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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.

Autonomous or highly automated road vehicles and all related technologies are under intensive research and development. Moreover, internationally a massive investment increase can be observed in the automotive industry. According to this megatrend, new automotive test tracks appear or older ones transform to be capable of testing and proving for autonomous vehicles. Therefore, the question emerges: what are the key areas for automated drive development, which must be financed in case of autonomous proving ground design? It is a real challenge to be able to make the right decisions due to a lack of numerous experiences in this field. In this research, experts of automated driving technology have been surveyed and their opinion and knowledge have been synthesized. As a strong purpose of gaining robust results, the conventional AHP has been amended by the Pareto approach to ensure that the derived weights correspond to the expert scoring intention so perfectly that it cannot be more improved. Since the non-Pareto optimal weight results might cause rank reversal in the final prioritization, the applied Pareto test guarantees that the final outcome reflects the expert evaluators’ incentive. The conducted analysis has indicated that the obtained results are robust not only from the sensitivity point of view but also from the Pareto optimality approach. The proposed hierarchical decision model is therefore applicable to assist decision making for autonomous proving ground developments. The main contribution of the article, however, is to present the first reliable prioritization of the autonomous proving ground elements to extend the body of professional knowledge.

The paper proposes an eco-cruise control strategy for urban public transport buses. The aim of the velocity control is ensuring timetable adherence, while considering upstream queue lengths at traffic lights in a probabilistic way. The contribution of the paper is twofold. First, the shockwave profile model (SPM) is extended to capture the stochastic nature of traffic queue lengths. The model is adequate to describe frequent traffic state interruptions at signalized intersections. Based on the distribution function of stochastic traffic volume demand, the randomness in queue length, wave fronts, and vehicle numbers are derived. Then, an outlook is provided on its applicability as a full-scale urban traffic network model. Second, a shrinking horizon model predictive controller (MPC) is proposed for ensuring timetable reliability. The intention is to calculate optimal velocity commands based on the current position and desired arrival time of the bus while considering upcoming delays due to red signals and eventual queues. The above proposed stochastic traffic model is incorporated in a rolling horizon optimization via chance-constraining. In the optimization, probabilistic guarantees are formulated to minimize delay due to standstill in queues at signalized intersections. Optimization results are analyzed from two particular aspects, (i) feasibility and (ii) closed-loop performance point of views. The novel stochastic profile model is tested in a high fidelity traffic simulator context. Comparative simulation results show the viability and importance of stochastic bounds in urban trajectory design. The proposed algorithm yields smoother bus trajectories at an urban corridor, suggesting energy savings compared to benchmark control strategies.

In order to provide optimal choice of activity locations and travel time reduction, a daily activity chain optimization method has been elaborated, which includes temporal and spatial flexibility o...

Traffic control of urban areas is a current research topic. Different pricing regimes such as cordon pricing, time-based average cost zone pricing, distance-based average cost zone pricing or marginal cost zone pricing have been investigated so far. The economical best solution, however, cannot be used due to the lack of information. In the study, the future of road pricing together with traffic control is analysed with the assumption of widespread information and communications technology (ICT) usage, i.e. access to real-time information. The study proposes a utility-based dynamic road pricing for optimal traffic flow management. The research largely focused on the model and regulator definition [fixed, dynamic, and non-linear model predictive control (MPC)]. The theoretical research was conducted on a real-world traffic test network by applying different control methods and taking into account the time-delay effect of traffic forecasting. The simulation results show that time delay in response to vehicle concentration can result in significant oscillation on the concurrent routes in case of fixed and dynamic control, but can be compensated by non-linear MPC. As a major result, it is shown that with the market penetration of ICT, a new era of road tolling regimes could be introduced.

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.

This paper presents a model-based multiobjective control strategy to reduce bus bunching and hence improve public transport reliability. Our goal is twofold. First, we define a proper model, consisting of multiple static and dynamic components. Bus-following model captures the longitudinal dynamics taking into account the interaction with the surrounding traffic. Furthermore, bus stop operations are modeled to estimate dwell time. Second, a shrinking horizon model predictive controller (MPC) is proposed for solving bus bunching problems. The model is able to predict short time-space behavior of public transport buses enabling constrained, finite horizon, optimal control solution to ensure homogeneity of service both in time and space. In this line, the goal with the selected rolling horizon control scheme is to choose a proper velocity profile for the public transport bus such that it keeps both timetable schedule and a desired headway from the bus in front of it (leading bus). The control strategy predicts the arrival time at a bus stop using a passenger arrival and dwell time model. In this vein, the receding horizon model predictive controller calculates an optimal velocity profile based on its current position and desired arrival time. Four different weighting strategies are proposed to test (i) timetable only, (ii) headway only, (iii) balanced timetable - headway tracking and (iv) adaptive control with varying weights. The controller is tested in a high fidelity traffic simulator with realistic scenarios. The behavior of the system is analyzed by considering extreme disturbances. Finally, the existence of a Pareto front between these two objectives is also demonstrated.

