A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios

In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle’s position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods.

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Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Sensors ◽

10.3390/s19194236 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4236

Author(s):

Woosik Lee ◽

Hyojoo Cho ◽

Seungho Hyeong ◽

Woojin Chung

Keyword(s):

Motion Estimation ◽

Autonomous Vehicles ◽

Autonomous Navigation ◽

Urban Environments ◽

Global Navigation Satellite Systems ◽

Navigation Satellite ◽

Key Technology ◽

Satellite Systems ◽

Agricultural Robots ◽

Global Navigation Satellite

Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.

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Seamless Vehicle Positioning by Lidar-GNSS Integration: Standalone and Multi-Epoch Scenarios

Remote Sensing ◽

10.3390/rs13224525 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4525

Author(s):

Junjie Zhang ◽

Kourosh Khoshelham ◽

Amir Khodabandeh

Keyword(s):

Autonomous Driving ◽

Measurement Model ◽

Urban Environments ◽

Global Navigation Satellite Systems ◽

High Definition ◽

Satellite Systems ◽

The Road ◽

Gnss Receivers ◽

Vehicle Positioning ◽

Global Navigation Satellite

Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons.

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Assessment of the Cutting Performance of a Robot Mower Using Custom Built Software

Agronomy ◽

10.3390/agronomy9050230 ◽

2019 ◽

Vol 9 (5) ◽

pp. 230 ◽

Cited By ~ 4

Author(s):

Luisa Martelloni ◽

Marco Fontanelli ◽

Stefano Pieri ◽

Christian Frasconi ◽

Lisa Caturegli ◽

...

Keyword(s):

Autonomous Vehicles ◽

Global Navigation Satellite Systems ◽

Forward Speed ◽

Satellite Systems ◽

Different Shapes ◽

Global Navigation Satellite ◽

High Level ◽

Short Time ◽

Gnss Data ◽

Collection And Management

Before the introduction of positioning technologies in agriculture practices such as global navigation satellite systems (GNSS), data collection and management were time-consuming and labor-intensive tasks. Today, due to the introduction of advanced technologies, precise information on the performance of agricultural machines, and smaller autonomous vehicles such as robot mowers, can be collected in a relatively short time. The aim of this work was to track the performance of a robot mower in various turfgrass areas of an equal number of square meters but with four different shapes by using real-time kinematic (RTK)-GNSS devices, and to easily extract data by a custom built software capable of calculating the distance travelled by the robot mower, the forward speed, the cutting area, and the number of intersections of the trajectories. These data were then analyzed in order to provide useful functioning information for manufacturers, entrepreneurs, and practitioners. The path planning of the robot mower was random and the turfgrass area for each of the four shapes was 135 m2 without obstacles. The distance travelled by the robot mower, the mean forward speed, and the intersections of the trajectories were affected by the interaction between the time of cutting and the shape of the turfgrass. For all the different shapes, the whole turfgrass area was completely cut after two hours of mowing. The cutting efficiency decreased by increasing the time, as a consequence of the increase in overlaps. After 75 minutes of cutting, the efficiency was about 35% in all the turfgrass areas shapes, thus indicating a high level of overlapping.

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Context-Aware Sensor Uncertainty Estimation for Autonomous Vehicles

Vehicles ◽

10.3390/vehicles3040042 ◽

2021 ◽

Vol 3 (4) ◽

pp. 721-735

Author(s):

Mohammed Alharbi ◽

Hassan A. Karimi

Keyword(s):

Autonomous Vehicles ◽

Real Data ◽

Global Navigation Satellite Systems ◽

Learning Approach ◽

Context Aware ◽

Satellite Systems ◽

Sensor Performance ◽

Machine Learning Approach ◽

Global Navigation Satellite ◽

Data Acquisition Process

Sensor uncertainty significantly affects the performance of autonomous vehicles (AVs). Sensor uncertainty is predominantly linked to sensor specifications, and because sensor behaviors change dynamically, the machine learning approach is not suitable for learning them. This paper presents a novel learning approach for predicting sensor performance in challenging environments. The design of our approach incorporates both epistemic uncertainties, which are related to the lack of knowledge, and aleatoric uncertainties, which are related to the stochastic nature of the data acquisition process. The proposed approach combines a state-based model with a predictive model, where the former estimates the uncertainty in the current environment and the latter finds the correlations between the source of the uncertainty and its environmental characteristics. The proposed approach has been evaluated on real data to predict the uncertainties associated with global navigation satellite systems (GNSSs), showing that our approach can predict sensor uncertainty with high confidence.

