Matthias Röckl

ITETRIS: a modular simulation platform for the large scale evaluation of cooperative ITS applications

Cooperative ITS systems are expected to improve road traffic safety and efficiency, and provide infotainment services on the move, through the dynamic exchange of messages between vehicles, and between vehicles and infrastructure nodes. The complexity of cooperative ITS systems and the interrelation between its components requires their extensive testing before deployment. The lack of simulation platforms capable to test, with high modelling accuracy, cooperative ITS systems and applications in large scale scenarios triggered the implementation of the EU-funded iTETRIS simulation platform. iTETRIS is a unique open source simulation platform characterized by a modular architecture that allows integrating two widely adopted traffic and wireless simulators, while supporting the implementation of cooperative ITS applications in a language-agnostic fashion. This paper presents in detail the iTETRIS simulation platform, and describes its architecture, standard compliant implementation, operation and new functionalities. Finally, the paper demonstrates iTETRIS large scale cooperative ITS evaluation capabilities through the implementation and evaluation of cooperative traffic congestion detection and bus lane management applications. The detailed description and implemented examples provide valuable information on how to use and exploit iTETRIS simulation potential.

Performance of CAM based safety applications using ITS-G5A MAC in high dense scenarios

ETSI ITS-G5 is the current vehicle-to-vehicle communication technology in Europe, which will be standardized by ETSI TC ITS1. 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.

Communication Technologies for Vehicles

The proceedings contain 20 papers. The topics discussed include: VANET architectures and protocol stacks: a survey; behavior specification of a red-light violation warning application - an approach for specifying reactive vehicle-2-X communication applications; wireless protocol design for a cooperative pedestrian protection system; a vehicular mobility model based on real traffic counting data; driver-centric VANET simulation; simulative evaluation of the potential of Car2X-communication in terms of efficiency; performance study of an in-car switched Ethernet network without prioritization; degradation of communication range in VANETs caused by interference 2.0 - real-world experiment; real-world measurements of non-line-of-sight reception quality for 5.9GHz IEEE 802.11p at intersections; and interoperability testing suite for C2X communication components.

Information-centric opportunistic data dissemination in Vehicular Ad Hoc Networks

This paper addresses the problem of efficient data dissemination in Vehicular Ad Hoc Networks (VANETs), which particularly suffer from changing densities in the network topology due to congested and sparse traffic on the roads. We present a new network layer protocol in the family of geographic network protocols, which makes use of distance and time information following a dissemination strategy to efficiently distribute messages adapting to the varying densities in VANETs. We have evaluated the protocol in different road density scenarios and its performance has been proved in comparison to two other recent protocols of the art.

V2V Communications in Automotive Multi-Sensor Multi-Target Tracking

Todays 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.

The Bayeslet Concept for Modular Context Inference

In the development and design of ubiquitous computing many challenges are arising. While there is much research done on service management systems and context provisioning, less effort is spent on the methods to actually generate context information - the process of context inference. If we are considering this field of research, we have not only to consider the pure algorithmic problem to infer otherwise unknown information from available data, we also have totarget the challenges of large scale systems with millions of users possibly spread across the world and the users requirements who is neither willing nor able to wait more thana couple of seconds for his request to be served. In this work we consider shortcomings in todays context inference systems and analyze requirements for emerging architectures relying on probabilistic algorithms, more precisely static Bayesian networks. We postulate the fragmentation of large networks into smaller so called Bayeslets, that are modular, (un)pluggable, individualisable and easy to process, as they are small and processing can be parallelised. Further on, we propose a formalism to note those Bayeslets in the Bayeslet Language (BalL). Hence, we have a way to easily exchange and deploy Bayeslets and even give application developers a way to provide their own inference rules to the pervasive system.

Data Dissemination in Cooperative ITS from an Information-Centric Perspective

The success of cooperative Intelligent Transportation Systems (ITS) applications such as collision avoidance or adaptive cruise control stands or falls with the exchange of information between distributed and usually moving nodes. The extensive transmission of information contrasts with a limited channel bandwidth which has to be shared between all nodes. Thus, a tradeoff is required which cooperatively selects pieces of information for dissemination according to their worth for the receivers under consideration of the communication channel conditions. The tradeoff is achieved by an entropy-based evaluation of evidence in a dynamic probabilistic filter system. With this approach the dissemination priority is based on the uncertainty reduction which can be achieved by the reception of a piece of evidence in contrast to a pure prediction. The novelty of the approach as presented in this paper is exceptional due to its information-centric evaluation of the worth of evidence which has not been performed so far for cooperative ITS applications. It outperforms current state of the art by its generic and theoretically grounded approach for diverse applications, its inclusion of measurement uncertainty, its context-adaptability and its optimized cooperative radio bandwidth utilization.

Hybrid fusion approach combining autonomous and cooperative detection and ranging methods for situation-aware driver assistance systems

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.

Comparison of exact static and dynamic Bayesian context inference methods for activity recognition

This paper compares the performance of inference in static and dynamic Bayesian Networks. For the comparison both kinds of Bayesian networks are created for the exemplary application activity recognition. Probability and structure of the Bayesian Networks have been learnt automatically from a recorded data set consisting of acceleration data observed from an inertial measurement unit. Whereas dynamic networks incorporate temporal dependencies which affect the quality of the activity recognition, inference is less complex for dynamic networks. As performance indicators recall, precision and processing time of the activity recognition are studied in detail. The results show that dynamic Bayesian Networks provide considerably higher quality in the recognition but entail longer processing times.

Optimizing dynamic composition of Bayesian Networks for context sensing and inference

Breaking Bayesian Networks for Context Inference from Sensor Networks into smaller Bayeslets is a proven approach for optimizing performance in adaptive resource-constraint ubiquitous computing and networking environments. Automatic selection and composition of such Bayeslets faces the challenge that the related cost factors (inference time, memory consumption) grow exponentially with the number of components. The paper discusses optimising approaches to evaluate the added value of using a particular Bayeslet vs. its cost to prune the dynamic composition graph.