Visibility Estimation of Traffic Signals under Rainy Weather Conditions for Smart Driving Support

Abstract-The aim of this work is to support a driver by notifying the information of traffic signals in accordance with their visibility. To avoid traffic accidents, the driver should detect and recognize surrounding objects, especially traffic signals. However, when driving a vehicle under rainy weather conditions, it is difficult for drivers to detect or to recognize objects existing in the road environment in comparison with fine weather conditions. Therefore, this paper proposes a method for estimating the visibility of traffic signals for drivers under rainy weather conditions by image processing. The proposed method is based on the concept of visual noise known in the field of cognitive science, and extracts two types of visual noise features which ware considered that they affect the visibility of traffic signals. We expect to improve the accuracy of visibility estimation by combining the visual noise features with the texture feature introduced in a previous work. Experimental results showed that the proposed method could estimate the visibility of traffic signals more accurately under rainy weather conditions

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed

Road Infrastructure Challenges Faced by Automated Driving: A Review

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS

Exploratory analysis of injury severity under different levels of driving automation (SAE Level 2-5) using multi-source data

Vehicles equipped with automated driving capabilities have shown the potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated driving systems are ongoing, there is limited research investigating the difference between injury severity outcomes of the ADAS and ADS vehicles using real-world crash data. To ensure comprehensive analysis, a multi-source dataset that includes the NHTSA crash database (752 cases), CA DMV crash reports (498 cases), and news outlet data (30 cases) is used. Two random parameters multinomial logit models with heterogeneity in the means and variances are estimated to gain a better understanding of the variables impacting the crash injury severity outcome for the ADAS (SAE Level 2) and ADS (SAE Levels 3-5) vehicles. We found that while 56 percent of crashes involving ADAS vehicles took place on a highway, 84 percent of crashes involving ADS took place in more urban settings. The model estimation results indicate that the weather indicators, traffic incident or work zone indicator, differences in the system sophistication that are captured by both manufacture year and high or low mileage, type of collision, as well as rear and front impact indicators all play a significant role in the crash injury severity. The results offer an exploratory assessment of the safety performance of the ADAS and ADS equipped vehicles in the real-world environment and can be used by the manufacturers and other stakeholder to dictate the direction of their deployment and usage

LiDAR-based Weather Detection: Automotive LiDAR Sensors in Adverse Weather Conditions

Technologische Verbesserungen erhöhen den Automatisierungsgrad von Fahrzeugen. Der natürliche Schritt ist dabei, den Fahrer dort zu unterstützen, wo er es am meisten wünscht: bei schlechtem Wetter. Das Wetter beeinflusst alle Sensoren, die zur Wahrnehmung der Umgebung verwendet werden, daher ist es entscheidend, diese Effekte zu berücksichtigen und abzuschwächen. Die vorliegende Dissertation konzentriert sich auf die gerade entstehende Technologie der automobilen Light Detection and Ranging (LiDAR)-Sensoren und trägt zur Entwicklung von autonomen Fahrzeugen bei, die in der Lage sind, unter verschiedenen Wetterbedingungen zu fahren. Die Grundlage ist der erste LiDAR-Punktwolken-Datensatz mit dem Schwerpunkt auf schlechte Wetterbedingungen, welcher punktweise annonatatierte Wetterinformationen enthält, während er unter kontrollierten Wetterbedingungen aufgezeichnet wurde. Dieser Datensatz wird durch eine neuartige Wetter-Augmentation erweitert, um realistische Wettereffekte erzeugen zu können. Ein neuartiger Ansatz zur Klassifizierung des Wetterzustands und der erste CNN-basierte Entrauschungsalgorithmus werden entwickelt. Das Ergebnis ist eine genaue Vorhersage des Wetterstatus und eine Verbesserung der Punktwolkenqualität. Kontrollierte Umgebungen unter verschiedenen Wetterbedingungen ermöglichen die Evaluierung der oben genannten Ansätze und liefern wertvolle Informationen für das automatisierte und autonome Fahren

Arterial-level Real-time Safety Evaluation in the Context of Proactive Traffic Management

In the context of pro-active traffic management, real-time safety evaluation is one of the most important components. Previous studies on real-time safety analysis mainly focused on freeways, seldom on arterials. With the advancement of sensing technologies and smart city initiative, more and more real-time traffic data sources are available on arterials, which enables us to evaluate the real-time crash risk on arterials. However, there exist substantial differences between arterials and freeways in terms of traffic flow characteristics, data availability, and even crash mechanism. Therefore, this study aims to deeply evaluate the real-time crash risk on arterials from multiple aspects by integrating all kinds of available data sources. First, Bayesian conditional logistic models (BCL) were developed to examine the relationship between crash occurrence on arterial segments and real-time traffic and signal timing characteristics by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted from four urban arterials in Central Florida. Second, real-time intersection-approach-level crash risk was investigated by considering the effects of real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Orange County. Third, a deep learning algorithm for real-time crash risk prediction at signalized intersections was proposed based on Long Short-Term Memory (LSTM) and Synthetic Minority Over-Sampling Technique (SMOTE). Moreover, in-depth cycle-level real-time crash risk at signalized intersections was explored based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). All the possible real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather conditions. Above all, comprehensive real-time safety evaluation algorithms were developed for arterials, which would be key components for future real-time safety applications (e.g., real-time crash risk prediction and visualization system) in the context of pro-active traffic management

The perception system of intelligent ground vehicles in all weather conditions: A systematic literature review

Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system