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Dynamic GNSS Mission Planning Using DTM for Precise Navigation of Autonomous Vehicles

Journal of Navigation ◽

10.1017/s0373463316000679 ◽

2016 ◽

Vol 70 (3) ◽

pp. 483-504 ◽

Cited By ~ 5

Author(s):

Aleksander Nowak

Keyword(s):

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Digital Terrain Model ◽

Dead Reckoning ◽

Urban Environments ◽

Mission Planning ◽

Global Navigation Satellite Systems ◽

Satellite Systems ◽

Terrain Model ◽

Global Navigation Satellite

Nowadays, the most widely used method for estimating location of autonomous vehicles in real time is the use of Global Navigation Satellite Systems (GNSS). However, positioning in urban environments using GNSS is hampered by poor satellite geometry due to signal obstruction created by both man-made and natural features of the urban environment. The presence of obstacles is the reason for the decreased number of observed satellites as well as uncertainty of GNSS positioning. It is possible that in some sections of the vehicle route there might not be enough satellites necessary to fix position. It is common to use software for static GNSS measurement campaign planning, but it is often only able to predict satellite visibility at one point. This article presents a proposal for dynamic GNSS mission planning using a Digital Terrain Model (DTM) and dead reckoning. The methodology and sample results of numerical experiments are also described. They clearly show that proper dynamic GNSS mission planning is necessary in order to complete a task by an autonomous vehicle in an obstructed environment.

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Design, Implementation and Validation of a GNSS Measurement Exclusion and Weighting Function with a Dual Polarized Antenna

Sensors ◽

10.3390/s18124483 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4483

Author(s):

Daniel Egea-Roca ◽

Antonio Tripiana-Caballero ◽

José López-Salcedo ◽

Gonzalo Seco-Granados ◽

Wim De Wilde ◽

...

Keyword(s):

Precision Agriculture ◽

Time Management ◽

Autonomous Vehicles ◽

Weighting Function ◽

Modern Society ◽

Global Navigation Satellite Systems ◽

Satellite Systems ◽

Dual Polarized ◽

Global Navigation Satellite ◽

Dual Polarized Antenna

Global Navigation Satellite Systems (GNSSs) have become a ubiquitous tool for our modern society to carry out vital tasks such as transportation, civil engineering or precision agriculture. This breath has reached the realm of safety-critical applications such as time management of critical infrastructures or autonomous vehicles, in which GNSS is an essential tool nowadays. Unfortunately, current GNSS performance is not enough to fulfill the requirements of these professional and critical applications. For this reason, the FANTASTIC project was launched to boost the adoption of these applications. The project was funded by the European GNSS agency (GSA) in order to enhance the robustness and accuracy of GNSS in harsh environments. This paper presents the part related to the development of a weighting and exclusion function with a dual circularly polarized antenna. The idea is to reduce the effects of multipath by weighting and/or excluding those measurements affected by multipath. The observables and other metrics obtained from a dual polarized antenna will be exploited to define an exclusion threshold and to provide the weights. Real-world experiments will show the improvement in the positioning solution, using all available constellations, obtained with the developed technique.

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A visual approach towards forward collision warning for autonomous vehicles on Malaysian public roads

F1000Research ◽

10.12688/f1000research.72897.1 ◽

2021 ◽

Vol 10 ◽

pp. 928

Author(s):

Man Kiat Wong ◽

Tee Connie ◽

Michael Kah Ong Goh ◽

Li Pei Wong ◽

Pin Shen Teh ◽

...

Keyword(s):

Autonomous Vehicles ◽

Autonomous Driving ◽

Safe Driving ◽

Collision Warning ◽

Smart Transportation ◽

The Road ◽

Safety Mechanism ◽

Visual Approach ◽

On The Road ◽

Forward Collision Warning

Background: Autonomous vehicles are important in smart transportation. Although exciting progress has been made, it remains challenging to design a safety mechanism for autonomous vehicles despite uncertainties and obstacles that occur dynamically on the road. Collision detection and avoidance are indispensable for a reliable decision-making module in autonomous driving. Methods: This study presents a robust approach for forward collision warning using vision data for autonomous vehicles on Malaysian public roads. The proposed architecture combines environment perception and lane localization to define a safe driving region for the ego vehicle. If potential risks are detected in the safe driving region, a warning will be triggered. The early warning is important to help avoid rear-end collision. Besides, an adaptive lane localization method that considers geometrical structure of the road is presented to deal with different road types. Results: Precision scores of mean average precision (mAP) 0.5, mAP 0.95 and recall of 0.14, 0.06979 and 0.6356 were found in this study. Conclusions: Experimental results have validated the effectiveness of the proposed approach under different lighting and environmental conditions.

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Global reliance on GPS creates new risks

Emerald Expert Briefings ◽

10.1108/oxan-db241765 ◽

2019 ◽

Keyword(s):

Power Distribution ◽

Autonomous Vehicles ◽

Critical Infrastructure ◽

Organised Crime ◽

Global Navigation Satellite Systems ◽

Economic Losses ◽

Navigation Systems ◽

Content Type ◽

Satellite Systems ◽

Global Navigation Satellite

Subject Reliance on GPS. Significance Critical infrastructure, essential services and entire industries have become dependent on global navigation satellite systems (GNSSs). This dependence has emerged in an unplanned and unanticipated manner. GNSSs are vulnerable to disruption, with a risk of serious economic losses, loss of life and threats to national security. Impacts Reliance on GNSS will rise in established industries, notably finance, power distribution, agriculture and transportation. GNSS will be vital to the successful development of emerging industries such as autonomous vehicles, drones and new space systems. Demand will grow for high-quality GNSS receivers and software, and for backup systems such as ground-based radio navigation systems. Rising dependence on GNSS will incentivise efforts by national militaries and organised crime to exploit vulnerabilities. The greatest vulnerabilities may emerge in poorer countries where finances are more constrained and regulation is likely to be weaker.

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