Abstract: A method is provided for identifying an obstacle during a work operation of a tractor and attached implement combination. The method includes detecting an object during the work operation, and identifying the detected object as an obstacle in dependence on the predefined classification parameter for classification of the detected object or a predefined work parameter of the tractor or the implement.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a first utility metric associated with a region and first autonomous vehicle eligibility criteria, wherein the first utility metric is determined based on a first plurality of rides and a subset of the first plurality of rides that can be successfully executed within the region based on the first autonomous vehicle eligibility criteria. A second utility metric associated with the region and second autonomous vehicle eligibility criteria can be determined, wherein the second utility metric is determined based on a second plurality of rides and a subset of the second plurality of rides that can be successfully executed within the region based on the second autonomous vehicle eligibility criteria. An autonomous vehicle associated with the first autonomous vehicle eligibility criteria can be selected to drive in the region based on a comparison of the first utility metric and the second utility metric.

Abstract: The present invention provides a system (100) for extending voice commands and determining a user's location. The system (100) includes a plurality of interconnected user devices (10a, 10b, 10c, 10d, 10e, 10f, 10g, 10h, 10i, 10j, . . . ) forming a meshed network (1000), to receive a voice command from a user (1) for extending the range of voice command detection. Further, the system (100) includes a base unit (20) connected to one of the user devices, which receives the voice command from the user device (10) closest to the base unit (20) and calculates ranging data. A master controller (30), connected to multiple base units (20a, 20b, 20c, 20d, . . . ), calculates the user's location based on ranging data, processes the voice command to determine the user's intent, and performs an action based on the determined intent and sends the user's location to a central server (40), to visualize the user's location on map.

Abstract: The present disclosure provides a traffic prediction apparatus, system, method and program capable of predicting the number of vehicles and the speed of vehicles in a predetermined time and in a predetermined range based on waterfall data of vehicles. The traffic prediction apparatus comprises acquisition means for acquiring waterfall data comprising a generation position of a vibration, a generation time of the vibration and an amplitude of the vibration generated by a vehicle traveling on a road, pre-processing means for transforming the acquired waterfall data, generation means for training a portion of the plurality of processed waterfall data and at least one corresponding ground truth traffic property used as plurality of labels to generate a trained model, wherein the ground truth may be obtained from a secondary acquisition means, and prediction means for predicting at least one traffic property for a processed waterfall data.

Abstract: Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.

Abstract: A system and corresponding method for autonomous driving of a vehicle are provided. The system comprises at least one neural network (NN) that generates at least one output for controlling the autonomous driving. The system further comprises a main data path that routes bulk sensor data to the at least one NN and a low-latency data path with reduced latency relative to the main data path. The low-latency data path routes limited sensor data to the at least one NN which, in turn, employs the limited sensor data to improve performance of the at least one NN's processing of the bulk sensor data for generating the at least one output. Improving performance of the at least one NN's processing of the bulk sensor data enables the system to, for example, identify a safety hazard sooner, enabling the autonomous driving to divert the vehicle and avoid contact with the safety hazard.

Abstract: Systems and methods for real time routing of vehicles. In particular, systems and methods are provided for real time routing of vehicles based on encounters with vulnerable road users. Vehicle perception and communication systems can provide live feedback of vulnerable road user locations. Various vulnerable road user behaviors can be identified by vehicle perception systems and flagged for avoidance in a central mapping system. The feedback can be used to route other fleet vehicles around the vulnerable road users.

Abstract: Aspects of the invention may be related to a computer system and a computerized method of controlling a marine vessel. Embodiments may include: receiving, by a controller, a position of the marine vessel from a positioning system; receiving, by the controller, geographical data, from at least one database, the geographical data may include at least a map of seabed depths; receiving, by the controller, a heading direction and speed of the marine vessel; calculating, by the controller, a safety zone ahead of the marine vessel based on the position, the heading direction and the speed of the marine vessel; identifying a location of a critical seabed depth inside the safety zone based on the received, the geographical data; and changing, by the controller, a state of a propelling unit of the marine vessel when a critical seabed depth was identified inside the safety zone.

