Fabian de Ponte Müller

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.

Characterization of a Laser Scanner Sensor for the Use as a Reference System in Vehicular Relative Positioning

Advanced Driver Assistance Systems (ADAS) play an important role in increasing the safety on today’s roads. Forward collision warning systems, lane change assistants or cooperative adaptive cruise control are examples of safety relevant applications that rely on accurate relative positioning between vehicles. Current solutions found in commercial automobiles estimate the position of surrounding vehicles by measuring the distance with RADAR, cameras or IR-sensors. It is envisioned that the advent of inter-car communication will provide on-board relative positioning systems with further information about other vehicles in the surrounding area. While performing research in this field, the need of a proper reference system for testing new approaches originates. In the ideal case, such a reference system would yield the exact and continuous 3D baseline between two vehicles at any time in any circumstance. In this paper we will characterize the use of a laser scanner as a reference system for relative vehicle positioning.

Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles

Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability.Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability.

Standalone inertial pocket navigation system

Positioning applications became more important in recent years not only for security applications, but also for the mass market. Having a pedestrian navigation system embedded in a mobile phone is a realistic solution since it is equipped with low-cost sensors and the smartphone is located in a non-obstructive way. The location of the smartphone is important, since the position estimation process depends on it. Therefore, we propose to distinguish between pocket or bag, phoning, texting and swinging. We present a standalone inertial pocket navigation system based on an inertial measurement unit. For the computation of the orientation, we have developed an attitude estimator based on an unscented Kalman filter. The update stage has two different updates based on the acceleration and the magnetic field. Therefore, a zero acceleration detector, a magnetic disturbances detector and a static periods detector have been developed. The odometry in our navigation system is computed through an extended Kalman filter. The position is predicted with a movement model which is periodically updated through position corrections computed by the position computer. It comprises a step detector and a step length estimator based on the norm of the acceleration. The performance of our attitude estimator in comparison with the ground truth orientation is shown. The rest of the handheld positions are also tested for orientation. Likewise, we show pocket odometries of different users with the floor plan superimposed.

Bayesian cooperative relative vehicle positioning using pseudorange differences

Forward collision warning systems, lane change assistants or cooperative adaptive cruise control are examples of safety relevant applications that rely on accurate relative positioning between vehicles. Current solutions estimate the position of surrounding vehicles by measuring the distance with a RADAR sensor or a camera system. The perception range of these sensors can be extended by the exchange of GNSS information between the vehicles using an inter-vehicle communication link. In this paper we analyze two competing strategies against each other: the subtraction of the absolute positions estimated in each vehicle and the differentiation of GNSS pseudoranges. The aim of the later approach is to cancel out correlated errors in both receivers and, thus, achieve a better relative position estimate. The theoretical analysis is backed with Monte-Carlo simulations and empirical measurements in real world scenarios. Further on, two Bayesian approaches that make use of pseudorange differences are proposed. In a Kalman Filter pseudorange and Doppler measurements are used to estimate the baseline between two vehicles. This is extended in a second filter using on-board inertial and speed sensors following a multisensor fusion approach. The performance is evaluated in both, a highway and an urban scenario. The multisensor fusion approach proves to be able to stabilize the baseline estimate in GNSS challenging environments, like urban canyons and tunnels.

Evaluation of AHRS algorithms for inertial personal localization in industrial environments

This paper presents a comparison among several state-of-the-art Attitude and Heading Reference Systems (AHRS). These algorithms can be used for 3D orientation and position estimation of users or devices. The robust performance of these AHRS algorithms is of paramount importance, specially in environments with potential external perturbations, such as industrial environments. The comparison among AHRS algorithms presented in this paper also includes an algorithm recently proposed by the authors (DLR-AHRS). In this paper the performance of the different AHRS will be studied, including the effect of magnetic perturbations on the performance of orientation estimation, and the effect of using different patterns of motion when the sensor is carried by a user at different locations (pocket, foot/shoe, hand). These AHRS algorithms are also compared with the Kalman-based commercially available AHRS algorithm of Xsens. The performance of the AHRS algorithms depends strongly on the strategies used to reject perturbations (sudden accelerations or deformations of the Earth magnetic field) and the ability of the systems to estimate the biases of the gyroscopes.

