Abstract: A device and a method that prevent a tracking target tracked by a mobile device from deviating from a field of view at a fork, thereby enabling reliable tracking, are provided. When a control parameter determination unit of a mobile device cannot discriminate one of a plurality of routes constituting a fork that a tracking target selects and moves along, the control parameter determination unit calculates a goal position for bringing the tracking target within a viewing angle of the mobile device and a goal posture at the goal position, and generates a control parameter including the calculated goal position and goal posture. The control parameter determination unit calculates a position and posture that enable the tracking target to be within the viewing angle of the mobile device regardless of a route that the tracking target selects and moves along.

Abstract: A testing method for a blind spot detection system for an automobile includes: applying an electromagnetic interference signal to the tested automobile through an electromagnetic interference signal application device, meanwhile, simulating a static obstacle or a moving obstacle within a detection range through a testing device, and if the blind spot detection system for the tested automobile is activated, determining that the testing is passed. Repeatable and reproducible testing for the electromagnetic safety of the blind spot detection system may be realized.

Abstract: A trailer sideswipe avoidance system for a vehicle towing a trailer includes a sensor system configured to detect an object in an operating environment of the vehicle. The trailer sideswipe avoidance system also includes a controller that processes information received from the sensor system to determine whether an object detected in the operating environment of the vehicle is in a forward travel path of the towed trailer as the vehicle turns. The trailer sideswipe avoidance system further includes a visual display that displays bird's eye view representations of the vehicle and the trailer and a collision warning band that extends around at least a portion of the bird's eye view representation of the trailer. A portion of the collision warning band changes color based on the controller determining that the object is in the travel path of the towed trailer.

Abstract: Systems and methods use cameras to provide autonomous navigation features. In one implementation, a method for navigating a user vehicle may include acquiring, using at least one image capture device, a plurality of images of an area in a vicinity of the user vehicle; determining from the plurality of images a first lane constraint on a first side of the user vehicle and a second lane constraint on a second side of the user vehicle opposite to the first side of the user vehicle; enabling the user vehicle to pass a target vehicle if the target vehicle is determined to be in a lane different from the lane in which the user vehicle is traveling; and causing the user vehicle to abort the pass before completion of the pass, if the target vehicle is determined to be entering the lane in which the user vehicle is traveling.

Abstract: A method for intersection assistance, the method may include obtaining sensed information regarding an environment of the vehicle; determining an occurrence of an intersection related situation, based on the sensed information; generating one or more intersection driving related decisions; wherein the generating comprises processing, by one or more narrow AI agents of a group of narrow AI agents, at least one out of (a) at least a first part of the sensed information, and (b) an outcome of a pre-processing of at least a second part of the sensed information; and responding to the one or more intersection driving related decisions; wherein the responding comprises at least one out of (a) executing the one or more intersection driving related decisions, and (b) suggesting executing the one or more intersection driving related decisions.

Abstract: 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.

Abstract: An aerial vehicle landing method includes controlling to decelerate, with aid of one or more processors and in response to at least two of a plurality of conditions being satisfied, the aerial vehicle to cause the aerial vehicle to land autonomously. The plurality of conditions includes determining that an external signal related to a human is detected via one or more sensors; determining that a location/orientation change of the aerial vehicle is detected while the aerial vehicle is airborne; and determining that an external contact from an external object is exerted upon the aerial vehicle, the external object being an object that is not part of the aerial vehicle.

Abstract: A method at an infrastructure unit of signaling a presence of a vulnerable road user, the method including receiving at least one report from a sensor unit, the at least one report indicating the presence of the vulnerable road user; composing a message indicating the presence of the vulnerable road user; and transmitting the message to road users proximate the infrastructure unit on a channel.

