Digital Training Platform for Comprehensive Traffic Simulation

The paper deals with the development of software for traffic flow simulation combining the widest spectrum of mathematical approaches used in this field. Macro- and microscopic models, models of cellular automata as well as different numerical methods of their computer implementation are incorporated into a digital platform. Original developments of the authors of the paper such as quasi-gas dynamic traffic model and multilane cellular automata model take the main place. Potential users of the software are students and researchers. The platform possesses an intuitive graphical interface ensuring interactivity. Microsoft Visual Studio with C# is chosen as the development environment, the Unity 3D engine is employed for visualization and collaboration with WinForm projects. In the future, the platform can be transformed into a network computer laboratory providing access to information resources via Internet.

Power-law distribution found in city-scale traffic flow simulation

Origin of a power-law in traffic-volume distribution found in traffic simulations of Kobe city was studied. The traffic distribution which was obtained from a shortest path search with randomized OD (origin-destination) set in Kobe city digital map obeys power-law. The toy model that Cayley tree is embedded in the network is also verified. It is theoretically shown that the traffic distribution with all possible OD set in a Cayley tree obeys power-law like distribution. With randomized OD set, the distribution is diffused from the theoretical point sets. Relationship between these facts and the origin of power-law is discussed.

Report on design of modules for the stochastic traffic simulation

As part of the AutoDrive project, OpenPASS is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. The deliverable D4.20 is about the design of the modules for the stochastic traffic simulation. This initially includes an examination of the existing traffic simulations described in chapter 2. Subsequently, the underlying tasks of the driver when crossing an intersection are explained. The main part contains the design of the cognitive structure of the road user (chapter 4.2) and the development of the cognitive behaviour modules (chapter 4.3).

Report on integration of the stochastic traffic simulation

As part of the AutoDrive project, the OpenPASS framework is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. This framework was adapted and further developed. The deliverable D5.13 deals with the construction of the stochastic traffic simulation. At this point of the process, the theoretical design aspects of D4.20 are implemented. D5.13 explains the operating principles of the different modules. This includes the foundations, boundary conditions, and mathematical theory of the traffic simulation.

Report on layout of the traffic simulation and trial design of the evaluation

Within the AutoDrive project, openPASS is used to develop a cognitive stochastic traffic flow simulation for urban intersections and highway scenarios, which are described in deliverable D1.14. The deliverable D2.16 includes the customizations of the framework openPASS that are required to provide a basis for the development and implementation of the driver behavior model and the evaluated safety function. The trial design for the evaluation of the safety functions is described. Furthermore, the design of the driver behavior study is introduced to parameterize and validate the underlying driver behavior model.

OPTIMIZATION OF THE TRAFFIC FLOW SIMULATION MODEL AT A REGULATED INTERSECTION

In this paper the traffic flow simulation model at a regulated intersection presents (on the example of the intersection of Kalinina and Tenistaya streets in Blagoveshchensk). The study of the simulation model was carried out in the AnyLogic program. An optimization experiment was carried out to minimize the travel time of the intersection by vehicles.

Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems

The increasingly used approach of combining different simulation softwares in testing of automated driving systems (ADSs) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSPs) provide the possibility to cover the high demand for testing kilometers for ADSs by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand for testing kilometers is not enough to choose a suitable CSP. The complexity levels of the vehicle, object, sensors, and environment models used are essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, environments, objects, and sensors in the VSS, as well as traffic parameters in the TFSS, can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper, we present a software design for a CSP based on the Model–View–Controller (MVC) design pattern as well as an implementation of a complex CSP for virtual testing of ADSs. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive (CarMaker) and the TFSS from the PTV Group (Vissim). The results showed that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADSs.