A Novel Simulation Framework for the Design and Testing of Advanced Driver Assistance Systems

The number and complexity of newly developed automated driving systems has been constantly rising over the past decade. Especially the introduction of vehicle-to-everything (V2X) communication is expected to further potentiate this development. In order to be deployed, the functional safety of the developed systems has to be assured previously. However, the testing in a representative number of field tests is costly and time-consuming. For this reason, virtual test drives have risen as an important option for design and testing of automated driving technologies, leaving only the final validation to test with real vehicles and thus significantly reducing the overall expenditure. The authors of the work at hand introduce a simulation framework based on the vehicle simulator CarMaker, complemented with the middleware platform Robot Operating System (ROS) and fed with real traffic data, which allows to automatically test advanced driver assistance systems for a large number of real world scenarios by varying topology, vehicle and communication parameters, among others. The simulation framework is then used to demonstrate the benefit of collective perception (i.e. sharing of on-board sensor data among nearby vehicles by V2X communication) for a vehicle merging into a freeway, with metrics such as the vehicle awareness on spot and the time it has to plan and execute its maneuver.

[1]  Justin Dauwels,et al.  An Integrated Simulation Environment for Testing V2X Protocols and Applications , 2016, ICCS.

[2]  Daniel Krajzewicz,et al.  Simulation of V2X applications with the iTETRIS system , 2012 .

[3]  Klaus C. J. Dietmayer,et al.  The Ko-PER intersection laserscanner and video dataset , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[4]  Panagiotis Papadimitratos,et al.  TraNS: realistic joint traffic and network simulator for VANETs , 2008, MOCO.

[5]  Teerawat Issariyakul,et al.  Introduction to Network Simulator NS2 , 2008 .

[6]  Marco Bertini,et al.  Parallel and distributed simulation of wireless vehicular ad hoc networks , 2006, MSWiM '06.

[7]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[8]  Björn Schünemann,et al.  V2X simulation runtime infrastructure VSimRTI: An assessment tool to design smart traffic management systems , 2011, Comput. Networks.

[9]  Twan Basten,et al.  Co-simulation Framework for Control, Communication and Traffic for Vehicle Platoons , 2018, 2018 21st Euromicro Conference on Digital System Design (DSD).

[10]  Lutz Eckstein,et al.  The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[11]  Falko Dressler,et al.  Plexe: A platooning extension for Veins , 2014, 2014 IEEE Vehicular Networking Conference (VNC).

[12]  Aakash Arora,et al.  A Roadmap to Safer Driving Through Advanced Driver Assistance Systems , 2016 .

[13]  Michał Maciejewski,et al.  A comparison of microscopic traffic flow simulation systems for an urban area , 2010 .

[14]  Florian Alexander Schiegg,et al.  Analytical Performance Evaluation of the Collective Perception Service in C-V2X Mode 4 Networks , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[15]  Peter Vortisch,et al.  Microscopic Traffic Flow Simulator VISSIM , 2010 .

[16]  Daniel Krajzewicz,et al.  SUMO - Simulation of Urban MObility An Overview , 2011 .

[17]  Rudolf Hornig,et al.  An overview of the OMNeT++ simulation environment , 2008, Simutools 2008.

[18]  Ignacio Llatser,et al.  Object Detection Probability for Highly Automated Vehicles: An Analytical Sensor Model , 2019, VEHITS.

[19]  Jan Erik Stellet,et al.  Generation of Scenes in Intersections for the Validation of Highly Automated Driving Functions , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).