Abstract: A vehicle controller includes a memory configured to store map information including information on a predetermined area used in autonomous driving control of a vehicle; and a processor configured to: refer to the map information to identify an autonomous-driving practicable point at which a planned travel route from the current position of the vehicle outside the predetermined area to a destination of the vehicle enters the predetermined area, calculate a manual-driving section length from the current position of the vehicle to the autonomous-driving practicable point along the planned travel route, and notify the calculated manual-driving section length to a driver of the vehicle via a notification device provided in the interior of the vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a robot to perform a custom real-time action that uses a callback function. One of the methods comprises receiving a definition of a custom real-time control function that specifies a custom callback function, an action, and a custom reaction that references the custom callback function; providing a command to initiate the action; repeatedly executing, by the control layer of the real-time robotics control framework, the custom real-time control function at each tick of a real-time robotics system driving one or more physical robots, including: obtaining current values of one or more state variables, evaluating the custom reaction specified by the custom real-time control function according to the current values of the one or more state variables, and whenever the one or more conditions of the custom reaction are satisfied, invoking the custom callback function.

Abstract: Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.

Abstract: A first distinct vehicle operational scenario is identified for an autonomous vehicle (AV). A first set of candidate vehicle control actions are received from a model that provides a first solution to the first distinct vehicle operational scenario. An action is selected from the first set of candidate vehicle control actions. The AV is controlled based on the action. The first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV.

Abstract: A framework for offline learning from a set of diverse and suboptimal demonstrations operates by selectively imitating local sequences from the dataset. At least one embodiment recovers performant policies from large manipulation datasets by decomposing the problem into a goal-conditioned imitation and a high-level goal selection mechanism.

Abstract: Autonomous driving system methods and devices which trigger vehicular actions based on the monitoring of one or more occupants of a vehicle are presented. The methods, and corresponding devices, may include identifying a plurality of features in a plurality of subsets of image data detailing the one or more occupants; tracking changes over time of the plurality of features over the plurality of subsets of image data; determining a state, from a plurality of states, of the one or more occupants based on the tracked changes; and triggering the vehicular action based on the determined state.

Abstract: An autonomous off-road vehicle, upon encountering an obstruction while navigating a route, can apply a first machine-learned model to identify the obstruction. In the event that the first machine-learned model cannot identify the obstruction, the autonomous off-road vehicle can apply a second machine-learned model configured to determine whether or not the obstruction can be ignored, for instance based on dimensions of the obstruction. If the obstruction can be ignored, the autonomous off-road vehicle can continue navigating the route. If the obstruction cannot be ignored, the autonomous off-road vehicle can modify the route, can stop, can flag the obstruction to a remote human operator, can modify an interface of a human operator to display a notification or a video feed from the vehicle, and the like.

Abstract: A driving analysis server may be configured to receive vehicle operation data from mobile devices respectively disposed within the vehicles, and may use the data to group the vehicles into multiple groups. A driving pattern for each vehicle may be established and compared against a group driving pattern of its corresponding group to identify outliers. A driver score the outliers may be adjusted positively or negatively based on whether the outlier behaved in a manner more or less safe than its group. Further, unsafe driving events performed by the outlier that were the result of another vehicle's unsafe driving event may be ignored or positively accounted for in determining or adjusting the outlier's driver score.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle to enter a driveway. As an example, an instruction to pickup or drop off a passenger at a location may be received. It may be determined that the vehicle is arriving in a lane on an opposite side of a street as the location, the lane having a traffic direction opposite to a traffic direction of a lane adjacent to the location. A difficulty score to maneuver the vehicle to the lane adjacent to the location may be determined, and the difficulty score may be compared to a predetermined difficulty score. Based on the comparison, it may be determined to an available driveway on the same side of the street as the location. The vehicle may be controlled to enter the available driveway.

Abstract: A method may include obtaining, by an autonomous driving system, input information relating to an autonomous vehicle (AV). The method may include determining, by the autonomous driving system, one or more driving signals that describe operations of the AV. The method may include instructing the AV to move according to the driving signals and simulating, by a virtual driving system, movement of the AV based on the driving signals, wherein the simulating the movement of the AV occurs concurrently with the AV moving according to the driving signals as instructed.