Abstract: A control method of a vehicle may include: performing a calibration of a plurality of cameras mounted on a vehicle to obtain a parameter of each of the plurality of cameras; generating, based on the obtained parameter, a plurality of distance value tables representing a projection relationship between pixel coordinates in an image of each of the plurality of cameras and actual coordinates in surrounding area of the vehicle; calculating, based on the plurality of distance value tables, an accuracy of actual distance coordinates included in the plurality of distance value tables for a specific area; and generating an accuracy map representing a distribution of the accuracy.

Abstract: There is provided an information processing apparatus and an information processing method that can provide more useful information for an action plan of an autonomous mobile body, the information processing apparatus including an action recommendation unit configured to present a recommended action recommended to an autonomous mobile body, to the autonomous mobile body that performs an action plan based on situation estimation. The action recommendation unit determines the recommended action on the basis of an action history collected from a plurality of the autonomous mobile bodies, and on the basis of a situation summary received from a target autonomous mobile body that is a target of recommendation. The information processing method includes presenting, by a processor, a recommended action recommended to an autonomous mobile body, to the autonomous mobile body that performs an action plan based on situation estimation.

Abstract: A vehicle control method causes a controller to execute: vehicle speed determination processing of determining whether or not vehicle speed of an own vehicle is reduced to less than a predetermined speed threshold; lateral deviation detection processing of detecting lateral deviation of the own vehicle with respect to a lane center or a lane boundary line; target trajectory generation processing of, when determining that the vehicle speed is reduced to less than the speed threshold, generating a first target travel trajectory of the own vehicle in such a way as to suppress change in the lateral deviation at and after a vehicle speed reduction time point; avoidance target detection processing of detecting an avoidance target; and avoidance processing of avoiding the avoidance target by, after the vehicle speed reduction time point, adjusting deceleration of the own vehicle while the own vehicle travels along the first target travel trajectory.

Abstract: A vehicle control system includes: an on-board control module, configured to control driving of the vehicle according to a control instruction sent by a vehicle dispatching command platform; a vehicle state module, configured to collect state information of the vehicle; an environment sensing module, configured to collect first road environment information of the vehicle; a UAV scanning module, configured to collect second road environment information of the vehicle; a data center module, configured to generate fusion information according to the state information, the first road environment information, and the second road environment information; a map module, configured to generate a driving route map of the vehicle according to the fusion information; and a vehicle dispatching command platform, configured to generate the control instruction according to the fusion information and the driving route map.

Abstract: A travel assistance device includes: an inner area prediction unit predicting a with-vehicle interaction that is a behavior taken, in response to a state of the subject vehicle, by a moving object within an inner area around the subject vehicle; an outer area prediction unit predicting a with-environment interaction that is a behavior taken by a moving object according to surrounding environment of the moving object within an outer area further to the subject vehicle than the inner area; an outer area planning unit planning a future behavior of the subject vehicle based on the predicted with-environment interaction, wherein the future behavior is a behavior pattern of the subject vehicle realized by traveling control; and an inner area planning unit planning a future trajectory of the subject vehicle in accordance with the future behavior based on the predicted with-vehicle interaction.

Abstract: A lane change control method applied to a vehicle-mounted device is provided. The method includes calculating a first risk factor of a preceding vehicle based on a first vehicle data and a second vehicle data, calculating a second risk factor of an adjacent preceding vehicle based on the first vehicle data and a third vehicle data. A risk weight is obtained according to the first risk factor and the second risk factor. A lane-change weight is calculated based on a target lane line. A collective weight is obtained according to the lane-change weight and the risk weight. Once a plurality of reference vehicle values is obtained, the host vehicle is controlled to change lanes based on the plurality of reference vehicle values, the lane-change weight, and the first vehicle data.

Abstract: A travel route setting system is provided. A passage time estimation section estimates a passage time at which a vehicle passes each of stop lines located before traffic lights present in a traveling direction on each of routes from a vehicle position to a destination. A solar position calculation section calculates a position of the sun at the passage time of each of the stop lines based on the position and the passage time of each stop line. The backlight margin value calculation section calculates a backlight margin value. A route backlight margin value calculation section calculates a route backlight margin value based on the backlight margin value of each of the stop lines present on each of the routes from the vehicle position to the destination. A route determination section determines a travel route from the vehicle position to the destination using the route backlight margin values.

