Traffic Situation Research Articles

Quality assessment of segment based sensor data at motorways

Road Operators like the ASFINAG in Austria use the advantages of various sensor technologies to record traffic on the motorway network. As the existing cross-sectional sensors do have some shortcomings, ten novel 24 GHz radar sensors with ultra-high resolution 4D/UHD+ technology and inbuilt video camera were tested at the motorway network to answer the question whether they have the capability to add valuable information to the current status of traffic situation detection for enhancing traffic management. This kind of segment based sensor systems can provide cross-sectional traffic count data as well as trajectory data. Therefore, the main objective of this publication was to show and evaluate the sensor quality during field tests. This quality assessment builds the knowledge base for further adoptions of new radar sensors within the ASFINAG road network in the future.

Distributed Multi‐MMW Radar Fusion for Target Detection and Tracking in Highway Traffic Environment

High‐resolution millimeter‐wave (MMW) radar is viewed as a low‐cost and highly reliable sensor compared to camera, lidar, etc., in moving scenarios and thus has been selected by highway stakeholders as an important roadside detector to detect the movement of traffic vehicles and monitor traffic flow in real time. However, the echo signal of MMW radar in complex highway environment contains not only the signal reflected by target but also spurious signals and other interference signals, which significantly affects the estimation of the target movement state. To solve this problem, an improved vehicle tracking method is designed to simultaneously estimate the polar angle and polar radius in coordinator of MMW radar. Moreover, considering the movement patterns of target vehicles in dynamic uncertain traffic situations, a set of state space models, such as CA, CV, and CT are combined to represent the vehicle movement. In addition, based on the enhanced detection performance of a single radar, the combination of multiple MMW radars’ information was performed to determine the sequential trajectory of the target vehicle on the continuous road sections; then, the historical trajectory of the target vehicle was correlated and fused. Real experiments in highway scenarios show that the method used in this study is effective in deriving the trajectory of the vehicle and improving the positioning accuracy and reliability when the vehicle performs heavy maneuvers.

Targeted deep learning: Framework, methods, and applications

Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic situations for which they were not specifically trained. In general, the ability to cope with a broad spectrum of unseen test inputs is called generalization. Generalization is definitely important in applications where the possible test inputs are known but plentiful or simply unknown, but there are also cases where the possible inputs are few and unlabeled but known beforehand. For example, medicine is currently interested in targeting treatments to individual patients; the number of patients at any given time is usually small (typically one), their diagnoses/responses are still unknown, but their general characteristics (such as genome information, protein levels in the blood, and so forth) are known before the treatment. We propose to call deep learning in such applications In this paper, we introduce a framework for targeted deep learning, and we devise and test an approach for adapting standard pipelines to the requirements of targeted deep learning. The approach is very general yet easy to use: It can be implemented as a simple data‐preprocessing step. We demonstrate on a variety of real‐world data that our approach can indeed render standard deep learning faster and more accurate when the test inputs are known beforehand.

ПРОГНОЗИРОВАНИЕ ДТП КАК ОДИН ИЗ СПОСОБОВ СНИЖЕНИЯ СМЕРТНОСТИ

This paper analyzes the statistics of traffic accidents involving pedestrians, children and drivers who are intoxicated. Pedestrians are the most unprotected category of all road users, and therefore they are given special attention when analyzing statistical data. To reduce road deaths, a mathematical model was developed for predicting accident rates based on a two-parameter Weibull distribution, which makes it possible to carry out a comprehensive assessment of the traffic situation for making management decisions.

Research of drivers' attitudes to the front brake light

The front brake light (FBL) is not an entirely new idea. It appeared already in 1920. However, the front brake light did not enter traffic for almost a century. The breakthrough came only in 2017. At that time, a practical experiment began at the Tegel airport in Berlin, Germany. This experiment required the installation of green LED lights on airport vehicles. Most airport employees expressed positive opinions about this new device. Therefore, the next stage of the project takes place in Slovakia. Such an experiment requires a much larger number of vehicles with FBL in as small an area as possible due to their concentration. This article describes the principle of operation of FBL, its execution and justification of the colour of the light. It also theoretically describes examples of traffic situations in which FBL is helpful. The research in this article uses a traffic-sociological questionnaire, which is described and summarily evaluated. Many respondents were professional drivers. Up to 80% of them would like to have FBL in traffic. Other results, described in the text, are also interesting. We also confirmed the hypothesis that drivers assume the positive impact of FBL on road safety.

