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Usage of simulation techniques like Vehicle-in-the-Loop, Scenario-in-the-Loop, and other mixed-reality systems are becoming inevitable in autonomous vehicle development, particularly in testing and validation. These methods rely on using digital twins, realistic representations of real vehicles, and traffic in a carefully rebuilt virtual world. Recreating them precisely in a virtual ecosystem requires many parameters of real vehicles to follow their properties in a simulation. This is especially true for vehicle dynamics, where these parameters have high impact on the simulation results. The paper's objective is to provide a method that can help reverse engineering a real car's suspension characteristics with the help of a genetic algorithm. A detailed description of the method is presented, guiding the reader through the whole process, including the meta-heuristic function's settings and how it interfaces with IPG Carmaker. The paper also presents multiple measurements, which can be effortlessly recreated without expensive devices or the need to disassemble any vehicle parts. Measurements are reproduced in two separate simulation tools with special scenarios providing an efficient way to analyze and verify the results. The provided method creates vehicle suspension characteristics with adequate quality, opening up the possibility to use them in the creation of digital twins or creating virtual traffic with realistic vehicle dynamics for high-quality visualization. Results show satisfying accuracy when tested with OpenCRG.

The paper describes a mixed reality environment for testing highly automated vehicle functions. The proposed Scenario-inthe-Loop test system connects real-time computer simulation with real elements being applicable at automotive proving ground. The paper outlines necessary hardware and software requirements and proposes basic system architecture. The limitation and bottleneck of the system are also identified: latency of the wireless communication constraints the accuracy of the test system. However, the presented framework can contribute to efficient development, testing and validation of automated cars. The Scenario-In-The-Loop architecture has also been justified by real-world demonstration using an experimental 5G New Radio network technology.

It is believed that autonomous vehicles will replace conventional human drive vehicles in the next decades due to the emerging autonomous driving technology, which will definitely bring a massive transformation in the road transport sector. Due to the high complexity of traffic systems, efficient traffic simulation models for the assessment of this disruptive change are critical. The objective of this paper is to justify that the common practice of microscopic traffic simulation needs thorough revision and modification when it is applied with the presence of autonomous vehicles in order to get realistic results. Two high-fidelity traffic simulators (SUMO and VISSIM) were applied to show the sensitivity of microscopic simulation to automated vehicle’s behavior. Two traffic evaluation indicators (average travel time and average speed) were selected to quantitatively evaluate the macro-traffic performance of changes in driving behavior parameters (gap acceptance) caused by emerging autonomous driving technologies under different traffic demand conditions.

Urban commuters have been suffering from traffic congestion for a long time. In order to avoid or mitigate the congestion effect, it is significant to know how the introduction of autonomous vehicl...

As highly automated and autonomous vehicles (AVs) become more and more widespread, inducing the change of traffic dynamics, significant changes occur in traditional traffic control. So far, automotive testing has been done mostly in real-world or pure virtual simulation environment. However, this practice is quite obsolete as testing in real traffic conditions can be quite costly, moreover purely simulation based testing might be inadequate for specific goals. Accordingly, a hybrid concept of the Vehicle-inthe-Loop (ViL) was born recently, in accordance with the Hardware-in-the-Loop concept, i.e. in the ViL concept the vehicle is the 'hardware' within the simulation loop. Furthermore, due to the development of software capabilities, a novel approach, the Scenarioin-the-Loop (SciL) concept evolves based on the ViL approach. The paper defines the main purposes and conditions related to implementing ViL and SciL concepts from the perspective of traffic simulation and traffic control.

The massive demand for road transportation will cause traffic congestion. This issue is especially severe when the motorway transitions to the urban road network. In view of the characteristics of the connection between urban road networks and motorway import and export, and intending to increasing the utilization rate of roads, alleviating road traffic congestion, and reducing motor vehicle pollutant emissions, this paper evaluated the effects of speed limit reduction in an area of Budapest, Hungary. The evaluation is based on an integrated model that combines the microscopic traffic simulation model VISSIM with the emission model COPERT. By applying the variable speed limit system, a reasonable speed limit value is formulated for the upstream road section of the congested section according to the traffic performance measured at the congested area. The results show a considerable reduction in pollutant emissions and traffic delays.

