With system Without system
Time between activation of the brake pedal and maximum deceleration [s]
1.40 1.20 1.00 0.80 0.60 0.40 0.20
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Figure 4.18: Time from beginning of brake pedal activation to maximum deceleration.
The basic idea behind this situation was to present a realistic pedestrian accident scenario (crossing from the right, no visibility obstruction, urban setting, daylight, ...) in the driving simulator. The drivers went through a long period of driving without any special event, were not informed about the research questions, and were visually distracted by a secondary task during the highly critical situation. The objective for this situation was to test whether an accident situation can be reproduced in a stable way in such an experimental environment. The difference to other studies is that not a critical situation but an accident situation should be created. As the results show, it was not possible to bring the subjects reliably and repeatably into the critical accident situation (TTC at braking should have been at least below 1.0 s TTC) as only a few accidents did happen under baseline conditions.
Several possible explanations and ideas for further studies have been developed. The subjects probably found a strategy to work on the secondary task and react properly to the traffic situations. A possible design change would be to further increase the level of distraction. The experimental situation itself, i.e., taking part in an experiment in the driving simulator, could additionally have influenced the subjects as well as the perception of the environment and the traffic situation in the simulator. Another technical possibility would be a visibility obstruction of the pedestrian. This deviates from the realistic scenario as described in Section 3.2, but could help bring the subject into the accident situation and thus be an experimental necessity.
simu-4.5 Conclusion
lator. As the age characteristics of the sample used here range from 22 to 60, an interesting extension of this study might especially consider a population of older drivers, due to their possibly changed perceptual and reaction characteristics.
Everyday normal situations were used to assess the levels of discomfort or hazard while passing a pedestrian walking in the same direction as the vehicle moves or approaching a crossing pedestrian. The drivers could perceive the pedestrians from a great distance, passed them at an average lateral clearance of 1.5 m and started to brake on average at a TTC of 4 s. The implication of these findings is that a system issuing warnings within these boundaries (laterally and longitudinally) has a high chance of acceptance, if the driver did not react himself in advance. The consequence of low acceptance could be a deactivation of the system (if possible) or reluctance to purchase the system in a future vehicle (if optional equipment). In both cases the safety benefit would be negatively affected by low acceptance.
The acceptance ratings revealed that the subjects regarded situations as especially un-acceptablewhere an endangerment of the pedestrian was not obvious to them. The reasons given by the subjects were that the pedestrian is not moving or is far away from the current position of the vehicle or its present trajectory. Situations were regarded ashazardous for the surrounding traffic when false system actions were unpredictable for the drivers, the situations included higher vehicle speeds or the situations involved complex maneuvers. If the drivers’ attention was already high in a situation, a false system action was regarded as less hazardous.
An important finding is that the investigation of highly critical situations in the driv-ing simulator proves to be challengdriv-ing. The drivers reacted about 0.1 s earlier (difference is non-significant) at an average TTC of 1.44 s with the preventive pedestrian protection system installed than without. All drivers observed the pedestrian and reacted before or nearly at the time of the acoustical warning. The braking reaction in terms of timing, maximum deceleration, or duration between initiation of the braking and the maximum deceleration could not be evaluated regarding system effectiveness, as the driving simu-lator does not allow for an interpretation of these measurements. The missing realistic kinesthetic feedback due to the technically necessary scaling of the real accelerations is responsible for the magnitude of the braking reactions, not the experimental conditions.
The drivers brake much harder than in real accidents and also tend to push the brake pedal very fast, which is also suspected by experts not to be the case in real critical situations.
Overall, the highly critical situation hardly led to any accidents in the baseline condition, which gives an indication about the challenges of bringing the subjects into the situation in a driving simulator. These results shows that the realistic and reliable construction of anaccident situation (in contrast tocritical situations as used in many other experiments) is challenging even if a tested secondary task and an optimized test design are used.
Possible solutions are a stronger distraction of the subjects or a visibility obstruction of the pedestrian. Clearly, more research regarding the methodology of subject testing in accident situations is needed.
The technological limitations of the driving simulator as a method in general as well as of the specific one used here become evident when evaluating brake reactions. On the one hand, the time series of braking itself cannot be evaluated. On the other hand, the driving simulator is the method of choice to do subject testing in accident or critical situations.
Since this experiment had the driver in the focus, it is obvious that the behavior of the pedestrian is also an important field of research, although not part of this thesis. His actions do influence the situation itself as well as the system actions (e.g., prediction of collision probability).