Abstract: A system, an image analyzer and a method for diagnosing a presence of a disease or a condition in an image of a subject, for example, a veterinary patient, are provided including: classifying the image to a body region, and obtaining a classified, labeled, cropped, and oriented sub-image; directing the sub-image to artificial intelligence processor for obtaining an evaluation result, and comparing the evaluation result to a database library of evaluation results and matched written templates or a dataset cluster to obtain at least one cluster result; measuring the distance between the cluster result and the evaluation result to obtain at least one cluster diagnosis; and assembling the cluster diagnosis and the matched written templates to obtain a report to display the report to a radiologist. These system, analyzer and method are achieved in greatly reduced lengths of time and are useful for cost and time savings.

Abstract: A trajectory for an autonomous machine may be evaluated for safety based at least on determining whether the autonomous machine would be capable of occupying points of the trajectory in space-time while still being able to avoid a potential future collision with one or more objects in the environment through use of one or more safety procedures. To do so, a point of the trajectory may be evaluated for conflict based at least on a comparison between points in space-time that correspond to the autonomous machine executing the safety procedure(s) from the point and arrival times of the one or more objects to corresponding position(s) in the environment. A trajectory may be sampled and evaluated for conflicts at various points throughout the trajectory. Based on results of one or more evaluations, the trajectory may be scored, eliminated from consideration, or otherwise considered for control of the autonomous machine.

Abstract: A drive assist apparatus includes: an avoidance target recognition portion that recognizes an avoidance target which is a target with which a host vehicle should avoid coming into contact when traveling on a road; a steering control portion that assists steering of the host vehicle at a time of the host vehicle and the avoidance target passing each other; a margin detection portion that detects a margin indicating a distance between the host vehicle and the avoidance target at the time of passing each other; and a level change portion that changes a steering assistance degree of the host vehicle by the steering control portion at subsequent times of passing each other based on the margin detected by the margin detection portion.

Abstract: The technology relates controlling an autonomous vehicle through a multi-lane turn. In one example, data corresponding to a position of the autonomous vehicle in a lane of the multi-lane turn, a trajectory of the autonomous vehicle, and data corresponding to positions of objects in a vicinity of the autonomous vehicle may be received. A determination of whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane may be made based on a position of the autonomous vehicle in the lane relative to the positions of the objects. The trajectory of the autonomous vehicle through the lane may be adjusted based on whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane. The autonomous vehicle may be controlled based on the adjusted trajectory.

Abstract: A driving assistance device reflects, in autonomous driving control on a vehicle, a driving characteristic of a driver who drives the vehicle learned during manual driving of the vehicle. The driving assistance device includes a storage that stores information on an individual risk potential set for the vehicle by learning a risk sensed by the driver for each of one or more obstacles during the manual driving, a vehicle risk calculator that sets, for the vehicle, a vehicle risk map that reflects the individual risk potential of an occupant of the vehicle during autonomous driving of the vehicle, and a driving condition setter that sets a driving condition for the autonomous driving of the vehicle based on information on the vehicle risk map and information on an obstacle risk map that reflects an obstacle risk potential set for the each of the one or more obstacles.

Abstract: This application is directed to aerial view generation for vehicle control. A vehicle obtains a forward-facing view of a road captured by a front-facing camera of a vehicle and applies a machine learning model to process the forward-facing view to predict determine a trajectory of the vehicle and a road layout based on a Frenet-Serret coordinate system of the road for the vehicle. The trajectory of the vehicle is combined with the road layout to predict an aerial view of the road, and the aerial view of the road is used to at least partially autonomously drive the vehicle. In some embodiments, the machine learning model is applied to process the forward-facing view to determine a first location of an obstacle vehicle in the Frenet-Serret coordinate system. The obstacle vehicle is placed on the aerial map based on the first location of the obstacle vehicle.

