A crucial step for reducing emissions in the transport sector is the shift towards public transportation and bicycling. However, due to the lack of perceived safety, people are reluctant to commute by bicycle. A potential solution to this problem could be group cycling, allowing cyclists to form a group with others in order to ride together. Depending on local traffic laws, such groups allow for cycling next to each other and for special rights for intersection crossing. In this paper, we outline how group cycling commutes may be coordinated using communication capabilities of contemporary smartphones. We showcase how group cycling can reduce waiting times and, thus, improve ride comfort and safety in a simulation-based case study.

Platooning is a promising cooperative driving application for future intelligent transportation systems. In order to assign vehicles to platoons, some algorithm for platoon formation is required. Such vehicle-to-platoon assignments have to be computed on-demand, e.g., when vehicles join or leave the freeways. In order to get best results from platooning, individual properties of involved vehicles have to be considered during the assignment computation. In this paper, we explore the computation of vehicle-to-platoon assignments as an optimization problem based on similarity between vehicles. We define the similarity and, vice versa, the deviation among vehicles based on the desired driving speed of vehicles and their position on the road. We create three approaches to solve this assignment problem: centralized solver, centralized greedy, and distributed greedy, using a Mixed Integer Programming (MIP) solver and greedy heuristics, respectively. Conceptually, the approaches differ in both knowledge about vehicles as well as methodology. We perform a large-scale simulation study using PlaFoSim to compare all approaches. While the distributed greedy approach seems to have disadvantages due to the limited local knowledge, it performs as good as the centralized solver approach across most metrics. Both outperform the centralized greedy approach, which suffers from synchronization and greedy selection effects. The centralized solver approach however assumes global knowledge and requires a complex MIP solver to compute vehicle-to-platoon assignments. Overall, the distributed greedy approach achieves close to optimal results but requires the least assumptions and complexity. Therefore, we consider the distributed greedy approach the best approach among all presented approaches.

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version ofPlexe, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naïve approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments.

The age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). The most frequently used definition of AoI, however, does only account for the generation time of the data but not for application-specific aspects. In ITS, for example, the distance of vehicles is not considered and nodes farther away may experience an increased AoI due to effects of the wireless communication channel. We propose a new way of interpreting the AoI in such a context, also considering the location of the transmitting vehicle as a metric of importance to the information. In particular, we introduce a weighting coefficient used in combination with the peak age of information (PAoI) metric to describe the AoI requirement, emphasizing on packets from more important neighbors. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. We use the derived model to focus on timely updates of relevant vehicles for meeting a given AoI requirement, which can save resources on the wireless channel while keeping the AoI minimal.

Road traffic is continuously increasing worldwide and Vulnerable Road Users (VRUs)such as bicyclists are ever more susceptible to injuries from crashes. Vehicular Ad-hoc Network (VANET)technologies in combination with Advanced Driver Assistance Systems (ADAS)are currently being evaluated to enhance road traffic safety. The same technology is now also considered for warning cyclists about imminent crashes. Prototypes for such VANET -enabled bicycles already exist, however, the primary problem here is verification and test due to immediate safety concerns for the participants. Computer simulation is considered a more feasible alternative; however, realistic behavior models of VRUs are not available in the required quality and quantity. We developed a virtual cycling environment to address this issue, allowing to study and record cyclists' behavior. We collected (and make available)traces from a variety of subjects cycling towards an intersection for three different safety critical scenarios. As proof of concept, we then used these traces in our vehicular networking simulation framework to assess the impact of a simple VANET solution on the cyclists' safety. Our results demonstrate the need for such an integrated framework for empirical studies as well as for simulation-based exploration of system configurations.