The disclosure is related to a method and a system for assisting a driver of a road vehicle. A data processing unit receives data representing at least a relative position of the road vehicle, outputs, through a display device and/or an interior lighting device, a visual alert when the data processing unit determines that the data fulfill a first condition, and outputs, through a sound-emitting device, an audible alert when the data processing unit determines that the data fulfill a second condition after fulfilling the first condition.

A vehicle control device includes a processor. The processor is configured to: detect a mobile body located in a region that is not a host vehicle lane in which a host vehicle is traveling, out of regions that are adjacent to an adjacent lane that is adjacent to the host vehicle lane; detect entry operation of the mobile body to enter the adjacent lane; and when the processor detects the entry operation, perform steering control for the host vehicle such that a travel position of the host vehicle within the host vehicle lane in a width direction of the host vehicle lane is moved in a direction away from the adjacent lane before the entry operation is completed.

An system and method of operating an adaptive cruise control system for a vehicle. In one example, the system includes a rearward facing sensor, a speed control, and a controller. The controller receives at least one parameter indicative of a road condition or a traffic condition. The controller then activates a coasting mode based on the at least one parameter. The controller receives a signal from the rearward facing sensor indicative of a presence of a target vehicle positioned behind the host vehicle and restricts the coasting mode when the signal from the rearward facing sensor detects the target vehicle is positioned behind the host vehicle. The controller performs coasting via the speed control when the signal from the rearward facing sensor does not detect the target vehicle positioned behind the host vehicle and when the coasting mode is active.

Example embodiments relate to user interface techniques for recommending remote assistance actions. A remote computing device may display a representation of the forward path for an autonomous vehicle based on sensor data received from the vehicle. The computing device may augment the representation of the forward path to further depict one or more proposed trajectories available for the autonomous vehicle to perform. Each proposed trajectory conveys one or more maneuvers positioned relative to road boundaries in the forward path. The computing device may receive a selection of a proposed trajectory from the one or more proposed trajectories available for the autonomous vehicle to perform and provide navigation instructions to the vehicle based on the proposed trajectory.

A method of defining an autonomous vehicle path according to a disclosed exemplary embodiment includes, among other possible things, defining a boundary that utilizes sensor data indicative of features constraining a possible vehicle path, generating a reference path with a vehicle autonomous control system based on the defined boundary, defining a clearance distance for the vehicle relative to the reference path, detecting a conflict at a point along the reference path that is responsive to a distance between the reference path and the defined boundary being less than the defined clearance, and communicating the potential conflict to the vehicle autonomous control system.

A method of autonomously defining a vehicle path according to a disclosed exemplary embodiment includes, among other possible things, detecting an object that is disposed within a predetermined path with a sensor system within a vehicle, defining an avoidance clearance around the detected object with a vehicle control system within the vehicle, defining a look-ahead clearance centered on a portion of the vehicle with the vehicle control system, detecting intersections between the avoidance clearance and the look-ahead clearance with the vehicle control system, and changing the vehicle path based on the intersections between the avoidance clearance and the look-ahead clearance.

A system and method are provided for operating an autonomous or semi-autonomous host vehicle. The method includes receiving data measured from a plurality of sensors, wherein the measured data relates to one or more target vehicles in the host vehicle's field of view, calculating a desired speed command based on a driver-selected set-speed and the measured data, detecting initiation of a host vehicle lane change to a desired adjacent lane, and in response to initiation of the lane change, selecting an acceleration profile based on at least one set of operating conditions, calculating a modified speed command by adjusting the desired speed command according to the selected acceleration profile; and controlling a host vehicle speed based on the modified speed command.

System, methods, and computer-readable media for training an object path prediction model to reduce an uncertainty of a predicted path when the predicted path of an object adjacent to another object. The training penalizes an uncertainty area prediction associated with a predicted future location of a nearby object to an autonomous vehicle (AV) when the uncertainty area prediction overlaps with another object to which the first detected object would be adjacent at the predicted future location. The training also penalizes a set of predicted future locations that implies improbable vehicle kinematics, whereby the object path prediction model becomes trained to avoid predicting similar sets of predicted future locations with improbable vehicle kinematics.

Depth data processing systems and methods are disclosed. A mapping system receives, from one or more depth sensors, depth sensor data that includes a plurality of points corresponding to an environment. The mapping system uses one or more trained machine learning models to perform semantic segmentation of the plurality of points, to classify a first subset of the points into a first category and to classify a second subset of the points into a second category. The mapping system clusters the plurality of points into a plurality of clusters based on the semantic segmentation. At least some of the first subset of the points are clustered into a first cluster and at least some of the second subset of the points are clustered into a second cluster. The mapping system generates a map of at least a portion of the environment based on the plurality of clusters.

The subject disclosure relates to techniques for improving performance of a machine learning algorithm that at least receives data descriptive of a 3-D object and provides an output, where the 3-D object occurs infrequently in a training dataset. A process of the disclosed technology can include determining that the machine learning algorithm performed below a threshold performance score when receiving data descriptive of the 3-D object in a real-world scene, wherein the 3-D object is classified as a first type of object, creating at least one 3-D representation of the first type of object for use in a simulation, modifying a plurality of simulated scenes to include the at least one 3-D representation of the first type of object, and training the machine learning algorithm with the modified simulated scenes, whereby the machine learning algorithm has greater exposure to the first type of object.