Bi-Level l 1 Optimization-Based Interference Reduction for Millimeter Wave Radars

With the increasing number of radar-equipped vehicles in dense traffic situations, millimeter wave radars are suffering from serious interference problems. Therefore, this article presents a novel bi-level <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> optimization based approach for reducing interferences between automotive radars for range, velocity and angle measurement. Firstly, sparse difference analysis between the interfering signal and the target signal is investigated. According to the analysis results, one bi-level based signal optimization model is further derived by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm penalized least squares. This bi-level optimization enables a trade-off between suppressing interference and preserving radar targets. Specifically, in the first <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> level, the interfering signal is first optimized as the “desired signal”, while the target is considered as “noise”. Meanwhile, sparse optimization is applied on the target signals in the frequency domain at the second <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> level. Finally, the iterated soft-thresholding algorithm is used to optimize the proposed model. In real road interference suppression experiments, the proposed method improves the signal to interference plus noise ratio (SINR) for the target from 5.06 dB to 19.26 dB in the range-Doppler domain and from 6.86 dB to 22.10 dB in the azimuth spatial domain.

Ethical and legal challenges of automated driving: The prioritization of socio-political values

Much research on the ethics of automated driving (AD) focusses on moral decision-making processes in extreme traffic situations. However, more likely scenarios involving daily trade-offs among different values in the design and implementation of AD are also worth considering. Our main goal was to provide an in-depth analysis of how diverse experts and stakeholders prioritize different socio-political values. Through a series of workshops, semi-structured interviews, and focus groups, we assessed today's major social, ethical, and legal concerns related to AD, as well as their relative prioritization in specific near-future realistic traffic scenarios. We summarize experts’ and lay-people's opinions on the trade-offs among six socio-political values (privacy, autonomy, safety, security, performance, and costs) and present overall recommendations for the design of future AD technologies.

On Security and Privacy in Vehicle Speed-Limiting Services in the Internet of Vehicles

Currently, the number of vehicles in road traffic is increasing, which enhances the variety of traffic situations and needs related to road traffic and safety. A cooperative intelligent transport system (C-ITS) can improve road safety and benefit drivers and traffic participants. The C-Roads specification defines many different use cases for informing road vehicles about speed limits, impending danger, road work, signalized intersections, and more. In this article, we propose a new use case targeting the need for Integrated Rescue System services to slow down or stop selected vehicles that are violating rules. Unlike most use cases in C-Roads, in our case, it is not enough to only notify the driver’s vehicle; it must also be slowed down or stopped completely. This intervention requires addressing issues from a technical and a procedural/legal point of view. In this work, we define the use case itself, following the templates of the specification in C-Roads. Then, we discuss the issues in terms of secure communication between vehicles and privacy. We also analyze existing protection security techniques against typical cybersecurity attacks and privacy countermeasures. Next, we propose a system architecture concerning appropriate C-ITS messages and communication between vehicles and present an onboard diagnostics simulator implementing the proposed architecture for the core needs of a given use case. Furthermore, we create a practical demonstrator of our proposal based on real vehicle components. Finally, we discuss open research questions and the ongoing development of the use case before the application on the Internet of Vehicles.

ИНФОРМАЦИОННАЯ МОДЕЛЬ АНАЛИЗА ДОРОЖНОЙ СИТУАЦИИ КАНДИДАТОМ В ВОДИТЕЛИ НА ЭТАПЕ ПРАКТИЧЕСКОЙ ПОДГОТОВКИ

This article presents the causes that have a direct impact on the mechanism of occurrence of road accidents, identifies the factors that affect the reliability of perception of road information when driving a vehicle, at the stage of movement along the training route in a populated area, and outside a populated area. The information model of the analysis of the road traffic situation by the candidate for drivers at the stage of practical training is considered, the increasing sequence of passing road signs at the stage of practical training by the candidate for drivers in the training center is established.

Разработка имитационной дискретно-событийной модели транспортной инфраструктуры с использованием инструментов оптимизации

The paper describes a discrete-event simulation model of transport and logistics infrastructure on the example of a road intersection at Profsoyuznaya Street, 64 k.2 / Nametkina Street, 6. The model allows the studying of traffic processes at the intersection with a high degree of accuracy, drawing analytical conclusions and making correct management decisions to improve the traffic situation on the basis of these conclusions. The developed simulation model consists of road network elements, agent generation system, process diagram functional blocks, control elements and infographics, etc. A number of simple experiments were performed with the model, including optimization, as a result of which the most optimal duration of the traffic light phases was determined, which ensures the minimum average travel time of vehicles. In addition, the model includes ground crossings that allow the study of the behavior of pedestrian masses when crossing the carriageway. The proposed solutions to the organization of traffic and pedestrian flows can be used to implement simulation models for a wide range of issues of modeling transport and logistics infrastructure, including railway transport, for example, when developing simulation models of transport interchange nodes. Software that was used in the study: the simulation environment AnyLogic 8 and the object-oriented programming language Java. The conducted research and preparation of the publication were carried out as part of the implementation of a grant from Russian Railways for the development of scientific and pedagogical schools in the field of railway transport.

