Cycling provides individual and societal benefits, such as improved health [1], faster intra-urban commuting [2], lower C02 emissions [3] and all in all lower societal costs [4] compared to most other traffic modes. However, the national average of the cycling mode share was only around 10% in 2008 and has not increased remarkably ever since [5]. Several studies indicate that the lack of subjective safety may be a crucial reason to refuse using the bicycle [6, 7].While there is evidence on how to improve subjective safety through infrastructure on road sections [8], there is none concerning intersections or roundabouts yet. To close that gap, we investigate subjective safety at junctions depending on different infrastructure designs. [From: Introduction]

The probability of a cycling crash is much higher at intersections as along the road. A number of reasons contribute to this difference, for example car drivers overlooking cyclists when taking a turn. There have been attempts to quantify the risk at prototypical, un-signaled intersections featuring different levels of cycling infrastructure, as well as cyclists' perception of risk of these intersections. However, these attempts are limited to regular, four-arm intersections, although irregular intersections featuring both a higher and a lower nwnber of anns as weil as odd angles are likely to pose additional challenges for cyclists. There appears tobe little research on the question how the complexity and layout of such intersection affects cyclists perception of risk, as weil as their allocation of attention towards the different arms of an intersection. In, we presented a first approach to taclde this issue in a virtua1 reality (VR) based setup. We found evidence that tbe type oftum affected the subjective risk (e.g. with. a higher risk associated with situations requiring a sharp turn or to continue to an offset road), but no effects of the general position of an intersection arm in relation to the cyclist' traveling trajectory. However, the repeated exposure to the same intersection in this stu.dy limits the conclusiveness of the findings. We thus developed a more flexible virtual environment allowing us to investigate the attention allocation and risk. perception at various types of intersections.

Bicycle usage is increasing in urban (as well as rural) areas, which increases demand for better and safer infrastructure. Whilst the total number ofbicycle fatalities in European countries has been stable over the last ten years (:::: 2.000 fatalities per year for all European Union member states), bicycle fatalities and injuries in Germany have been increasing in this time. About two-thirds of all bicycle crashes in Germany occur at intersections, this proportion is highe:r than in Denmark and the N ethe:rlands (three-fi:fths). lntersections are tbus of high relevance for bicyclists' safety andin addition, they require sophisticated research methods because of their complex designs and the high numbers and types of uscr interactions and conflicts compared to street sections. This study analyses determinants of bicycle crashes at 269 signalised intersections in two major eitles in Germany (Dresden, Munich) as the basis for developing evidence-based recomm.endations for improving bicyclists' safety at existing intersections and for ensuring high safety levels at newly planned intersections from the very beginning. This study is part ofthe research project SiRou (nrvp.de/21520). The project is funded by the German Federal Ministry for Digital and Transport within the National Cycling Plan 2020(NRVP).

This master thesis has been done in the field of Advanced Driver Assistance Systems and presents a method to assist cross traffic at road junctions. An accurate tracking of crossing objects is necessary in order to assist traffic at road junctions. At Continental, the stable tracking of crossing objects is available, but the system still gives false alarms for non-colliding objects (e.g. Target Braking at crossroads). Hence the main focus of this thesis is on the reduction of false alarms for non colliding objects. Radar based Maneuver Detection function has been developed for Crossing Emergency Brake Assist system, which uses radar measurement parameters to detect the maneuvering of target objects in order to differentiate between collision and non-collision cases. Different crossing scenarios have been created in a Matlab environment and the algorithm is tested. Secondly, the algorithm is tested by using the measurement data from real recordings and evaluation is made. The proposed algorithm has reliably detected the non-collided objects (in normal cases) and helped in reducing the false alarm rate significantly.