Abstract: The present invention relates to a device having at least one computing unit, which is configured to ascertain a specified target power profile curve (213) for a vehicle, to determine a tolerance band (211) for the target power profile curve (213), wherein the tolerance band (211) is limited by an upper limit line (215) and a lower limit line (217), to determine an expectation characteristic curve (219) for a power of the vehicle to be expected in the future by extrapolating a power development of the vehicle at a current setting of the vehicle for a specified temporal prediction window (229), and to enable a control command (315, 319) to be provided by the driver model in order to modify the setting of the vehicle in the event that the expectation characteristic curve (219) intersects at least one of the upper limit line (215) and the lower limit line (217) of the tolerance band (211) within the temporal prediction window (229).

Abstract: A controller detects partitioning lines OL and BL of a road and a road edge E based on an image taken by a camera, generates a pair of left and right virtual lines ILL and ILR that extend from the side to the front of a vehicle along the partitioning lines OL and BL, controls the steering so the vehicle travels between the left and right virtual lines ILL and ILR, moves the left virtual line ILL to a position in proximity to the road edge E and fixes the left virtual line ILL to this position, and then, moves the right virtual line ILR to a position separated from the left virtual line ILL by the width of the vehicle and fixes the right virtual line ILR to this position, and controls brakes to stop the vehicle after fixing the left and right virtual lines ILL and ILR.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating safety trajectories.

Abstract: A distance measurement apparatus comprises a light emitter that emits a plurality of light beams toward a scene in different directions and at different timings, a light receiver that includes an array of a plurality of light-receiving elements and detects reflected light from the scene produced by the emission of each light beam with the plurality of light-receiving elements, and a signal processing circuit that generates and outputs output data including measurement data indicating the positions or distances of a plurality of points in the scene on the basis of a signal outputted by the light receiver. The output data includes the data of a plurality of blocks, and individual time data is attached to each of the plurality of blocks.

Abstract: A merging support device includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: execute control of the first vehicle corresponding to behavior when a second vehicle merges into a first lane, based on a vehicle speed and a vehicle position of a second vehicle traveling in a second lane that merges into a first lane in which a first vehicle is traveling; and notify a driver of the first vehicle about information on a control content of the first vehicle by the hardware processor.

Abstract: A vehicle is provided. A detector detects an object at least ahead of the vehicle. A display is provided on an instrument panel of the vehicle and displays a signal at a position corresponding to a direction of the object detected by the detector. The display includes a part recessed forward in a vehicle longitudinal direction in plan view.

Abstract: A protective warning system for a vehicle is provided. A device comprises: a long-range camera and a stereoscopic camera positioned in a housing to image external objects in a rear-facing direction when the housing is mounted to a vehicle. A controller detects, using images from the long-range camera, an external object in the rear-facing direction and processes stereoscopic images from the stereoscopic camera to determine when the external object is located within a first zone or a second zone extending in the rear-facing direction, the second zone being closer to the stereoscopic camera than the first zone. In response to determining that the external object is located within the first zone or the second zone, the controller controls the one or more notification devices to provide one or more first or second notifications associated with a first or second urgency level.

Abstract: An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. A distribution of a plurality of positions of the obstacle at a point of time may be predicted. A range of positions of the plurality of positions of the obstacle may be determined based on a confidence level of the range. A modified shape with a modified length of the obstacle may be determined based on the range of positions of the obstacle. A trajectory of the ADV based on the modified shape with the modified length of the obstacle may be planned. The ADV may be controlled to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.