Comparison of dilemma zone length for car drivers between PTV Vissim and visual observation

There is a zone known as the dilemma zone located on each arm of a signalized intersection, where in this zone, the vehicle at the start of the yellow light is hesitant to stop safely, before the stop line or continues past the intersection with the start of the red light. In the dilemma zone, there is a decision to go through the intersection at the risk of colliding with a car coming from the other arm, or a decision to stop suddenly which might result in a rear-end collision. The longer the dilemma zone, the higher the risk that the driver will pass through the intersection. Therefore, the purpose of this study is to compare the results between visual observations and PTV Vissim results regarding the yellow light dilemma zone for 3 seconds at the 4-arm intersection for car drivers. using the Gaziz method. The dilemma zone for passenger cars from visual observation compared to PTV Vissim simulation has a result of 55% longer with a distance of 6,15m to 28,65 m, where PTV Vissim shows a distance of 2,79 m to 11,25 m. This distance is based on the denser traffic situation during peak hour.

A collision avoidance algorithm with intention prediction for inland waterways ships

Collision avoidance is one of the most challenging problems in developing a Maritime Autonomous Surface Ship. This paper proposes a pro-active collision avoidance algorithm for inland waterway ships. The algorithm addresses the problem of avoiding collisions with dynamic obstacles by applying a situation-based intention prediction for neighboring ships. This prediction algorithm allows own ship to be aware of future collision threats caused by the course changes of nearby ships. The prediction scheme is integrated into a scenario-based model predictive control scheme that has been modified for application in inland waterway traffic. The proposed algorithm can deal with complex traffic situations that a ship can encounter, and the performance of the proposed algorithm is evaluated in several experiment simulations.

How can a digital twin sustainably enhance integration and service quality for road authorities?

Towards the vision of cooperative, connected and automated mobility (CCAM) smart road services enabling communication between participants at the information level seem to be necessary. This paper aims at providing a framework for the integration of key concepts like operational design domains (ODD) and infrastructure-based support for automated driving (ISAD) into an umbrella digital twin as developed in the project DIGEST. A prototype concept for advanced forms of information management for a homogenous traffic situation representation is proposed. The concept of ODD Aware Traffic Management provides the ground for the digital twin, which shall support all vehicles in their decision-making. The intermediate project results contribute to the conference theme ‘Innovative Infrastructure for Europe 2030’ with the use of intelligent, resilient and cooperative infrastructure systems.

Investigation on the Cognitive Reaction Time of the Human Drivers Under Real Traffic Situations

Investigation on the Cognitive Reaction Time of the Human Drivers Under Real Traffic Situations

Possibility of use of artificial intelligence in the whole claim settlement process on insurance companies’ vehicle claims

The paper introduces the possibility of use of Artificial Intelligence in the whole claim settlement process on insurance companies’ vehicle claims. A fully automated vehicle claim settlement needs to develop the automatic liability check of the accident, which is now only made by human experts. The main points of this article are the main directions of the possible future development, such as to recognize the traffic situation, and to make a proper accident reconstruction.

Computation of Solution Spaces for Optimization-Based Trajectory Planning

The nonlinear vehicle dynamics and the non-convexity of collision avoidance constraints pose major challenges for optimization-based trajectory planning of automated vehicles. Current solutions are either tailored to specific traffic scenarios, simplify the vehicle dynamics, are computationally demanding, or may get stuck in local minima. This work presents a novel approach to address the aforementioned shortcomings by identifying collision-free driving corridors that represent spatio-temporal constraints for motion planning using set-based reachability analysis. We derive a suitable formulation of collision avoidance constraints from driving corridors that can be integrated into arbitrary nonlinear programs as well as (successive) convexification procedures. When combining our approach with existing motion planning methods based on continuous optimization, trajectories can be planned in arbitrary traffic situations in a computationally efficient way. We demonstrate the efficacy of our approach using scenarios from the CommonRoad benchmark suite.

Methodology and Performance Assessment of Three-Dimensional Vehicular Ad-Hoc Network Simulation

Packet-based simulation is a key tool for the research and development of Vehicular Ad-hoc Networks (VANETs). Over the last decade, many models throughout the communication stack have been presented, which have increased the degree of realism that can be achieved with popular simulation frameworks. Nevertheless, the three-dimensional aspects of many real-world traffic situations barely find consideration. In this paper, we present a holistic approach to simulate large-scale three-dimensional VANET scenarios. We briefly summarize our previously presented models covering different aspects of communication in 3D scenarios, including an environmental diffraction model, an n-ray ground interference model, and the consideration of multi-floor communication. We then describe the principle of a model selector, which applies the appropriate models depending on the environment of the currently transmitted packet. Subsequently, we use the outlined methodology implemented in our Veins 3D framework to simulate a large urban reference scenario. The results differ significantly from comparable 2D simulations, demonstrating the necessity of three-dimensional considerations. However, they also show strongly increased execution times. Therefore, we further suggest different approaches to improve the simulation performance. Based on these optimizations, simulation durations in the same order of magnitude as a comparable 2D simulation can be achieved.

Improving the traffic situation by shifting the turn and adjusting the traffic lights

The article deals with the problem of the formation of a traffic jam on the one of the street Krasnoyarsk cities. According to statistics, the largest set of accidents occur when turning the vehicle. For convenience and traffic priority, traffic lights are set up and additional signals or sections of traffic lights are introduced. In Europe, additional lanes are added, designed separately for turning, where cars can continue to wait for their turn, but if there are a lot of cars, then the additional lane ceases to cope with the flow of cars and begins to load the lane. Another option, when all left turns are prohibited, you need to turn right, where there is one-way traffic, look for a place to turn around and return from another street in order to drive straight to the opposite side of the road where the turn was prohibited. These manipulations can affect the ecology near residential areas and overload small streets, where there is already 1 lane for traffic. The paper proposes the option of transferring the turn to the supermarket to the next intersection in order to increase throughput at large traffic flows, and also uses analytical optimization methods to minimize travel time and maximize passing cars per hour.

IVF-Net: An Infrared and Visible Data Fusion Deep Network for Traffic Object Enhancement in Intelligent Transportation Systems

Infrared and visible data fusion (IVF) aims to generate a fused output that simultaneously highlights salient thermal radiation features and preserves texture information, which can not only grasp the necessary information for traffic movement, but also highlight the invisible objects that need to be dodged in intelligent transportation system (ITS). Therefore, IVF is capable of improving the environmental perception ability for various challenging traffic situations, e.g., foggy scenarios, rainy environments, and low-light illumination. However, current available IVF algorithms cannot offer a theoretical manner to integrate a priori knowledge and the network structure into a unified model. Moreover, they always fail to handle infrared and visible data pairs with different resolutions, which is a common occurrence in real ITS scenarios. To this end, this study develops a novel model-inspired unsupervised network termed IVF-Net. Specifically, an enhanced IVF model (IVFM), which pays more attention on detailed texture information and salient objects, is first established. According to proximal gradient theory, then we map this model into a deep network with learnable feature extraction parameters, aiming to draw on the strengths of the fusion model and deep learning to better describe the IVF task. Finally, a multiple task-driven loss function is designed to train the mapped network. Unlike previous work, our IVF-Net is motivated by IVFM, each layer in which has a semantic interpretability and a clear mission, thereby leading to a significantly enhanced fusion effect. Another advantage is that it is only composed of simple convolution-based structures, which ensures its lightweight and efficiency. Experiments demonstrate that IVF-Net can have a stronger ability to capture the key traffic information and highlight the salient feature of imperceptible objects, which makes it an excellent candidate to improve the reliability of subsequent applications in ITS.

Solving the Problem of Crossing Traffic Flows at the Northern Entrance to the City of Rostov-on-Don

The M4 “Don” highway is one of the main federal highways in Russia, it passes through the city of Rostov-on-Don. At the entrance and exit from the city of Rostov-on-Don there is a difficult traffic situation: many kilometers of traffic jams, various traffic accidents. One of the problem areas is the northern entrance to the city of Rostov-on-Don, namely the intersection of Yantarnaya Street and Aksaisky Prospekt in the city of Aksai, Rostov Region. There is a conflict between traffic flows, low road capacity. The article considers theoretical studies of this intersection and proposes an option for separating traffic flows using an overpass.