As mentioned in the previous section, comfort is one crucial human challenge for vehicle automation. To achieve high user-acceptance and market penetration in the domain of autonomous driving, the design of automated driving functions is crucial and should offer flexibility and adaptability (Griesche et al., 2016). The importance of driving comfort is highlighted by The European Road Transport Advisory Council (2017) next to safety and efficiency (Hartwich et al., 2018). However, there is still a
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lack of knowledge about how the driver wants to be driven and which manoeuvres are perceived as uncomfortable (Scherer et al., 2015).
Ride comfort is a major challenge in the development and acceptance of AVs (Kraus et al., 2010; Kuderer et al., 2015; Lefèvre, et al., 2015a). Ride comfort is a subjective concept understood as a state achieved by the removal or absence of uneasiness and distress. It is a subjective, pleasant state of relaxation given by confident and apparently safe vehicle operation (Constantin et al., 2014). A global definition including psychological aspects describes comfort as ‘a pleasant state of physiological, psychological and physical harmony between a human being and the environment’ (Hartwich et al., 2018). Although, when considering driver comfort, we must not omit safety precautions. Safety is far more important than comfort under any circumstance (Wu et al., 2009).
Comfort may vary considerably among drivers since human drivers adopt different driving styles based on their personality, age, gender, etc. (Kuderer et al., 2015; Powell and Palacín, 2015). Nevertheless, there have been many attempts in the literature to evaluate it and discover which factors affect it. Some of these factors are noise, temperature, air quality, car seat and motion, i.e. vibrations (Martin and Litwhiler, 2008; Constantin et al., 2014; Elbanhawi et al., 2015). Those form the traditional ergonomics factors (Figure 2.2). Vibration has been widely studied as a comfort measurement. Vibrations can be transmitted through the seat surface, backrest and through the floor and can occur in all 3 axes (longitudinal, lateral and vertical) (Park and Subramaniyam, 2013). There are 4 different standards throughout the world today designed to evaluate ride comfort with respect to human response to vibration. Those standards are the ISO 2631 standard, which is used mainly in Europe, the British Standard BS 6841 used in the United Kingdom, VDI 2057 used in Germany and Austria and the Average Absorbed Power mainly used in the United States of America and their overall purpose is to evaluate the trip as a whole in respect to ride comfort (Els, 2005). In the work of Constantin et al. (2014), most of the traditional ergonomics factor were analysed and the seat, the space inside the car, climate and noise were found to be the most important ergonomic factors affecting the comfort.
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Traditional ergonomic factors have been investigated a lot, although, with the development of AVs, factors beyond ergonomics such as naturality, disturbances, apparent safety and motion sickness will affect the comfort level (Figure 2.2). Naturality is connected to executing familiar to the passenger maneuverers by mimicking the human driving style. Apparent safety does not refer to the vehicle behaving in a safe manner but to the feeling of the passengers that it actually does. Regarding disturbances, they can result from vertical forces (road disturbances) or horizontal forces (load disturbances). Last but not least, motion sickness is apparent when what the passenger sees and expects does not agree with what the vehicle does (Elbanhawi et al., 2015). Therefore, a single subjective evaluation of ride comfort and investigation of traditional ergonomics factors are no longer considered an acceptable and competitive way to assess the passenger experience (Elbanhawi et al., 2015).
Figure 2.2: Factors influencing ride comfort in autonomous cars
Another approach to addressing comfort is to focus on manoeuvre-based analysis, instead of trip-based, which is the most common. If the AV executes manoeuvres familiar to the passenger, it would undoubtedly contribute to the passengers’ comfort enhancement, since they will not have the feeling of being driven by a robotic operator (Elbanhawi et al., 2015; Bellem et al., 2016, 2017, 2018). This would improve the factor naturality. The most common analysed manoeuvres are deceleration, acceleration and lane changing. As far as the apparent safety is concerned, it can be improved by suitable development of the driver-machine interface, to inform the driver early about the next movements and to reassure the driver that it detects a possible danger.
