Christian Wewetzer

Data aggregation and roadside unit placement for a vanet traffic information system

In this paper we investigate how a VANET-based traffic information system can overcome the two key problems of strictly limited bandwidth and minimal initial deployment. First, we present a domain specific aggregation scheme in order to minimize the required overall bandwidth. Then we propose a genetic algorithm which is able to identify good positions for static roadside units in order to cope with the highly partitioned nature of a VANET in an early deployment stage. A tailored toolchain allows to optimize the placement with respect to an application-centric objective function, based on travel time savings. By means of simulation we assess the performance of the resulting traffic information system and the optimization strategy.

Experimental Evaluation of UMTS and Wireless LAN for Inter-Vehicle Communication

The rapid development of mobile communication technologies in recent years allows their application for inter-vehicle communication (IVC). In this paper, we evaluate the suitability of two wireless technologies for IVC, namely Universal Mobile Telecommunication System (UMTS) and IEEE 802.11 Wireless Local Area Network (WLAN). We analyze the communication properties of these technologies and point out the requirements of applications for IVC. We present a series of practical experiments to evaluate the eligibility of each technology for applications of IVC. To obtain comparable results, we performed measurements based on IP-unicast communication, since the lower layers of the two communication technologies differ drastically. Based on the recorded data of radio coverage, latency and throughput, we draw conclusions on currently feasible and infeasible applications for each technology.

VANET Simulation Environment with Feedback Loop and its Application to Traffic Light Assistance

Traffic applications, in which vehicles are equipped with a radio interface and communicate directly with each other and the road traffic infrastructure are a promising field for ad-hoc network technology. Vehicular applications reach from entertainment to traffic information systems, including safety aspects where warning messages can inform drivers about dangerous situations in advance. As performance tests of the real system are very expensive and not comprehensive, todays evaluations are based on analysis and simulation via traffic simulators. In order to investigate the impact of traffic information systems there are two options: First, traffic simulators can be extended by application code and a simplified model for wireless communication. Second, existing network simulators can be coupled with existing traffic simulators. We favor the coupling of existing and well known simulators as we believe that the wireless communication characteristics influence the data transfer significantly and an oversimplified transmission model can lead to flawed results. In this paper we describe the feedback loop between traffic and network simulators named traffic control interface (TraCI) and outline its versatility. We explain its use to determine possible energy consumption reduction when traffic lights send their phase schedules to vehicles.

Learning Traffic Light Phase Schedules from Velocity Profiles in the Cloud

Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the traffic light phase schedule, they could predict the traffic light state at arrival time and could reduce fuel consumption. To acquire information like traffic light phase schedules, our vision is that drivers share their velocity profiles in a digital cloud, and in return benefit from smart algorithms evaluating the collected data. We present one such algorithm, Traffic Light State Estimation (TLSE), that operates on the velocity profiles to backward-estimate phase schedules of traffic light signal groups operating with fixed cycle length (representing about 80% of all traffic lights in the US). We present simulation results showing that phase schedule prediction on the base of TLSE is correct more than 90% of the time.

Driving More Efficiently - The Use of Inter-Vehicle Communication to Predict a Future Velocity Profile

Fuel-efficient driving is difficult in unknown or complex environments. To aid the driver with this task, we present a novel method of tactical route optimization by calculating a short-term fuel-reduced velocity profile. This profile is based on knowledge of location-dependent velocity profiles that are collected by the vehicles over time and shared with other vehicles. To determine a fuel-efficient velocity profile, we first split the planned route into segments. We cluster the historical velocity profiles within each segment using a Dynamic Time Warping algorithm, obtaining classes of velocity profiles and their probabilities. We construct a transition graph between velocity profile classes from adjacent segments and calculate the most probable path through the next segments ahead. This path represents the most likely future velocity profile under the assumption that the driver behaves like previous drivers on the same segment. Given this profile, we calculate the fuel-reduced velocity profile with help of a shortest-path algorithm in a vehicle-specific fuel-consumption graph. First results in an urban environment indicate possible fuel savings of about 8.3% compared to the most probable profile.

The Feasibility of a Search Engine for Metropolitan Vehicular Ad-Hoc Networks

Applications based on vehicular ad-hoc networks (VANETs) rely on information exchanged within the network. There are two different methods of exchanging information among vehicles, i.e. distributing it proactively or requesting it on-demand. Proactive distribution of information is suitable for distributing small amounts of data, comprising information of common interest for a large number of vehicles. In contrast, proactive distribution of data is not advisable if information contained therein is relevant only to a small number of vehicles or if it would require large amounts of data. A more efficient alternative in this case is on-demand information distribution, which allows information of interest to be located, requested and sent on-demand. On-demand information retrieval in sparse metropolitan-wide VANETs is challenging, as vehicles stay in the metropolitan area only for a short period of time and message exchange is impacted by communication delays. In this paper, we introduce three different concepts of a search engine for metropolitan VANETs and explain how these concepts could make it possible to allow on-demand information exchange. We compare the concepts for their applicability by evaluating their performance with respect to latency and network load.

Analyzing vehicle traces to find and exploit correlated traffic lights for efficient driving

Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the situation at arrival time, they could adapt their velocity and thus reduce the number of unnecessary stops and fuel consumption. To predict the influence of the traffic light ahead on the velocity of an approaching vehicle, our vision is that drivers share their vehicle traces in a digital cloud, and in return benefit from algorithms evaluating the collected data. With Traffic Light Coordination Analysis (TLCorA), we present one such algorithm analyzing vehicle traces. When a vehicle is approaching a traffic light, TLCorA finds traces of vehicles similar to that of the vehicle at the previous traffic light, and calculates from their approach to the upcoming traffic light whether there is a representative approaching trace. For this purpose, TLCorA classifies the approaching traces with help of a clustering algorithm based on dynamic time warping. We implement TLCorA in simulations of different traffic light signalization algorithms, and study the calculated approach probabilities depending on the respective traffic light correlation level in the scenarios.