Abstract: A method for operating an autonomous driving function of a vehicle. The vehicle includes a computer unit and sensors for detecting surroundings data. The computer unit is configured to determine a setpoint trajectory for the vehicle, based on the detected surroundings data. In step a), an actual trajectory, and distances from objects in the surroundings, are detected. In step b), an ascertainment of the quality of the autonomous driving function takes place by comparing the actual trajectory to the setpoint trajectory and monitoring the detected distances from objects in the surroundings. In step c), a control of the quality to a predefined target value takes place by selecting sensors to be used for the autonomous driving function from the plurality of sensors and/or by changing a measuring rate, at which measurements are carried out, of at least one sensor from the plurality of sensors.

Abstract: Embodiments of the present disclosure provide a method for lane changing decision for vehicles on expressways considering safety risks in a networked environment, which is applicable to a multi-lane expressway. The method includes collecting a demand for lane change of a target vehicle; collecting a roadway and a lane where the target vehicle is located; collecting intervals between the target vehicle and a front vehicle and a rear vehicle on a lane that is switching into; determining a lane change direction of the vehicle; and executing a lane change operation by the target vehicle. A lane changing decision model provided in the embodiments of the present disclosure helps to improve the efficiency of lane change and the safety of lane change in expressways and provides a support for changing lanes in a multi-lane expressway.

Abstract: A method for determining a usable distance between a host vehicle and a moving object is provided. According to the method, a current obstacle free distance is detected in front of the host vehicle, wherein the current obstacle free distance is limited by a current position of the moving object, and a current velocity of the moving object is determined. An extension distance is estimated based on the current velocity of the moving object, and the usable distance is determined based on the current obstacle free distance and the extension distance.

Abstract: A method for validating an autonomous vehicle performance using nearby traffic patterns includes receiving remote vehicle data. The remote vehicle data includes at least one remote-vehicle motion parameter about a movement of a plurality of remote vehicles during a predetermined time interval. The method further includes determining a traffic pattern of the plurality of remote vehicles using the at least one remote-vehicle motion parameter. The method includes determining a similarity between the traffic pattern of the plurality of remote vehicles and movements of the host vehicle. Further, the method includes determining whether the similarity between the traffic pattern of the plurality of remote vehicles and movements of the host vehicle is less than a predetermined threshold. Also, the method includes commanding the host vehicle to adjust the movements thereof to match the traffic pattern of the plurality of remote vehicles.

Abstract: An unmanned device acquires sensing data of surrounding obstacles; determines, for each obstacle, at least one predicted track of the obstacle in a future period of time based on the sensing data; determines, for each moment in the future period of time and according to the predicted track corresponding to the obstacle, a collision probability that a collision with the obstacle occurs at each position in a target region at the moment; and determines a global collision probability that the collision with the obstacle occurs in the entire target region at the moment. According to the global collision probability corresponding to each obstacle at each moment, the unmanned device controls the unmanned device in the future period of time.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a trajectory for the autonomous vehicle to traverse in order to follow a route to a destination may be generated. A first error value for a boundary of an object, a second error value for a location of the autonomous vehicle, a third error value for a predicted future location of the object may be received. An uncertainty value for the object may be determined by combining the first error value, the second error value, and the third error value. A lateral gap threshold for the object may be determined based on the uncertainty value. The autonomous vehicle may be controlled in an autonomous driving mode based on the lateral gap threshold for the object.

Abstract: A method for detecting a moving object in a scene includes providing sampled ultrasonic echo signals from the scene; determining echo envelopes of the sampled ultrasonic echo signals; determining differentials of successive echo envelopes for providing echo envelope differentials; determining the absolute values of the echo envelope differentials; and conducting a classification based on the determined absolute values of the echo envelope differentials for determining a relative movement of the moving object.

Abstract: A system obtains, from an autonomous vehicle, point cloud data, projects the point cloud data onto a two-dimensional plane, and determines an optimized center parameter value of an optimized circle and an optimized radius parameter value of the optimized circle that, collectively, maximizes a probability distribution of a center parameter and a radius parameter across the point cloud data. The system determines whether one or more of the data points of the point cloud data are located within the optimized circle. If there are, the system assigns a pedestrian class value to the point label of the data point. If there are data points of the point cloud data that are located outside of the optimized circle, they system assigns a non-pedestrian class value to the point label of the data point.

