Abstract: A method of determination of the alignment angles of two or more road vehicle (1) borne radar sensors (4) for a road vehicle radar auto-alignment controller (3) starting from initially available rough estimates of alignment angles. From at least two radar sensors (4) are obtained signals related to range, azimuth and range rate to detections. The detections are screened (5) to determine detections from stationary targets. From the determined detections from stationary targets is derived a linearized signal processing model involving alignment angles, longitudinal and lateral velocity and yaw-rate of the road vehicle (1). A filter algorithm is applied to estimate the alignment angles. Based on the estimated alignment angles are produced signals suitable for causing a road vehicle (1) radar auto-alignment controller (3) to perform radar offset compensation.

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: Described herein is a method of determining a current location speed limit in a road vehicle (1) speed limit information system (20). The method comprises receiving (6): one or more signals corresponding to respective candidate speed limits (7); and one or more signals related to observed vehicle dynamics affecting changes (8) in one or more vehicles (1, 5) pre and post a most recently passed anticipated speed limit change location (2). It further comprises evaluating (9) the confidence of the different candidate speed limits (7) based on the signals related to observed vehicle dynamics affecting changes (8) and validating (10a) or discarding (10b) candidate speed limits (7) based on the evaluated confidences. A signal (13) corresponding to a highest confidence validated speed limit is output.

Abstract: Disclosed is a method of providing a scenario-based overlay torque request signal in a steer torque manager (1) during driver-override of an auxiliary steering assistance system (2) function in a road vehicle (3) having an EPAS system (4). The steer torque manager (1) has a wheel angle controller (1b) for providing an assistance torque request related signal, and a driver-in-the-loop functionality (1a) for determining driver-override and providing a driver-override related signal.

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.).

Abstract: Method for encoding/decoding a signal at a first and second communication node (N1; N2) in a road vehicle. A signal (1) from an on-board sensor (10) is encoded using a first encoding scheme (a), encoding the formed single encoded sensor signal (1a) using a second encoding scheme (b), decoding this double encoded sensor signal (1ab) in the second communication node (N2) based on the second encoding scheme (b), forming a decoded single encoded sensor signal (1a?). In the first communication node (N2), performing a comparison analysis, comprising at least one of the following: comparing the decoded single encoded sensor signal (1a?) with a stored single encoded sensor signal (1a), or after encoding the decoded single encoded sensor signal (1a?) with the second encoding scheme (b) comparing (110) the thus formed double encoded sensor signal (1a?b) with a stored double encoded sensor signal (1ab).

Abstract: Described herein is a method and arrangement (11) for sourcing of location information, generating and updating maps (16) representing the location. From at least two road vehicle (12) passages at the location is obtained (1, 2) vehicle registered data on the surrounding environment from environment sensors and positioning data from consumer-grade satellite positioning arrangements and from at least one of an inertial measurement unit and a wheel speed sensor. The positioning data is smoothed (3) to establish continuous trajectories for the respective vehicles (12). Individual surrounding environment maps are created using the data from each respective vehicle (12) passage at the location. From the individual surrounding environment maps are identified submaps (15) sharing area segments. Pairs of submaps (15) sharing area segments are cross-correlated (6).

Abstract: Described herein is a method and arrangement (1) for generating validated control commands (2) for an autonomous road vehicle (3). An end-to-end trained neural network system (4) is arranged to receive an input of raw sensor data (5) from on-board sensors (6) of the autonomous road vehicle (3) as well as object-level data (7) and tactical information data (8). The end-to-end trained neural network system (4) is further arranged to map input data (5, 7, 8) to control commands (10) for the autonomous road vehicle (3) over pre-set time horizons. A safety module (9) is arranged to receive the control commands (10) for the autonomous road vehicle (3) over the pre-set time horizons and perform risk assessment of planned trajectories resulting from the control commands (10) for the autonomous road vehicle (3) over the pre-set time horizons. The safety module (9) is further arranged to validate as safe and output validated control commands (2) for the autonomous road vehicle (3).

Abstract: Method for improving global positioning performance of a first road vehicle (10), the method comprising, by means of a data server (3, 4, 4?): acquiring data from onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g) arranged on the first road vehicle (10) and on at least two neighbouring road vehicles (10?, 10?, 10??), the data comprising data on relative positions and data on heading angle and velocity of the road vehicles (10, 10?, 10?, 10??), and acquiring global positioning data of at least two of the road vehicles (10, 10?, 10?, 10??), processing (102) data comprising the global positioning data, the data, with corresponding timestamp, acquired from the onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g), and a motion model for each of the first road vehicle (10) and the at least two neighbouring road vehicles (10?, 10?, 10??) using a data fusion algorithm, calculating adjusted global positioning data for the first road vehicle (10) and communicating (104) the adjusted global positioning data to a positioning system (6

Abstract: Described herein is a method and system for assisting a driver of a vehicle (1) to drive with precaution. Vehicle environment monitoring sensors (3a, 3b) determines other road users and particular features associated with a traffic situation of the vehicle (1) and hypotheses are applied related to hypothetical threats that may arise based thereupon. A driver level of attention, required to handle the hypothetical threats, and a time until that level will be required is estimated. A current driver level of attention is derived, from driver-monitoring sensors (4). If determined that the estimated required driver level of attention exceeds the current and the time until the estimated driver level of attention will be required is less than a threshold-time (tthres), there is produced at least one of visual (5), acoustic (6) and haptic (7) information to a vehicle driver environment, and/or triggered at least one of automated braking (8) and steering (9) of the vehicle (1).