Abstract: Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.

Abstract: An example operation includes one or more of traveling, by a first transport, in a first lane, determining, by the first transport, that a speed of a second transport is greater than a speed of the first transport when the second transport is behind the first transport, determining, by the first transport, that no other transports are ahead of the first transport by a first distance in the first lane and beside the first transport by a second distance in a second lane, maneuvering, by the first transport, to the second lane allowing the second transport to pass the first transport in the first lane, and maneuvering, by the first transport, to the first lane when there are no other transports traveling in the first lane at a third distance behind the first transport and at or near the speed of the second transport.

Abstract: According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver—the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated.

Abstract: A lane change assistance method is provided. The method includes recognizing styles of two traffic lines corresponding to a lane where a vehicle is located. Whether the vehicle violates traffic rules is determined according to the styles of the two traffic lines when the vehicle is performing an operation of changing lanes. A prompt is issued when the vehicle violates traffic rules.

Abstract: An approach is provided for identifying special areas with significant vehicle activities that do not represent established roads (e.g., construction areas). The approach involves, for example, retrieving probe data collected from sensors of probe devices traveling within a geographic area including geographic partitions. The approach also involves dividing each of the geographic partitions into grid cells. The approach further involves, for each grid cell, detecting directional groupings of the probe data that share a respective common direction of travel. The approach further involves detecting a set of grid cells with a count of the directional groupings that is above a count threshold. The approach further involves designating an area associated with the set of grid cells as an unconstrained area or a sub-area of the unconstrained area. The approach further involves providing the unconstrained area and/or the sub-area as a mapping data output.

Abstract: A navigation planning device configured to receive a navigation start point, a navigation destination point for a navigation route of at least one of a vessel or an aircraft, and a plurality of waypoints between the navigation start point and the navigation destination point, where the plurality of waypoints may be received in at least one of random order or in sequential order. The navigation planning device is configured to determine a positional relationship between the plurality of waypoints, and determine at least one indicator data corresponding to at least one waypoint data of the plurality of waypoints, based on the positional relationship between the plurality of waypoints. The navigation planning device is further configured to output the at least one waypoint data and the corresponding at least one indicator data for the navigation route to a display.

Abstract: A system and method of detecting and mitigating an erratic vehicle by a host vehicle. The method includes gathering sensor information on a calibratable external region surrounding the host vehicle; analyzing the sensor information to detect a target vehicle traveling in a lane and a movement of the target vehicle in the lane; determining whether the movement of the target vehicle in the lane is erratic; if erratic then designating target vehicle as erratic vehicle; assigning a risk score to the erratic vehicle; and implementing a predetermined mitigating action correlating to the assigned risk score to the erratic vehicle. The mitigating action includes one or more of: warning an operator of the host vehicle, warning a vehicle proximal to the host vehicle, and taking at least partial control of the host vehicle to further distance the host vehicle apart from the erratic vehicle.

Abstract: The automated driving system includes a vehicle speed detection device for detecting a speed of a vehicle, a display device capable of displaying a running lane and an adjacent lane of the vehicle, and a processor configured to control display content of the display device, and execute an autonomous lane change of the vehicle. The processor is configured to prohibit the autonomous lane change of the vehicle when a speed of the vehicle is less than a predetermined value. The processor is configured to hide the adjacent lane when the autonomous lane change is prohibited and start displaying the adjacent lane before the speed of the vehicle reaches the predetermined value if it is planned for the vehicle to accelerate from a speed below the predetermined value to a speed equal to or greater than the predetermined value in order to carry out the autonomous lane change.

Abstract: While operating a host vehicle in an area, a target vehicle is identified in a sub-area based on detecting a visual cue from the target vehicle. A future location of the target vehicle is predicted to intersect a path of the host vehicle. A stop position of the host vehicle is determined based on the future location of the target vehicle. At least one of the target vehicle is determined to have moved or the path of the host vehicle is updated. Then, the host vehicle is operated to the stop position or along the updated path.

Abstract: A utility vehicle includes a frame and a plurality of ground-engaging members supporting the frame. Each of the plurality of ground-engaging members is configured to rotate about an axle. The utility vehicle further includes a powertrain assembly supported by the frame and a braking system configured to operate in a normal run mode and an anti-lock braking mode. The braking system includes an anti-lock braking control module operably coupled to the plurality of ground-engaging members and configured to automatically engage the anti-lock braking mode in response to a predetermined condition.

