scholarly journals A Cost-Effective Photonic Radar Under Adverse Weather Conditions for Autonomous Vehicles by Incorporating a Frequency-Modulated Direct Detection Scheme

2021 ◽  
Vol 9 ◽  
Author(s):  
Abhishek Sharma ◽  
Sushank Chaudhary ◽  
Jyoteesh Malhotra ◽  
Muhammad Saadi ◽  
Sattam Al Otaibi ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Direct Detection ◽  
Signal To Noise Ratio ◽  
Autonomous Vehicle ◽  
Cost Effective ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Detection Scheme ◽  
Available Bandwidth ◽  
Adverse Weather

In recent years, there have been plenty of demands and growth in the autonomous vehicle industry, and thus, challenges of designing highly efficient photonic radars that can detect and range any target with the resolution of a few centimeters have been encountered. The existing radar technology is unable to meet such requirements due to limitations on available bandwidth. Another issue is to consider strong attenuation while working under diverse atmospheric conditions at higher frequencies. The proposed model of photonic radar is developed considering these requirements and challenges using the frequency-modulated direct detection technique and considering a free-space range of 750 m. The result depicts improved range detection in terms of received power and an acceptable signal-to-noise ratio and range under adverse climatic situations.

scholarly journals Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather

2021 ◽  
Vol 11 (7) ◽  
pp. 3018
Author(s):  
Shih-Lin Lin ◽  
Bing-Han Wu
Keyword(s):  
Kalman Filter ◽  
Autonomous Vehicles ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Weather Data ◽  
Stereo Images ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
On The Road

A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in adverse weather by using a combination of a Kalman filter and nearby point cloud denoising. The overall LiDAR framework was tested in experiments in a space 2 m in length and width and 0.6 m high. Normal weather and three kinds of adverse weather conditions (rain, thick smoke, and rain and thick smoke) were simulated. The results show that this system can be used to recover normal weather data from data measured by LiDAR even in adverse weather conditions. The results showed an effective improvement of 10% to 30% in the LiDAR stereo images. This method can be developed and widely applied in the future.

Quality Assurance Methods Applied For Exposed-Aggregate Concrete Pavement Construction

2021 ◽  
Author(s):  
Adam Glinicki ◽  
Michal Glinicki
Keyword(s):  
Quality Assurance ◽  
Early Stage ◽  
Traffic Noise ◽  
Cost Effective ◽  
Weather Conditions ◽  
Special Treatment ◽  
Aggregate Concrete ◽  
Adverse Weather ◽  
Pavement Construction

The exposed aggregate pavement technology for construction of concrete highways is used in European countries, including Poland, mostly for heavy trafficked roads. It is mainly a two-lift slip-form technology with a special treatment of the top surface after the final smoothing operation. This is a demanding technology that leaves a little margin for mistakes. When properly done the pavement layer with exposed aggregates ensures designed skid resistance for vehicle wheels even in adverse weather conditions without excessive traffic noise. The challenge is to provide its cost-effective long term performance including both the adequate roughness and the desired smoothness of the pavement. The paper presents tools and methods for construction quality assurance specific for exposed aggregate concrete pavements. Required monitoring of the stability of concrete mix properties is discussed. The importance of concrete curing is analyzed in respect to the long term durability in wet-freeze regions with heavy use of deicing salts. Macrotexture assessment at the early stage of pavement construction is seen as the key factor for assurance of the proper skidding resistance. Local evaluation of smoothness is also a useful approach to assure the target IRI. Examples of quality assurance efforts applied on concrete highways recently constructed in Poland are presented.

scholarly journals PECULIARITIES OF CONSTRUCTION OF GEODESIC NETWORK FOR DEVELOPMENT OF OPEN MINERAL DEPOSITS

2020 ◽  
Vol 11 (87) ◽  
Author(s):  
Oksana Serant ◽  
◽  
Olena Kubrak ◽  
Nataliia Yarema ◽  
Maksym Batura ◽  
...  
Keyword(s):  
Geodetic Network ◽  
Cost Effective ◽  
Weather Conditions ◽  
Reference Network ◽  
Satellite Measurement ◽  
Geodetic Networks ◽  
Advantages And Disadvantages ◽  
Adverse Weather ◽  
The Creation ◽  
Combination Of Methods

The creation of geodetic networks for open deposits has its own characteristics, in contrast to the creation of conventional geodetic networks. Surveying networks of support points for groups of quarries and individual quarries located in developed mining regions, as well as in large industrial, hydraulic and agricultural structures adjacent to cities, are being developed on the basis of existing networks of higher-class triangulation points. In the absence of higher-class triangulation points, open source support networks are created independently. The study of geodetic monitoring in mining, especially in deposits that are developed in an open way. The design of geodetic reference networks depends entirely on the shape of the quarry and the system of its opening. According to its form, choose the method of creating a planned geodetic basis. For the most part, a backbone network is created to further condense and create a film network.After analyzing the methods of creating a spatial reference network for open deposits, we concluded that the classical methods of creating a planned-height geodetic network on the territory of the mining enterprise are time-consuming, long-term and economically unprofitable. The GNSS method is the best for creating such networks at present. Of course, it cannot fully replace all methods due to various constraints, such as interference, lack of communication, and adverse weather conditions. Therefore, given the advantages and disadvantages of the methods analyzed in the article to create spatial networks in open fields, the authors consider it appropriate to combine the GNSS method with polygonometry, as the use of only satellite measurement method is impractical, but in combination with polygonometry -altitude networks for geodetic works. This combination significantly reduces measurement time, is less time-consuming, cost-effective and meets the accuracy requirements of the relevant networks. Approbation of the combination of methods for the creation of a spatial geodetic network for monitoring the open field was carried out at the Vilnohirsk mining and metallurgical plant.