Abstract: Lane-level matching is provided. For a time index of a time slot of a period of receiving consecutive position information, a lateral distance of the vehicle to each of a plurality of lanes of a roadway is calculated using data from sensors of a vehicle. For each lane of the plurality of lanes, a positional uncertainty of the vehicle in an orthogonal direction to a lane bearing of the lane is calculated, a lane confidence based on an average of confidence values of the lane over the period of receiving consecutive position information is calculated, and a lane probability based on the lane confidence is calculated. For the time index, it is identified which of the lanes has a highest probability as being the lane to which the vehicle is matched.

Abstract: A lift device includes a chassis, tractive elements, a platform, a lift assembly, a first set of proximity sensors, and a controller. The tractive elements are rotatably coupled to the chassis and support the chassis. The platform is disposed above the chassis and includes a deck for supporting an operator. The lift assembly couples the platform to the chassis and can selectably move the platform between a lowered position and a raised position. The first set of proximity sensors are coupled to the platform and detect a distance of an obstacle relative to the platform. The controller is operably coupled with an alert system and receives obstacle detection data from the first set of proximity sensors. The controller selects a subset of the first set of proximity sensors to analyze based on an active function of the lift device and determines whether the obstacle is within the minimum allowable distance.

Abstract: A method for determining a roll angle (?) of an optoelectronic sensor of a motor vehicle, wherein the optoelectronic sensor comprises at least one transmitter device, at least one receiver unit and at least one evaluation unit is disclosed. The method involves emitting light beams into surroundings of the vehicle by the transmitter device, receiving light beams reflected at an object by the receiver unit, wherein the received light beams are represented by the evaluation unit as scan points in a sensor image of the surroundings of the motor vehicle, wherein the roll angle (?) is determined by the evaluation unit between at least one scan axis and at least one reference axis, wherein the scan axis is formed by at least one scan point of a ground structure and a reference point of the reference axis of the optoelectronic sensor.

Abstract: A controller for an agent of a group of agents, in particular for a group of autonomous or semi-autonomous vehicles, and to a computer program implementing such a controller. A temporal deep network for such a controller and to a method, a computer program and an apparatus for training the temporal deep network. The controller includes a temporal deep network designed to calculate a desired trajectory for the agent, a nonlinear model predictive controller designed to calculate commands for the agent based on the desired trajectory and desired trajectories of the other agents of the group of agents, and an augmented memory designed to integrate historic system states of the group of agents for the temporal deep network.

Abstract: Systems and methods for predicting actions of an actor before entering a conflicted area are disclosed. The methods may include detecting the presence of an actor in an environment of an autonomous vehicle while the autonomous vehicle and the actor are approaching the conflicted area, determining whether the autonomous vehicle has precedence over the actor for traversing the conflicted area, assigning a kinematic target to the moving object that requires the moving object to come to a stop at an yield point before entering the conflicted area if the autonomous vehicle has precedence over the actor, determining whether a plurality of forecasted trajectories for the actor need to be generated, and controlling movement of the autonomous vehicle to traverse the conflicted area accordingly.

Abstract: The present disclosure relates to a motor vehicle including at least one optical surroundings sensor which has a detection range in a detection direction. The motor vehicle further includes a deflection arrangement for the surroundings sensor. The deflection arrangement includes an optical arrangement configured to deflect the detection direction and the entire detection range, and an adjustment device configured to move the optical arrangement into and out of the detection range of the surroundings sensor.

