This chapter started with a review of the data collection methods used in driving behaviour analysis. Moreover, it presented the datasets that will be employed to conduct statistical and cluster analysis. The purpose of this work required naturalistic driving data, so the data were obtained from two FOT and an NDS. The data that were employed represented normal driving, i.e. absence of emergency events and were representing different scenarios, considering the road type, the reason for braking, the traffic situation, the initial kinematics and the drivers. Comparing the ideal dataset described in Section 3.2 with the datasets that were used in this research and were described in detail in this Chapter, it is concluded that all the dataset are satisfactory. To begin with, they provide naturalistic driving data and are consist of many drivers having conducted many trips. Moreover, most of the kinematic, driver, trip, event variables are available except for weather and light conditions and some driver variables, such as education level, income, sentimental state. However, not all the variables were easily accessible since to obtain some of them, complicated calculation or time-consuming processes (i.e. the examination of the trip videos) were essential. Finally, outliers and missing values were included in the datasets, which were detected and excluded.
In more detail, the TeleFOT project consist of 25 drivers conducting 44 trips in different conditions, the OEM had 12 drivers undertaking 130 trips and finally, from the UDRIVE NDS, 49 UK drivers were selected conducting 470 trips (Table 3.20). The deceleration events detection algorithm for the analysis of the braking characteristics had as outcome almost 10000 deceleration events, 869 for the TeleFOT project, 1690 for the OEM project and 7162 for the UDRIVE. However, the detection algorithm for the ride
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comfort analysis resulted in 21600 deceleration events. The datasets that were developed for each project consists of as many observations as the deceleration events and includes the deceleration characteristics, the kinematic values at the beginning and during the event, the driver characteristics, the trip characteristics and the situational factors that were obtained from the videos and from the developed algorithm in MATLAB. Analytically, the variables that were extracted and imported in the models along with their availability for each dataset are presented in Table 3.19.
Table 3.19: The extracted variables for each dataset Variable
category Variable TeleFOT OEM UDRIVE
Driver level
driver ID X X X
gender X X X
age categories X X X Arnett Inventory of
Sensation Seeking (AISS) X Driver behaviour
Questionnaire (DBQ) X
Trip level
Trip ID X X X
Trip duration (min) X X X Trip distance (km) X X X
Car_model X X
Trip level Road Type (rural, urban,
motorway) X X X Event level Initial speed X X X GPS latitude X X X GPS longitude X X X Speed limit X Traffic density X X Traffic light X X Time X X X Covered distance X X X Driver's reaction X X Traffic congestion X Arrive at traffic
congestion/ stops at car
block X X X
Reason for braking:
Roundabout X X
T-junction X X
Cross- junction X X Intersection X X X Pedestrian crossing X X X
77 Dynamic-obstacle X X Other X X X Cyclist X Ptw X Direction X
Number of lanes positive X Number of lanes negative X One direction road X Maximum steering angle X
Jerk X
TTC X
THW X
Headway X
Lead vehicle speed X Following a car X
Finally, Table 3.20 summarizes the characteristics of the three datasets. Specifically, the number of drivers, trips and events is outlined for each dataset along with the drivers’ characteristics. It can be concluded that the UDRIVE data gives a bigger number of different drivers and trips comparing to the other two datasets. Moreover, a balance regarding the gender of the drivers can be observed which doesn’t happen in the age since the younger age group has only 8 drivers in comparison with 42 for the middle and 32 for the old age group. Moreover, the statistical values (average, standard deviation, minimum and maximum) of some important variables are displayed in Table 3.20 and some differences along the datasets can be observed. First, in the TeleFOT dataset, the deceleration value was smaller in absolute value than in the other datasets, indicating softer braking. The average duration, as well as the standard deviation of the duration, shows a significantly shorter duration of the braking events in the TeleFOT dataset. Finally, as far as the speed is concerned, higher initial and final speed (both average and maximum values) are observed in the OEM and UDRIVE datasets. The lower speed values of the TeleFOT dataset might be due to the low percentage of observations happening in a motorway (only 7.5%). Regarding the frequency of the variables in the observation, it should be noticed that there is a satisfactory percentage for almost all the variables. The pedestrian and the motorway are the exceptions. Only in the UDRIVE dataset, there is a big percentage of observations happening in a motorway and braking occurring due to the presence of a pedestrian. Furthermore, only in 6.5% and 8.1% of the observations in the
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TeleFOT and OEM datasets respectively there is high traffic density, which might undervalue the effect of high traffic density in the braking event. Accordingly, the results of the modelling might be influenced by the lack of observations in some variables.
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Table 3.20: Comparison of the characteristics of the three datasets
TeleFOT OEM UDRIVE
Drivers 25 12 49
Trips 44 130 470
Events 869 1690 7163
Age
young middle old young middle old young middle old
4 13 8 2 6 4 7 23 20
Gender
male female male female male female
14 11 6 6 24 26
Variable Mean SD Minimum Maximum Mean SD Minimum Maximum Mean SD Minimum Maximum
Max deceleration (m/s2) -2.38 0.40 -4.89 -2.00 -2.62 0.52 -7.08 -2.00 -2.59 0.59 -11.30 -2.00
Duration (sec) 4.26 1.98 0.74 14.95 8.65 4.80 0.85 24.80 7.58 4.10 0.10 28.40 Final speed (km/h) 13.57 11.84 0.00 78.49 16.16 17.99 0.00 116.00 14.06 17.03 0.00 119.90 Initial speed (km/h) 34.66 14.99 4.10 107.51 49.25 19.72 2.60 142.03 46.87 19.60 4.16 128.85 Frequency (Percentage) Frequency (Percentage) Frequency (Percentage)
intersection 38.4 37.3 57.0
Pedestrian 5.2 2.2 16.0
arrive at traffic congestion 11.1 12.3 10.3
road typo: urban 44.8 40.5 57.4
rural 47.6 44.4 22.1
motorway 7.5 15.1 20.5
Traffic density: low 65.6 68.0
medium 27.9 23.8
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4 Methodology
According to the literature review, it is really important for the wide acceptance of (semi) AVs that passengers feel safe and comfortable inside them. Moreover, deceleration events are crucial for comfort and should be carried out in a way that resembles human behaviour. Therefore, this study focuses on the analysis of the deceleration events observed within normal driving with the aim of identifying acceptable thresholds and relationships with different factors associated with braking behaviour under different driving and operational conditions. The factors that will be tested are human factors (i.e. age, gender, driven miles per year and driver behaviour indices from questionnaire), traffic (e.g. traffic density), situational (e.g. reason for braking) and kinematic factors (i.e. speed, TTC, THW, headway at the beginning of the event) and road network conditions. The purpose of this analysis is to identify and explain the affecting factors at a deceleration event, i.e. the factors that influence the maximum deceleration and the duration of the event. Moreover, the comfort level of the deceleration events is analysed, using different thresholds, to determine which thresholds best explain the comfort level and to recognise the comfort influencing factors. All this information is useful for informing vehicle manufacturers about the deceleration behaviour observed during normal driving and suggesting how this could be transformed into (semi) AVs so as to ensure comfortable and safe braking operations.