Forecasting a meteorological indicator of pollutant dispersion in surface air

2021 ◽  
Vol 3 ◽  
pp. 131-149
Author(s):  
I.N. Kuznetsova ◽  
◽  
Yu.V. Tkacheva ◽  
I.Yu. Shalygina ◽  
M.I. Nakhaev ◽  
...  
Keyword(s):  
Air Pollution ◽  
Pollutant Dispersion ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Probabilistic Forecasting ◽  
Pollution Dispersion ◽  
Temperature Changes ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Accumulation Of Pollutants

An improved algorithm for calculating a meteorological indicator of pollution dispersion in surface air (MIPD) using the COSMO-Ru7 configuration forecast data with a discreteness of 1 hour is presented. Using the MIPD as a function of the transport rate and thermal stratification in the atmospheric boundary layer, precipitation and advective temperature changes, the entire range of atmospheric conditions affecting the dispersion of pollutants is divided into three types: weak (the first type), moderate (the second type), and strong (the third type) dispersion. The worst conditions for the pollutant dispersion are provided by the MIPD of the first type; the set of meteorological parameters that determines it corresponds to adverse weather conditions (AWC) that contribute to the accumulation of pollutants in surface air. The proposed detailing within each type of MIPD in the form of subtypes can be useful for predicting AWC for single sources. Illustrations of the MIPD connection with fluctuations in the level of air pollution during the AWC episodes are given using automated measurements of pollutant concentration and fixed network measurements. An algorithm for the probabilistic forecasting of the MIPD, that allows taking into account the uncertainty of the forecast when issuing AWC warnings, is proposed and implemented. Keywords: meteorological conditions of air pollution, adverse weather conditions, numerical prediction

Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving

2017 ◽  
Vol 36 (3) ◽  
pp. 292-319 ◽  
Author(s):  
Ryan W Wolcott ◽  
Ryan M Eustice
Keyword(s):  
Autonomous Vehicle ◽  
Probabilistic Method ◽  
Three Dimensional ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Gaussian Mixture ◽  
Road Surface ◽  
Vehicle Localization ◽  
Adverse Weather ◽  
Self Driving Cars

This paper reports on a fast multiresolution scan matcher for local vehicle localization of self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a 3D light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint (e.g. puddles and snowdrifts), poor road surface texture, or when road appearance degrades over time. We present a generic probabilistic method for localizing an autonomous vehicle equipped with a three-dimensional (3D) LIDAR scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the [Formula: see text]-height and reflectivity distribution of the environment—which we rasterize to facilitate fast and exact multiresolution inference. Results are shown on a collection of datasets totaling over 500 km of road data covering highway, rural, residential, and urban roadways, in which we demonstrate our method to be robust through heavy snowfall and roadway repavements.

scholarly journals Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4503
Author(s):  
Jose Roberto Vargas Rivero ◽  
Thiemo Gerbich ◽  
Boris Buschardt ◽  
Jia Chen
Keyword(s):  
Autonomous Vehicles ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Real Data ◽  
Weather Conditions ◽  
Point Clouds ◽  
Training Data ◽  
Driver Assistance Systems ◽  
Solid Objects ◽  
Adverse Weather

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types.

Performance Evaluation of System in Free Space Optic Utilizing Gaussian Optical Filter in Different Detection Scheme

2019 ◽  
Vol 41 (1) ◽  
pp. 31-36
Author(s):  
H. Djellab ◽  
A. Bouarfa ◽  
S. Bojanic
Keyword(s):  
Wavelength Division Multiplexing ◽  
Free Space ◽  
Direct Detection ◽  
Optical Filter ◽  
Weather Conditions ◽  
Spectral Amplitude ◽  
Free Space Optical ◽  
Detection Scheme ◽  
High Data ◽  
Code Division Multiple

Abstract In recent years, free space optical communication (FSO) has become a leader for its unique characteristics: large bandwidth, unlicensed spectrum, simple implementation, low power and high data rate. However, we use as a transmission medium for Spectral Amplitude Coding-Optical Code Division Multiple Access SAC OCDMA system. In this paper, we investigate the optimum received power of FSO communication system employing SAC OCDMA, by using different detection technique, such us Spectral Direct Detection SDD and Single Photodiode Detection (SPD) technique under optical Gaussian filter decoder schemes with Modified Double Weight code (MDW). In this work, the adverse effects of atmospheric channel limit the possibility of a large FSO communication, moderate turbulence and hazy weather conditions are considered. The results show that the performance of the proposed system with wavelength-division-multiplexing (WDM) multiplexer (MUX) based on Gaussian optical filter with SDP detection fares better than the system employing SDD technique.

The Impact of Adverse Weather Conditions on Autonomous Vehicles: How Rain, Snow, Fog, and Hail Affect the Performance of a Self-Driving Car

2019 ◽  
Vol 14 (2) ◽  
pp. 103-111 ◽  
Author(s):  
Shizhe Zang ◽  
Ming Ding ◽  
David Smith ◽  
Paul Tyler ◽  
Thierry Rakotoarivelo ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
The Impact

scholarly journals Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3370
Author(s):  
Răzvan-Cătălin Miclea ◽  
Vlad-Ilie Ungureanu ◽  
Florin-Daniel Sandru ◽  
Ioan Silea
Keyword(s):  
Autonomous Vehicles ◽  
Scientific Literature ◽  
Optical Power ◽  
Weather Conditions ◽  
Mobile Systems ◽  
Power Measurement ◽  
Short Period ◽  
Adverse Weather ◽  
Scientific Papers ◽  
Visibility Enhancement

In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.

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