Detection of the deceleration events

One of the challenges of this research was to correctly detect deceleration events from a large volume of traffic data (25 million observations for the TeleFOT and OEM projects with observation frequency 100Hz and 7 million observations from the UDRIVE project with frequency 10Hz) that were obtained from the projects that were used. More specifically, it was difficult to choose an appropriate threshold which will indicate the occurrence of a deceleration event within normal driving conditions. Studies documented in the literature show that most drivers decelerate at a rate greater than 4.5 m/s2 _{when confronted with the need to stop for an unexpected object}

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stay within the driving lane and maintain steering control during the braking manoeuvre.

Many studies through the literature have used different thresholds for describing a deceleration event depending on the purpose of each study and on the nature of the available data. For example, Naito et al. (2009) and Miyajima et al. (2011) applied a high threshold rate: i.e. 0.3g (i.e. 2.94 m/s2_{), for describing and categorising}

deceleration events because the purpose of their study was to evaluate the driver’s risk judging the way the driver brakes in emergency situations. On the other hand, Wu et al. (2009) focused on normal driving and therefore set a lower threshold value of 2 m/s2 _{for comfortable longitudinal deceleration. These thresholds are between the limits}

of the thresholds using in Japan for detecting deceleration events, which are between 1.96 m/s2 _{and 3.92 m/s}2 _{(Naito et al., 2009). Different thresholds were suggested by}

the Institution of Transportation Engineers (3.0 m/s2_{) and by the American Association}

of State Highway and Transportation Officials (AASHTO) (3.4 m/s2_{) (Maurya and}

Bokare, 2012).

Most of the deceleration rates observed in all projects in this work are relatively low and this may be due to the nature of naturalistic driving data, from the two FOTs and the UDRIVE NDS, which reflects driver’s normal braking and does not include many safety-critical events. Therefore, the threshold was set at 2m/s2_{, which is the lowest}

value found in the literature to detect deceleration events. This forms the first criterion in the detection of deceleration events.

Apart from the criterion in order to consider something as a deceleration event and detect it, the definition of the beginning and the end of a deceleration event plays an important role. Therefore, it was essential to set some more criteria. The beginning of the deceleration event is defined from the time onwards where absolute deceleration values are greater or equal to 0.1 m/s2_{. In addition, the deceleration event ends when}

the absolute deceleration values are greater or equal to 0.1m/s2 _{(criterion 2). That}

threshold was defined in order to exclude random noise to the actual event, since a deceleration rate which is less than -0.1 m/s2 _{may just be part of normal driving and}

not of a deceleration event. By having only these thresholds two different problems arise: the first has to do with braking following by not fully releasing the brake and then

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decelerating again (Figure 4.2-a) and the other has to do with keeping a really small, (but still greater than 0.1 m/s2_{) constant deceleration either before the braking or after}

which should not be included in the deceleration event in order to correctly calculate the deceleration profiles (Figure 4.2-b).

Figure 4.2: Examples of the problems during defining the deceleration events

These problems were solved by using another threshold-criterion. This threshold had to do with the rate of change of the acceleration-deceleration, i.e. jerk. Therefore, if the absolute value of the time derivative of the deceleration was smaller than 0.1 m/s3

continuously for 0.5 sec then this will demarcate the end of the event or from this point and onwards the beginning of it (criterion 3). The values for the last threshold were obtained empirically from some of the detected deceleration events and their problematic profiles. Furthermore, this threshold agrees with the one that Murphey et al. (2009) have used to classify the driver’s style using the jerk and specifically they used this threshold to specify calm from normal drivers. So, by combining the criteria 2 and 3 (Table 4.2), the start and the end of the deceleration event are defined. Having the criteria clarified, an algorithm for the detection of the deceleration events, which satisfies those criteria (Table 4.2), was developed and was implemented through the Matlab software package. The steps that the algorithm follows are:

1. First, the algorithm detects in the dataset a deceleration event by applying the criterion 1.

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2. Then, starting from the point specified from criterion 1 (i.e. a=-2 m/s2_{), the}

algorithm searches the dataset backwards and by simultaneously applying both criteria 2 and 3, it sets the beginning of the deceleration event.

3. Finally, to define the end of the deceleration event, the algorithm finds again the point specified from criterion 1 (i.e. a=-2 m/s2_{) and this times it moves}

forward in the dataset until the concurrent satisfaction of the criteria 2 and 3. In addition, the algorithm computes the duration of braking events, the maximum deceleration rate (m/s2_{) and the travelled distance (m) of each event.}

Table 4.2: The criteria for the detection of the deceleration events

Criterion Purpose

1 𝑎 ≤ −2 𝑚/𝑠2 Detection of the deceleration event.

2 𝑎 ≤ −0.1 𝑚/𝑠2 Set the beginning and the end of the

event and exclude the noise.

3

𝑑𝑎

𝑑𝑡 ≤ 0.1 𝑚/𝑠

3 _{𝑓𝑜𝑟 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛 = 0.5 𝑠𝑒𝑐, (𝑑𝑡}

= 0.1𝑠)

Set the beginning and the end of the event and deal with the problematic profiles.

This procedure was followed to detect the deceleration events from normal driving in order to analyse them and reveal the influencing factors. Although, to analyse the comfort level of each deceleration event for the UDRIVE project, a different threshold for the maximum deceleration was set. Specifically, the first criterion changed to 𝑎 ≤ −1 𝑚/𝑠2_{, whereas the other two criteria remained the same. The purpose of reducing}

the threshold is that more soft braking events needed to be included in order to represent the most comfortable ones. The selection of the threshold for comfort analyses is explained thoroughly in Section 4.5.1.