Slipstream cooperative adaptive cruise control — A conceptual ITS application for electric vehicles

The Electric Vehicle is seen to be one of the most important enablers for a more environmentally friendly mobility of people. Unfortunately, state of the art electric vehicles suffer from a series of problems, with facing a very limited traveling distance compared to gasoline vehicles being one of the most relevant ones. In this paper we present an approach how to reduce the energy consumption while traveling over longer distances by using the slipstream effect behind a vehicle ahead. We show how this can be implemented as a specialized form of cooperative adaptive cruise control, one of the innovative Intelligent Transportation System applications. The paper elaborates in detail on the reliability of the application from the perspective of the current ITS communication technology, by means of two example scenarios, and outlines also on other aspects of implementing Slipstream Cooperative Adaptive Cruise Control for electric vehicles.

Use of the Magnetic Field for Improving Gyroscopes' Biases Estimation

An accurate orientation is crucial to a satisfactory position in pedestrian navigation. The orientation estimation, however, is greatly affected by errors like the biases of gyroscopes. In order to minimize the error in the orientation, the biases of gyroscopes must be estimated and subtracted. In the state of the art it has been proposed, but not proved, that the estimation of the biases can be accomplished using magnetic field measurements. The objective of this work is to evaluate the effectiveness of using magnetic field measurements to estimate the biases of medium-cost micro-electromechanical sensors (MEMS) gyroscopes. We carry out the evaluation with experiments that cover both, quasi-error-free turn rate and magnetic measurements and medium-cost MEMS turn rate and magnetic measurements. The impact of different homogeneous magnetic field distributions and magnetically perturbed environments is analyzed. Additionally, the effect of the successful biases subtraction on the orientation and the estimated trajectory is detailed. Our results show that the use of magnetic field measurements is beneficial to the correct biases estimation. Further, we show that different magnetic field distributions affect differently the biases estimation process. Moreover, the biases are likewise correctly estimated under perturbed magnetic fields. However, for indoor and urban scenarios the biases estimation process is very slow.An accurate orientation is crucial to a satisfactory position in pedestrian navigation. The orientation estimation, however, is greatly affected by errors like the biases of gyroscopes. In order to minimize the error in the orientation, the biases of gyroscopes must be estimated and subtracted. In the state of the art it has been proposed, but not proved, that the estimation of the biases can be accomplished using magnetic field measurements. The objective of this work is to evaluate the effectiveness of using magnetic field measurements to estimate the biases of medium-cost micro-electromechanical sensors (MEMS) gyroscopes. We carry out the evaluation with experiments that cover both, quasi-error-free turn rate and magnetic measurements and medium-cost MEMS turn rate and magnetic measurements. The impact of different homogeneous magnetic field distributions and magnetically perturbed environments is analyzed. Additionally, the effect of the successful biases subtraction on the orientation and the estimated trajectory is detailed. Our results show that the use of magnetic field measurements is beneficial to the correct biases estimation. Further, we show that different magnetic field distributions affect differently the biases estimation process. Moreover, the biases are likewise correctly estimated under perturbed magnetic fields. However, for indoor and urban scenarios the biases estimation process is very slow.

Measurement and Analysis of ITS-G5 in Railway Environments

In this paper we present first measurement results of a novel approach in Train-to-Train T2T communications. For this measurement campaign an Intelligent Transport System ITS-G5 communication link was used to investigate the influences of a railway environment on a Car-to-Car C2C communication standard based system. The measurements cover a wide range of scenarios from urban to rural environments, forest to open field as well as tunnels and crossings under bridges. The investigated measurement categories are channel characteristics, system performance and environmental aspects. The results should clarify, if a technology transfer from road to railway traffic communications would be expedient.

Precise relative positioning of vehicles with on-the-fly carrier phase resolution and tracking

Forward collision warning systems, lane change assistants, and cooperative adaptive cruise control are examples of safety relevant applications that rely on accurate relative positioning between vehicles. Current solutions estimate the position of an in-front driving vehicle by measuring the distance with a radar sensor, a laser scanner, or a camera system. The perception range of these sensors can be extended by the exchange of GNSS information between the vehicles using an intervehicle communication link. One possibility is to transmit GNSS pseudorange and carrier phase measurements and compute a highly accurate baseline vector that represents the relative position between two vehicles. Solving for the unknown integer ambiguity is specially challenging for low-cost single-frequency receivers. Using the well-known LAMBDA (Least-squares AMBiguity Decorrelation Adjustment) algorithm, in this paper, we present a method for tracking the ambiguity vector solution, which is able to detect and recover from cycle slips and cope with changing satellite constellations. In several test runs performed in real-world open-sky environments with two vehicles, the performance of the proposed Ambiguity Tracker approach is evaluated. The experiments revealed that it is in fact possible to track the position of another vehicle with subcentimeter accuracy over longer periods of time with low-cost single-frequency receivers.