‘Understanding the driver’ in this research means determining what handling behavior the driver considers as 'good', as explained in section 2.1.2, but also, how this handling behavior influences his perception and subsequently his subjective evaluation. This is especially important owing to the adapting behavior of the driver, which can result in tests with similar objective handling behavior but with different subjective assessments. Is the driver forced to adapt much to the vehicle behavior? Does he experience a high workload? Is he required to put in much effort or is it relatively easy to perform well? Does he have to anticipate much? Getting answers to these perception related questions will provide this understanding of the driver, which can contribute to developing well handling and therefore safe vehicles.
In this chapter, the three methods for understanding the driver that are followed in this research, including background information, are described. The first two methods relate objective to subjective measures to provide information about what handling behavior is considered as good handling behavior. The third method models the driver in the closed-loop relationship with the vehicle to provide information about the perception of the driver to clarify how this handling behavior influences the driver.
3.1 Answering the ‘What’- Question: Relating Objective to
Subjective Measures
Seen from a system modeling view, the vehicle handling behavior could be considered as the input to the driver. The driver perceives this input and makes his assessment, which is the process of interest. This process results in the driver's subjective evaluation of this handling behavior, which can be defined as the output. This is shown in the top part of Fig. 20. As explained in section 2.3, objective assessment methods convert handling behavior through vehicle dynamics measurements to characteristic data (left-hand side of Fig. 20). Subjective assessments methods convert the driver's opinion to data, like scores on handling aspects (right-hand side of Fig. 20).
Figure 20. Relating objective to subjective measures to get ‘what’-information about the driver’s perception.
Studying the relationship between the data from objective and subjective assessment is a frequently used method to obtain information about the driver's assessment process (middle part of Fig. 20). Such studies (Aurell et al., 2000; Bergman, 1973; Chen et al., 1996; Crolla et al., 1998; Hill, 1987; Jaksch, 1977; Lincke et al., 1973; Seghers, 2006; Sharp, 1999; Van Randwijk et al., 1991; Vos, de et al., 1999) often use correlation or regression methods for analyzing this relation. Usually, the set of characteristic data coming from the vehicle dynamics measurements is large and not independent, making statistical analyses difficult. Therefore, this set is often reduced by using factor analyses or principal component analyses and sometimes combined with additional clustering techniques. Although using these reduction techniques can facilitate finding relations, their application is not standardized and requires significant knowledge. Additionally, there is no clear interpretation of the results. Still, these studies of objective-subjective relationship of handling have resulted in global indicators for good handling, as presented in section 2.1.2.
Next to using different methods for relating the objective data to subjective data, also the conversions from real life to numerical data differ for the approaches taken in the past. This conversion largely determines the amount of information that can be gathered by studying the relation. For example, if no steering wheel data are measured during objective assessment, the information about the important steering wheel feel of a driver is strongly restricted. This also applies for the subjective data, if the steering wheel feel is not part of a
handling aspect that is rated by the driver, this information will not show up by analyzing the relation. All these differences in these objective-subjective approaches make comparison and overall conclusions difficult. Sharp (Sharp, 1999) evaluates several of these approaches (Bergman, 1973; Crolla et al., 1998; Jaksch, 1977; Lincke et al., 1973), concluding that although correlations are found, the results are in most cases not applicable outside the context in which they were derived. Additional, there is no clear interpretation and standardization of this relation. He states that the only exceptions are objective indicators that have a sound fundamental basis of understanding for them (see section 2.1.2). Regarding to tire handling assessment, Pauwelussen (Pauwelussen, 1999a) evaluated several studies which included objective- subjective relations. He came to similar conclusions. He also found a lack of clear interpretation and standardization for this relation. Also in these studies, there is a general understanding that there are objective indicators that are important for subjective evaluation (see section 2.1.2).
To bypass some of the limitations mentioned here for this research some other approaches were followed, as mentioned at the end of section 1.1.
In the first method described in chapter 6, the driver's subjective handling assessment is predicted based on vehicle dynamics measurements with a General Regression Neural Network (GRNN). By using a GRNN for this, the previously mentioned limitations for using regression can be circumvented (see section 6.2 for explanation and references).
The second method described in chapter 7, focuses on the driver, more specifically, on his workload as an indication for the subjective assessment. This derives from the fact that the driver adapts to changing vehicle handling behavior (see section 2.4 for explanation and references). The assumption made and validated in this research is, that the more driver adaptation is required, the more workload the driver perceives, the lower his vehicle handling assessment score will be. Because workload cannot be measured directly, indirect measures must be used. This includes both physical as mental workload measures.
3.2 Answering the ‘How’-Question: Driver Model Method
Although studying the relationship between objective-subjective data is able to provide information on what the influence of vehicle's handling behavior on driver's evaluation is, it does not give much information on how this influences the driver's evaluation. To obtain this information a driver model method is derived in chapter 8. The basic idea is not to model the driver's assessment process as a high level open-loop input-process-output system, as done in the other methods and shown in Fig. 20, but to go one step deeper and to model and simulate the driver in its closed-loop connection with the vehicle, as shown in Fig. 21. Based on the adaptive nature of a human controller (McRuer & Krendel, 1962; McRuer & Weir, 1969), human control parameters can reflect control behavior, but also different levels of workload (Jagacinski & Flach, 2003), and can be related to subjective evaluation (Abe, Kano, & Shibahata, 2009). This inspired this research for the third method. It focuses on the driver but not by
looking at measures from 'outside' the driver, like workload measures, but by modeling the driver behavior and looking at driver parameters 'inside' the driver (model), as shown in Fig. 21.
Figure 21. Driver model method: relating driver model parameters to subjective evaluation scores to get ‘how’-information about the driver’s perception.
For this method, models of the vehicle including tires are run in closed-loop with a driver model, to simulate a handling maneuver. If the driver is modelled with parameters that are related to certain driver's characteristics, like effort or workload, these could relate to the subjective assessment of tires. If different tires (tire models) result in different (fitted) driver parameters, these 'tire dependent driver parameters' could provide information about the amount of effort and/or workload a driver perceives. This, in turn, could give an indication of the subjective evaluation of the tire handling behavior.