Mobility has a vital impact on the quality of life in a city. Yet, traditional modes of car-centric transportation models generate large externalities that must be tackled by cities - such as congestion, noise and air pollution. The Free and Hanseatic City of Hamburg in Germany is striving for a mobility transition, making mobility more sustainable and environmentally friendly. The city wants to change the mobility behaviour by strengthening the means of transport that are causing less impact on the environment and climate. By 2030, the goal is to increase the share of cycling, walking and public transport to a total of 80 per cent of all routes travelled. Cycling, which is especially cost- and space-efficient, plays a crucial role here. More specifically, the share of all joumeys made by bike should be increased by 25-30 per cent within this decade. Within the framework of Hamburg's strategy of lntelligent Transport Systems (ITS), the city fosters, develops and conducts ITS-projects that focus, amongst others, on cycling. In order to increase the proportion of cycling, it is essential to promote its attractiveness. A cycling infrastructure that ensures smooth and easy cycling within the city is vital for a competitive alternative to motorised private transport. Furthermore, people enjoy cycling when they feel comfortable and safe [e.g. 3]. The ITS-project PrioBilce-HH follows this approach and addresses both topics: cycling comfort and safety. This abstract focuses on the aspect of safety.

This master thesis has been done in the field of Advanced Driver Assistance Systems and presents a method to assist cross traffic at road junctions. An accurate tracking of crossing objects is necessary in order to assist traffic at road junctions. At Continental, the stable tracking of crossing objects is available, but the system still gives false alarms for non-colliding objects (e.g. Target Braking at crossroads). Hence the main focus of this thesis is on the reduction of false alarms for non colliding objects. Radar based Maneuver Detection function has been developed for Crossing Emergency Brake Assist system, which uses radar measurement parameters to detect the maneuvering of target objects in order to differentiate between collision and non-collision cases. Different crossing scenarios have been created in a Matlab environment and the algorithm is tested. Secondly, the algorithm is tested by using the measurement data from real recordings and evaluation is made. The proposed algorithm has reliably detected the non-collided objects (in normal cases) and helped in reducing the false alarm rate significantly.

The development of driver assistance and automated vehicle control is in process and finds its way more and more into urban traffic environments. Here, the complexity of traffic situations is highly challenging and requires system approaches to comprehend such situations. The key element is the process of situation assessment to identify critical situations in advance and derive adequate warning and intervention strategies. This thesis introduces a system approach to establish a situation assessment process with the focus on the prediction of the driver intention. The system design is based on the Situation Awareness model by Endsley. Further, a prediction algorithm is created using Hidden Markov Models. To define the parameters of the models, an existing database is used and previously analyzed to identify reasonable variables that indicate an intended driving direction while approaching the intersection. Here, vehicle dynamics are used instead of driver inputs to enable a further extension of the prediction, i.e.