Abstract: A method and a system for maneuvering vehicles using adjustable ultrasound sensors is disclosed. The method includes determining at least one first position of an object relative to a vehicle using at least one high range sensor and determining for each of at least one first position of the object non-conformance with one or more object position criteria. The method further includes determining at least one second position of object relative to vehicle, when at least one first position of the object is in non-conformance with one or more object position criteria. The method further includes assigning at least one ultrasound sensor to dynamically focus on the object and maneuvering the vehicle on a trajectory plan based on the at least object attribute. Each of plurality of ultrasound sensors is deployed on rotatable mount and assigned at least one ultrasound sensor is dynamically focused by rotating on associated rotatable mount.

Abstract: The disclosed embodiments are directed to improving the persistence of pre-accident data in vehicles. In one embodiment a method is disclosed comprising receiving events broadcast over a vehicle bus; classifying the events using a machine learning model, the classifying comprising indicating that a collision is imminent; and copying data from a cyclic buffer of a black box device into a long-term storage device in response to the classifying.

Abstract: A transverse steering method is for moving a vehicle comprising active steering to a target position. The method includes: performing distance and/or angle measurements between the vehicle and the target position enabling the derivation of location and orientation data; deriving the location and orientation data; filtering the location and orientation data into current values, which include current location values and current orientation values; performing control which derives a target steering angle from the current values; and realization of the target steering angle by acting on the active steering of the vehicle.

Abstract: Trajectory generation for controlling motion or other behavior of an autonomous vehicle may include alternately determining a candidate action and predicting a future state based on that candidate action. The technique may include determining a cost associated with the candidate action that may include an estimation of a transition cost from a current or former state to a next state of the vehicle. This cost estimate may be a lower bound cost or an upper bound cost and the tree search may alternately apply the lower bound cost or upper bound cost exclusively or according to a ratio or changing ratio. The prediction of the future state may be based at least in part on a machine-learned model's classification of a dynamic object as being a reactive object or a passive object, which may change how the dynamic object is modeled for the prediction.

Abstract: A method includes receiving a series of road images from a side-view camera sensor of the autonomous driving vehicle. For each object from objects captured in the series of road images, a series of bounding boxes in the series of road images is generated, and a direction of travel or stationarity of the object is determined. The methods and apparatus also include determining a speed of each object for which the direction of travel has been determined and determining, based on the directions of travel, speeds, or stationarity of the objects, whether the autonomous driving vehicle can safely move in a predetermined direction. Furthermore, one or more control signals is sent to the autonomous driving vehicle to cause the autonomous driving vehicle to move or to remain stationary based on determining whether the autonomous driving vehicle can safely move in the predetermined direction.

Abstract: This disclosure relates to systems and techniques for identifying collisions, such as relatively low energy impact collisions involving an autonomous vehicle. Sensor data from a first sensor modality in a first array may be used to determine a first estimated location of impact and second sensor data from a second sensor modality in a second array may be used to determine a second estimated location of impact. A low energy impact event may be configured when the first estimated location of impact corresponds to the second estimated location of impact.

Abstract: A system includes at least one electronic control unit, which performs a method including consecutively or simultaneously, determining a future turning maneuver of the ego vehicle, detecting information relating to the lane markings and the number of available lanes in the environment in front of and next to the ego vehicle, and determining whether at least one further road user is in a relevant lane next to or behind the lane of the ego vehicle. If so, a future intended movement of the road user is determined from the information relating to the lane markings and the number of available lanes. And if it is determined that the ego-vehicle and the road user are on an at least two-lane turning lane, or that the road user stops before the turning maneuver of the ego-vehicle or leaves its lane, it is determined that there is no probability of a collision.

Abstract: In various embodiments, while a self-driving model operates a vehicle, a user monitoring subsystem acquires sensor data associated with a user of the vehicle and a vehicle observation subsystem acquires sensor data associated with the vehicle. The user monitoring subsystem computes values for a psychological metric based on the sensor data associated with the user. Based on the values for the psychological metric, a feedback application determines a description of the user over a first time period. The feedback application generates a dataset based on the description and the sensor data associated with the vehicle. Subsequently, a training application performs machine learning operation(s) on the self-driving model based on the dataset to generate a modified self-driving model. Advantageously, the dataset enables the training application to automatically modify the self-driving model to account for the impact different driving actions have on the user.

Abstract: Based on a model and an identity of an operator of a vehicle, a setting for a cruise control to maintain a distance between the vehicle and a preceding vehicle can be determined. The model can be based on historical distances maintained by the operator. The model can also be based on information about an environment in which the vehicle is operating or information about a state of the operator. The identity can be received from an interface configured to receive information associated with the identity, a cloud computing platform, or a biometric system configured to determine the identity. The cruise control can be caused to operate according to the setting. A rate of energy consumption by the vehicle when the vehicle is controlled by the cruise control can be less than the rate of energy consumption when the vehicle lacks being controlled by the cruise control.

