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ATS International Journal
Editor in Chief: Prof. Alessandro Calvi
Address: Via Vito Volterra 62,
00146, Rome, Italy.
Mail to: alessandro.calvi@uniroma3.it

A simulation design of an integrated GNSS/INU, vehicle dynamics, and microscopic traffic flow simulator for automotive safety

G. Dedes, S. Wolfe, D. Guenther, B.B. Park, J.J. So, K. Mouskos, D. Grejner-Brzezinska, C. Toth, X. Wang, G. Heydinger
Pages: 41-52

Abstract:

This paper presents the development of a comprehensive, integrated GNSS/INU traffic simulator consisting of a microscopic traffic simulator based on VISSIM, a vehicle dynamics simulator based on CarSim, and a GNSS/INU simulator. This GNSS/INU traffic simulator provides an integrated design, test and evaluation platform for exploring new ideas, developing advanced concept designs, and investigating the impact of existing and emerging Global Navigation Satellite Systems (GNSS) and Inertial Navigation Unit (INU) technologies for enhanced automotive safety at the vehicle and network levels. For the simulation of hazardous conditions, VISSIM identifies situations where safety warning events are generated on the basis of surrogate safety indicators (e.g., time to collision). These events are intercepted by the vehicle dynamics simulator CarSim which generates simulated 'ground truth' vehicle trajectory and orientation information based on VISSIM’s simulated initial driving conditions, vehicle type, driver aggressiveness and road geometry. The simulated 'ground truth' vehicle trajectories and orientation are passed to a GNSS/INU simulator for the computation of the GNSS/INU instrumental, environmental and system errors. The simulated GNSS/INU trajectories in conjunction with the simulated 'ground truth' vehicle trajectories and orientation are processed through a “Driver-Vehicle Control Intervention Module” which simulates the driver and/or automated vehicle response for avoiding potential crashes. Based on the results of the drivervehicle simulated response, the individual safety is estimated through an “Individual Vehicle Surrogate Crash” estimator. For the estimation of network safety, a trained Neural Network (NN) is used as a nonparametric surrogate crash estimator with input from a Vehicle-2-Vehicle (V2V) and V2I simulators.
Keywords: micro-simulation; vehicle; GNSS/INU; VISSIM; CarSim; collision

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