Abstract: When executing the autonomous lane change control of lane change of the subject vehicle from the subject vehicle lane in which the subject vehicle travels to adjacent lane, the autonomous lane change control is executed so as to accelerate the lateral speed of the subject vehicle in the subject vehicle lane and thereafter decelerate the lateral speed in the subject vehicle lane. As a result, it takes longer for the driver of the following vehicle to confirm the lateral movement of the preceding subject vehicle during the lane change of the subject vehicle from the start of the lane change than a lane change in which the lateral speed is decelerated in the adjacent lane. Therefore, it becomes easier to recognize the lane change.

Abstract: This disclosure describes techniques to count objects across multiple zones of arbitrary shapes. The techniques include operations comprising: detecting an object in a first zone of a plurality of zones of an area; determining that the object has moved from the first zone to a second zone of the plurality of zones; determining that movement of the object from the first zone to the second zone fails to satisfy a zone transition criterion for updating a count value and time criterion; and in response to determining that the movement of the object from the first zone to the second zone fails to satisfy the zone transition criterion, preventing the count value from being updated.

Abstract: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a second convolutional neural network (CNN) training dataset by determining an underrepresented object configuration and an underrepresented noise factor corresponding to an object in a first CNN training dataset, generate one or more simulated images including the object corresponding to the underrepresented object configuration in the first CNN training dataset by inputting ground truth data corresponding to the object into a photorealistic rendering engine and generate one or more synthetic images including the object corresponding to the underrepresented noise factor in the first CNN training dataset by processing the simulated images with a generative adversarial network (GAN) to determine a second CNN training dataset. The instructions can include further instructions to train a CNN to using the first and the second CNN training datasets.

Abstract: A driving apparatus includes a body; at least one sensor that is exposed to an outside of the body and configured to sense a user input for controlling driving of the body; a controller configured to generate at least one control command for driving of the body according to the sensed user input; and a motor that is configured to generate a driving force for driving the body according to the generated at least one control command.

Abstract: In a vehicle teleoperation session, a speed limit is determined for which the vehicle can be safely teleoperated. A safety system senses data relating to the vehicle environment and generates a depth map from which obstacles in the vicinity of the vehicle can be identified. Based on the detected obstacles and a motion state of the vehicle, a speed limit is determined at which the teleoperator is predicted to be able to utilize an emergency braking command to avoid a collision. The speed limit may be automatically applied to the vehicle or may be provided to the teleoperator to enable the teleoperator to adjust the vehicle speed.

Abstract: Efficient and effective workspace condition analysis systems and methods are presented. In one embodiment, a method comprises: accessing information associated with an activity space, including information on a newly discovered previously unmodeled entity; analyzing the activity information, including activity information associated with the previously unmodeled entity; forwarding feedback on the results of the analysis, including analysis results for the updated modeled information; and utilizing the feedback in a coordinated path plan check process. In one exemplary implementation the coordinated path plan check process comprises: creating a solid/CAD model including updated modeled information; simulating an activity including the updated modeled information; generating a coordinated path plan for entities in the activity space; and testing the coordinated path plan. The coordinated path plan check process can be a success.

Abstract: In one embodiment, a method for monitoring a localization function in an autonomous driving vehicle (ADV) can use known static objects as ground truths to determine when the localization function encounter errors. The known static objects are marked on a high definition (HD) map for the real-time driving environment. When the ADV detects one or more known static objects, the ADV can use sensor data, locations of the one or more static objects, and one or more error tolerance parameters to create a localization error tolerance area surrounding a current location of the ADV. The ADV can project the tolerance area on the HD map, performs a localization operation to generate an expected location of the ADV on the HD map, and determines whether the generated location falls within the projected tolerance area.

Abstract: A number of illustrative variations may include a method or product for sensing driver intervention in an autonomous steering system.

Abstract: A method (400) and a control unit (210) for ground bearing capacity analysis. The method (400) steps include determining (401) a shape of the terrain segment (130) ahead of a vehicle (100), based on sensor measurements; predicting (402) a distance between a sensor (120) of the vehicle (100) and the ground (110) at the terrain segment (130), before the vehicle (100) moves into the terrain segment (130); measuring (403) the distance between the sensor (120) of the vehicle (100) and the ground (110) when the vehicle (100) has moved into the terrain segment (130); and determining (404) that the terrain segment (130) is to be avoided due to insufficient bearing capacity when the predicted (402) distance between the sensor (120) and the ground (110) exceeds the measured (403) distance between the sensor (120) and the ground (110). Also, a method (600) and control unit (210) for route planning of the vehicle (100) are described.