Abstract: Disclosed are an autonomous driving apparatus and method for an ego vehicle that autonomously travels. The autonomous driving apparatus includes a first sensor to detect a nearby vehicle nearby an ego vehicle, a memory to store map information, and a processor including a driving trajectory generator to generate a first driving trajectory of the ego vehicle and a second driving trajectory of the nearby vehicle based on the map information stored in the memory and a control processor configured to control autonomous driving of the ego vehicle based on the first and second driving trajectories generated by the driving trajectory generator.

Abstract: Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.

Abstract: A method for safely determining a position information of a train on a track includes an on-board system determining appearance characteristics, current distances relative to the train and current angular positions relative to the train of passive trackside structures with a first sensor arrangement of a first localization stage of the on-board system. The on-board system stores a map data base in which georeferenced locations and appearance characteristics of the passive trackside structures are registered. A first position information about the train is derived from a comparison of determined current distances and current angular positions and the registered locations of allocated passive trackside structures by the first localization stage. A second position information about the train is derived from satellite signals determined by a second sensor arrangement of a second localization stage. The first and second position information undergo a data fusion resulting in a consolidated position information.

Abstract: A driving analysis server may be configured to receive vehicle operation data from mobile devices respectively disposed within the vehicles, and may use the data to group the vehicles into multiple groups. A driving pattern for each vehicle may be established and compared against a group driving pattern of its corresponding group to identify outliers. A driver score the outliers may be adjusted positively or negatively based on whether the outlier behaved in a manner more or less safe than its group. Further, unsafe driving events performed by the outlier that were the result of another vehicle's unsafe driving event may be ignored or positively accounted for in determining or adjusting the outlier's driver score.

Abstract: A trajectory for a vehicle can be generated using a lateral offset bias. The vehicle, such as an autonomous vehicle (AV), may be directed to follow reference trajectory for through an environment. The AV may determine a segment associated with the reference trajectory based on curvatures of the reference trajectory, determine a lateral offset bias associated with the segment based at least in part on, for example, one or more of a speed or acceleration of the vehicle, and determine a candidate trajectory for the autonomous vehicle based at least in part on the lateral offset bias. The candidate trajectory may then be used to control the autonomous vehicle.

Abstract: In one embodiment, a method includes accessing a plurality of content objects, generating a plurality of voxelized representations for the plurality of content objects, respectively, generating one or more building blocks based on one or more sparse convolutions, which includes determining one or more active sites for each of the plurality of content objects based on the voxelized representation of each of the plurality of content objects and applying the one or more sparse convolutions to the one or more active sites, and training a machine-learning model based on a convolutional network including the one or more building blocks.

Abstract: A virtual teaching system includes a semi-autonomous device to perform a task such as cleaning a target environment. The semi-autonomous device includes one or more sensors configured to record environmental data from the target environment that can be used to construct a virtual environment. The semi-autonomous device is operably coupled to an analysis system. The analysis system includes a processor to perform multiple functions, such as constructing the virtual environment from the recorded environmental data and supporting operation of a user interface. The user interface can be operably coupled to the processor, allowing a human operator to teach a virtual device in the virtual environment to perform an action sequence. Once the virtual device has been taught an action sequence in the virtual environment, the analysis system can transfer the recorded action sequence to the semi-autonomous device for use in the target environment.

Abstract: A system and corresponding method for autonomous driving of a vehicle are provided. The system comprises at least one neural network (NN) that generates at least one output for controlling the autonomous driving. The system further comprises a main data path that routes bulk sensor data to the at least one NN and a low-latency data path with reduced latency relative to the main data path. The low-latency data path routes limited sensor data to the at least one NN which, in turn, employs the limited sensor data to improve performance of the at least one NN's processing of the bulk sensor data for generating the at least one output. Improving performance of the at least one NN's processing of the bulk sensor data enables the system to, for example, identify a safety hazard sooner, enabling the autonomous driving to divert the vehicle and avoid contact with the safety hazard.

