The performances of individual drivers are evaluated using a number of longitudinal and lateral control parameters taken from the vehicle, which reflect manoeuvring and control of driving behaviour. These parameters or driving performance measurements are illustrated in Table 6.1.
Table 6.1 Measurements of driving performance
Measurements on the Longitudinal Controls
Mean speed
Standard deviation of speed Maximum speed
Mean acceleration/deceleration
Free-Driving
Maximum acceleration/deceleration Mean distance headway
Standard deviation of distance headway Minimum distance headway
Mean time headway
Standard deviation of time headway Minimum time headway
Car-Following
Time-To-Collision (TTC)
Measurements on the Lateral controls
Standard deviation of steering wheel angle
Steering
Steering wheel Reversal Rate (RR) Mean lateral position (MSLP)
Lane Keeping
Standard deviation of lateral position (SDLP)
From Table 6.1, the longitudinal control measures can be classified into two types according to the driving Scenarios of Free-Driving and Car-Following. In Free-Driving, speed plays an important role in the risk of accidents: the higher the speed, the increased risk of accidents, and a power function has been suggested by (Aarts & van Schagen, 2006b) to describe the
relationship between speed and accident risk. As described in the same paper, the speed
deviation is also closely related to accidents because the risk increases with more variations, and
hence the deviation in speed is also considered as an indicator for road safety. In the Car- Following scenario, the distance/time headway provides a better measure of the safety margins than speed, and (Vogel, 2003) for example, demonstrated that small time headways result in higher potential danger. According to Summala’s results (Summala, 1981) in an actual driving
situation, drivers’ average reaction time is around 2.5 seconds, yet 2 seconds is the
recommended minimum time headway in most driving manuals. Even worse, most drivers tend to keep a headway shorter than the recommended minimum, and short headways are therefore fairly common, but still considered to be much riskier (Evans, 1991).
As shown in Table 6.1, the lateral control parameters can be classified into two groups by steering and lane-keeping behaviour, in which the measures of steering represent the rate of steering wheel adjustments and the measures of lane-keeping indicate the increased accident probability. Previous research (Johansson et al., 2004) showed that, when the steering wheel adjustments increase, especially small steering corrections, the effort spent on the lateral control task (e.g. on a narrow road) will also increase. The standard deviation of lateral position (SDLP) is on one of the most consistent and commonly used measures to evaluate the lane keeping and in-vehicle technology, which historically used as a well-established proxy for risk, i.e., the probability of the vehicle getting out of the lane (Janssen et al., 2004; Wewerinke & Hogema, 2003). It was expected that SDLP would increase when performing In-Vehicle secondary tasks, because of the decreased attention to the primary driving task.
6.2.1 Measurements for Longitudinal Control
The following measures of longitudinal controls were used as measures for driving performance and further analysis:
• Mean speed (MNSP) and speed deviation (SDSP): In the Car-Following scenario, the speed deviation was the standard deviation of speed. While in the Free-Driving Scenario, as the instruction given to drivers was to maintain a constant speed at 55 miles per hour (i.e. 88 km/h), therefore the speed deviation was calculated using the following formula instead:
2 1 1 ( 88) n i i SDSP v n = =
∑
− (6.1)where n is the number of the readings in each case.
The deviation in the Free-Driving Scenario still used the abbreviation of SDSP for convenience.
• Mean and deviation of distance headway (MNHW and SDHW), mean and deviation of
time headway (MNTHW and SDTHW), the minimum distance and time headways
(MinHW and MinTHW). These parameters are important in the Car-Following Scenario, because drivers were instructed to maintain a constant distance from the vehicle in front, and therefore any major change or deviation can be an indicator of driving performance deterioration. These headway parameters are not relevant to Free-Driving.
6.2.2 Measurements for Lateral Control
In terms of lateral controls, the following measures were computed and analysed:
Standard Deviation of Lane Position (SDLP): SDLP presents the extent to which drivers are
able to keep a stable position in the lane they are driving in, i.e. the variation in lane position; therefore a lower SDLP value denotes a more stable lane control. The lane position was calculated from the left and right lane offsets, which are the distances from the centre of the IV to the left and right lane markings respectively. The IV records left lane offsets as negative values and right as positive, lane position was therefore calculated by:
Right Lane Offset
Left Lane Offset
Lane Position
Left Lane Offset
2
−
=
+
(6.2) Figure 6.1 shows an example the SDLPs for the three task types (baseline, performing auditory and visual task) in the Free-Driving Scenario for subject #5. It can be seen from the figure that that the lane keeping was the most stable in baseline driving, and less stable when performing the visual task, whereas the SDLP value was nearly twice as much as for the baseline.
SDLP for SubNum_5 in Free-Driving
0 5 10 15 20 25
Baseline Auditory Task Visual task
SD
LP (
cm
)
Figure 6.1 SDLPs of three task types for Subject #5 in Free-Driving
Steering Behaviour was assessed by the steering wheel Reversal Rates larger than 1.5 and 3, 5,
6, 7, and 8 degrees (i.e. the number of steering wheel adjustments per minute which are larger than these degree) or RR1.5, RR3, RR5, RR6, RR7, RR8; and Steering Entropy (SE).