Abstract: Techniques are described for an autonomous vehicle to provide the autonomous vehicle's health-related information, identification information, and/or inspection information to a device that may be used by law enforcement or a government agency or a third-party so that the autonomous vehicle can comply with government regulations and can continue to operate on the road.

Abstract: A crest detection system (10, 19, 185C) for a vehicle (100), the system comprising a processing means (10, 19) arranged to receive, from terrain data capture means (185C) arranged to capture data in respect of terrain ahead of the vehicle by means of one or more sensors, terrain information indicative of the topography of an area extending ahead of the vehicle (100), wherein the processing means (10, 19) is configured to, in dependence upon a predicted path of the vehicle (100) over said terrain extending ahead of the vehicle (100), determine that a crest exists in the predicted path in dependence at least in part on the detection of a discontinuity in terrain along the predicted path ahead of the vehicle (100) based on the terrain information, and information in respect of slope of terrain before the discontinuity.

Abstract: An example system includes a sensor for obtaining information about an object in an environment and one or more processing devices configured to use the information in generating or updating a map of the environment. The map includes the object and boundaries or landmarks in the environment. The map includes a static score associated with the object. The static score represents a likelihood that the object will remain immobile within the environment. The likelihood may be between certain immobility and certain mobility.

Abstract: In response to a request for a three-point turn, a forward turning path from a current location and heading direction of the ADV is generated. In generating the forward turning path, a forward curvature is determined based on the maximum forward turning angle of the ADV by applying a full steering command. The forward turning path is determined based on the forward curvature from the current location of the ADV. A forward speed profile is calculated for the forward turning path based on perception information that perceives a driving environment surrounding the vehicle at the point in time. In addition, a backward turning path is generated from an end point of the forward turning path based on a maximum backward turning angle associated with the ADV. The three-point turn path is then generated based on the forward turning path and the backward turning path to drive the vehicle to make the three-point turn.

Abstract: A system for method for future forecasting using action priors that include receiving image data associated with a surrounding environment of an ego vehicle and dynamic data associated with dynamic operation of the ego vehicle. The system and method also include analyzing the image data and detecting actions associated with agents located within the surrounding environment of the ego vehicle and analyzing the dynamic data and processing an ego motion history of the ego vehicle. The system and method further include predicting future trajectories of the agents located within the surrounding environment of the ego vehicle and a future ego motion of the ego vehicle within the surrounding environment of the ego vehicle.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for visually annotating rendered multi-dimensional representations of robot environments. In various implementations, an entity may be identified that is present with a telepresence robot in an environment. A measure of potential interest of a user in the entity may be calculated based on a record of one or more interactions between the user and one or more computing devices. In some implementations, the one or more interactions may be for purposes other than directly operating the telepresence robot. In various implementations, a multi-dimensional representation of the environment may be rendered as part of a graphical user interface operable by the user to control the telepresence robot. In various implementations, a visual annotation may be selectively rendered within the multi-dimensional representation of the environment in association with the entity based on the measure of potential interest.

Abstract: A controller for an unmanned aerial vehicle (UAV) comprising an image capture means, the controller comprising: inputs arranged to receive: positional data relating to the UAV, a vehicle and a user device; image data captured by the image capture means; a processor arranged to process the received positional data to determine the relative locations of the UAV, vehicle and user device; an output arranged to output a control signal for controlling the UAV and to output an image signal comprising captured image data; wherein the processor is arranged to: generate the control signal for the UAV such that the image data captured by the image capture means comprises at least an image of an obscured portion of the vehicle that is obscured from a field of view of a user of the user device.

Abstract: A method is described for creating observation data, in particular by at least one vehicle, traveled road segments being ascertained by the vehicle, lanes of the road segments traveled by the vehicle being ascertained by the vehicle, and the ascertained road segments together with the ascertained traveled lanes being transmitted as observation data from the vehicle to an external server unit. A method for ascertaining a number of traffic lanes, to a system, to an external server unit, and to a control unit are also described.