Abstract: The parking assist apparatus comprises a first vehicle stop apparatus operated for a purpose of stopping a vehicle, a controller configured to be capable of performing parking assist control for parking a vehicle in a parking position and to finish the parking assist control by performing control for instructing a driver of the vehicle to operate the first vehicle stop apparatus or by controlling the first vehicle stop apparatus, and a second vehicle stop apparatus operated for a purpose of stopping the vehicle. When it is determined while the parking assist control is being performed that there is a malfunction in an apparatus for detecting whether or not operation of the first vehicle stop apparatus has been performed, the controller is configured to finish the parking assist control by performing control for instructing the driver to operate the second vehicle stop apparatus or by controlling the second vehicle stop apparatus.

Abstract: A Light Detection And Ranging (LIDAR) apparatus includes a pulsed light source to emit optical signals, a detector array comprising single-photon detectors to output respective detection signals indicating times of arrival of a plurality of photons incident thereon, and processing circuitry to receive the respective detection signals.

Abstract: An autonomous driving vehicle (ADV) receives instructions for a human test driver to drive the ADV in manual mode and to collect a specified amount of driving data for one or more specified driving categories. As the user drivers the ADV in manual mode, driving data corresponding to the one or more driving categories is logged. A user interface of the ADV displays the one or more driving categories that the human driver is instructed collect data upon, and a progress indicator for each of these categories as the human driving progresses. The driving data is uploaded to a server for machine learning. If the server machine learning achieves a threshold grading amount of the uploaded data to variables of a dynamic self-driving model, then the server generates an ADV self-driving model, and distributes the model to one or more ADVs that are navigated in the self-driving mode.

Abstract: A device receives network constraints associated with a network connected to a vehicle device, data collection constraints, and vehicle device constraints. The device determines a first sampling rate, a first time period, a second sampling rate, and a second time period for collecting vehicle data based on the network constraints, the data collection constraints, and the vehicle device constraints, wherein the first sampling rate is different than the second sampling rate. The device receives first vehicle data, provided at the first sampling rate and for the first time period, and second vehicle data, provided at the second sampling rate and for the second time period, and processes the first and second vehicle data, with a machine learning model, to identify a driving behavior or a vehicle issue. The device performs one or more actions based on the driving behavior or the vehicle issue.

Abstract: A training system for training a trained model for use by a navigating robot to perform visual navigation includes memory including N base virtual training environments, each of the N base virtual training environments including a field of view at a location within an indoor space, where N is an integer greater than 1. A randomization module is configured to generate N varied virtual training environments based on the N base virtual training environments, respectively, by varying at least one characteristic of the respective N base virtual training environments. A training module is configured to train the trained model for use by the navigating robot to perform visual navigation based on a training set including: the N base virtual training environments; and the N varied virtual training environments.

Abstract: In one embodiment, a system perceives an environment surrounding an ADV including a vehicle in front of the ADV. The system determines whether the vehicle in front is slipping backwards based on the perception. The system determines whether the vehicle in front is situated on a road with a slope based on map information. The system determines whether a tail light or a brake light of the vehicle in front is turned on based on the perception. If it is determined that the vehicle in front is situated on a sloped road, is slipping backwards, and the tail light or the brake light is not turned on, the system calculates a time to impact or a distance to impact based on the distance and speed of the vehicle in front.

Abstract: An autonomous mobile device (AMD) moves through a physical space without human intervention. Data from sensors on the AMD are used to determine an occupancy map indicative of obstacles and their locations in the physical space. A navigation map is created from the occupancy map by inflating the apparent size of the obstacles indicated by the occupancy map. In one implementation the inflation process dynamically skips some portions of the occupancy map based on the presence of an obstacle and a designated inflation distance. The extent of the inflation is based on a size of the AMD. The navigation map is then used for path planning.