\\ to predict the driving intention of other vehicles detected by sensors. High prediction rates at temporal distances of several seconds before entering the intersection are accomplished. The prediction is integrated in a system for situation assessment including an intersection model. A Matlab tool is created with an interface to the vehicle CAN bus and the intersection modeling which uses digital map data to establish a representation of the intersection. To identify differences and similarities in the process of approaching an intersection dependent on the intersection shape and regulation, a naturalistic driving study is conducted. Here, the distance to the intersection and velocity is observed on driver inputs related to the upcoming intersection (leaving the gas pedal, pushing the brake, using the turn signal). The findings are used to determine separate prediction models dependent on shape and regulation of the upcoming intersection. The system runs in real-time and is tested in a real traffic environment. / Die Entwicklung von Fahrerassistenz und automatisiertem Fahren ist in vollem Gange und entwickelt sich zunehmend in Richtung urbanen Verkehrsraum. Hier stellen besonders komplexe Verkehrssituationen sowohl für den Fahrer als auch für Assistenzsysteme eine Herausforderung dar. Zur Bewältigung dieser Situationen sind neue Systemansätze notwendig, die eine Situationsanalyse und -bewertung beinhalten. Dieser Prozess der Situationseinschätzung ist der Schlüssel zum Erkennen von kritischen Situationen und daraus abgeleiteten Warnungs- und Eingriffsstrategien. Diese Arbeit stellt einen Systemansatz vor, welcher den Prozess der Situationseinschätzung abbildet mit einem Fokus auf die Prädiktion der Fahrerintention. Das Systemdesign basiert dabei auf dem Situation Awareness Model von Endsley. Der Prädiktionsalgorithmus ist mit Hilfe von Hidden Markov Modellen umgesetzt. Zur Bestimmung der Modellparameter wurde eine existierende Datenbasis genutzt und zur Bestimmung von relevanten Variablen für die Prädiktion der Fahrtrichtung während der Kreuzungsannäherung analysiert. Dabei wurden Daten zur Fahrdynamik ausgewählt anstelle von Fahrereingaben um die Prädiktion später auf externe Fahrzeuge mittels Sensorinformationen zu erweitern. Es wurden hohe Prädiktionsraten bei zeitlichen Abständen von mehreren Sekunden bis zum Kreuzungseintritt erzielt. Die Prädiktion wurde in das System zur Situationseinschätzung integriert. Weiterhin beinhaltet das System eine statische Kreuzungsmodellierung. Dabei werden digitale Kartendaten genutzt um eine Repräsentation der Kreuzung und ihrer statischen Attribute zu erzeugen und die der Kreuzungsform entsprechenden Prädiktionsmodelle auszuwählen. Das Gesamtsystem ist als Matlab Tool mit einer Schnittstelle zum CAN Bus implementiert. Weiterhin wurde eine Fahrstudie zum natürlichen Fahrverhalten durchgeführt um mögliche Unterschiede und Gemeinsamkeiten bei der Annäherung an Kreuzungen in Abhängigkeit der Form und Regulierung zu identifizieren. Hierbei wurde die Distanz zur Kreuzung und die Geschwindigkeit bei Fahrereingaben im Bezug zur folgenden Kreuzung gemessen (Gaspedalverlassen, Bremspedalbetätigung, Blinkeraktivierung). Die Ergebnisse der Studie wurden genutzt um die Notwendigkeit verschiedener Prädiktionsmodelle in Abhängigkeit von Form der Kreuzung zu bestimmen. Das System läuft in Echtzeit und wurde im realen Straßenverkehr getestet.