Road traffic congestion has become an everyday phenomenon in today's cities all around the world. The reason is clear: at peak hours, the road network operates at full capacity. In this way, growing traffic demand cannot be satisfied, not even with traffic-responsive signal plans. The external impacts of traffic congestion come with a serious socio-economic cost: air pollution, increased travel times and fuel consumption, stress, as well as higher risk of accidents. To tackle these problems, a number of European cities have implemented reduced speed limit measures. Similarly, a general urban speed limit measure is in preparatory phase in Budapest, Hungary. In this context, a complex preliminary impact assessment is needed using a simulated environment. Two typical network parts of Budapest were analyzed with microscopic traffic simulations. The results revealed that speed limits can affect traffic differently in diverse network types indicating that thorough examination and preparation works are needed prior to the introduction of speed limit reduction.

Developing a vehicle is always a long and complex task. This is especially true for autonomous cars. Tasks performed by the driver are taken over by the vehicle and must be performed with maximum reliability. Developing these systems is a difficult task, especially due to limited testing capabilities. Testing a vehicle in a closed environment is safe and controlled, but the variety of test scenarios is limited. By using mixed reality environments, one can create a diverse environment around a real test vehicle, with traffic, obstacles, and unexpected situations. The real movement of the test vehicle allows testing decision and motion planning level vehicular functions. Information from the virtual world can be considered as input to the vehicle sensor. Mixed reality or digital twin simulation environments can greatly assist the autonomous vehicle development process and also serve as a basis for validation procedures for such systems. This article introduces a mixed reality simulation environment that integrates a real test vehicle into a virtual environment, can handle other real and virtual obstacles, contains traffic simulations, and visualizes all of these.

The paper presents a novel simulation concept for autonomous and highly automated road vehicles, called Scenario-in-the-Loop (SciL) testing. SciL can contribute to a more efficient development, testing and validation of driverless cars, which is a pressing question of our days. SciL based testing introduces a new approach capable to simulate and control realistic traffic scenarios around the autonomous vehicle under test realizing a Digital Twin technology for testing. For realistic traffic generation a high fidelity microscopic traffic simulator (SUMO) and for visualization the Unity 3D game engine are involved. The proposed testing methodology was proved with a real world autonomous car. As a test environment for SciL demonstration ZalaZONE Smart City Zone was used. Two different traffic scenarios (platooning and valet parking with pedestrian dummy) have been successfully tested and demonstrated.

Road traffic monitoring at intersections is important in traffic engineering practice. Measured data together with traffic estimation represents base input information for traffic management or for road infrastructure development. Measuring turning rates is problematic in roundabouts due to their special geometry. This needs laborious manual traffic counts or other special methods such as automatic number-plate recognition by image processing or Bluetooth sensing, which are more costly compared to the simple automatic cross-sectional detection. As a cost-effective solution to this problem, a hybrid solution is suggested, i.e. using cross-sectional detection combined with advanced estimation. The paper investigated different methods. Traditional iteration based approach as well as estimators adopted from control theory were benchmarked and validated on real-world traffic data as well as via microscopic traffic simulation. Considering different error metrics, it is shown that constrained Kalman Filter and Moving Horizon Estimation provide reliable solution to the turning rate estimation problem in roundabouts.

In the near future, autonomous vehicles will face with new challenges in several fields. One of the most exciting changes will be represented by the network-wide optimal traffic control. When driverless vehicles take over the road, classical road signalization schemes will become superfluous. Accordingly, the paper’s aim is to propose a control design methodology for autonomous vehicles in urban traffic network by considering the network-wide performance. The proposed two-level control strategy solves a tractable optimization problem for a network wide traffic control. On the one hand, a local intersection controller is designed which ensures safe crossings of vehicles and aims to reduce traffic emission in the junction area. On the other hand, the local controllers also optimize the network performance by minimizing the queues in all road links. The traffic is therefore modeled in a two-level fashion. A microscopic dynamics is considered in junctions and macroscopic model is applied for the whole traffic network. The control strategy is tested and evaluated based on microscopic traffic simulation.

