Bruno Augusto

Design and Experimental Validation of a Cooperative Driving System in the Grand Cooperative Driving Challenge

In this paper, we present the Cooperative Adaptive Cruise Control (CACC) architecture, which was proposed and implemented by the team from Chalmers University of Technology, Göteborg, Sweden, that joined the Grand Cooperative Driving Challenge (GCDC) in 2011. The proposed CACC architecture consists of the following three main components, which are described in detail: 1) communication; 2) sensor fusion; and 3) control. Both simulation and experimental results are provided, demonstrating that the proposed CACC system can drive within a vehicle platoon while minimizing the inter-vehicle spacing within the allowed range of safety distances, tracking a desired speed profile, and attenuating acceleration shockwaves.

A basic vehicle dynamics model for driving simulators

Driving simulators are a useful research tool in studies of vehicle and transport-related human machine interface. A vital part of the driving simulator is the vehicle dynamics model. In the present paper, such a model is developed. The model implements basic first order phenomena, but is yet complete in the terms of modelled parts of the vehicle. A major challenge is the trade-off between simplicity and accuracy imposed by the capabilities of the driving simulators motion platform. The modelling language Modelica was used for readability reasons. The model has been validated toward both measurements of a real car and subjectively in a driving simulator. It is shown that the model is well capable of fulfilling the imposed requirements, despite its simplicity. The model is open and available upon request.

Prepositioning of driving simulator motion systems

Motion base driving simulators have limited space in which to recreate the motions of the simulated road vehicle. Conventional motion cueing algorithms strive to centre the cabin in the simulator motion envelope to accommodate accelerations in a worst case scenario while respecting the physical boundaries. Using information about the road ahead one can preposition the cabin to an off-centre point, virtually increasing the available space so that larger motions are made possible. The prepositioning algorithm presented here was developed as an addition to a classical motion cueing algorithm and makes use of road data and vehicle speed to adjust the simulator displacement. Simulations show that the amount of acceleration presented by an x, y-sled system can, with prepositioning, be increased by up to 25% in longitudinal and 53% in lateral direction for an example road. A comparative study including 12 test subjects indicates that the perceived realism is rated higher with prepositioning.