David Betaille

Lane-Level Integrity Provision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps

Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.

High-precision application of GPS in the field of real-time equipment positioning

Abstract In the frame of its research concerning real-time positioning and control of road construction equipment, the Laboratoire Central des Ponts et Chaussees , has carried out in 1996 a study to know more about the actual vertical accuracy that a real-time kinematic (RTK) global positioning system (GPS) sensor could reach, under work site conditions. This study has widely used the dedicated testing facility called SESSYL, built to perform high-accuracy and real-time evaluation tests on positioning systems. It has been performed in collaboration with the French road contractor COLAS and the Ecole Superieure des Geometres et Topographes (ESGT). First, there is the proposed adapted geodetic transformation procedure, compatible with the high accuracy requirements. Then, the main results of a special SESSYL tests program are presented, where the impacts of several influencing parameters on the vertical accuracy have been carefully examined. The core part of the paper is the analysis of the typical RTK GPS set of data, from which we have tried to extract two different components: a high-frequency noise, rather easy to filter, and a low-frequency bias. This bias, given its good repeatability, can be modelled and used in prediction to improve in real-time the raw accuracy of the data. As a full-scale validation of our study, a site experiment is finally described, carried out this time on a real piece of equipment (an asphalt paver) during real roadwork.

Creating Enhanced Maps for Lane-Level Vehicle Navigation

The concept of enhanced maps (Emaps) was introduced with one main objective: It should characterize roads, first, with more completeness and, second, with more accuracy than standard maps to fulfill the requirements of new challenging road safety applications and advanced driver-assistance systems (ADAS). This paper introduces a paradigm for Emap definition and creation on which every road lane is represented and topologically connected to the rest of lanes. Following this approach, a number of Emaps have been created in France, Germany, and Sweden. The experiments carried out in these test sites with the Emaps show the capability of our Emap definition to assist with the determination of the vehicle position at the lane level. Details of the processes of extraction and connection of the road segments are given in the core of this paper, as well as a discussion of the elaboration process and future guidelines in the conclusion.

Fusing GNSS, Dead-Reckoning, and Enhanced Maps for Road Vehicle Lane-Level Navigation

Nowadays, it is common that road vehicle navigation systems employ maps to represent the vehicle positions in a local reference. The most usual process to do that consists in the estimation of the vehicle positioning by fusing the Global Navigation Satellite System (GNSS) and some other aiding sensors data, and the subsequent projection of these values on the map by applying map-matching techniques. However, it is possible to benefit from map information also during the process of fusing data for positioning. This paper presents an algorithm for lane-level road vehicle navigation that integrates GNSS, dead-reckoning (odometry and gyro), and map data in the fusion process. Additionally, the proposed method brings some benefits for map-matching at lane level because, on the one hand, it allows the tracking of multiple hypothesis and on the other hand, it provides probability values of lane occupancy for each candidate segment. To do this, a new paradigm that describes lanes as piece-wise sets of clothoids was applied in the elaboration of an enhanced map (Emap). Experimental results in real complex scenarios with multiple lanes show the suitability of the proposed algorithm for the problem under consideration, presenting better results than some state-of-the-art methods of the literature.

About Non-Line-Of-Sight Satellite Detection and Exclusion in a 3D Map-Aided Localization Algorithm

Reliable GPS positioning in city environment is a key issue actually, signals are prone to multipath, with poor satellite geometry in many streets. Using a 3D urban model to forecast satellite visibility in urban contexts in order to improve GPS localization is the main topic of the present article. A virtual image processing that detects and eliminates possible faulty measurements is the core of this method. This image is generated using the position estimated a priori by the navigation process itself, under road constraints. This position is then updated by measurements to line-of-sight satellites only. This closed-loop real-time processing has shown very first promising full-scale test results.

Enhancement of global vehicle localization using navigable road maps and dead-reckoning

This paper presents a data fusion strategy for the global localization of car-like vehicles. The system uses raw GNSS measurements, dead-reckoning sensors and road map data. We present a new method to use the map information as a heading observation in a Kalman filter. Experimental results show the benefit of such a method when the GPS information is not available. Then, we propose a conservative localization strategy that relies mainly on dead-reckoned navigation. The GNSS measurements and the map information are not used when consistency tests are doubtful. Experimental tests indicate that the performance is effectively better when using only the available consistent information.

Making an Enhanced Map for Lane Location Based Services

Latest investigations show the benefits of fusing map information with GNSS (Global Navigation Satellite System) and dead-reckoning measurements for road vehicle navigation. However, to achieve success enhanced maps (Emaps) that take into account this new capability must be developed. In this paper, we present a method to create an Emap that includes road shape information, capable to serve as an input of a combined Fusion/Map-Matching algorithm. Details of the processes of road segments extraction and connection are given in the text. The analysis of the results obtained using the proposed method and future guidelines conclude the paper.

GPS multipath detection and exclusion with elevation-enhanced maps

The reflections of satellite signals in the environment of Global Positioning Systems (GPS) receivers cause significant errors in their position estimates. Particularly critical are the errors due to the so called non-line-of-sight (NLOS) satellites. In a NLOS situation, the only way the satellite signal reaches the receiver is by means of reflections on plane surfaces, typically buildings, causing overestimates of the pseudoranges between the satellites and the antenna. Receivers not always distinguish between the direct signals and the multipath effects, leading to un-modelled GPS errors. This paper presents a solution to the problem of multipath effects in urban areas, by means of simple elevation models of the environment. The description of the buildings is stored in a elevation-enhanced digital map (EEMap) that can be consulted to decide whether a certain satellite may be in direct view or not. The validity of the concept is proven by means of real experiments in built-up areas of Spain.

A Multi-Sensor Acquisition Architecture and Real-Time Reference for Sensor and Fusion Methods Benchmarking

Localization is a key functionality for advance driving assistance systems (ADAS) as well as for vehicle-vehicle or vehicle-infrastructure cooperation. Indeed, depending on the accuracy and integrity of the localization process, applications such as driver information, driver assistance or even fully autonomous driving can be performed. This paper presents a multisensor acquisition architecture for localization. Special attention has been given to main parameters that can affect the accuracy of the localization system. Several sensor technologies have been used and special care to intrinsic, spatial and temporal calibration was given. Since a timestamping synchronizations error induces an error in space on the configuration of a mechanical system, it is necessary to combine synchronized sensor data. A suitable way to handle such problem is to timestamp sensor information in the same time reference frame. The originality of the approach is the use of the SensorHub, a parallel hardware electronic device to perform data acquisition and timestamping in coordinated universal time (UTC). In complement with the SensorHub, the authors demonstrate the real time estimation of a reference trajectory computed from a real time kinematic (RTK) GPS receiver and a hi-grade inertial navigation system (INS) that also timestamp information in UTC time scale. Several sensor databases corresponding to different driving scenarios (environment, speed) were recorded and were used in the future to benchmark a set of fusion methods for localization of road vehicles

GNSS Autonomous Localization: NLOS Satellite Detection Based on 3-D Maps

One of the main drawbacks of global navigation satellite systems (GNSSs) in urban environments is that signals may arrive at the receiver antenna only in nonline-of-sight (NLOS) conditions, leading to biased pseudorange estimates when they are taken for granted by the receiver and, eventually, wrong positioning. This article presents a study on the benefits of using three-dimensional (3-D) maps of cities to decide whether the GNSS signal coming from each tracked satellite is reliable. Based on this principle, two different 3-D maps and two methodologies are presented and compared. The results show the benefits of this approach.