Abstract: A driver assist device and a method for operating the device are provided. The driver assist device includes a processor and a non-transitory storage medium containing program instructions executed by the processor. When a vehicle changes a lane from a traveling lane to a target lane, the processor determines a possibility of collision between the vehicle and another vehicle in the target lane and calculates an expected amount of deceleration of the vehicle with respect to a target vehicle in the target lane when there is no possibility of collision between the vehicle and the another vehicle. The processor compares the calculated expected amount of deceleration of the vehicle with a deceleration criterion, and determines whether to perform lane change assist control based on the comparison result.

Abstract: The vehicle control system is configured to execute autonomous driving and collision avoidance assistance. The autonomous driving is a process in which an ego-vehicle is autonomously driven based on necessary information for traveling including map information and information on a surrounding environment of the ego-vehicle. The collision avoidance assistance is a process in which the ego-vehicle is operated to avoid a collision in response to that a risk of collision of the ego-vehicle with a forward obstacle exceeds a threshold. The vehicle control system is configured to lower the threshold when the ego-vehicle travels, by the autonomous driving, in an area in which either one of an ego-lane and an opposite lane adjacent to the ego-lane is not travelable.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that determine yield behavior for an autonomous vehicle. An agent that is in a vicinity of an autonomous vehicle can be identified. An obtained crossing intent prediction characterizes a predicted likelihood that the agent intends to cross a roadway during a future time period. First features of the agent and of the autonomous vehicle are obtained. An input that includes the first features and the crossing intent prediction is processed using a machine learning model to generate an intent yielding score that represents a likelihood that the autonomous vehicle should perform a yielding behavior due to the intent of the agent to cross the roadway. From at least the intent yielding score, an intent yield behavior signal is determined and indicates whether the autonomous vehicle should perform the yielding behavior prior to reaching the first crossing region.

Abstract: Techniques are disclosed for systems and methods to provide graphical user interfaces for assisted and/or autonomous navigation for mobile structures. A navigation assist system includes a user interface for a mobile structure comprising a display and a logic device configured to communicate with the user interface and render a docking user interface on the display. The logic device is configured to monitor control signals for a navigation control system for the mobile structure and render the docking user interface based, at least in part, on the monitored control signals. The docking user interface includes a maneuvering guide with a mobile structure perimeter indicator, an obstruction map, and a translational thrust indicator configured to indicate a translational maneuvering thrust magnitude and direction relative to an orientation of the mobile structure perimeter indicator.

Abstract: Techniques for determining whether to yield a vehicle to an oncoming object based on a stationary object are discussed herein. A vehicle can determine a drivable area in an environment through which the vehicle is travelling. A trajectory associated with the vehicle can be determined. A probability that the oncoming object will intersect a drive path associated with a vehicle trajectory can be determined. In some case, the probability can be based on various distance metric(s) (e.g., dynamic distance metrics) of the environment. If the oncoming object is determined to intersect the drive path, a stopping location for the vehicle can be determined to allow the oncoming object to traverse around the stationary object. The vehicle can be controlled based on sensor data captured by the vehicle and the probability.

Abstract: This application is directed to aerial view generation for vehicle control. A vehicle obtains a forward-facing view of a road captured by a front-facing camera of a vehicle and applies a machine learning model to process the forward-facing view to predict determine a trajectory of the vehicle and a road layout based on a Frenet-Serret coordinate system of the road for the vehicle. The trajectory of the vehicle is combined with the road layout to predict an aerial view of the road, and the aerial view of the road is used to at least partially autonomously drive the vehicle. In some embodiments, the machine learning model is applied to process the forward-facing view to determine a first location of each of an obstacle vehicle in the Frenet-Serret coordinate system. The first location of the obstacle vehicle is converted to a vehicle location on the aerial view of the road.

Abstract: A vehicle safety system for helping to protect a pedestrian in the event of a vehicle frontal collision with the pedestrian includes a plurality of crash sensors for sensing a vehicle frontal collision, an actuatable pedestrian protection device, a controller for controlling actuation of the pedestrian protection device, and at least one active sensor for determining the presence of a pedestrian in the path of the vehicle.