Abstract: A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

Abstract: A method, system and computer program product for determining a map position of an ego-vehicle are disclosed. The method includes acquiring map data comprising a road geometry, initializing at least one dynamic landmark by measuring a position and velocity, relative to the ego-vehicle, of a surrounding vehicle, and determining a first map position of the surrounding vehicle based on this measurement and the geographical position of the ego-vehicle. Further, the method includes predicting a second map position of the surrounding vehicle, and measuring a location, relative to the ego-vehicle, of the surrounding vehicle when it is estimated to be at the second map position, whereby the geographical position of the ego-vehicle can be computed and updated.

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: There is presented a method for controlling an activation threshold of a safety system of a vehicle. The method comprises receiving map data from a remote data repository, said map data comprising a geographical location of a dynamic object located in a surrounding area of an expected path of the vehicle, determining a geographical location of the vehicle by means of a localization system of the vehicle, and lowering an activation threshold value of the safety system when the geographical location of the vehicle is within a predefined distance from the dynamic object. The presented method provides for an efficient means for preparing e.g. an emergency brake assist system of a vehicle in potentially critical situations.

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 trajectory planning for a vehicle. The method includes obtaining a reference trajectory over a finite time horizon, where the reference trajectory includes a speed reference over time for the finite time horizon. Further, the method includes determining a back-up stop trajectory within the finite time horizon. The back-up stop trajectory has a starting state and terminating in a final state, where the final state is defined as a safe state. The method further includes forming a terminal set of states within the finite time horizon based on at least one predefined constraint, wherein the terminal set of states includes a terminal state that corresponds to the starting state for the back-up stop trajectory. Moreover, the method includes generating a nominal trajectory for at least a portion of the finite time horizon based on a constraint controlled technique.

Abstract: A method for controlling vehicle system of a vehicle is disclosed. The method comprises determining an expected path of the vehicle, determining a vehicle trajectory for the determined expected path, and determining at least one required control parameter value of a driver assistance system based on the determined vehicle trajectory. Further, the method comprises comparing the at least one required control parameter value to a predefined threshold scheme associated with the driver assistance system, and sending a signal to a Human Machine Interface, HMI, of the vehicle based on the comparison. Then, the method comprises receiving a feedback signal originating from a user of the vehicle, and controlling the driver assistance system based on the comparison and the received feedback signal.

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 for path planning for an autonomous or semi-autonomous vehicle. The method including obtaining a drivable area of a surrounding environment of the vehicle, and generating a path within the drivable area for a time step t based on a predefined set of characteristics for the path and a predefined set of constraints. The predefined set of constraints includes at least one constraint based on a current pose of the vehicle.

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: A method for path planning for an autonomous or semi-autonomous vehicle. The method includes obtaining a drivable area of a surrounding environment of the vehicle, and generating a path within the drivable area for a time step t based on a predefined set of characteristics for the path and a predefined set of constraints. The predefined set of constraints includes at least one constraint based on a path generated for a previous time step.

Abstract: A method for providing information about traffic regulations in a vehicle is disclosed. The method comprises identifying a road sign in a surrounding area of the vehicle (e.g. by means of a perception system of the vehicle). Further, the method comprises identifying a traffic regulation associated with the identified road sign, in a current jurisdiction of vehicle, and retrieving traffic regulation data comprising information about a set of predefined road signs and associated predefined traffic regulations in a predefined jurisdiction. Still further, the method comprises comparing the identified traffic regulation with the retrieved traffic regulation data in order to determine if the identified road sign is not comprised in the set of predefine road signs, or if the identified traffic regulation differs from the associated predefined traffic regulations.