Abstract: Techniques for determining a location and type of traffic-related features and using such features in route planning are discussed herein. The techniques may include determining that sensor data represents a feature such as a traffic light, road segment, building type, and the like. The techniques further include determining a cost of a feature, whereby the cost is associated with the effect of a feature on a vehicle traversing an environment. A feature database can be updated based on features in the environment. A cost of the feature can be used to update costs of routes associated with the location of the feature.

Abstract: The present disclosure provides a method comprising identifying at least one of a characteristic and an identity of an item for delivery from an origin to a destination; identifying a plurality of possible routes between the origin and the destination using mapping information, the mapping information including for each of the plurality of possible routes, a characterization of each of a plurality of route segments comprising the possible route; evaluating the plurality of possible routes in view of the identified at least one of the item characteristic and the item identity to select one of the plurality of possible routes; and providing the selected one of the plurality of possible routes to a vehicle, wherein the vehicle delivers the item from the origin to the destination via the identified route.

Abstract: The travel control device, when determining that the traffic congestion is occurred in the other lane, detects, behind another vehicle in the other lane, an approach space; determines whether the approach space meets a predetermined condition; when determining that the approach space meets the predetermined condition, sets the target posture of the own vehicle at the target position behind the other vehicle in the other lane based on the shape of the approach space; and generates a target traveling trajectory from a current position of the own vehicle to the target position. The travel control device controls the motion of the own vehicle so that the own vehicle tracks the target traveling trajectory.

Abstract: In general, the present disclosure is directed to a time of flight (ToF) sensor arrangement that may be utilized by a robot (e.g., a robot vacuum) to identify and detect objects in a surrounding environment for mapping and localization purposes. In an embodiment, a robot is disclosed that includes a plurality of ToF sensors disposed about a housing of the robot. Two or more ToF sensors may be angled/aligned to establish overlapping field of views to form redundant detection regions around the robot. Objects that appear therein may then be detected by the robot and utilized to positively identify, e.g., with a high degree of confidence, the presence of the object. The identified objects may then be utilized as data points by the robot to build/update a map. The identified objects may also be utilized during pose routines that allow the robot to orient itself within the map.

Abstract: Systems and methods for vehicle spatial path sampling are provided. The method includes obtaining an initial travel path for an autonomous vehicle from a first location to a second location and vehicle configuration data indicative of one or more physical constraints of the autonomous vehicle. The method includes determining one or more secondary travel paths for the autonomous vehicle from the first location to the second location based on the initial travel path and the vehicle configuration data. The method includes generating a spatial envelope based on the one or more secondary travel paths that indicates a plurality of lateral offsets from the initial travel path. And, the method includes generating a plurality of trajectories for the autonomous vehicle to travel from the first location to the second location such that each of the plurality of trajectories include one or more lateral offsets identified by the spatial envelope.

Abstract: The present application relates to providing alert notifications of high-risk driving areas in a connected vehicle by calculating a first navigational route between a host vehicle location and a destination, transmitting the first navigational route via a wireless network, receiving via a wireless network an indication of a high-risk area in response to the first navigational route, generating a second navigational route in response to the high-risk area, presenting the second navigational route to a vehicle occupant via a user interface and prompting user action.

Abstract: A controller of a drone includes a storage unit that stores a scene information database containing a plurality of scene information sets arranged in time series of a video, in each of the scene information sets, a relative position with respect to a vehicle in capturing a scene of the video and a duration of the scene are associated with each other, a vehicle information acquisition unit that receives vehicle information from the vehicle, a traveling position estimation unit that estimates a future traveling position of the vehicle based on the vehicle information, and a flight path calculation unit that, based on the estimated traveling position and the scene information database, calculates, for each of the scenes, a flight path that passes through the relative position with respect to the vehicle.

Abstract: A robot moves about an environment and may interact with a user. A waypoint specifies where the robot is to move to with respect to the user while a proxemic cost map is used to plan the path to the waypoint. User input or preferences may be used to modify the waypoint or the proxemic cost map. The waypoint may specify a particular distance and bearing with respect to the user. The proxemic cost map may be oriented with respect to the user and specifies costs for particular areas. For example, an area immediately behind the user may have a very high cost while an area in front of the user may have a low cost. Based on the waypoint and the proxemic cost map, a path is selected and the robot moves along that path, avoiding the high cost areas in favor of the low cost areas if possible.

