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. Abstract In his meta-analysis, Thomas Winkle documents exemplary analyses of potential safety-enhancing vehicle systems with low degrees of automation. However, a safety prognosis of highly or fully automated vehicles depends on assumptions, as so far no series applications of such features exist. For testing methods to develop and validate safe automated vehicles with reasonable expenditure, the author recommends combining worldwide traffic accident-, weather-, vehicle operation data and traffic simulations. Based on these findings, a realistic evaluation of internationally prospective, and statistically relevant real world traffic scenarios as well as error processes and stochastic models can be analysed (in combination with virtual tests in laboratories and driving simulators) to control critical driving situations. Advancing vehicle automation promises new opportunities to better meet society’s future mobility demands.
New, extended concepts for interaction with machines are arising in certain areas. A prerequisite for this is further technological development of assistance systems with more capable sensor and information technologies, allowing for a steady automation of driving tasks in vehicle control, right up to self-driving vehicles. Initially the following meta-analysis documents exemplary investigation of potential safety-enhancing vehicle systems with low degrees of automation. However, a safety prognosis of highly or fully automated vehicles depends on assumptions, as so far no series applications of such features exist. For testing methods in order to develop and validate safe automated vehicles with reasonable expenditure, the author recommends combining area-wide traffic, accident, weather, and vehicle operation data as well as traffic simulations.
Based on these findings, a realistic evaluation of internationally and statistically relevant real world traffic scenarios as well as error processes and stochastic models can be analyzed (in combination with virtual tests in laboratories and driving simulators) to control critical driving situations in the future. 17.1.1 Motivation In terms of advancing automation, automobile manufacturers have been offering active steering-assistance systems (Lane Keeping Assistance Systems—LKAS) in combination with adaptive cruise control for series production vehicles since as far back as the turn of the millenium.
The combined functionality was available on the Japanese market for right-hand drive vehicles such as the Nissan Cima (2001) and the Honda Inspire (2003). When using both assistance systems, short-term, partially automated driving (see Sect. ) of up to 20 seconds was possible under the supervision of the driver (author’s test drives in 2003). Since 2008, German manufacturers, starting with the VW Passat CC, have also been selling active steering systems optionally in selected models.
Opportunities for greater traffic safety increase with rising vehicle automation. Further market penetration of standard equipped safety-enhancing driver-assist systems will lead to a further reduction in road accidents (see p.
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According to figures from the Federal Statistical Office of Germany, 3475 people were killed in road accidents in Germany in 2015. On average around nine people a day lose their lives in this way on German roads alone. Among these accidents are some that can be prevented by automated vehicles in future. A potential safety benefit can be determined on the basis of accident data. The examples given in this article demonstrate the possibilities and limits of analyzing this data. What is meant here by potential safety benefits is the predicted fall in accident-related damage.
Prerequisites in order to determine specific potential safety benefits are basic assumptions about the overall traffic situation and about the proportion of automated driven mileage with its corresponding functional limits. Traffic accident research is carried out worldwide by various organizations. Their research encompasses the subfields of accident surveys/-statistics, accident reconstruction, and accident analysis. Accident investigation, carried out by the police in all federal states, forms the basis for accident research in Germany. Furthermore, other institutions such as the Traffic Accident Research Institute of TU Dresden GmbH (Verkehrsunfallforschung, or VUFO) and the Hannover Medical School, as well as vehicle manufacturers and the German insurance industry, all carry out their own accident research.
Central to this is investigating accidents directly at the scene, statistically recording and analyzing them according to certain characteristics, and, where needed, using this to further develop future vehicle automation. Regarding automated vehicles’ potential safety benefits, the following elaboration exemplarily demonstrates potentials, limits of findings and predictions given by accident-data collections. What significance do analyzes and findings from road-accident research hold for the introduction of automated vehicles?. How can the potential safety benefits of automated vehicles be established? 17.1.2 Categorizing the Levels of Driving Automation Three categories for levels of driving automation (concerning the degree of vehicle guidance) are outlined briefly below, and will later be used as examples to illustrate the potentials and limits of accident data analysis. For use in subsequent sections here the categorization of the previous BASt-project group “Legal consequences of an increase in vehicle automation” published in 2012 is sufficient. These five degrees of automation begin with the original conventional vehicle guidance, termed “driver only”, where the driver is permanently responsible for the vehicle’s longitudinal and lateral motion.
