The trend towards urbanization increases the need for highly available public transportation. Nevertheless, the majority of society demands for individual transport too. To address these demands in urban areas, intelligent transportation systems (ITS) aim at increasing capacity and safety while reducing costs, accidents, and environmental impact. To that end, both the railway industry and the automotive industry focus on automation, digitization, and wireless communications to cope with increasing numbers of vehicles and passengers. These two industries may rely on cooperative ITS (C-ITS) communicating in the same frequency band. Without appropriate measures, interference between the different radio technologies must be assumed and reliable communication for safety-critical applications cannot be guaranteed. To develop accurate and realistic interference models for current and future radio technologies, we conducted a four-day measurement campaign with the Deutsche Bahn (DB) advanced TrainLab on the Berlin “Süd-Ring” tracks. In this paper, we present an overview on C-ITS radio technologies, the measurement campaign, first results, and conclusions. An initial data analysis shows that adjacent channel interference can cause severe performance degradation on urban rail C-ITS if generated in line-of-sight (LOS) to the train with a significant number of interfering signals.

Cooperative safety applications require dedicated short-range communications (DSRC) to provide position-awareness of neighboring vehicles at a specific level of reliability, i.e., awareness-quality, up to a given distance, i.e., awareness-range. However, heavy communication loads negatively impact such awareness requirements due to communication impairments, ranging from strict capacity limitations of DSRC channels to correlated packet collisions due to periodic communication patterns. Transmission control strategies may adapt power or rate to control such impairments but risk missing the requirements of cooperative safety applications. In this paper, we design a new awareness control strategy by implementing a spatial awareness framework. Specifically, we adapt the distribution of the awareness-quality as a function of the awareness-range. Therefore, we first propose random transmit power control (RTPC), which manages to provide different levels of awareness-quality at different ranges, while mitigating correlated packet collisions by randomizing them in space. As RTPC is able to reduce the channel load, we secondly propose to combine RTPC with transmit rate control (TRC) and to benefit from the gained channel resources by subsequently increasing the update-rate and by implication, the quality of position-awareness. The spatial awareness control capability of RTPC+TRC has been evaluated through simulations. We discuss the influence of RTPC+TRC on cooperative safety applications exemplarily for the forward collision warning (FCW) application.

The avoidance of train collisions is vital for human safety in railway transportation. Technical approaches are general train control or collision avoidance systems as well as semi-automated or fully autonomous trains. These systems rely on robust and exact train localization as well as an accurate map of the track network.We present Simultaneous Localization and Mapping relying exclusively on train-side sensors. RailSLAM, implemented as a probabilistic filter, uses measurements from multiple sensors and computes a track map. We rely heavily on sensors that are not affected by the harsh environmental conditions often experienced in this application, in particular a low-cost MEMS Inertial Measurement Unit (IMU). Rail vehicle localization methods based on these sensors require a dedicated map with detailed geometric track features in combination with the topological track connections. If this feature map does not exist apriori, it needs to be created. If it does, it may suffer from incompleteness, insufficient accuracy or outdated information. RailSLAM addresses the creation and maintenance of this special track map by a simultaneous estimation of the probabilistic geometric-topological feature-rich track map and the train state. A first proof of concept implementation of mapping is given based on the use of an Extended Kalman Filter with measurements from Global Navigation Satellite System (GNSS) and an IMU.

The localization of trains in a railway network is necessary for train control or applications such as autonomous train driving or collision avoidance systems. Train localization is safety critical and therefore the approach requires a robust, precise and track selective localization. Satellite navigation systems (GNSS) might be a candidate for this task, but measurement errors and the lack of availability in parts of the railway environment do not fulfill the demands for a safety critical system. Therefore, additional onboard sensors, such as an inertial measurement unit (IMU), odometer and railway feature classification sensors (e.g. camera) are proposed. In this paper we present a top-down train localization approach from theory. We analyze causal dependencies and derive a general Bayesian filter. Furthermore we present a generic algorithm based on particle filter in order to process the multi-sensor data, the train motion and a known track map. The particle filter estimates a topological position directly in the track map without using map matching techniques. First simulations with simplified particular state and measurement models show encouraging results in critical railway scenarios.

Information dissemination in pure Vehicular Ad Hoc NETworks (VANETs) such as ITS-G5 becomes problematic when the network is sparse. In situations where the number of vehicles, that can act as a communication node, is insufficiently low, e.g. in rural areas, during nighttime or because of a low market penetration of the technology in the early years of market introduction, certain range limits (unavailability of forwarding nodes) or timing limits (store-and-forward techniques) are stressed. Due to the limited communication range, VANETs start to build separated clusters, if the density of equipped vehicles is too low. Consequently, information dissemination without delay-massive store-and-forwarding is only possible within one cluster, but not beyond. This paper investigates the integration of Car-to-Car (C2C) with an additional satellite communication technology, referred to as Car-to-Satellite (C2S). A realistic sparse vehicular network scenario has been simulated and evaluated with respect to the in-time reception of safety-related information. The results show that information dissemination can be significantly improved through a limited number of vehicles which are additionally equipped with satellite terminals. In fact, even the market introduction of VANET-based ITS can be significantly accelerated with just a few vehicles equipped with non-VANET communication technology.