Abstract: A vehicle computing system may identify a scenario in an environment that violates an operating constraint. The vehicle computing system may request remote guidance from a guidance system of a service computing device. The vehicle computing system may receive input from the guidance system including one or more waypoints and/or associated orientations for the vehicle to navigate through the scenario. The vehicle computing system may be configured to validate the input. A validation may include processing the input to determine whether the waypoint(s) and/or orientation(s) associated therewith may cause the vehicle to violate a safety protocol. Based on a determination that the input will not cause the vehicle to violate the safety protocol, the vehicle computing system may control the vehicle according to the input, such as by causing a drive system to operate the vehicle to each waypoint at the associated orientation.

Abstract: A lawn mower robot may include a plurality of distance sensor units. Each distance sensor unit detects distance information between the lawn mower robot and a fence for partitioning a region. A controller calculates adjacent direction information using each detected distance information. In addition, the lawn mower robot includes a position sensor module. The position sensor module detects position information related to the lawn mower robot. The controller detects whether the lawn mower has deviated from a preset region by using the position information. When the lawn mower robot has deviated, the lawn mower robot can be moved in a direction according to the calculated adjacent direction information and return along the shortest path.

Abstract: A robotic device including one or more proximity sensing systems coupled to various portions of a robot body. The proximity sensing systems detect a distance of an object about the robot body and the robotic device reacts based on the detected distance. The proximity sensing systems obtain a three-dimensional (3D) profile of the object to determine a category of the object. The distance of the object is detected multiple times in a sequence to determine a movement path of the object.

Abstract: Systems and methods for determining appropriate energy replenishment and controlling autonomous vehicles are provided. An example computer-implemented method can include obtaining one or more energy parameters associated with an autonomous vehicle. The method can include determining a refueling task for the autonomous vehicle based at least in part on the energy parameters associated with the autonomous vehicle. The refueling task comprises a first refueling task that is interruptible by a vehicle service assignment or a second refueling task that is not interruptible by the vehicle service assignment. The method can include communicating data indicative of the refueling task to the autonomous vehicle or to a second computing system that manages the autonomous vehicle. The method can include determining whether the refueling task for the autonomous vehicle has been accepted or rejected.

Abstract: The present application relates to a method and apparatus for controlling an ADAS equipped vehicle including a sensor configured for determining a first distance to a first proximate vehicle and a second distance to a second proximate vehicle, a user input operative to receive a user preference, a memory operative to store a map data, a processor operative to generate a current lane score and an adjacent lane score in response to the first distance, the second distance, the user preference, and the map data, the processor being further operative to generate a lane change control signal in response to the adjacent lane score exceeding the current lane score and a vehicle controller operative to perform a lane change operation from a current lane to an adjacent lane in response to the lane change control signal.

Abstract: A cart-based workflow system includes: a robot operating in a facility; a server, the server operably connected to the robot, the server configured to do one or more of send the robot a cart transfer location usable for transferring the cart and instruct the robot to specify the cart transfer location; a graphic user interface (GUI) comprising a map of the facility, the GUI operably connected to the server, the GUI configured to do one or more of receive input from a human user and provide output to the human user, the GUI further configured to be usable by the user to coordinate movement of one or more of robots and carts.

Abstract: A device and method for acquiring captured image data obtained by an unmanned aerial vehicle (UAV), identifying a direction that a target appearing in the captured image data faces, outputting an approach curve used to move the UAV from a current UAV position to a UAV position closer to the face of the person based on the identified direction that the face of the person faces, where the approach curve includes an arc portion connecting the current position to a straight line portion at an approach contact point located a predetermined distance in front of the face of the person, the straight line portion connecting the approach contact point to the position closer to the face of the person.

Abstract: A system, method, infrastructure, and vehicle for performing automated valet parking enable an unmanned vehicle to autonomously move to and park at an empty parking space by communicating with a parking infrastructure. The system, method, infrastructure, and vehicle enable an unmanned vehicle to autonomously move from a parking space to a pickup zone by communicating with a parking infrastructure.