The gradations continue with driver assistance (“assisted”) and partial automation (“partially automated”), with constant driver supervision at all times. Lastly, the levels of high automation (“highly automated”) and full automation (“fully automated”) permit humans to stay out of the vehicle-guidance process some or all of the time. Another five levels were also defined by the American NHTSA agency. Subsequently the SAE International, formerly the Society of Automotive Engineers, developed further six distinctions, as described in its J 3016 informational report.
They are being increasingly used in today’s research projects. These levels correspond precisely to the BASt levels published previously in 2012, with two key differences. First, the names are different. Second, SAE adds level 5 (full automation), at which the automated driving system performs the entire dynamic driving task under all conditions that can be managed by a human driver (see below Fig. 17.3.1 Level of Data Collection Versus Number of Cases The validity of accident data regarding potential safety benefits varies considerably depending on the collection method. In-depth surveys are mostly carried out in cooperation with qualified interdisciplinary teams.
Particularly well-founded results are achievable when function developers, accident analysis experts, doctors, and traffic psychologists work together on analyzing individual cases. But this level of data collection is usually restricted to a low number of cases, hampering its statistical validity. Evaluations from accident databases indicate which measures are needed to increase traffic safety. A detailed accident analysis including an accident reconstruction encompasses a retrograde calculation of speeds from traces of the accident, an investigation into how the accident arose, a check for accident fraud, consideration of how avoidable it was, and biomechanics. An evaluation of future systems’ potential benefits based on this requires extensive knowledge of the given conditions and framework.
Until now, forward-thinking ideas on improving vehicle safety have primarily come from combining accident analysis, existing experience and extensive research work. Accident research is one way to determine the efficiency of existing automated vehicle functions and the need for new safety-enhancing ones. In what follows, basic terms of accident-data evaluation are explained. 17.3.2 The Validity of Areas of Action Compared to Areas of Efficiency When comparing various accident-data analyses, both the way in which data is collected and the way it is processed must be distinguished.
Frequently, areas of action adopted under optimal conditions are confused with areas of efficiency under real conditions. An area of action covers the accidents that a system can influence.
The area of action can vary depending on how precise a system’s specification is defined. As a result it is an initial estimation for the maximum achievable potential of the regarded level of automation. The actual resulting efficiency of a function, on the other hand, is usually significantly lower. Efficiency here is the effect that a specified system actually has. It is either proved in the occurrence of accidents (a posteriori) or predicted by a simulation (a priori).
(17.1) 17.3.3 Potential Safety Benefits Depending on the Level of Automation and Degree of Efficiency Some analyses of potential safety impacts using accident databases examine the maximum assumed area of action described above. In contrast, analyzing the degree of efficiency comes closer to reality by assessing an area of efficiency for its actual benefit.
The resulting safety benefits of automated vehicles only arise, though, after all risks have been taken into consideration. The benefit complies with the reduction of accident frequency and severity. New risks exist in terms of as-yet nonexistent accidents that may arise with increasing automation.
On the one hand, when looking at the actual total consumer-relevant potential as regards the safety benefit of automated vehicles, this increases in accordance with the degree of efficiency up to the maximum area of efficiency (proof by accident data analysis and knowledge of functions). On the other hand, the risks may increase in line with the level of automation (“Driver” vs. These in turn reduce the actual safety benefit (see Fig. To minimize potential risks, manufacturers carry out risk management (see Chap. ) that takes accident data into account. Using examples, the following meta-analysis shows what is and is not possible when drawing conclusions about potential benefits on the basis of various accident data. As there is no existing experience of analyzing highly and fully automated vehicles, systems without automation (“driver only”/“no automation”) or with low levels of automation referring to the main driving task (“assisted”/“partially automated”) are considered first, divided into a-posteriori- and a-priori-analyzes.
Section describes examples of a-posteriori-statements on accident data collected so far. In the definition used here, figures “gained from experience” can be used directly for interpretations. In contrast, the a-priori-forecasts defined in Sect. Are based on accident-data collections to assess the potential benefits of future levels of automation, exclusively using assumptions “obtained by logical reasoning”. 17.4.1 A-Posteriori-Analyzes of Accident Data for “Driver Only”/“No Automation” Past and present a-posteriori-analyzes of accident-data collections with conventionally (human-) driven vehicles form the basis for direct insights into accident black spots and changes in real-life traffic accidents. In this “driver-only”/ “no-automation” category, there are neither warnings nor interventions in longitudinal and lateral guidance on the basis of environmental sensors. To illustrate this, the change in the numbers of accident deaths serves as one example (see Sect.