ETSI ITS-G5 is the current vehicle-to-vehicle communication technology in Europe, which will be standardized by ETSI TC ITS. It is based on IEEE 802.11p and therefore uses a CSMA/CA scheme for Media Access Control (MAC). In this paper we analyze the performance of Cooperative Awareness Message (CAM) based safety applications using the ETSI ITS-G5 MAC technology in a challenging scenario with respect to MAC issues: A suitable freeway segment with 6 lanes in each direction. The freeway scenario is thoroughly modeled and implemented in the well known ns-3 simulation environment. Based on this model, the paper shows the performance of CAM based safety applications under MAC challenging conditions. We provide a set of simulation results resting upon a particular performance metric which incorporates the key requirements of safety applications. Finally we analyze two concrete example scenarios to determine how reliable CAM based safety applications are in high dense traffic scenarios with respect to MAC issues.

The design of a collision avoidance system for trains implies the design of a MAC layer for their specific requirements. It should be efficient, reliable, use broadcast and support wireless mobile ad hoc networks (MANETs) with high user speeds. Therefore we are using awareness techniques, which allow a certain channel assignment, despite the absence of infrastructure. This paper presents a new MAC layer protocol designed for broadcast MANETs called COMB (Cell-based Orientation-aware MANET Broadcast). In principle, COMB allows the realization of a collision free transmission, high speed is supported and no handshake is required. COMB is based on localization aware cross layer dimensioned CDMA cells, and uses the SOTDMA protocol as intra cell scheme, while the inter cell scheme relies on direction and speed awareness.

Current driver assistance systems such as Adaptive Cruise Control (ACC) and in particular future assistance systems such as Collision Warning make high demands on reliability of detection and ranging methods for vehicles within the local vicinity. Autonomous systems such as Radar which are already integrated into a multitude of vehicles meet these requirements to only a limited extent. As an alternative, cooperative systems for detection and ranging will be enabled by future Vehicle-2-Vehicle communication. But cooperative detection and ranging also has drawbacks regarding reliability due to positioning and transmission errors if it is applied in a standalone way. Thus, the solution presented in this paper is a hybrid approach combining autonomous and cooperative methods for detection and ranging within a common architecture. A particle filter is used for state estimation. The results are a higher detection effectiveness and a lower position error compared to using standalone autonomous or cooperative detection and ranging methods.

This paper presents a channel model for direct train- to-train communication appropriate for the 400 MHz band. Extrapolation of theoretical and experimental results obtained for the planning of other railway communication systems like GSM-R is not obvious due to the difference in frequencies, antenna height and absence of base stations. In this paper, the analysis of the channel model covers different radio phenomena including path loss, Doppler, fading, and delay spread. Concretely we consider three scenarios (train stations, shunting yards and regional networks), for which the propagation channel characteristics are discussed. Furthermore, influence of special railway environments like cuttings which can be found near cities and towns, tunnels and bridges are encountered.

Today's automotive sensor systems for in-vehicle based target tracking, i.e. radar, lidar, camera, are limited to a field of view which is restricted by distance, angle and line-of-sight. Future driver assistance systems such as predictive collision avoidance or situation-aware adaptive cruise control require a more complete and accurate situation awareness in order to detect hazardous and inefficient situations in time. Therefore, we introduce multi-target tracking including vehicle-2-vehicle communications as a complementing sensor for future driver assistance systems. The paper presents first simulation results of our algorithm which show promising outcomes.

Today's automotive sensor systems for in-vehicle based target tracking, i.e. radar, lidar, camera, are limited to a field of view which is restricted by distance, angle and line-of-sight. Future driver assistance systems such as predictive collision avoidance or situation-aware adaptive cruise control require a more complete and accurate situation awareness in order to detect hazardous and inefficient situations in time. Therefore, we introduce multi-target tracking including vehicle-2-vehicle communications as a complementing sensor for future driver assistance systems. The demonstration presents set-up, functionality and results of multi-sensor multi-target tracking based on particle filtering.

Current driver assistance systems merely use a minimum set of information. By using additional information of the environment hazardous situations can be detected earlier, more reliably and with a higher precision. This situational information has a significant impact not only on the hazard detection, but also on other modules such as the human-machine-interface or knowledge distribution between vehicles over suitable networks. For a number of reasons situational information is inherently subject to uncertainty. This may be handled by the use of dynamic Bayesian networks, which we suggest can be used to represent these dynamic complex systems. The objective of this paper is to provide an architecture for driver assistance systems that is aware of uncertain situational information in order to prevent accidents and reduce the number of traffic fatalities. It introduces the concepts of utility-based knowledge exchange based on the consideration of other partners' knowledge, the amount of inference that has taken place to identify a situation and current network conditions. Furthermore, we suggest a representation of hazard descriptions using Bayesian network fragments.