The introduction of automated vehicles (AVs) changes the way road users interact and communicate. In AVs, informal communication such as eye contact or gestures with other road users is omitted. Because interaction should still be objectively and subjectively safe, many studies are currently focusing on the communication processes between (automated) vehicles and predominantly vulnerable road users (VRUs), like pedestrians and cyclists [1 ]. These road users are highly at risk of being fatally injured in road traffic accidents, with the WHO reporting pedestrians and cyclists account for 32 % of all fatalities in Europe [2].... This shows why it is so important to study the interaction processes between VRUs, such as cyclists, and A Vs in real traffic. The algorithms of the A Vs must be able to anticipate the behavior of VRUs and thus ensure a subjectively and objectively safe interaction (cyclists should feel and be safe a.round them). This is the aim of the present study. How do cyclists behave when they encounter an apparent A V for the first time? How do they assess the situation and on what basis do they decide to cross? To answer these questions, a field study will take place in Munich in the summer of 2022, in which such interactions will be observed and the cyclists will be interviewed afterward. The study takes place within the TEMPUS project funded by the BMDV (German Federal Ministry for Digital and Transport). [From: Introduction]

An optimal-control based path planning algorithm has been developed recently for Connected and Automated Vehicles (CAVs) driving on a lane-free highway, including vehicle nudging. That vehicle movement strategy considers, in the lateral direction, a lateral desired speed that had been set to zero in previous works; in other words, vehicles avoid lateral movement if this is not helpful in achieving some of their goals, e.g. achieving a longitudinal desired speed by overtaking slower vehicles. In this work, a lateral positioning strategy for the vehicles is proposed, aiming to improve the vehicles’ longitudinal speeds and the traffic flow, mainly at intermediate densities, by distributing laterally the vehicles based on their longitudinal desired speeds. The intention is to leverage the existing optimal control formulation to move the CAVs to appropriate lateral positions, while respecting other, higher-priority sub-objectives, such as avoiding crashes. First, the longitudinal desired speed of each vehicle is mapped to a lateral desired position under the premise “faster vehicles drive farther left”. Then, the value of the desired lateral speed is updated in real-time in dependence on the vehicle’s current versus the desired lateral position, letting the optimal control problem, with the given sub-objective priorities, decide on the actual vehicle path. The proposed strategy is demonstrated via traffic simulations, involving various traffic densities, on a ring-road. Several quantities, such as the reached average flows and statistical measures of the error in the lateral position are computed for evaluation and comparison purposes.

Cycling offers many benefits, such as reducing traffic congestion, Iower emissions and health benefits. To further promote cycling, the cyclists' perceived safety needs to be addressed. In this context, automated vehicles offer high potential for designing safe and comfortable interactions with cyclists in the future. A key parameter in these interactions constitutes the proximity of vehicles passing cyclists to avoid causing discomfort. To evaluate specific scenarios with varying proximity, cycling simulators provide a safe and standardized environment for traffic safety research. Therefore, there are numerous efforts to implement cycling simulators for use in research. However, it is important to verify the simulator validity to ensure the generalizability of results. In this work, an implementation of a virtual reality (VR) cycling simulation is presented and it is aimed to investigate the simulator validity in terms of perceived criticality in traffic conflict scenarios as well as the participants' experience of presence within the VR cycling simulation. [from Introduction]

Interpreting the subtleness and complexity of vulnerable road user (VRU) behaviour is still a significant challenge for automated vehicles (AVs). Solutions for facilitating safe and acceptable interactions in future automated traffic include equipping AVs and VRUs with human-machine interfaces (HMl.s), such as awareness and notification systems, and connecting road users to a network of A Vs and infrastructure. The research on these solutions, however, primarily focuses on pedestrians. There is no overview ofthe type of systems or solutions supporting cyclists in future automated traffic. The objective ofthe present study is to synthesise current literature and provide an overview ofthe state-ofthe-art support systems available to cyclists. The aim is to identify, classify, and count the types of communicative technologies, systems, and devices capable of supporting the safety of cyclists in automated traffic. The overall goal is to understand A V-cyclist interaction better, pinpoint knowledge gaps in current literature, and develop strategies for optimising safe and pleasant cycling in future traffic environments with AVs.

