#Vi postview simulator
The last phase of the thesis is dedicated to the study of Adaptive Cruise Control (ACC) with on-road and simulator experimentation. The improvement on the driver’s yielding behaviour towards an un-signalized crossing during night-time and their reaction to an integrated lighting-warning system was evaluated in the case study VI. The drivers’ performance measures such as perception reaction time and gaze behaviour were used to assess the safety level of the crossing equipped with standard and innovative signalling systems.
#Vi postview driver
Case study V analyzed the driver yielding behaviour in approach phase to a bicycle priority crossing with the use of surrogate safety measures.
#Vi postview drivers
Significant attention is given to the safety of vulnerable drivers in urban areas during the naturalistic driving behaviour study. The eye-tracking data were evaluated in both studies in order to identify a driver visual attention indicator based on the participants gaze position and duration. These same driving monitoring instruments were used for evaluating the improvement of a pedestrian crossing at the roundabout in case study IV. The real road experiment with drivers was carried out in an urban arterial road in order to evaluate the proposed approach through innovative driver monitoring techniques. Case study III aims to integrate the traditional road safety auditing with an innovative driver behaviour monitoring system. During this phase, several case studies were developed to monitor drivers’ behaviour in the naturalistic environment. The driver visual behaviour was recorded with the use of a head mounted eye-tracking device, while the vehicle trajectory was registered with an instrumented vehicle equipped with Global Positioning System (GPS). In the second part of the research, the driver performance and visual behaviour were studied on the road under different scenarios. The results of these studies are discussed in the case studies I and II. During this phase of the research, motion cueing algorithms were developed to control the simulator movements and the effect of the motion cues on drivers’ behaviour was analysed through experimentation. The vehicle dynamics model has been developed in MATLAB-Simulink and has the ability of real-time calculation of the vehicle states and control the motion platform. The first part of the research is focused on improving the physical fidelity of the two DOF driving simulator with particular attention to motion cueing and vehicle dynamics model. The activities are divided into two macro areas the driving simulation studies conducted in Gustave Eifel University (IFSTTAR) and on-road experiments organized by the University of Bologna.
The related research activities were carried out in collaboration with the University of Bologna, Paris-Est University and Gustave Eiffel University (IFSTTAR) in the form of a cotutelle PhD. Therefore, in its current form, our MRAC model can be used to approximate human adaptation in pursuit tracking tasks when a change in the dynamics of the controlled system requires significant (rate) feedback controller adaptation to maintain satisfactory closed-loop control performance. The MRAC model was indeed able to accurately capture the transient dynamics exhibited by the participants when the system changed from an approximate single to a double integrator, however, for the opposite transition the MRAC gains were always adapted too slowly. Participants' control behavior rapidly changed when the dynamics of the controlled system changed, in particular for transitions from single to double integrator dynamics. MRAC's adaptive control gains, the internal model parameters, and the learning rates were estimated from the experiment data using non-linear optimization aimed at maximizing the quality-of-fit of participants' control outputs. Our proposed MRAC controller is composed of a feedforward and a feedback controller and an internal reference model that is used to drive an adaptive control policy. Ten participants took part in an experiment in which they controlled a time-varying system whose dynamics changed twice between approximate single and double integrator dynamics, and vice versa. This paper evaluates the effectiveness of a model-based adaptive control technique, Model Reference Adaptive Control (MRAC), for describing the adaptive control policy used by human operators while controlling a time-varying system in a pursuit-tracking task. View Video Presentation: Improved understanding of human adaptation can be used to design better (semi-)automated systems that can support the human controller when task characteristics suddenly change.
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