For the optimization of daily activity chains a novel method has been elaborated, where flexible demand points were introduced. Some activities are not necessarily fixed temporally and spatially, t...

This paper presents a velocity control/advise algorithm relying on vehicle-to-vehicle communication, to ensure the headway homogeneity of buses on a joint corridor, i.e. when multiple lines merge and travel on the same route. The proposed control method first schedules merging buses prior to entering a common line. Second, based on the position and velocity of the bus ahead of the controlled one, a shrinking horizon model predictive controller (MPC) calculates a proper velocity profile for the merging bus. The model is able to predict short time- space behavior of public transport buses enabling constrained, finite horizon, optimal control solution to reach the merging point with equidistant headways, taking all buses from different lines into account. The controller is tested in a high fidelity traffic simulator with realistic scenarios.

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.

There is a growing interest in the literature regarding the flexibility of activities and reduction of travel times in an urban environment. For the optimization of daily activity chains a method has been elaborated, which includes the temporal and spatial flexibility of activities. In the course of the optimization process possible alternatives are searched using a modified version of the Traveling Salesman Problem with Time Window. The algorithm provides an optimal order of activities as the minimum of the pre-defined cost function. In our case the cost function is travel time. The optimization process results in travel time reduction of the users. In order to provide some insight regarding the performance of the optimization, application oriented simulations are realized on a real transportation network using real travel time information. Three transportation modes are considered: car, public transport and combined (public transport with car-sharing option). We conclude that with the use of the optimization algorithm approximately 20-25% travel time reduction can be achieved, but the exact value highly depends on the original location of the activities.

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.

The era of autonomous vehicles infer new challenges in several fields. When autonomous vehicles take over the road in a large volume, consumer preferences around car-ownership will transform, traffic modeling and control will need correction, and moreover hackers will appear. These are just a few impacts which are expectable in the near future. Accordingly, the paper’s aim is to enlighten trends and upcoming challenges of driverless vehicles and automated transportation system from a transportation engineering perspective.

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic. First Published Online:4 Sept 2017

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.

The Research Center for Autonomous Road Vehicles (RECAR) was founded in 2015 upon the initiative of the Faculty of Transportation Engineering and Vehicle Engineering of Budapest University of Technology and Economics. The research center is supported by industrial partners and other academic partners targeting research and educational purposes. In complement to this project, the construction of a new automotive test track is also under development especially for autonomous road vehicle testing serving as automotive proving ground in Zalaegerszeg, Hungary. Accordingly, an intensive research has been started in RECAR center in the field of autonomous vehicle technology. The paper’s goal is to share the main practical and methodological experiences with the scientific audience as well as the industrial sector. Based on the initial research actions we intend to enlighten the upcoming research challenges of driverless vehicles and automated intelligent transport system. Basically, three main topics are concerned. Firstly, the main issues concerning autonomous vehicle research are summarized. Secondly, the requirements for autonomous test track design are concluded. Thirdly, the legal questions that emerge with the appearance of driverless vehicles are investigated, especially concerning liability.

Autonomous vehicular technology significantly stresses the issue of safety. Although the use of driverless cars raises considerable expectations of a general improvement in safety, new challenges concerning the safety aspects stem from the changing context. On the one hand, the paper addresses regulatory issues raised by the impact of technological changes, particularly standardization problems. On the other hand, the issue of liability questions is investigated as it might cause today’s main legal obstacle for the wide spreading of autonomous cars, especially as autonomous cars might jeopardize the existing approaches to vehicular liability. The aim of this paper is to scrutinize the basic problems in both fields. We provide what, at the current state-of-the-art, appear to be reasonable recommendations from the perspective of technological regulation and law, in order to deal with the main problems that might hamper the development of autonomous transport technology.