Abstract: Disclosed herein is an artificial intelligence cleaner. The artificial intelligence cleaner includes a memory configured to store a simultaneous localization and mapping (SLAM) map for a cleaning space; a driving unit configured to drive the artificial intelligence cleaner; and a processor configured to collect a plurality of cleaning logs for the cleaning space, divide the cleaning space into a plurality of cleaning areas using the SLAM map and the collected plurality of cleaning logs, determine a cleaning route of the artificial intelligence cleaner in consideration of the divided cleaning areas, and control the driving unit according to the determined cleaning route.

Abstract: A method and apparatus is provided for controlling the operation of an autonomous vehicle. According to one aspect, the autonomous vehicle may track the trajectories of other vehicles on a road. Based on the other vehicle's trajectories, the autonomous vehicle may generate a pool of combined trajectories. Subsequently, the autonomous vehicle may select one of the combined trajectories as a representative trajectory. The representative trajectory may be used to change at least one of the speed or direction of the autonomous vehicle.

Abstract: An automatic diversion management system on-board an aircraft is provided. The diversion management system is configured to automatically detect a need for the aircraft to divert from a primary airport to a diversion airport; automatically initiate diversion planning to a suitable diversion airport responsive to detecting conditions that can cause a need for diversion; automatically create a diversion flight plan; automatically send a clearance request to air traffic control (ATC) for a first type of conditions causing a need for diversion and send a clearance request to ATC, responsive to flight crew action, for a second type of conditions causing a need for diversion; and automatically activate the diversion flight plan after receipt of ATC clearance for the first type of conditions causing a need for diversion and activate the diversion flight plan, responsive to flight crew action, for the second type of conditions causing a need for diversion.

Abstract: A parking space can be identified. A lateral clearance can be identified when a vehicle is parked in the parking space based on identifying a first door of the vehicle to be provided the lateral clearance when the vehicle is parked. A parking position of the vehicle in the parking space can be determined, including a parking bias angle ? between a vehicle longitudinal axis and a parking space longitudinal center axis, and a lateral offset of the vehicle with respect to the parking space longitudinal center axis, based on dimensions of the parking space and the lateral clearance.

Abstract: Methods and systems for controlling a vehicle are herein disclosed. A method includes receiving vehicle data and external data from a vehicle control system of a vehicle and generating an environment representation of an area of the transportation network proximate to the vehicle location. The method includes displaying the environment representation in a GUI and receiving a solution path via the graphical user interface, the solution path indicating a route and one or more stop points. The method includes transmitting the route to the vehicle including a respective geolocation of each of the one or more stop points. The vehicle receives the route and begins traversing the transportation network based on the solution path. The method includes receiving updated vehicle data and/or updated external data from the vehicle and updating the environment representation based thereon. The method includes displaying, the updated environment representation via the graphical user interface.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for controlling vehicle driving. The method includes: generating a global path of a driving site of a vehicle; executing following controlling: generating a local path of a site in front of a current position of the vehicle based on the global path, the local path following a direction of the global path, controlling the vehicle to drive along the local path until reaching an end point of the local path, determining whether the vehicle reaches an end point of the global path, and terminating the controlling if the vehicle reaches the endpoint of the global path; and continuing, in response to determining the vehicle failing to reach the end point of the global path, executing the controlling.

Abstract: In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Abstract: A method for determining an idealized passing maneuver includes receiving first data values that represent a passing maneuver of a first vehicle to pass a second vehicle, requesting and receiving surroundings data values that represent instantaneous and/or future surroundings of the first vehicle and/or of the second vehicle, determining a surroundings model of the first vehicle based on the surroundings data values and on a digital map that represents the instantaneous and/or future surroundings of the first vehicle and/or of the second vehicle, determining an idealized passing maneuver for the first vehicle to carry out the passing maneuver based on the surroundings model, and providing the idealized passing maneuver in the form of second data values for receipt by the first vehicle.

Abstract: Autonomous vehicles are capable of executing missions that abide by on-street rules or regulations, while also being able to seamlessly transition to and from “zones,” including off-street zones, with their our set(s) of rules or regulations. An on-board memory stores roadgraph information. An on-board computer is operative to execute commanded driving missions using the roadgraph information, including missions with one or more zones, each zone being defined by a sub-roadgraph with its own set of zone-specific driving rules and parameters. A mission may be coordinated with one or more payload operations, including zone with “free drive paths” as in a warehouse facility with loading and unloading zones to pick up payloads and place them down, or zone staging or entry points to one or more points of payload acquisition or placement. The vehicle may be a warehousing vehicle such as a forklift.