), the impact of Electric Stability Control, or ESC, is another (see Sect. 17.4.1.1 Traffic Statistics: Accident Fatalities Versus Registered Motor Vehicles One example of what currently available accident data can show is the relationship of traffic accident deaths to vehicles registered, taken from data of the German Federal Statistics Office. This demonstrates that numbers have been dropping in Germany since the dramatic figure of 21,332 traffic accident fatalities in 1970. In summary, neither reliable specifications for OEM (Original Equipment Manufacturers) mass production solutions ready for market nor concrete information on the functional limits of highly and fully automated driving are currently available. To date, therefore, numerous assumptions have had to be made in forecasting potential safety benefits. Reliable data is also lacking on market launch and penetration.
Thus today’s projections of potential safety benefits, based on accident data, only have limited validity. It is hence recommended to combine in-depth accident data collections (e.g.
GIDAS) with all worldwide available accident data collections and analyzes, traffic simulations, related weather information and vehicle operation data (see Fig. Fig. 17.7 Today 93.5% of accidents results from human error. With full automation, there would be no more human error. However, the proportion of technical faults may be perceived considerably enlarged in future. Source GIDAS. Image rights: Author During fully automated driving sections, the number of accidents caused by driver error is ruled out completely.
The “technical failure” category could become proportionally larger, with the new technical risks of fully automated driving. This will lead to the public giving it greater attention (see Fig. Further assessment and overcoming of human failure-processes in real traffic situations—in addition to worldwide relevant test scenarios based on comprehensively linked up geographically defined accident, traffic, weather, and vehicle operation data collections (see Sect., Figs.
And )—will support virtually traffic simulations for safe development, tests and validation of automated vehicles in the future. 17.5.2 Potential Safety Benefits–Human and Machine Performance Vehicles’ road safety today essentially depends on the performance of humans supported by safety-enhancing systems. Fully automated vehicles will only rely on the capabilities of machines. Depending on the degree of automation, technical systems will replace humans’ perceptions, experience, judgment and capacity to react. Both the potential safety benefits and risks of increasingly automated vehicle guidance result from the various strengths and weaknesses of humans and machines. Machines, for example, cannot react to unknown situations or interpret the movements of children (see Chap. In comparison, humans can be inattentive, judge distances and speeds badly and their eyes only see a restricted field of vision.
17.5.2.1 Machine Versus Human Perception Limits and Consequences for Testing. To illustrate the limited performance of technological perception compared to that of humans, a heavily simplified model of currently used sensor technologies is described below. Sensors are needed for a vehicle to be able to collect information about its environment, and are classifiable according to their physical measuring principle. The automobile sector mainly uses radar, lidar, near and far infrared, ultrasound sensors, and cameras. The upper and center image of Fig. Show simplified and color-coded the limited machine perception of individual measuring principles.
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Compared with this, the lower image superimposes all these above-named measurements onto what humans perceive among difficult light- and weather conditions (sun, backlight, wet road surface, spray/splashing water, icing/contamination of windshield/sensors, road markings only partially visible). Close investigation reveals that the lefthand radar reflection point (blue) is a false detection, caused by a reflection in the opposite lane (see , ). Fig. 17.8 Machine versus human perception ( upper image radar in blue with lidar in yellow, center image addition with camera-image processing in green and red, lower image overlay machine with human perception).
Image rights: Author Figure illustrates that the outcome of machine perception and interpretation of complex traffic situations continues to present development engineers with considerable technical challenges. These include detecting static and dynamic objects, physically measuring them as accurately as possible, and allocating the correct semantic meaning to the detected objects (see Chap. Difficult light- and weather conditions challenge human and machine perception in real traffic situations.
For this purpose area-wide accident data analyzes (see Sect. ) are able to indicate temporally and geographically related accident black spots.