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Moreover, executing the manoeuvers in a familiar way, especially the timing of the braking can help to raise apparent safety. Car sickness relates to the longitudinal and lateral acceleration, i.e. when driving at a constant speed, the possibility of experiencing car sickness is dropping. To prevent carsickness, the ability of the driver to anticipate the future motion plan should be maximized (Diels, 2014).
Moreover, the types of disturbances that passengers are exposed to and play an important role in their comfort can be categorised into road and load disturbances. Driver’s control of braking, acceleration, and turning results in serious disturbances and belong to load disturbances whereas road disturbances mostly include vertical vibrations (Elbanhawi et al., 2015). It can be concluded that a manoeuvre-based analysis focused on the deceleration, acceleration and steering can help to solve some of the problems regarding comfort that have been arising due to autonomous driving. There are many studies that focus on the comfort inside of Autonomous vehicles, which factors affect it and how can be achieved. One of the studies dealing with comfort in AVs was conducted by Yusof and Karjanto (2015). Its purpose was to make autonomous driving style comfortable for the passengers by discovering the relationship between two human driving styles (assertive and defensive) that were identified by questionnaires and three autonomous driving styles (light rail transit, assertive and defensive), which were tested in a field experiment in three different locations: junction, speed hump and roundabout. The comfort of the passengers is the main goal in the study of Dovgan et al. (2012) as well. They measured the comfort as the change of acceleration, i.e. the jerk and they developed a two-level multi-objective algorithm to optimise the control action with three objectives, i.e. travelling time, fuel consumption and comfort. They tested the algorithm on a real-world route. Comparing the results from the algorithm they developed with the ones from an algorithm which does not optimise the comfort, it was found that significant improvement on comfort can be made in control actions with low-fuel consumptions, but not with short travelling time.
Having the same general aim, Scherer et al. (2015) presented two studies related to how to model driving styles in highly automated vehicles. The first one was a simulator study with the additional use of questionnaires and its goal was to detect which driving
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parameters are essential for passengers to feel comfortable inside an autonomous car. It was revealed that the most frequently mentioned comfort parameters were the longitudinal safety margin, braking, velocity and acceleration proving that parameters of longitudinal control were affecting comfort the most. Therefore, those parameters, specifically braking and acceleration were examined in the second study which used real driving data.
One of the most important and critical factors of perceived safety and comfort inside vehicles is braking. This is because sharp deceleration is closely connected to collisions. It should be noted that deceleration is only one dimension of passengers’ ride experience while braking; others include vibration and jerk (Le Vine et al., 2015b; Bellem et al., 2016). It is strongly supported through the literature that vehicle acceleration/deceleration and the time rate of change of acceleration, i.e. the jerk can have a significant impact on passenger’s comfort and safety (Martin and Litwhiler, 2008; Wu et al., 2009; Jensen et al., 2011; Powell and Palacín, 2015). Those factors could result in excessive external forces applied to the passengers, which affect passenger’s stability and discomfort (Powell and Palacín, 2015). Table 2.2 presents the most common features that have been used through the literature to identify comfort during manoeuvre-based analyses.
Table 2.2: Kinematic factors connecting to the disturbances Kinematic factors affecting the
comfort
Explanation Factorial Literature Review Evidence
Acceleration and deceleration Describe the longitudinal control and are expressed as the change of speed.
(Diels, 2014; Scherer et al., 2015; Bellem et al., 2016, 2017) Jerk The rate of deceleration/
acceleration.
(Dovgan et al., 2012; Bellem et al., 2016, 2017, 2018)
TTC, Headway distance, TTMD (time to minimum distance)
Factors connecting to following a car situation.