The development of driver assistance and automated vehicle control is in process and finds its way more and more into urban traffic environments. Here, the complexity of traffic situations is highly challenging and requires system approaches to comprehend such situations. The key element is the process of situation assessment to identify critical situations in advance and derive adequate warning and intervention strategies. This thesis introduces a system approach to establish a situation assessment process with the focus on the prediction of the driver intention. The system design is based on the Situation Awareness model by Endsley. Further, a prediction algorithm is created using Hidden Markov Models. To define the parameters of the models, an existing database is used and previously analyzed to identify reasonable variables that indicate an intended driving direction while approaching the intersection. Here, vehicle dynamics are used instead of driver inputs to enable a further extension of the prediction, i.e.\\ to predict the driving intention of other vehicles detected by sensors. High prediction rates at temporal distances of several seconds before entering the intersection are accomplished. The prediction is integrated in a system for situation assessment including an intersection model. A Matlab tool is created with an interface to the vehicle CAN bus and the intersection modeling which uses digital map data to establish a representation of the intersection. To identify differences and similarities in the process of approaching an intersection dependent on the intersection shape and regulation, a naturalistic driving study is conducted. Here, the distance to the intersection and velocity is observed on driver inputs related to the upcoming intersection (leaving the gas pedal, pushing the brake, using the turn signal). The findings are used to determine separate prediction models dependent on shape and regulation of the upcoming intersection. The system runs in real-time and is tested in a real traffic environment.:Contents List of Figures Acronyms 1 Introduction 1.1 Motivation 1.2 Outline 2 Fundamentals 2.1 Traffic Intersections 2.2 Situation Assessment 2.3 Prediction of Driver Intention 2.3.1 Methods Overview 2.3.2 Hidden Markov Models 2.4 Localization 3 Driving Behavior 3.1 Data Analysis 3.1.1 Data selection and processing 3.1.2 Results 3.1.3 Conclusion 3.2 Naturalistic Driving Study 3.2.1 Background 3.2.2 Methods 3.2.3 Results 3.2.4 Discussion and Conclusion 4 Prediction Algorithm 4.1 Framework 4.2 Input data 4.3 Evaluation 4.4 Validation 4.5 Conclusion 5 System Approach 5.1 Sensing 5.2 Situation analysis 5.3 Prediction 5.3.1 Implementation 5.3.2 Graphical User Interface (GUI) 5.3.3 Testing and Outlook 6 Conclusion and Outlook Bibliography / Die Entwicklung von Fahrerassistenz und automatisiertem Fahren ist in vollem Gange und entwickelt sich zunehmend in Richtung urbanen Verkehrsraum. Hier stellen besonders komplexe Verkehrssituationen sowohl für den Fahrer als auch für Assistenzsysteme eine Herausforderung dar. Zur Bewältigung dieser Situationen sind neue Systemansätze notwendig, die eine Situationsanalyse und -bewertung beinhalten. Dieser Prozess der Situationseinschätzung ist der Schlüssel zum Erkennen von kritischen Situationen und daraus abgeleiteten Warnungs- und Eingriffsstrategien. Diese Arbeit stellt einen Systemansatz vor, welcher den Prozess der Situationseinschätzung abbildet mit einem Fokus auf die Prädiktion der Fahrerintention. Das Systemdesign basiert dabei auf dem Situation Awareness Model von Endsley. Der Prädiktionsalgorithmus ist mit Hilfe von Hidden Markov Modellen umgesetzt. Zur Bestimmung der Modellparameter wurde eine existierende Datenbasis genutzt und zur Bestimmung von relevanten Variablen für die Prädiktion der Fahrtrichtung während der Kreuzungsannäherung analysiert. Dabei wurden Daten zur Fahrdynamik ausgewählt anstelle von Fahrereingaben um die Prädiktion später auf externe Fahrzeuge mittels Sensorinformationen zu erweitern. Es wurden hohe Prädiktionsraten bei zeitlichen Abständen von mehreren Sekunden bis zum Kreuzungseintritt erzielt. Die Prädiktion wurde in das System zur Situationseinschätzung integriert. Weiterhin beinhaltet das System eine statische Kreuzungsmodellierung. Dabei werden digitale Kartendaten genutzt um eine Repräsentation der Kreuzung und ihrer statischen Attribute zu erzeugen und die der Kreuzungsform entsprechenden Prädiktionsmodelle auszuwählen. Das Gesamtsystem ist als Matlab Tool mit einer Schnittstelle zum CAN Bus implementiert. Weiterhin wurde eine Fahrstudie zum natürlichen Fahrverhalten durchgeführt um mögliche Unterschiede und Gemeinsamkeiten bei der Annäherung an Kreuzungen in Abhängigkeit der Form und Regulierung zu identifizieren. Hierbei wurde die Distanz zur Kreuzung und die Geschwindigkeit bei Fahrereingaben im Bezug zur folgenden Kreuzung gemessen (Gaspedalverlassen, Bremspedalbetätigung, Blinkeraktivierung). Die Ergebnisse der Studie wurden genutzt um die Notwendigkeit verschiedener Prädiktionsmodelle in Abhängigkeit von Form der Kreuzung zu bestimmen. Das System läuft in Echtzeit und wurde im realen Straßenverkehr getestet.:Contents List of Figures Acronyms 1 Introduction 1.1 Motivation 1.2 Outline 2 Fundamentals 2.1 Traffic Intersections 2.2 Situation Assessment 2.3 Prediction of Driver Intention 2.3.1 Methods Overview 2.3.2 Hidden Markov Models 2.4 Localization 3 Driving Behavior 3.1 Data Analysis 3.1.1 Data selection and processing 3.1.2 Results 3.1.3 Conclusion 3.2 Naturalistic Driving Study 3.2.1 Background 3.2.2 Methods 3.2.3 Results 3.2.4 Discussion and Conclusion 4 Prediction Algorithm 4.1 Framework 4.2 Input data 4.3 Evaluation 4.4 Validation 4.5 Conclusion 5 System Approach 5.1 Sensing 5.2 Situation analysis 5.3 Prediction 5.3.1 Implementation 5.3.2 Graphical User Interface (GUI) 5.3.3 Testing and Outlook 6 Conclusion and Outlook Bibliography