Abstract: A method and processor for training a Machine Learning Algorithm (MLA) for predicting steering wheel positions for controlling steering of a Self-Driving Car (SDC). The method comprises: acquiring a plurality of training data sets a given one of which including (i) a training input and (ii) a respective label, the training input being indicative of (i) a training target state and (ii) a second target state, the respective label being indicative of an actual steering wheel position at a respective moment in time for arriving from the training target state to a sequential target state of a training trajectory; and inputting the given training data set into the MLA for determining a predicted steering wheel position for arriving from the training target state at the respective moment in time to the sequential target state while taking into account that the training trajectory is to end with the second target state.

Abstract: Method and device for controlling operation of a Self-Driving Car. The method includes determining route-level and lane-level information for generating a graph-structure, applying a first model for assigning costs to respective edges, determining scores for vertices based on the costs, storing the edges with the costs and the vertices with the scores. The method also includes at a given moment in time during operation: acquiring a lane path indicative of a lane segment extending from the current location without a lane-changing manoeuvre. The method includes, for the lane path: identifying a series of vertices covered by the lane segment, applying a second model for assigning additional costs to lane-path-departing edges of the series of vertices thereby determining locally-increased costs, determining a locally-adjusted scores for vertices in the series of vertices based on the locally-increased costs, and identifying a lane path score for the lane path based on the locally-adjusted scores.

Abstract: A method and processor for controlling steering of a Self-Driving Car (SDC) are disclosed. The method includes: acquiring SDC state data associated with vertices in a graph-structure, using the SDC state data for building a polynomial curve, using the polynomial curve for simulating a candidate trajectory of the SDC as if the SDC is steered in accordance therewith and such that a closest allowed steering wheel position to a non-allowed steering wheel position is used instead of the non-allowed steering wheel position, determining an offset between the simulated state and a target state of the SDC, selecting candidate steering profile as a target steering profile of the SDC based on the offset, and using the target steering profile of the SDC for controlling steering of the SDC. A method of training a Machine Learning Algorithm for predicting steering wheel positions to control steering of the SDC is also disclosed.

Abstract: Methods and devices for controlling operation of a Self-Driving Car (SDC) are disclosed. The method includes, at a first moment in time during an approach of the SDC to a crosswalk: identifying, an object-inclusion zone in proximity to the crosswalk, determining presence of an object in the object-inclusion zone, determining an interval of time for the object based on movement data of the object, using the interval of time and movement data of the SDC for determining operation-control data for controlling operation of the SDC, and assigning decision data to the object-inclusion zone indicative of a decision.

Abstract: Method and device for controlling operation of a Self-Driving Car. The method includes determining route-level and lane-level information for generating a graph-structure, applying a first model for assigning costs to respective edges, determining scores for vertices based on the costs, storing the edges with the costs and the vertices with the scores. The method also includes at a given moment in time during operation: acquiring a lane path indicative of a lane segment extending from the current location without a lane-changing manoeuvre. The method includes, for the lane path: identifying a series of vertices covered by the lane segment, applying a second model for assigning additional costs to lane-path-departing edges of the series of vertices thereby determining locally-increased costs, determining a locally-adjusted scores for vertices in the series of vertices based on the locally-increased costs, and identifying a lane path score for the lane path based on the locally-adjusted scores.

Abstract: Methods and devices for controlling operation of a Self-Driving Car (SDC) are disclosed. The method includes, at a first moment in time during an approach of the SDC to a crosswalk: identifying, an object-inclusion zone in proximity to the crosswalk, determining presence of an object in the object-inclusion zone, determining an interval of time for the object based on movement data of the object, using the interval of time and movement data of the SDC for determining operation-control data for controlling operation of the SDC, and assigning decision data to the object-inclusion zone indicative of a decision.

Abstract: A method and processor for controlling steering of a Self-Driving Car (SDC) are disclosed. The method includes: acquiring SDC state data associated with vertices in a graph-structure, using the SDC state data for building a polynomial curve, using the polynomial curve for simulating a candidate trajectory of the SDC as if the SDC is steered in accordance therewith and such that a closest allowed steering wheel position to a non-allowed steering wheel position is used instead of the non-allowed steering wheel position, determining an offset between the simulated state and a target state of the SDC, selecting candidate steering profile as a target steering profile of the SDC based on the offset, and using the target steering profile of the SDC for controlling steering of the SDC. A method of training a Machine Learning Algorithm for predicting steering wheel positions to control steering of the SDC is also disclosed.