(Bellem et al., 2016, 2017)
Martin & Litwhiler (2008) support that accurate control of the braking profile can result in significant improvements in the safety and comfort of the passengers. In their work, Powell & Palacín (2015) found that there is considerable variation between the perceptions and stability of different individuals and therefore there are no precise limits for comfort longitudinal acceleration. This is further supported by the review of
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Hoberock (1976) and Gebhard (1970a) on ground transportation vehicles, where they found that there is wide variability in passenger acceptance of any specific acceleration-jerk profile (Gebhard, 1970; Hoberock, 1976). However, it was concluded that the range 0.11g to 0.15g is considered comfortable deceleration for more studies and regarding the jerk, the value should not exceed 0.3g/s to be perceived as acceptable. For electric rapid transit cars in the U.S. normal braking is set from 0.12 g to 0.14 g and emergency braking from 0.14 g to 0.30 g (Hoberock, 1976).
Le Vine et al. (2015a) used different scenarios in simulation to identify how AVs will influence the intersection capacity and level-of-service if they travel according to the maximum acceleration/deceleration rates of rail transport. Maximum typical rates of acceleration and deceleration during revenue service for light rail speed rail: 1.34 m/s2
(Le Vine et al., 2015a). In another paper, the purpose is to identify metrics that enable the parameterisation of a safe, functional and comfort automated driving style (Bellem et al., 2016). They split the trip into manoeuvres and into highways or urban/rural scenarios, underlying the importance of a manoeuvre-based analysis. Bellem et al. (2016) concluded that acceleration, jerk, quickness and headway distance are the essential components to build a comfortable highly automated driving style. Trying to investigate how highly automated vehicles should drive to ensure driving comfort for the now passive drivers, Bellem et al. (2018) rated and analysed different variations of three central manoeuvres, i.e. lane change, acceleration and deceleration. The variations were configured by manipulating the longitudinal and lateral jerk in simulators studies. Also, personality traits, as well as the driver’s age and gender, resulted in having no effect on manoeuvres preferences.
In automated or semi-automated vehicle networks, fast starts and stops will be necessary in order to merge vehicles into high-speed traffic at close headways. Passenger tolerances to longitudinal acceleration and jerk loads will thus affect not only the design of the vehicle propulsion and braking system, but also the central headway, speed, and scheduling controls for the entire network (Hoberock, 1976). The values for the comfort deceleration limits suggested for public transportation are smaller in absolute value than those found in motor cars in different studies. For instance, Shen et al. (2000) set the minimum acceleration at which a passenger feels discomfort at 0.25g and the acceleration at which a passenger cannot stand at 0.5g.
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Moreover, it was reported by Abernethy et al. (1977) that 95% of the passengers were able to remain securely in their seats when the deceleration was less than 4.12 m/s2
(=0.42g) (Abernethy et al., 1977). Also, they set the limit of emergency deceleration on 1.96 m/s2 (=0.2g). On the contrary, Wu et al. (2009) set that limit (2m/s2=0.204g)
as a critical value for a comfortable longitudinal deceleration (Wu et al., 2009). Bogdanović and Ruškić (2013) defined normal vehicle acceleration as acceleration values from 0 to 3.5 m/s2. In two important projects, the EuroFOT and the 100-Car
NDS, the limit of 4 m/s2 was used for the identification of “hard” braking, which is
assumed to be perceived as uncomfortable. Comfortable decelerations on surface streets vary from 1.47 m/s2 to 4.12 m/s2 (0.15g to 0.4g) whereas on freeways where
speeds are higher, decelerations from 0.98 m/s2 to 1.96 m/s2 (0.1g to 0.2g) can be
considered high (McLaughlin et al., 2009). Nevertheless, in some naturalistic driving studies, braking at 5.88 m/s2 (=0.6g) or higher was common, based on the driver and
the driving situation.
Through the literature, there are different descriptors used to characterise deceleration and ride comfort. One example is: insensible, just sensible, noticeable, slightly uncomfortable, very uncomfortable. Interestingly, in a study by Urabe and Nomura, three correlated measures, i.e. perception, comfort and acceptability were used to address ride comfort during a deceleration event (Gebhard, 1970; Hoberock, 1976).