Abstract: Aspects of the disclosure provide for the determining whether to assign a driver to a trip. For instance, a route for a trip between a pickup location and a destination location for a passenger may be determined. Branch routes for the route may be determined based on one or more lane changes of the route. A probability of following each determined branch route may be determined. A probability of requiring assistance for each determined branch route may be determined. Whether to assign a driver to the trip may be determined based on the probabilities for each determined branch route. A vehicle of a fleet of vehicles may be assigned to the trip based on the determine of whether to assign a driver.

Abstract: A method for determining a lane change, performed by an apparatus for determining a lane change of an object located around a driving vehicle with which is equipped a sensor, the method including, detecting a plurality of objects located around the driving vehicle using scanning information obtained repeatedly at every predetermined period of time by the sensor scanning surroundings of the driving vehicle, selecting at least one candidate object estimated to change lanes among the plurality of objects based on previously detected lane edge information and determining whether the candidate object changes lanes based on information on movement of the candidate object.

Abstract: An information processing method is to be executed by a computer. The information processing method includes: receiving, from an autonomous moving body, a first processing result that is a result of first preprocessing of travel control processing in autonomous movement processing of the autonomous moving body, and sensing data received by the autonomous moving body; executing second preprocessing based on the sensing data to receive a second processing result, the second preprocessing being more advanced than the first preprocessing; determining a difference between the first processing result and the second processing result; and outputting, to the autonomous moving body based on the difference determined, a change request to change the first processing result to the third processing result, the third processing result being received based on at least one of the first processing result or the second processing result.

Abstract: Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

Abstract: A robotic system includes a controller configured to obtain image data from one or more optical sensors and to determine one or more of a location and/or pose of a vehicle component based on the image data. The controller also is configured to determine a model of an external environment of the robotic system based on the image data and to determine tasks to be performed by components of the robotic system to perform maintenance on the vehicle component. The controller also is configured to assign the tasks to the components of the robotic system and to communicate control signals to the components of the robotic system to autonomously control the robotic system to perform the maintenance on the vehicle component.

Abstract: At least one object is detected, via a sensor, in a direction of movement ahead of a host vehicle. A current sensor range is determined based on the detected at least one object. A maximum sensor range is determined based on the sensor. The current sensor range is compared with the maximum sensor range. Upon determining the current sensor range is less than the maximum sensor range by at least a predetermined threshold, driving parameters of the host vehicle are updated based on the current sensor range.

Abstract: A traveling lane planning device has a processor configured to assess whether or not an approach zone exists on an adjacent lane between an event location and the current location of a vehicle, when the traveling lane plan includes a lane change from the traveling lane to the adjacent lane before the event location, to create a lane change plan in which the vehicle moves from the traveling lane to the adjacent lane after the vehicle has passed along the approach zone of the adjacent lane when it has assessed that the approach zone exists and to cause the vehicle to carry out a notification action which gives notice to the surrounding area of the vehicle that the vehicle will make a lane change from the traveling lane to the adjacent lane while the vehicle is traveling in the traveling lane along the approach zone of the adjacent lane.

Abstract: An Aerial Robotics Network (ARN) Management Infrastructure (MI) (also referred to as ARNMI) that provides a mechanism for the management of aerobots.

Abstract: A system for evaluating behavior of vehicles is provided. The system includes an input device configured to receive a request for evaluation of behavior of vehicles, one or more processors, one or more memory modules communicatively coupled to the one or more processors; and machine readable instructions stored in the one or more memory modules. The machine readable instructions, when executed by the one or more processors, cause the system to: collect log files related to vehicles from a plurality of sources, a format of log files from one source being different from a format of log files from another source, extract one or more parameters for the vehicles from the collected log files based on the request for evaluation, and evaluate behavior of the vehicles based on the extracted one or more parameters, wherein the one or more parameters include time series and time intervals.

Abstract: Methods, devices and systems enable controlling an autonomous vehicle by identifying a vehicle that is within a threshold distance of the autonomous vehicle, determining an autonomous capability metric (ACM) the identified vehicle, determining whether the ACM of the identified vehicle is greater than a first threshold, determining whether the ACM of the identified vehicle is less than a second threshold, and adjusting a driving parameter of the autonomous vehicle so that the autonomous vehicle is more or less reliant on the capabilities of the identified vehicle based on whether the ACM of the identified vehicle exceeds the thresholds.