Investigating the Use of Visual Information in Lane Keeping Tasks: A replication study of the Land & Horwood experiment

In car driving, manual control to keep a vehicle within its lane is mainly performed based on visual information of the road ahead. Linear models describing behavior in such tasks can therefore be directly based on the human perception of the visual scene, although it is currently unclear how this perception guides control behavior. In literature, occlusion experiments have investigated this connection by artificially restricting the field of view and measuring the difference in driver performance compared to full-visual driving, but never managed to describe changes in underlying driver control dynamics. Therefore, in this MSc thesis project a human-in-the-loop experiment was performed in the SIMONA Research Simulator in which drivers steered along a curved road under varying occlusion conditions, showing either a single, or two separate, horizontal slits (1-deg vertical view angle) of the visual scene at varying vertical positions in the visual scene. The measured steering behavior was analyzed using a recently developed parametric model of driver steering, including the estimation of the driver Frequency Response Functions (FRFs). This model explicitly captures drivers’ individual responses to road preview, lateral position and heading angle information. Complementary, the eye gaze is measured and compared to the estimated driver model parameters. For the first time, insight is obtained in the behavioral changes under various occlusion conditions with respect to full-visual behavior, directly in relation to where drivers look. The experiment shows that drivers adapt their modelled aim points and eye gaze to the available road geometry if only a single occlusion slit is present. For double-slit conditions, drivers place both the gaze and aim points between the occlusion slits, effectively interpolating the available visual information while still responding to a single metric. In contrast to earlier reported findings in literature, these results show a strong adaptability to the visual scene and provide no indication of often-suspected two-level driver control.Aerospace Engineering | Control & Simulatio

Quality comparison of motion cueing algorithms for urban driving simulations

When designing driving simulation experiments with motion cueing, it is often necessary to make choices between Motion Cueing Algorithms (MCAs) without being fully able to know how well an MCA will perform during the experiment. Choices between MCAs can therefore be greatly supported by previous measurements or predictions of motion cueing quality. This paper describes a data collection experiment on a nine degree-of-freedom motion-base simulator, in which participants are asked to continuously rate the motion cueing quality during a pre-recorded drive through an urban environment. Three benchmark MCAs are compared: a Model-Predictive Control (MPC) algorithm with infinite prediction horizon, a Classical Washout Algorithm (CWA) tuned for the use-case, and the same algorithm (CWA), but with the tilt-coordination channels turned off. By comparing ratings for the whole scenario, as well as ratings for each maneuver individually, the results show a preference of the presence of tilt-coordination, as well as a preference for the optimization-based MPC algorithm over the CWA condition. The collected data will be used directly for modeling and predicting motion cueing quality for future experiments at BMW, such that the best-suited MCA and parameter setting can be selected before experiments.Control & Simulatio