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.

Effect of adverse weather conditions and pointing error on the performance of 2-D WH/TS OCDMA over FSO link

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Bithi Mitra ◽  
Md. Jahedul Islam
Keyword(s):  
Free Space ◽  
Atmospheric Condition ◽  
Optical Power ◽  
Weather Conditions ◽  
Free Space Optical ◽  
Central Wavelength ◽  
Pointing Error ◽  
Adverse Weather ◽  
Code Division Multiple ◽  
Ber Performance

AbstractIn this paper, the performance of two-dimensional (2-D) wavelength-hopping/time-spreading (WH/TS) optical code division multiple access (OCDMA) system over free space optical (FSO) channel is analyzed in the presence of pointing error and different weather conditions. Prime code scheme is employed for both wavelength-hopping and time-spreading to address user code-matrix. The operating central wavelength of 1550 nm is considered to demonstrate the bit error rate (BER) performance of the proposed system as a function of various system parameters. The required optical power of the proposed system is determined to maintain a BER value of 10−9. The numerical evaluation interprets that the BER performance is highly dependent on transmission length, transmitted power, pointing error angle as well as the number of simultaneous user. It is also observed that the 2-D OCDMA system over free space needs minimum required optical power in case of rainy atmospheric condition, but it is maximum for foggy atmospheric condition.

Investigating the Temporal Instability in Injury Severity Outcomes of Clear and Adverse Weather Crashes on Rural Mountainous Highways

2021 ◽  
pp. 036119812110570
Author(s):  
Irfan U. Ahmed ◽  
Mohamed M. Ahmed
Keyword(s):  
Monte Carlo ◽  
Injury Severity ◽  
Statistical Significance ◽  
Weather Conditions ◽  
Warning Signs ◽  
Parameter Estimates ◽  
Connected Vehicle ◽  
Temporal Instability ◽  
Short Period ◽  
Adverse Weather

Analysis of driver injury severity based on weather conditions on rural highways is limited in the literature. Such analyses provide insights useful to policymakers in optimizing the allocation of limited resources based on weather conditions. Furthermore, if there is a possibility of factors exhibiting temporal instability, then an aggregate analysis can lead to erroneous allocation of funds. In this study, separate models for clear and adverse weather conditions were developed for each of the years from 2015 to 2019 using crash data from a rural mountainous highway corridor. A random-intercept Bayesian logistic approach was used to analyze the dichotomous injury severity response and capture the between-crash variance. An efficient Markov chain Monte Carlo sampling technique known as the No-U-Turn Hamiltonian Monte Carlo was employed to sample the posterior distributions of parameter estimates. Likelihood ratio tests provided statistical significance of the temporal instability and also the differences in driver injury severities resulting from clear and adverse weather crashes. While most of the variables demonstrated temporal instability, some factors exhibited temporal stability over a short period of time and only during clear weather conditions. Findings from the separate models suggest that there are major differences in both the combination and magnitude of the significant contributing factors. Implementation of confirmatory warning signs, variable message signs, connected vehicle technology, strict enforcements during different times and locations, and driver awareness programs have been recommended as suitable countermeasures. The findings and recommendations could potentially help in guiding the respective agencies in formulating injury severity mitigation policies and strategies.

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.

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.

THE INFLUENCE OF HONEYBEE POLLINATION ON FRUIT SET OF THE LOWBUSH BLUEBERRY

1961 ◽  
Vol 41 (2) ◽  
pp. 332-335 ◽  
Author(s):  
G. W. Wood
Keyword(s):  
Fruit Set ◽  
Weather Conditions ◽  
Adverse Weather Conditions ◽  
Lowbush Blueberry ◽  
Short Period ◽  
Adverse Weather ◽  
Plant Stand

Inconsistent results were obtained when the fruit set was determined in blueberry fields with and without the service of honeybees. Adverse weather conditions curtailed pollinator activity and this was reflected in poor fruit set. The advantage of using honeybees was more evident in the season which had a short period of bloom. No significant increase in percentage fruit set was obtained when the number of colonies of honeybees per acre was increased. There was also no correlation between percentage fruit set and composition of plant stand.

scholarly journals BLIND CONTRAST ENHANCEMENT ASSESSMENT BY GRADIENT RATIOING AT VISIBLE EDGES

2011 ◽  
Vol 27 (2) ◽  
pp. 87 ◽  
Author(s):  
Nicolas Hautière ◽  
Jean-Philippe Tarel ◽  
Didier Aubert ◽  
Éric Dumont
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Image Quality Assessment ◽  
Weather Conditions ◽  
Reference Image ◽  
Tone Mapping ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Before And After ◽  
Visibility Enhancement

The contrast of outdoor images acquired under adverse weather conditions, especially foggy weather, is altered by the scattering of daylight by atmospheric particles. As a consequence, differentmethods have been designed to restore the contrast of these images. However, there is a lack of methodology to assess the performances of the methods or to rate them. Unlike image quality assessment or image restoration areas, there is no easy way to have a reference image, which makes the problem not straightforward to solve. In this paper, an approach is proposed which consists in computing the ratio between the gradient of the visible edges between the image before and after contrast restoration. In this way, an indicator of visibility enhancement is provided based on the concept of visibility level, commonly used in lighting engineering. Finally, the methodology is applied to contrast enhancement assessment and to the comparison of tone-mapping operators.

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 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.

Effective Range Assessment of Lidar Imaging Systems for Autonomous Vehicles Under Adverse Weather Conditions with Stationary Vehicles

2021 ◽  
Author(s):  
Jamil Abdo ◽  
Spencer Hamblin ◽  
Genshe Chen
Keyword(s):  
Data Collection ◽  
Technology Implementation ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Imaging Systems ◽  
Extreme Conditions ◽  
Commercial Vehicles ◽  
Performance Quality ◽  
Adverse Weather Conditions ◽  
Adverse Weather

Abstract Light detection and ranging (Lidar) imaging systems are being increasingly used in autonomous vehicles. However, the final technology implementation is still undetermined as major automotive manufacturers are only starting to select providers for data collection units that can be introduced in commercial vehicles. Currently, testing for autonomous vehicles is mostly performed in sunny environments. Experiments conducted in good weather cannot provide information regarding performance quality under extreme conditions such as fog, rain, and snow. Under extreme conditions, many instances of false detection may arise because of the backscattered intensity, thereby reducing the reliability of the sensor. In this work, lidar sensors were tested in adverse weather to understand how extreme weather affects data collection. Testing setup and algorithms were developed for this purpose. The results are expected to provide technological validation for the commercial use of lidar in automated vehicles. The effective ranges of two popular lidar sensors were estimated under adverse weather conditions, namely, fog, rain, and snow. Results showed that fog severely affected lidar performance, and rain too had some effect on the performance. Meanwhile, snow did not affect lidar performance.

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