Abstract: Method and server for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A server (101; 500) provides (301) a virtual environment (200) simulating an environment relevant for operation of vehicles having said AD/ADAS functionality and in which is operating: fully computer controlled movable virtual objects (230a-c), human controlled movable virtual objects (220a-c) and at least one virtual AD/ADAS vehicle (210) operating according to said AD/ADAS functionality. The server (101; 500) allows devices (101-103) to remotely connect to the server (105; 500) and users of said devices (101-103) to, via user interfaces of the devices (101-103), control said human controlled movable virtual objects (220a-c), respectively, in the virtual environment (200), and thereby cause generation of scenarios that said at least one virtual AD/ADAS vehicle (210) is subjected to.

Abstract: The present disclosure relates to systems and methods capable of adaptively estimating time-to-take over during ODD exit events, by estimating the recovery time for driver and the action time required to safely handle the situation. In more detail, the proposed system allows for field monitoring for online verification (i.e., in-vehicle verification) of adaptive hand over time, and for facilitated updating of the systems predicting the hand-over time (i.e., Action Time Network and Reaction Time Network) in a decoupled manner by efficient use of data from field monitoring.

Abstract: Method and device for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A device (101; 500) provides (301) a virtual environment (200) simulating an environment relevant for operation of vehicles having said AD/ADAS functionality and in which is operating: fully computer controlled movable virtual objects (230a-c), human controlled movable virtual objects (220) and at least one virtual AD/ADAS vehicle (210) operating according to said AD/ADAS functionality. The device allows (303) a user of the device (101; 500) to, via user interface (506), control said one or more human controlled movable virtual objects (220) during operation and thereby cause generation of scenarios that the virtual AD/ADAS vehicle (210) is subjected to.

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: 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: 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: Method and server for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A server (101; 500) provides (301) a virtual environment (200) simulating an environment relevant for operation of vehicles having said AD/ADAS functionality and in which is operating: fully computer controlled movable virtual objects (230a-c), human controlled movable virtual objects (220a-c) and at least one virtual AD/ADAS vehicle (210) operating according to said AD/ADAS functionality. The server (101; 500) allows devices (101-103) to remotely connect to the server (105; 500) and users of said devices (101-103) to, via user interfaces of the devices (101-103), control said human controlled movable virtual objects (220a-c), respectively, in the virtual environment (200), and thereby cause generation of scenarios that said at least one virtual AD/ADAS vehicle (210) is subjected to.

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: Method and device for supporting generation of scenarios for testing autonomous driving and/or advanced driver assistance system, AD/ADAS, functionality for real world vehicles. A device (101; 500) provides (301) a virtual environment (200) simulating an environment relevant for operation of vehicles having said AD/ADAS functionality and in which is operating: fully computer controlled movable virtual objects (230a-c), human controlled movable virtual objects (220) and at least one virtual AD/ADAS vehicle (210) operating according to said AD/ADAS functionality. The device allows (303) a user of the device (101; 500) to, via user interface (506), control said one or more human controlled movable virtual objects (220) during operation and thereby cause generation of scenarios that the virtual AD/ADAS vehicle (210) is subjected to.

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).

Abstract: A method, a central control system and a non-transitory computer-readable storage medium are provided for determining driving assisting data in relation to a vehicle based on sensor data and map data in relation to other vehicles. Sensor data are obtained from a sensor system of a first vehicle, the sensor data comprising a pose and a velocity of a second vehicle located in a surrounding environment of the first vehicle. Furthermore, map data are obtained comprising a road geometry of the surrounding environment of the first vehicle. The pose and the velocity of the second vehicle are compared with the map data, and driving assisting data in relation to a third vehicle are determined based on the comparison.

Abstract: Described herein is a method of determining a current location speed-limit in a road-vehicle (1) speed-limit information system (2). One or more signals (3, 4) corresponding to respective candidate speed-limits for the current location are received. A parametrized heuristic algorithm with an associated cost function is applied (7) to decide which candidate speed-limit (3, 4), if any, that is applicable. If available for the current location, a cloud service (8) supplied estimated true speed-limit (9) and an associated confidence in this estimate is received. An online learning or a reinforcement learning method is used to, based on the cloud service (8) supplied estimated true speed-limit (9) and the associated confidence in that estimate, constantly fit (10) the heuristic's parametrization to, with a high confidence, reproduce the cloud service (8) supplied estimated true speed-limit (9). A speed-limit information signal (11) corresponding to the decision of the parametrized heuristic algorithm is output.

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: 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: A system and method for identifying a route to a location for a vehicle including generating a topological map of a parking area having a plurality of parking spaces and a plurality of roads. A plurality of nodes are established on the topological map, wherein each of the nodes has latitude and longitudinal coordinates. A plurality of links are established on the topological map. The links each extend between two of the nodes. One of the nodes is established as a starting node at which the vehicle starts and one of the nodes is established as a target node. A route is established to the target node that follows a series of the links to the target node using Dijkstra's algorithm. The route is determined based on a cost associated with each link for purposes of establishing the route with Dijkstra's algorithm.

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 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).