Abstract: A driving simulation system may perform simulations using one or more of a virtual driving simulator or a log-based driving simulator. A log-based simulator may replay data from logs to test whether a control algorithm is successfully capable of navigating a scenario. However, when such log-based simulations are invalid (e.g., based on the results of the simulations or evaluations of conditions of the simulation), a virtual simulation may be generated for the vehicle control system during which new log data may be captured. Such a virtual simulation may comprise sensor simulations and more sophisticated object control for ensuring convergence to a scenario to be tested. The new log data may be used for additional log-based simulations, thereby improving the durability and flexibility for testing and/or validation, while reducing the computational overhead of driving simulation scenarios.

Abstract: In one embodiment, an open space model is generated for a system to plan trajectories for an ADV in an open space. The system perceives an environment surrounding an ADV including one or more obstacles. The system determines a target function for the open space model based on constraints for the one or more obstacles and map information. The system iteratively, performs a first quadratic programming (QP) optimization on the target function based on a first trajectory while fixing a first set of variables, and performs a second QP optimization on the target function based on a result of the first QP optimization while fixing a second set of variables. The system generates a second trajectory based on results of the first and the second QP optimizations to control the ADV autonomously according to the second trajectory.

Abstract: A method performed by an apparatus is described. The method includes receiving remote data. The method also includes adapting model processing based on the remote data. The method further includes determining a driving decision based on the adapted model processing. In some examples, adapting model processing may include selecting a model, adjusting model scheduling, and/or adjusting a frame computation frequency.

Abstract: The present disclosure relates to a method, device, computer readable storage medium, and electronic device for planning velocity of a mobile apparatus. The method for planning velocity of a mobile apparatus provided by embodiments of the present disclosure comprises: acquiring a target weight coefficient according to a target linear velocity of the mobile apparatus; determining a motion central angle of the mobile apparatus according to a current pose, a target pose, and the target weight coefficient of the mobile apparatus; and calculating a target angular velocity of the mobile apparatus according to the motion central angle and the target linear velocity. The method for planning velocity of a mobile apparatus provided by embodiments of the present disclosure may realize real-time planning of velocity of the mobile apparatus to reach the target pose more accurately.

Abstract: Controller of a vehicle uses control functions to transition the current state of the vehicle into a target state. A control function is probabilistic to output a parametric probability distribution over the target state defined by a first moment and at least one higher order moment. The controller submits the current state into at least a subset of control functions consistent with the next driving decision to produce a subset of parametric probability distributions over the target state, combines the subset of parametric probability distributions to produce a joint parametric probability distribution of the target state, and determines the control command based on the first moment and at least one higher order moment of the joint parametric probability distribution of the target state.

Abstract: An autonomous vehicle, system and method of operating the autonomous vehicle. The system includes an episodic memory, a hyper-association module and a navigation system. The episodic memory stores a plurality of episodes, recalls a plurality of candidate episodes in response to receiving a partial prefix and recalls a hypothesis episode in response to receiving an intermediate episode. The hyper-association module receives the plurality of candidate episodes from the episodic memory and obtains the intermediate episode from the plurality of candidate episodes. The navigation system navigates the autonomous vehicle using the hypothesis episode.

Abstract: A method of operating a mobile robot includes generating a segmentation map defining respective regions of a surface based on occupancy data that is collected by a mobile robot responsive to navigation of the surface, identifying sub-regions of at least one of the respective regions as non-clutter and clutter areas, and computing a coverage pattern based on identification of the sub-regions. The coverage pattern indicates a sequence for navigation of the non-clutter and clutter areas, and is provided to the mobile robot. Responsive to the coverage pattern, the mobile robot sequentially navigates the non-clutter and clutter areas of the at least one of the respective regions of the surface in the sequence indicated by the coverage pattern. Related methods, computing devices, and computer program products are also discussed.

Abstract: A vehicle system in a host vehicle, comprising a vehicle transceiver located in the host vehicle and configured to receive from a remote server a selectable maneuver in response to data indicative of a driving path of one or more remote vehicles identifying one or more hazards. The system may include a processor in communication the vehicle transceiver and programmed to output one or more selectable maneuvers responsive to the data indicative of the driving path of one or more remote vehicles in response to the data indicative of the driving path identifies one or more hazards in a vicinity of the host vehicle, and a display in communication with the processor and configured to display the one or more selectable maneuvers.

