Abstract: A method of adapting tuning parameter settings of a system (2) functionality (3) for road vehicle (1) speed adjustment control starting from initially selected settings and applying a training set of speed adjustment profiles obtained from manually negotiated road segments and road segment data for these. For each of these road segments: —a simulated speed adjustment profile is calculated using the selected settings and the road segment data; —the manual and the simulated speed adjustment profiles are compared to obtain a residual; —a norm of the residual is calculated. For all of the road segments of the training set: —a norm of the norms of the residuals is calculated; —at least one of optimization, regression analysis or machine-learning is performed to minimize the norm of the norms of the residuals by selecting different settings and iterating the above steps. Settings rendering a minimal training set norm are selected.

Abstract: A method of curve speed adjustment for a road vehicle includes obtaining data on: current ego velocity; distance and curvature of an upcoming road segment, represented by a set of control points to be negotiated; road property of a road comprising the road segment; environmental properties; and driver properties. The obtained data is continuously streamed to a data processing arrangement arranged to perform a translation to target velocities for the respective control points and, for each respective control point, a translation from target velocity for that control point and distance to that control point and obtained current ego velocity, to a target acceleration to reach that control point at its target velocity. The resulting target accelerations are continuously streamed to a control unit of the road vehicle to adjust the road vehicle acceleration to reach each respective control point at its target velocity.

Abstract: A method of adapting tuning parameter settings of a system (2) functionality (3) for road vehicle (1) speed adjustment control starting from initially selected settings and applying a training set of speed adjustment profiles obtained from manually negotiated road segments and road segment data for these. For each of these road segments: —a simulated speed adjustment profile is calculated using the selected settings and the road segment data; —the manual and the simulated speed adjustment profiles are compared to obtain a residual; —a norm of the residual is calculated. For all of the road segments of the training set: —a norm of the norms of the residuals is calculated; —at least one of optimization, regression analysis or machine-learning is performed to minimize the norm of the norms of the residuals by selecting different settings and iterating the above steps. Settings rendering a minimal training set norm are selected.

Abstract: The present disclosure relates to a method for controlling an autonomous driving configuration or driving assistance configuration comprising at least two adjustable parameters. The method includes, at an electronic device with a display, displaying on the display a user interface. The user interface includes a graphical representation of a current setting of the at least two adjustable parameters associated with the autonomous driving configuration or driving assistance configuration. The method further includes detecting via one or more input devices, a user input directed towards a first adjustable parameter of the at least two adjustable parameters, and updating the graphical representation on the display based on the detected user input. More specifically, the updating of the graphical representation is done by modifying the current setting of the first parameter of the at least two adjustable parameters and modifying at least one characteristic of a second adjustable parameter.

Abstract: Described herein is a method and arrangement of curve speed adjustment for a road vehicle (1). Obtained is data on: current ego velocity (vE), distance (d) and curvature (r) of an upcoming road segment, represented by a set of control points (Pn, Pn+1, etc.) to be negotiated; road property of a road comprising the road segment; environmental properties; and driver properties. The obtained data is continuously streamed to a data processing arrangement (12) arranged to perform a translation to target velocities (vroad, n, vroad, n+1, etc.) for the respective control points (Pn, Pn+1, etc.) and, for each respective control point (Pn, Pn+1, etc.), a translation from target velocity (vroad, n, vroad, n+1, etc.) for that control point (Pn, Pn+1, etc.) and distance (dn, dn+1, etc.) to that control point (Pn, Pn+1, etc.) and obtained current ego velocity (vE), to a target acceleration (an, an+1, etc.) to reach that control point (Pn, Pn+1, etc.) at its target velocity (vroad, n, vroad, n+1, etc.).