Cycling as an active mode of transport is increasing across all Europe [1]. Multiple benefits are coming from cycling both for the single user and the society as a whole. With increasing cycling, we expect more conflicts to happen between cyclists and vehicles, as it is also shown by the increasing cyclists' share of fatalities, contrary to the passenger cars' share [2]. Understanding cyclists' behavioral patterns can help automated vehicles (AVs) to predict cyclist's behavior, and then behave safely and comfortably when they encounter them. As a result, developing reliable predictive models of cyclist behavior will help AVs to interact safely with cyclists.

Connected and automated vehicles (CAVs) will behave fundamentally differently than human drivers. In mixed traffic, this could lead to inefficiencies and safety-critical situations since neither human drivers nor CAVs will be able to fully anticipate or predict surrounding traffic dynamics. Thus, some researchers proposed to separate CAVs from conventional vehicles by dedicating exclusive lanes to them. However, the separation of road infrastructure can negatively impact the system’s capacity. While the effects of CAV lanes were addressed for freeways, their deployment in urban settings is not yet fully understood. This paper systematically analyzes the effects of CAV-lanes in an urban setting accounting for the corresponding complexities. We employ microscopic traffic simulation to model traffic flow dynamics in a detailed manner and to be able to consider a wide array of supply-related characteristics. These concern intersection geometry, public transport operation, traffic signal control, and traffic management. Our study contributes to the existing literature by revealing the potential of CAV lanes in an urban setting while accounting for the behavioral and topological complexities. The results of this study can support decision-makers in the design of future urban transportation systems and to prepare cities for the upcoming era of automation in traffic.

Falls are responsible for a large proportion of serious injuries and deaths among cyclists [1-4]. A common fall scenario is loss of balance during an emergency braking maneuver to avoid another vehicle [5-7]. Automated Vehicles (AV) have the potential to prevent these critical scenarios between bicycle and cars. However, current Threat Assessment Algorithms (TAA) used by AVs only consider collision avoidance to decide upon safe gaps and decelerations when interacting wih cyclists and do not consider bicycle specific balance-related constraints. To date, no studies have addressed this risk of falls in safety critical scenarios. Yet, given the bicycle dynamics, we hypothesized that the existing TAA may be inaccurate in predicting the threat of cyclist falls and misclassify unsafe interactions. To test this hypothesis, this study developed a simple Newtonian mechanics-based model that calculates the performance of two existing TAAs in four critical scenarios with two road conditions. Tue four scenarios are: (1) a crossing scenario and a bicycle following lead car scenario in which the car either (2) suddenly braked, (3) halted or (4) accelerated from standstill. These scenarios have been identified by bicycle-car conflict studies as common scenarios where the car driver elicits an emergency braking response of the cyclist [8-11] and are illustrated in Figure 1. The two TAAs are Time-to-Collision (TTC) and Headway (H). These TAAs are commonly used by AVs in the four critical scenarios that will be modelled. The two road conditions are a flat dry road and also a downhill wet road, which serves as a worst-case condition for loss of balance during emergency braking [12].

Public transport prioritization is used at signalized intersections to reduce travel times and increase the attractiveness of public transport. In the future, analog communication technologies for public transport prioritization are soon to be replaced by the promising vehicle-to-everything (V2X) technology. This abstract presents a holistic approach using V2X communication in public transport prioritization for an automated vehicle. In order to take full advantage of the V2X technology, this means to V2X-enable the traffic infrastructure and change the way of communication as well as the traffic light control. The approach was implemented and tested under real-life conditions at the research intersection Tostmannplatz in Braunschweig.

Effective communication between automated vehicles (AVs) and human road users (HRU) in mixed traffic is essential for ensuring safety, trust and acceptance. However, existing research on external Human-Machine Interfaces (eHMI) for AVs often overlooks design factors and their interconnections, leading to suboptimal designs. This article presents a comprehensive framework of Human-Machine Interaction in mixed traffic, integrating different relevant stakeholders, influencing factors, and relationships. By visualizing the interactions during communication and with the surrounding environment, the framework serves as a valuable tool for research and development of eHMI, maintaining a comprehensive perspective. Key challenges include determining optimal design features, such as message transmission methods and integration into the vehicle exterior design, and considering diverse human factors, such as age, culture, and cognitive abilities. By addressing these challenges, future eHMI designs can enhance user acceptance and trust in AVs, contributing to safer and more efficient mixed traffic environments. Further research will delve into the detailed examination of design factors and the interaction between interior and exterior vehicle interfaces.