Abstract: A system is disclosed for providing rerouting information based, in part, on a probability of route acceptance. In accordance with further embodiments, the rerouting information is based, in part, on decision tree analyses involving decisions to request and decisions to not request a reroute.

Abstract: Localization error handling using output is described. A computing system associated with a vehicle can receive sensor data from a sensor associated with vehicle. The computing system can determine, based at least partly on the sensor data, a first instruction for controlling the vehicle during a first period of time and a difference in pose information associated with a pose of the vehicle. Based at least partly on determining the difference, the computing system can retrieve a second instruction for controlling the vehicle during a second period of time prior to the first period of time and, based at least partly on comparing the first instruction and the second instruction, the computing system can determine whether the vehicle is to follow the first instruction or perform an alternate operation.

Abstract: A method for controlling an unmanned aerial vehicle (UAV) to track a monitored person is provided. The method includes directing the UAV toward a target location the target location being based on past or present location information provided by a personal monitoring device attached to a monitored person, the location information representing the location of the personal monitoring device; assuming with the UAV a surveillance position relative to the target location; and determining that the monitored device is proximate to the target location by receiving signals from the personal monitoring device and/or observing the monitored person through a camera on the UAV.

Abstract: A control apparatus of a work vehicle includes a course data acquisition unit acquiring course data indicating a traveling condition of a work vehicle that includes a travel route, a travel range data acquisition unit acquiring travel range data indicating a travel range of the work vehicle that is defined with a preset travel width based on the travel route, a detection data acquisition unit acquiring detection data of a detection device that has detected a travel direction of the work vehicle, a prediction unit predicting, based on the detection data, a prescribed position distant from a current position of the work vehicle traveling according to the course data, a determination unit determining whether the prescribed position exists within the travel range, and a drive control unit stopping traveling of the work vehicle when it is determined that the prescribed position does not exist within the travel range.

Abstract: Aspects of the disclosure provide for localizing a vehicle. In one instance, a weather condition in which the vehicle is currently driving may be identified. A plurality of sensor inputs including intensity information, elevation information, and radar sensor information may be received. For each of the plurality of sensor inputs, an alignment score is determined by comparing the intensity information, elevation information, and radar sensor information to a corresponding pre-stored image for each of the intensity information, the elevation information, and the radar sensor information. A set of weights for the plurality of sensor inputs may be determined based on the identified weather condition. The alignment scores may then be combined using the set of weights in order to localize the vehicle.

Abstract: Provided is a multi-information provider system of a guidance robot and a method thereof. The multi-information provider system of a guidance robot may include a user database configured to receive and store user information transmitted from a terminal of a user, a robot database configured to receive pieces of robot information from one or more guidance robots and store the pieces of robot information, a valid robot list generator configured to list pieces of information of guidance robots located within a predetermined effective distance from a location of the user, a service matcher configured to match the user information and each of the pieces of robot information included in a valid robot list, and an outputter configured to output matched service information to the user terminal.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for generating a driving path. The method includes acquiring a two-dimensional image of a driving site obtained by a camera provided on an unmanned aerial vehicle through aerial photography, and a two-dimensional image of a site in front of a vehicle photographed by a camera provided on the vehicle; generating a global map based on the two-dimensional image of the driving site, and generating a local map based on the two-dimensional image of the site in front of the vehicle; and performing path planning based on the global map and the local map, to generate a global path and a local path, the local path following a direction of the global path.

Abstract: A method and system for automatically controlling a vehicle is provided. The method includes generating an original route of travel for a first vehicle for travel from an original location to a destination location. The vehicle is directed from the original location to the destination location such that the vehicle initiates motion and navigates the original route of travel towards the destination location in accordance with the original route of travel. Monitored vehicular attributes of the first vehicle are received and environmental attributes associated with the original route of travel are monitored with respect the first vehicle. Navigational issues associated with the vehicle traveling along the original route of travel are determined based on the monitored vehicular attributes and results of monitoring the environmental attributes. The navigational issues are used to determine if the vehicle should continue to travel along the original route of travel.