Abstract: A mobile home robot is provided. The mobile home robot includes a storage configured to store in-home map data, a communication interface comprising communication interface circuitry, a camera, a user interface, and a processor configured to, based on device information of a plurality of Internet of things (IoT) devices installed in the home and an image captured through the camera while the mobile home robot moves around in the home, generate location information of each of the plurality of IoT devices, to map the generated location information with the map data, and in response to a user command being received through the user interface, to provide an IoT device location-based service based on the map data with which the location information is mapped.

Abstract: A driving analysis server may be configured to receive vehicle operation data from mobile devices respectively disposed within the vehicles, and may use the data to group the vehicles into multiple groups. A driving pattern for each vehicle may be established and compared against a group driving pattern of its corresponding group to identify outliers. A driver score the outliers may be adjusted positively or negatively based on whether the outlier behaved in a manner more or less safe than its group. Further, unsafe driving events performed by the outlier that were the result of another vehicle's unsafe driving event may be ignored or positively accounted for in determining or adjusting the outlier's driver score.

Abstract: The vehicle control device includes a target detection sensor and a control device that controls the operation of actuators. The target detection sensor can detect the relative speed of a target, positioned in a detectable area, with respect to the vehicle but cannot detect the relative speed when the target exists in a short distance area. The control device calculates the change amount of the relative speed per unit time. When the target enters the short-distance region, the control device calculates the estimated relative speed based on the elapsed time from the last acquisition time, which is the time when the relative speed of the target was last acquired from the target detection sensor, to the current time, the last acquired relative speed that is the relative speed of the target at the last acquisition time, and the time rate of change.

Abstract: In some examples, artificial intelligence based plantable blank spot detection may include generating a plurality of clusters of input images of areas that are to be analyzed for plantable blank spot detection. For each cluster of the plurality of clusters, a model may be identified to analyze corresponding images of a cluster. A model may be selected, from the models identified for the plurality of clusters, to analyze the input images. Canal lines may be identified in the analyzed images. Plantable blank spots may be determined in the analyzed images. An operation of a drone may be controlled to validate the determination of the plantable blank spots.

Abstract: Delivery of a modularly reconfigurable vehicle may include receiving a request for the modularly reconfigurable vehicle, including a desired configuration associated with the modularly reconfigurable vehicle and a location associated with the request.

Abstract: An operation authority management apparatus manages switching between automatic operation and manual operation of a plurality of actuators (a steering actuator, an accelerator actuator, and a brake actuator) of a subject vehicle. In the operation authority management apparatus, a subject vehicle behavior prediction unit predicts a behavior of the subject vehicle. A determination unit determines, for each individual actuator, whether or not to permit switching of the actuator during the automatic operation to the manual operation on the basis of the behavior of the subject vehicle predicted by the subject vehicle behavior prediction unit. A notification unit notifies a driver of the subject vehicle of a determination result made by the determination unit.

Abstract: Disposed within the body of a robotic device are an output element, accelerometer, gyroscope, and processor. The accelerometer sends a signal to the processor in response to detection of a vibration external to the body. The gyroscope sends a signal to the processor in response to a detection of movement of the body. The processor then sends an action signal to the output element based on the signals received from the accelerometer and gyroscope.

Abstract: Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.

Abstract: Method and system for managing synchronized movement of a set of vehicles. Based on a set of parameters and goals for a vehicle having an agent component, negotiating with one or more other vehicles to form a coalition of vehicles with a set of common parameter values and common goals. The method may: synchronize with the other vehicles in the coalition to result in a positioning of the vehicle in relation to the other vehicles and to synchronize time; combine the agent component of the vehicle with one or more agent components of the other vehicles in the coalition to form a distributed agent system; control the coalition of vehicles with common commands from the distributed agent system in response to feedback from sensors in each of the vehicles; and enable an override of the coalition by the vehicle agent component in response to a breach of safety parameters.