Abstract

Localization is a fundamental technology embedded — often unbeknownst to users — in many devices and services. First, this book reveals how localization systems are driving modern society. The core focus is then on the Global Positioning System (GPS), which is constantly rising in importance due to its global availability and low receiver cost. Still, high energy consumption and susceptibility to fake or reflected signals are challenges for GPS receivers. The limited endurance of battery-powered GPS receivers can be addressed by snapshot receivers which require as little as one millisecond of received satellite signals to determine their location. Using short signals reduces the active time and thus the energy consumption of a GPS receiver drastically. But short signals reduce the received signal energy, which impairs the localization robustness. The collective detection maximum likelihood method performs well with short signals. Furthermore, a tiny and low power snapshot GPS receiver hardware design allows location tracking for months or years using a coin cell. Spoofed GPS satellite signals pose a threat to an increasing number of critical systems relying on GPS. Determining multiple likely localization solutions instead of only one enables correct receiver localization despite malicious signals. Finally, an experimental localization system is presented that leverages status messages transmitted by aircraft. It aims at combining the ubiquitous availability and low receiver cost of GPS with the high signal strength of local localization systems like those based on WiFi or LTE signals. As such, it gives the prospect of robust and possibly indoor localization without the high setup costs of short- range localization systems. This book is suitable for readers with a technical background, but no prior knowledge of localization systems. Readers familiar with GPS can skip the introductory chapters and directly dive into the advanced material.

Abstract

Wireless vehicular networks are to be deployed in both Europe and the USA within upcoming years. Such networks introduce a new promising source of information about vehicular environments to be used by cooperative advanced driver assistance systems (ADAS). However, development and evaluation of such cooperative ADAS is still challenging. Hence, we introduce a novel methodology for their development and evaluation processes. It is applied to evaluate the fulfillment of requirements on position accuracy information within exchanged messages. Such requirements are only roughly defined and not sufficiently evaluated in field tests. This holds especially for Global Navigation Satellite Systems (GNSS) optimized for maximum integrity of obtained positions. Such configuration is required to increase robustness and reliability of safety critical ADAS. We find that pure GNSS-based positioning cannot fulfill position accuracy requirements of studied ADAS in most test cases.

Abstract

Wireless vehicular networks are in the wake of mass deployment both in Europe and the USA. These networks introduce a new promising source of information about vehicular environments usable by cooperative advanced driver assistance systems (ADAS). However, development and evaluation of such ADAS is still challenging. Thus, we propose a methodology for their development and evaluation process. It is applied to evaluate the fulfillment of requirements on position accuracy information within the communicated data sets. Accuracy requirements are only roughly defined and not sufficiently evaluated in real world environments. This holds especially for GNSS (Global Navigation Satellite Systems) optimized for maximum integrity of obtained positions, which is required for safety critical ADAS to increase robustness and reliability. Our main goal is to determine whether position accuracy provided by GNSS is sufficient for cooperative ADAS. Thereby, we find that pure GNSS input cannot fulfill position accuracy requirements in most test cases.

Abstract

Global navigation satellite system (GNSS) is widely regarded as the primary positioning solution for intelligent transport system (ITS) applications. However, its performance could degrade, due to signal outages and faulty-signal contamination, including multipath and non-line-of-sight reception. Considering the limitation of the performance and computation loads in mass-produced automotive products, this research investigates the methods for enhancing GNSS-based solutions without significantly increasing the cost for vehicular navigation system. In this study, the measurement technique of the odometer in modern vehicle designs is selected to integrate the GNSS information, without using an inertial navigation system. Three techniques are implemented to improve positioning accuracy; (a) Time-differenced carrier phase (TDCP) based filter: A state-augmented extended Kalman filter is designed to incorporate TDCP measurements for maximizing the effectiveness of phase-smoothing; (b) odometer-aided constraints: The aiding measurement from odometer utilizing forward speed with the lateral constraint enhances the state estimation; the information based on vehicular motion, comprising the zero-velocity constraint, fault detection and exclusion, and dead reckoning, maintains the stability of the positioning solution; (c) robust regression: A weighted-least-square based robust regression as a measurement-quality assessment is applied to adjust the weightings of the measurements adaptively. Experimental results in a GNSS-challenging environment indicate that, based on the single-point-positioning mode with an automotive-grade receiver, the combination of the proposed methods presented a root-mean-square error of 2.51 m, 3.63 m, 1.63 m, and 1.95 m for the horizontal, vertical, forward, and lateral directions, with improvements of 35.1%, 49.6%, 45.3%, and 21.1%, respectively. The statistical analysis exhibits 97.3% state estimation result in the horizontal direction for the percentage of epochs that had errors of less than 5 m, presenting that after the intervention of proposed methods, the positioning performance can fulfill the requirements for road level applications. Document type: Article

Abstract

The lane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSSs) suffer from weak observation geometry or from the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera, a low-cost inertial measurement unit (IMU), and a single-frequency GNSS receiver. The sensor data fusion is implemented as a tightly coupled Kalman filter that corrects the IMU-based trajectory with GNSS observations while employing European Geostationary Overlay Service correction data. Further, we consider vision-based complementary data that serve as an additional source of information. In contrast to other approaches, the camera is not used to infer the motion of the vehicle, but rather for directly correcting the localization results under the usage of map information. More specifically, the so-called camera-to-map alignment is done by comparing virtual 3D views (candidates) created from projected map data with lane geometry features that are extracted from the camera image. One strength of the proposed solution is its compatibility with state-of-the-art map data, which are publicly available from different sources. We validate the approach on real-world data recorded in The Netherlands and show that it presents a promising and cost-efficient means to support future advanced driver assistance systems.