A Neural network approach to visibility range estimation under foggy weather conditions

© 2017 The Authors. Published by Elsevier B.V. The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution using a diverse set of images under various fog density scenarios. Our approach shows very promising results that outperform the classical method of estimating the maximum distance at which a selected target can be seen. The originality of the approach stems from the usage of a single camera and a neural network learning phase based on a hybrid global feature descriptor. The proposed method can be applied to support next-generation cooperative hazard & incident warning systems based on I2V, I2I and V2V communications. Peer-review under responsibility of the Conference Program Chairs

Estimating meteorological visibility range under foggy weather conditions: A deep learning approach

© 2018 The Authors. Published by Elsevier Ltd. Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination of an ANN model and a set of global feature descriptors. Our experimental results show that the proposed solution presents very promising results in support for next-generation situational awareness and cooperative collision avoidance systems. Hence it can potentially contribute towards safer driving conditions in the presence of fog

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201

Semantic Understanding of Foggy Scenes with Purely Synthetic Data

This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent years, it is mainly concentrated on clear weather outdoor scenes. Extending semantic segmentation methods to adverse weather conditions like fog is crucially important for outdoor applications such as self-driving cars. In this paper, we propose a novel method, which uses purely synthetic data to improve the performance on unseen real-world foggy scenes captured in the streets of Zurich and its surroundings. Our results highlight the potential and power of photo-realistic synthetic images for training and especially fine-tuning deep neural nets. Our contributions are threefold, 1) we created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor scenes, that we call Foggy Synscapes and plan to release publicly 2) we show that with this data we outperform previous approaches on real-world foggy test data 3) we show that a combination of our data and previously used data can even further improve the performance on real-world foggy data.Comment: independent class IoU scores corrected for BiSiNet architectur

Towards Night Fog Detection through use of in-Vehicle Multipurpose Cameras

Abstract — Compared to daytime, a larger proportion of accident happens during nighttime. The altered visibility of the road scene for the drivers may partially explain this situation. The latter becomes worse in fog presence. In this paper, two camera-based methods are proposed to detect the presence of night fog in images grabbed by in-vehicle multipurpose cameras. They rely on the visual effects of night fog. A first approach can assess the presence of fog around the vehicle thanks to the detection of the backscattered veil induced by the vehicle ego lights. It aims at detecting fog when the vehicle is alone in absence of exterior public lighting. A second approach can assess the presence of fog thanks to the detection of halos around light sources in the vehicle environment. It aims at detecting fog in presence of road traffic or public lighting. Both methods are presented and illustrated with actual images of fog. Their complementarity makes it possible to envision a complete night fog detection system. There are numerous applications for such a system: automation or adaptation of vehicle lights, contextual speed computation and reliability improvement for camera-based systems. I

Improving Safety under Reduced Visibility Based on Multiple Countermeasures and Approaches including Connected Vehicles

The effect of low visibility on both crash occurrence and severity is a major concern in the traffic safety field. Different approaches were utilized in this research to analyze the effects of fog on traffic safety and evaluate the effectiveness of different fog countermeasures. First, a Crash Risk Increase Indicator (CRII) was proposed to explore the differences of crash risk between fog and clear conditions. A binary logistic regression model was applied to link the increase of crash risk with traffic flow characteristics. Second, a new algorithm was proposed to evaluate the rear-end crash risk under fog conditions. Logistic and negative binomial models were estimated in order to explore the relationship between the potential of rear-end crashes and the reduced visibility together with other traffic parameters. Moreover, the effectiveness of real-time fog warning systems was assessed by quantifying and characterizing drivers\u27 speed adjustments through driving simulator experiments. A hierarchical assessment concept was suggested to explore the drivers\u27 speed adjustment maneuvers. Two linear regression models and one hurdle beta regression model were estimated for the indexes. Also, another driving simulator experiment was conducted to explore the effectiveness of Connected-Vehicles (CV) crash warning systems on the drivers\u27 awareness of the imminent situation ahead to take timely crash avoidance action(s). Finally, a micro-simulation experiment was also conducted to evaluate the safety benefits of a proposed Variable Speed limit (VSL) strategy and CV technologies. The proposed VSL strategy and CV technologies were implemented and tested for a freeway section through the micro-simulation software VISSIM. The results of the above mentioned studies showed the impact of reduced visibility on traffic safety, and the effectiveness of different fog countermeasures