Abstract: A method for instructing users of car-sharing services to operate a shared vehicle includes identifying a user of a selected vehicle, determining a customary vehicle of the user based on the identification, determining differences between the selected vehicle and the customary vehicle, and informing the user of the determined differences.

Abstract: A racing coach device stores a first path of travel along a racetrack over a first time period and a second path of travel along the racetrack over a second time period. The racing coach device identifies, for each of a plurality of geolocations along the racetrack, one of the first path of travel or the second path of travel that is associated with a shorter duration of time over which the user traversed a segment of the path of travel associated with each of the plurality of geolocations. The device determines an optimal path of travel along the racetrack based on the identified first and second path of travel for each segment of the path of travel at each of the plurality of geolocations that results in a calculated lap time to traverse the racetrack that is less than the first time period and the second time period.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to identify a current location of the vehicle in a map of a stopping area, collect data to calibrate a sensor while the vehicle is moving in the stopping area, calibrate the sensor based on the collected data, and actuate one or more vehicle components to move the vehicle to a stopping location in the stopping area based on the location of the vehicle in the map and data collected by the calibrated sensor.

Abstract: A method for predicting a trajectory of at least one secondary road user for avoiding a collision course with the secondary road user for a host vehicle. The method includes determining the present location for the host vehicle, retrieving a plurality of modelled clusters of trajectories for a present traffic situation, and detecting the position and speed of the at least one secondary road user. The method also includes predicting at least one feasible trajectory for the at least one secondary road user based on the position and the speed of the at least one secondary road user to the plurality of modelled clusters of trajectories and selecting at least one feasible trajectory of the feasible trajectories for each secondary road user based on a selection criterion. At least one action is performed based on the selected at least one feasible trajectory.

Abstract: In some implementations, a method is provided. The method includes determining a first set of data acquisition characteristics of a first sensor of an autonomous driving vehicle. The method also includes determining a second set of data acquisition characteristics of a second sensor of the autonomous driving vehicle. The method further includes synchronizing a first data acquisition time of the first sensor and a second data acquisition time of the second sensor, based on the first set of data acquisition characteristics and the second set of data acquisition characteristics. The first sensor obtains first sensor data at the first data acquisition time. The second sensor obtains second sensor data at the second data acquisition time.

Abstract: Methods and apparatus to generate vehicle warnings are disclosed. An example apparatus includes a sensor to detect a vehicle, where the sensor is associated with an observer of the vehicle, an object tracker to determine a motion of the vehicle, an accident estimator to calculate a likelihood of a collision of the vehicle based on the determined motion, and a transceiver to transmit a message to the vehicle upon the likelihood of the collision exceeding a threshold, where the message includes information pertaining to the collision.

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 system described herein may generate one or more models based on relationship data associated with multiple objects. The models may include one or more thresholds (e.g., which may indicate potential collisions between objects). The system may compare relationship data, associated with a particular vehicle and a particular object, with the one or more thresholds associated with the one or more models, and determine whether the relationship data associated with the particular vehicle and the particular object is rare data with respect to the one or more models. When the relationship data associated with the particular vehicle and the particular object is not rare data, the system may refraining from causing the particular vehicle to perform a collision prevention measure, and when the relationship data associated with the particular vehicle and the particular object is rare data, the system may cause the particular vehicle to perform the collision prevention measure.

Abstract: A platooning controller, a vehicle system including the same, and a method thereof perform control during platooning. The platooning controller includes a processor that, when an outside vehicle cuts in a platooning line, performs platooning control depending on an intention of a user to perform the platooning control and a driving situation with the cut-in vehicle that cuts in the platooning line. The platooning controller also includes a storage that stores the result of performing the platooning control performed by the processor and information about the driving situation.

Abstract: The present disclosure discloses a method for generating a parking model, an electronic device and a storage medium, and relates a field of autonomous parking technologies. The detailed implementing solution includes: obtaining multiple sample sets; constructing a parking cruise space for the target vehicle based on each sample set, and extracting a first parking trajectory corresponding to each sample set from each parking cruise space; recognizing an abnormal position on each first parking trajectory, and deleting driving data corresponding to the abnormal position from a sample set corresponding to each first parking trajectory to obtain target sample data in each sample set; and performing model training based on the target sample data in each sample set to generate a target parking model.

Abstract: A racing coach device stores a first path of travel along a racetrack over a first time period and a second path of travel along the racetrack over a second time period. The racing coach device identifies, for each of a plurality of geolocations along the racetrack, one of the first path of travel or the second path of travel that is associated with a shorter duration of time over which the user traversed a segment of the path of travel associated with each of the plurality of geolocations. The device determines an optimal path of travel along the racetrack based on the identified first and second path of travel for each segment of the path of travel at each of the plurality of geolocations that results in a calculated lap time to traverse the racetrack that is less than the first time period and the second time period.