A pre-analysis was conduced to decide which Reversal Rate parameters were best to describe driving performance. The analysis results showed that the reversal rate larger than 3° (RR3) was very similar to that of RR4, 5, 6, 7, and 8; and showed a better homogeneity in each task type. Hence in this thesis, the results of RR4, 5, 6, 7, and 8 will not be reported, given that they do not contradict the findings from RR3 and RR1.5, namely large and minor steering wheel
adjustments. However, by doing this, it is possible that significantly large fluctuations in
steering adjustments, e.g. RR7 or RR8, which can be a sign of driving performance deterioration, will sometimes be dampened or smoothed. But given SDLP is a more consistent measure of performance deterioration, the benefit of analysing RR larger than 3 degrees separately was deemed superfluous. It should be noted that when workload increases, drivers are often less able to control their vehicle, which can result in increased steering wheel adjustments. However, these adjustments are significantly large and tend to be abrupt in nature, i.e. larger than three degrees, and peak patterned. When the adjustments are relatively smaller (including RR1.5 and RR3), the increases accompanied by comparable SDLP reflect the extra effort of drivers to adjust their steering to keep lane position.
In addition, Steering Entropy can be another indicator for driving performance deterioration, as it has been well proved (Boer, 2000; Nakayama et al., 1999) to be sensitive to steering
behaviour changes, indicating unpredictability of steering, i.e. the higher the value, the less predictable the steering. Based on the definition by Nakayama et al. (Nakayama et al., 1999), Steering Entropy is related to the steering angle. In this research, the steering angles an are
recorded at a frequency rate of 10 Hz, which is larger than the lowest sampling frequency that could be used to reveal a human operator’s control response to manual tracking tasks
(Nakayama et al. 1999). Based on the measurement data of the steering angles an, a
second-order Taylor expansion on time is used to predict the steering angle at any given time. After a simple derivation, the steering angle bn can be predicted using three subsequent points in
the measurement data:
1 2 3
5
1
2
2
2
n n n nb
=
a
−−
a
−+
a
− ,and the prediction error is calculated by:
n n n
e
= −b
a
The prediction error en can be assumed to be normally distributed. Therefore the mean
prediction error is obtained using all error data within 1.2 standard deviations, to encompass 90% of the population. Suppose 90 percent of the population falls between -C and C, then the prediction error distribution between -C and C can be divided into nine bins, from which the steering entropy is derived (Nakayama et al. 1999):
9 9 1
log
p m m mH
p
p
== −∑
in which pm is the probability of being in the mth bin. Steering Entropy is therefore a measure of
the errors in the prediction of the steering angle based on smooth reversal behaviour, and a
is therefore expected to increase when drivers perform the secondary tasks, and takes into
account individual differences between drivers.
To summarise, of the measures taken from Table 6.1, speed, time/distance headway, steering wheel Reversal Rate (RR), Entropy (SE) and lane deviation (SDLP) can strongly indicate a driving performance change due to secondary tasks, especially as they are related to road safety. Besides these measurements, the deviations of speed and headway were also included in the metrics analysis because they are widely accepted in the evaluation of drivers’ performance (Brookhuis & De Waard, 2003; Ma & Kaber, 2005; Tornros & Bolling, 2006; Tornros & Bolling, 2005; Tonros & Bolling, 2005).
Typically, in Car-Following, a higher SDLP can be taken as an indicator of steering and driving performance deterioration, i.e. more erratic lane keeping or increased steering adjustments, which is usually accompanied by a higher RR (RR1.5 and/or RR3) by drivers. However, an
increase in RR that results in a similar or lower SDLP can be an indicator of a coping strategy
being employed, i.e. increased effort to obtain more stable lane control. This is especially important on busy dual-carriageways, i.e. the Car-Following Scenario, where stable lane keeping has a direct relevance to road safety. Other indicators of coping strategies being
employed included increasing the time and/or distance headway when dual-tasking compared to baseline driving.
In Free-Driving on a single carriageway, SDLP can be important, but less so than for
Car-Following. However, an increase in RR, especially RR3 (large sudden steering wheel turns) in Free-Driving indicates more frequent adjustments to a lack of vehicle control (or driver concentration), and therefore driving performance deterioration.
Given that drivers were also instructed to keep a constant headway (in Car-Following) or speed (in Free-Driving), a greater deviation in these two parameters, i.e. SDHW (standard deviation of headway) in Car-Following or SDSP (standard deviation of speed) in Free-Driving, can also indicate deterioration in driving performance. In addition, provided the SDHW or SDSP
remained constant (depending on the Scenario), a change to the value of either parameter (e.g. a higher headway or a lower speed), indicates a compensation strategy being adopted by drivers (e.g. to increase safety margins or the time required for decision-making). Also, by definition, SE represents the predictability of steering behaviour. Therefore, a high SE is used as an indication of deteriorated and less stable steering behaviour.
6.2.3 Controlling Driving Performance Measurements
When comparing performance differences across the various driver characteristics groups, it is important to look at all the driving performance parameters as a whole, and not focus solely on one measure, because of the potential impact of individual differences in each group. In order to further eliminate individual variability, the same driving performance measurements were collected in baseline driving for each subject or driver to use as control. Separate averages were calculated for each Scenario using three separate baseline collection periods, i.e. before, during and after the secondary tasks. The baseline average for each driver was then subtracted from the performance measurements while performing the secondary tasks, as recommended by Russell (Russell, 1990), cited by Pastor (Pastor et al., 2006), and these differences named after the original ones, with a suffix of “_Ctrl”. These difference-to-control values were reported only where they were more material to the absolute ones. In addition, when comparing different characteristics groups (where there are more than two groups) for certain parameters, e.g. SDLP, the incremental changes when performing secondary tasks were compared against a
reference group, for example Experience Group #3, to determine the relative difference
between Experience Groups.
Note: Most of the driving performance measurements referred to above are not measured directly by the IV, and the experimenter wrote complex programs to derive most of the values, e.g. for Steering Entropy.