Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System

Most vehicle controllers are developed and verified with V-model. There are several traditional methods in the automotive industry called “X-in-the-Loop (XIL)”. However, the validation of advanced driver assistance system (ADAS) controllers is more complicated and needs more environmental resources because the controller interacts with the external environment of the vehicle. Vehicle-in-the-Loop (VIL) is a recently being developed approach for simulating ADAS vehicles that ensures the safety of critical test scenarios in real-world testing using virtual environments. This new test method needs both properties of traditional computer simulations and real-world vehicle tests. This paper presents a Vehicle-in-the-Loop topology for execution in global Coordinates system. Also, it has a modular structure with four parts: synchronization module, virtual environment, sensor emulator and visualizer, so each part can be developed and modified separately in combination with other parts. This structure of VIL is expected to save maintenance time and cost. This paper shows its acceptability by testing ADAS on both a real and the VIL system.

[1]  Juergen Haering,et al.  Current Approaches in HiL-Based ADAS Testing , 2016 .

[2]  Tobias Bär,et al.  Consistent Test Method for Assistance Systems , 2014 .

[3]  P. G. Gipps,et al.  A MODEL FOR THE STRUCTURE OF LANE-CHANGING DECISIONS , 1986 .

[4]  Zsolt Szalay,et al.  Vehicle-In-The-Loop (VIL) and Scenario-In-The-Loop (SCIL) Automotive Simulation Concepts from the Perspectives of Traffic Simulation and Traffic Control , 2019 .

[5]  Robert Shorten,et al.  A Large-Scale SUMO-Based Emulation Platform , 2015, IEEE Transactions on Intelligent Transportation Systems.

[6]  Kai Hormann,et al.  The point in polygon problem for arbitrary polygons , 2001, Comput. Geom..

[7]  Berthold Färber,et al.  Standard Reactions – Driver Reactions in Critical Driving Situations☆ , 2015 .

[8]  Soo-Won Kim,et al.  Design of Pedestrian Target Selection With Funnel Map for Pedestrian AEB System , 2017, IEEE Transactions on Vehicular Technology.

[9]  P. G. Gipps,et al.  A behavioural car-following model for computer simulation , 1981 .

[10]  Johann Sienz,et al.  An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion , 2019, Sensors.

[11]  Stefan Bernsteiner,et al.  Radar Sensor Model for the Virtual Development Process , 2015 .

[12]  Ardalan Vahidi,et al.  A Vehicle-in-the-Loop (VIL) verification of an all-autonomous intersection control scheme , 2019, Transportation Research Part C: Emerging Technologies.

[13]  Ehud Rivlin,et al.  Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan † , 2019, Sensors.

[14]  Sang Hun Lee,et al.  Human-Automation Interaction Design for Adaptive Cruise Control Systems of Ground Vehicles , 2015, Sensors.

[15]  Eric Haines,et al.  Point in Polygon Strategies , 1994, Graphics Gems.

[16]  Sangsoo Jeong,et al.  Component-Based Interactive Framework for Intelligent Transportation Cyber-Physical Systems , 2020, Sensors.

[17]  Richard Hacker,et al.  Certification of Algorithm 112: Position of point relative to polygon , 1962, Commun. ACM.