A Car2X sensor model for virtual development of automated driving. (6th September 2017)

• Record Type: Journal Article
• Authors: Eichberger, Arno; Markovic, Gerald; Magosi, Zoltan; Rogic, Branko; Lex, Cornelia; Samiee, Sajjad
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 5(2017:Sep./Oct.)
• Publication Date: 2017-09-06
• Subjects: Car2X -- sensor models -- ADAS -- automated driving -- virtual development; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417725625
• Languages: English
• ISSNs: 1729-8806

Abstract

Automated driving requires a reliable digital representation of the environment, which is achieved by various vehicle sensors. Wireless devices for communication between vehicles and infrastructure (Car2X communication) provide additional data beyond the vehicle's sensor range. In order to reduce the amount of on-road testing, there has been an increased use of numerical simulation in the development of automated driving functions, which demands accurate simulation models for the sensors involved. The present research deals with the development of Car2X sensor models for conceptual, automated driving investigations based on relatively simple yet computationally efficient mathematical models featuring parameters derived from on-road hardware testing. For analysis purposes, variations in range and reliability in different driving situations were measured and depicted in Google Earth. For the sensor model, a combination of geometric and stochastic models was chosen. The modeling is based on a link budget calculation that considers system and path losses, where wave propagation is described using Nakagami probability density functions. For intersections, an additional term is added to account for the path loss with geometric parameters of the intersection. After model parametrization, an evaluation was conducted. In addition, as a sample case, Car2X was added to an adaptive cruise control, and the improved functionality was demonstrated using vehicle dynamics simulation. This extended adaptive cruise control used information from the indicator of surrounding vehicles to react faster to lane changes by these vehicles.