To analyze scenarios considering reduced visibility due to fog, rain, snow, darkness and glare from sun or headlights, the author carried out a fist-of-its-kind area-wide accident study in cooperation with Christian Erbsmehl from Fraunhofer Institute for Traffic and Infrastructure Systems (IVI) in Dresden. One finding of the case-by-case analysis was that in 95% of all cases no evasive actions to avoid accidents were documented. Only in 1% of the cases drivers were able to reduce the consequences of a collision by evasive maneuvers. Other evasive maneuvers failed (4%). Figure presents results of this study with relevant geographical accident scenes for virtual, proofing ground, and field tests of automated vehicles covering all police reports in Saxony from the years 2006 to 2014.
Fig. 17.9 Area-wide geographically related traffic accident scenes with difficult weather conditions and reduced visibility for human and machine perception (Geographical data © state-owned enterprise geo basic information and measurement Saxony 2015). Image rights: Author 17.5.2.2 Human Error Versus Machine Incertitudes Advancing vehicle automation of the main driver tasks result in new research questions. Attentive and vigilant drivers have substantial skills to deescalate dangerous traffic situations. Human’s capabilities provide significant input for traffic safety today.
Differentiated potential-benefit estimates would need to compare the performance of humans and machines. Especially take-over situations between driver and machine involve new challenges for design and validation of human-machine interaction. Initial tests at the professorial chair of Klaus Bengler, professor for ergonomics at the Technical University of Munich (TUM) demonstrate relevant ergonomic design requirements which will be continued. Fundamental correlations between automation and human performance can be evaluated by many methods. It is possible to identify the probability of a road accident by the use of a fault tree. Amongst others the probability includes human failure, inappropriate behavior and the existence of a conflicting object. The choice of actions to avoid a collision is greater if the potential road accident is less imminent.
High-speed Ii Pinball
The evaluation of driver behavior requires observations for a longer period. Regarding human failures analyzing the perception process chain provides in-depth knowledge. Such analyzes draw on evaluations of psychological data from road accidents. In terms of interdisciplinary accident analyzes, an error classification of five categories has been approved by practical experience in accident research. This five-steps method is a further development of ACASS (Accident Causation Analysis with Seven Steps). It was developed jointly with GIDAS along the lines of the seven-step principle by Jens Rasmussen, former system safety and human factors professor in Denmark, a highly influential expert within the field of safety science, human error, risk management and accident research. Using the five-steps method it is possible to identify human errors, define the time during the perception process from accessing the information to operation, and to evaluate the particular type of error (see Fig.
The associated questions concern: Information Access (was the relevant information of the traffic-situation objectively accessible to the driver? Was the field of vision clear?), Information Reception (did the driver observe the traffic situation properly and perceive/detect the relevant information subjectively?), Data Processing (did the driver correctly interpret the traffic situation according to the available information?), Objective Target (did the driver make a decision appropriate to the traffic situation?), and Operation (did the driver carry out his or her decision into operation properly?). For machine perception, Klaus Dietmayer, professor in Ulm at the Institute of Measurement, Control, and Microtechnology, Expert for Information fusion, Classification, Multi-Object Tracking, Signal processing and Identification (see Chap.
) names three essential domains of incertitudes corresponding to human Information Access as well as Data Processing. These three are: firstly State-, secondly Existence-, and thirdly Class uncertainty.
All three have a direct impact on machine performance. If the uncertainties in these areas increase beyond a yet to be defined “tolerable limit”, errors in the automatic vehicle guidance can be expected.
In terms of making forecasts, currently only an indication of trends is possible. While the currently known methods for estimating state and existence uncertainties do not enable a current estimation of the capability of the machine perception, in principle it is not possible to predict degeneration in the capability of individual sensors or even a failure of components. ) 17.5.3 Potential Safety Benefits of Fully Automatied Vehicles in Inevitable Incidents When analyzing the potential safety benefits of fully automated vehicles, it is also important to consider persistent risks in the area of complex traffic situations and today’s known inevitable incidents. These include accidents at poorly visible and unclear intersections or behind visual obstructions.
In a study of individual cases as part of a doctoral thesis at the University of Regensburg, visual obstruction was identified as a contributory cause in 19% of all cases. Examples include trees, bushes, hedges, and high grass. Obstructions for instance may also be the cause of an accident if a child is running out suddenly and unexpectedly in front of a car from between parked vehicles or a yard entrance.
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