In our days, vehicle automation is in a continuous evolutionary phase consisting of experiments, testing and validation. Accelerating the development and deployment of autonomous vehicles and infrastructure is a real demand as these technologies have a great potential to improve traffic safety and resolve road transport problems. The Vehicle-In-the-Loop testing is therefore indispensable throughout the development process. As a potential solution to this need, this paper introduces a new approach for test environment capable to simulate realistic traffic around the autonomous test vehicle. The test car therefore can be put into virtual transportation network by applying realtime microscopic traffic simulation, i.e., the method enables safe vehicle testing. The proposed test environment was proved with real world autonomous car.

In recent years, self-driving cars are being introduced to streets, which generated significant attention and discussion. The widespread adoption of autonomous vehicles (AV) brings changes in several fields. One of the most exciting changes is presented by the effect that driverless cars bring to the well-known traditional traffic model, the Macroscopic Fundamental Diagram (MFD). This is a key issue as MFD is a basic model for strategic traffic planning and also for real-time traffic control. In this paper therefore the impacts of autonomous vehicles that are relevant to the urban MFD are investigated through traffic simulation. The paper seeks the answer to a basic question, i.e. how the different percentage of autonomous vehicles among traditional vehicles and the autonomous driving levels influence the urban MFD. A detailed simulation study was carried with SUMO (microscopic traffic simulation software) in an artificial grid network.

For an urban bus network to operate efficiently, conflicting objectives have to be considered: providing sufficient service quality while keeping energy consumption low. The paper focuses on energy efficient operation of bus lines, where bus stops are densely placed, and buses operate frequently with possibility of bunching. The proposed decentralized, bus fleet control solution aims to combine four conflicting goals incorporated into a multi-objective, nonlinear cost function. The multi-objective optimization is solved under a receding horizon model predictive framework. The four conflicting objectives are as follows. One is ensuring periodicity of headways by watching leading and following vehicles i.e. eliminating bus bunching. Equal headways are only a necessary condition for keeping a static, predefined, periodic timetable. The second objective is timetable tracking. A conflicting objective to the former is minimizing passenger waiting time. When more than the expected passengers are waiting for the bus it is desirable to haste the vehicle in order to prevent bunching. The final objective is energy efficiency. To this end, an energy consumption model is formulated considering battery electric vehicles with recuperation during braking. Different weighting strategies are compared and evaluated through realistic scenarios, realized in a validated microscopic traffic simulation environment. Simulation results suggest 3–8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses.

Recently, computer simulation aided work has become a standard routine in all engineering fields. Accordingly, simulation plays a fundamental role even in road traffic engineering. A reliable simulator is able to provide effective analysis of a given traffic network if the applied simulation scenarios properly converge to the real-word situation. This requirement can be achieved based on the mixed use of prior real-world traffic measurements and proper simulation settings. The latter one, however, is not straightforward. Accordingly, the paper investigates a potential calibration technique to create realistic simulations. Basically, a tuning method with genetic algorithm is proposed to reproduce true-to-life traffic based on floating car speed data.

The paper focuses on the simulation of signal control problems based on VISSIM microscopic simulator. VISSIM is a commonly used software in practice for diverse problems related to road traffic control. Beyond the standard use of VISSIM, advanced options are available. VISSIM COM interface and VISSIM DLL API may be used as well providing freedom to the traffic engineer. By exploiting the programming possibilities efficient simulations may be achieved. As additional possibility MATLAB may be involved in the traffic simulation as well creating an integrated environment with VISSIM. Thus, complex mathematical problems can be calculated online by MATLAB and used for VISSIM simulation.

The paper investigates a designing method for urban traffic management system. A busy traffic area was chosen for test field in the 10^th district of Budapest. The control algorithm is based on model predictive control (MPC). The control aim is to relieve traffic congestion, reduce travel time and improve homogenous traffic flow. Theory and realization details of the control method are also presented. The MPC based control strategy was implemented into the test network´s management system. The applied environment contains microscopic traffic simulator, scientific mathematical software and some computational applications for the evaluation. The simulation results show that the system is able to ameliorate the network efficiency and reduce travel time. The designed MPC based traffic control strategy proves effectiveness by creating optimal flow in the network subjected to control input constraints.