Autonomes Fahren (AF) wird potenziell die Präferenzen für die im Auto verbrachte Zeit stark beeinflussen und dementsprechend den Wert der Reisezeit, der ein Schlüsselelement von Kosten-Nutzen-Analysen im Verkehr ist. Die Untersuchung dieses Aspekts des AF ist daher entscheidend für die Analyse potenzieller Auswirkungen der Technik auf die zukünftige Verkehrsnachfrage. Trotz der steigenden Anzahl an Studien zu diesem Thema, gibt es noch erhebliche Forschungslücken. Der Fokus der Dissertation ist die potenziellen Änderungen des Reisezeitwerts, die durch das AF entstehen, zu messen sowie ihre Determinanten zu analysieren. Es wurden sowohl qualitative Ansätze als auch quantitative Methoden verwendet. Dabei wurden zwei Konzepte von AF betrachtet: privates und geteiltes autnomes Fahrzeug. Die Ergebnisse der Analysen zeigen einen niedrigeren Wert der Reisezeitersparnis beim AF im Vergleich zum manuellen Fahren, allerdings nur auf Pendelwegen. Das private Fahrzeug wird als eine attraktivere Option als ein geteiltes Fahrzeug wahrgenommen, jedoch unterscheiden sich die Nutzerpräferenzen für geteilte Fahrzeug stark zwischen den durchgeführten Studien. Individuelle Charakteristiken, wie Erfahrung mit Fahrassistenzsystemen, beeinflussen stark die Wahrnehmung der Zeit im AF; andere sozio-demographischen Faktoren, wie Alter und Geschlecht haben vor allem einen indirekten Effekt auf den Reisezeitwert indem sie Einstellungen potenzieller Nutzer beeinflussen. Die Verbesserung des Fahrterlebnisses durch das AF und das Vertrauen in die Technik sind wichtige Determinanten der Reisezeitwahrnehmung. Fahrvergnügen und andere wahrgenommene Vorteile vom manuellen Fahren gleichen in einem gewissen Ausmaß den Nutzen vom AF aus. Es wurden Reisezeitwerte für unterschiedliche potenzielle Nutzersegmente berechnet. Abschließend wurden politische Implikationen, Empfehlungen für die Entwicklung von AF sowie Empfehlungen für künftige Studien und potenziellen Forschungsgebiete abgeleitet. / Autonomous driving will potentially strongly affect preferences for time spent in a vehicle and, consequently, the value of travel time savings (VTTS). As VTTS is a key element of cost-benefit analysis for transport, these interrelations are crucial for analysing the potential impact of the technology on future travel demand. Despite the increasing number of studies dedicated to this topic there are still many unanswered questions. The focus of the thesis is to measure potential changes in the VTTS resulting from the introduction of autonomous driving and analyse their determinants. Qualitative approaches and quantitative methods were used. Two concepts of AVs were considered: a privately-owned AV (PAV) and a shared AV (SAV). The analysis results suggest lower VTTS for autonomous driving compared to manual driving, but only on commuting trips. A PAV is perceived as a more attractive option than an SAV, but user preferences for SAVs vary between the conducted studies. Individual characteristics, such as experience with advanced driver assistance systems, strongly affect the perception of time in an AV; other socio-demographic factors, such as age and gender, affect mode choices and the VTTS mainly indirectly by influencing the attitudes of potential users. The improvement in travel experiences due to autonomous driving and trust in the technology are important determinants of the perception of travel time. Enjoyment of driving and other perceived benefits of manual driving partially counterbalance the utility of riding autonomously. VTTS for different potential user segments were calculated. In conclusion, several policy implications, development recommendations for AVs as well as recommendations for future studies and potential research avenues are derived from the findings.