinal profile: A pilot study. Int. J. Ind. Ergon. 1991, 8, 89–101.
  17. Kamijo, K.; Tsujimura, H.; Obara, H.; Katsumata, M. Evaluation of seating comfort. SAE Trans. 1982, 91, 2615–2620.
  18. Zhang, L.; Helander, M.G.; Drury, C.G. Identifying factors of comfort and discomfort in sitting. Hum. Factors 1996, 38, 377–389.
  19. De Looze, M.P.; Kuijt-Evers, L.F.; van Dieen, J. Sitting comfort and discomfort and the relationships with objective measures. Ergonomics 2003, 46, 985–997.
  20. Vér, I.L.; Beranek, L.L. Noise and Vibration Control Engineering: Principles and Applications; John Wiley & Sons: Hoboken, NJ, USA, 2005.
  21. Sezgin, A.; Arslan, Y.Z. 782. Analysis of the vertical vibration effects on ride comfort of vehicle driver. J. Vibroeng. 2012, 14, 1392–8716.
  22. Wicher, J.; Wieckowski, D. Influence of vibrations of the child seat on the comfort of child’s ride in a car. Eksploatacja i Niezawodnosc-Maint. Reliab. 2010, 48, 102–110.
  23. Flor, D.; Pena, D.; Pena, L.; de Sousa, V.A.; Martins, A. Characterization of noise level inside a vehicle under different conditions. Sensors 2020, 20, 2471.
  24. da Silveira Brizon, C.J.; Medeiros, E.B. Combining subjective and objective assessments to improve acoustic comfort evaluation of motor cars. Appl. Acoust. 2012, 73, 913–920.
  25. Nor, M.J.M.; Fouladi, M.H.; Nahvi, H.; Ariffin, A.K. Index for vehicle acoustical comfort inside a passenger car. Appl. Acoust. 2008, 69, 343–353.
  26. Junoh, A.K.; Nopiah, Z.; Muhamad, W.; Nor, M.; Fouladi, M. An optimization model of noise and vibration in passenger car cabin. Adv. Mater. Res. 2012, 383, 6704–6709.
  27. Walgama, C.; Fackrell, S.; Karimi, M.; Fartaj, A.; Rankin, G. Passenger thermal comfort in vehicles-a review. Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 2006, 220, 543–562.
  28. Danca, P.; Vartires, A.; Dogeanu, A. An overview of current methods for thermal comfort assessment in vehicle cabin. Energy Procedia 2016, 85, 162–169.
  29. Devonshire, J.M.; Sayer, J.R. Radiant heat and thermal comfort in vehicles. Hum. Factors 2005, 47, 827–839.
  30. Martinho, N.; Silva, M.; Ramos, J. Evaluation of thermal comfort in a vehicle cabin. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 2004, 218, 159–166.
  31. Russi, L.; Guidorzi, P.; Pulvirenti, B.; Aguiari, D.; Pau, G.; Semprini, G. Air Quality and comfort characterisation within an electric vehicle cabin in heating and cooling operations. Sensors 2022, 22, 543.
  32. Khanh, T.Q. Lighting Quality for Automotive Lighting. Light Eng. 2014, 22, 59–63.
  33. Anant, S.; Veni, S. Safe driving using vision-based hand gesture recognition system in non-uniform illumination conditions. J. ICT Res. Appl. 2018, 12, 2.
  34. Oh, G.T.; Lee, S.Y.; Ko, J.J. Design and implementation of gesture recognition and safe driving assistance system using wearable band based on in-vehicle HUD system. J. Korean Inst. Commun. Inf. Sci. 2018, 43, 2192.
  35. Naddeo, A.; Cappetti, N.; Vallone, M.; Califano, R. New trend line of research about comfort evaluation: Proposal of a framework for weighing and evaluating contributes coming from cognitive, postural and physiologic comfort perceptions. In Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics AHFE 2014, Kraków, Poland, 19–23 July 2014.
  36. Naddeo, A.; Cappetti, N.; D’Oria, C. Proposal of a new quantitative method for postural comfort evaluation. Int. J. Ind. Ergon. 2015, 48, 25–35.
  37. Da Silva, M.G. Measurements of comfort in vehicles. Meas. Sci. Technol. 2002, 13, R41.
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