A combination of findings obtained in different kinds of experiments – for example, using processes and techniques as described in Section 3.1 – is necessary to get a com-plete picture of the effects of a preventive system involving the vehicle as well as the driver.
5 Probabilistic modeling of pedestrian injury severity
5.1 Objective and research questions
This chapter presents the methodology necessary for the construction of evidence-based probability models for pedestrian injury severity in frontal vehicle crashes using empirical, in-depth accident data. The primary aim is thus to apply statistical methodology in or-der to estimate models predicting injury severity and mortality of pedestrians involved in vehicle crashes, based on the conditions of impact. The results are intended to improve and quantify the predictability of pedestrian injury severity during design and develop-ment phases of preventive pedestrian protection systems as well as to provide a basis for comparison with safety benefits due to measures of passive safety. The data sets used are the German In-Depth Accident Study (GIDAS) [10] and Pedestrian Crash Data Study (PCDS) [194] for the US.
It is well established that collision speed is the most important predictor for injury severity [45, 94, 160, 162, 190]. However, for constructing probability models for advanced applications, several additional research questions arise:
1. Which injury scale available in the data sets would be most suited for deriving prob-abilistic models?
2. Do multivariate models provide a better prediction than univariate models based solely on impact speed?
3. Does a splitting into subgroups defined by pedestrian age provide a better prediction than models comprising all ages?
The first of these issues refers to the choice of a metric to describe the injury scale.
In the data sets considered here, pedestrian injuries were originally coded according to the Abbreviated Injury Scale (AIS), revision 90 [12, 181, 182] (for cases 2008 and newer GIDAS also includes AIS coding following the 2005 revision [7, 8, 149]). Table 5.1 gives the AIS levels as well as the lethality rate associated with each level. The maximum AIS value (MAIS) of a person is separately coded and serves as an indicator for overall injury severity.
Another established injury coding scale is known as the Injury Severity Score (ISS) [20, 21, 181, 182]. The ISS is defined as the sum of the squares of the highest AIS scores in each of the three most severely injured body regions (out of six regions in total). It ranges from 0 to 75; 75 is the maximum and is defined if at least one body region has
Table 5.1: AIS codes and description [194] with corresponding lethality rate [124].
AIS Severity description Lethality rate [%]
0 not injured 0.00
1 minor injury 0.00
2 moderate injury 0.07
3 serious injury 2.91
4 severe injury 6.88
5 critical injury 32.32
6 maximum (untreatable) injury 100.00
an AIS of 6. There are strong indications in the medical literature that ISS gives a more precise estimate of the overall injury severity than MAIS [147, 185].
The second research question refers to the number of variables included in the models.
As stated above, impact speed is the most important predictor for injury severity and mor-tality. However, it is known, for example, that fatality risk can be predicted more precisely using pedestrian age in addition to impact speed [162]. Thus, considering the spectrum of variables coded in the databases, it is important to identify potential explanatory variables beyond impact speed that could improve the predictive accuracy of the models. Possible explanatory variables include vehicle kinematics (e.g., collision speed), vehicle characteris-tics (e.g., height of the front bumper), and pedestrian physiology (e.g., age).
The third research question takes the biomechanical differences due to pedestrian age into account. It is well known that the biomechanical response with respect to injury severity is dependent on age [101, 102, 153]. To this end, this study will also investigate whether a splitting of the population into subgroups depending on pedestrian age improves the quality of injury modeling.
In addition, two constraints concerning injury probability models are implemented. The first one is a simple definition: the injury or fatality probability for an impact speed vc = 0 kph is defined as zero. The second one is more subtle. In the case of several cumulative outcome categories (e.g., ISS9+, ISS16+, and ISS25+), the probability for a larger outcome category, e.g., ISS16+, must be at least as large as the probability for another smaller set, e.g., ISS25+ (pISS16+ ≥ pISS25+), which itself is a subset of the first one (ISS25+ ⊆ ISS16+). If that constraint has not been taken into account explic-itly in model development, then it needs to be tested to guarantee plausibility. To this end, a conditional probability simulation is introduced which generates synthetic vehicle-to-pedestrian accidents with all input parameters necessary for the models in question.
These two constraints are investigated, tested, and their implications are discussed to-gether with remarks for correct implementation of the models. A new methodology of constructing probability models for several cumulative outcome categories (e.g., ISS0-8, ISS9-15, ISS16-24, and ISS25+) by means of conditional probabilities is developed and tested for the constraints.