Abstract: A trajectory planning system for an autonomous vehicle includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle. The one or more controllers determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver. The one or more controllers select a trajectory from the plurality of relative state trajectories for the autonomous vehicle, where the autonomous vehicle follows the trajectory while performing the maneuver.

Abstract: A method for predicting an actor occupancy corridor includes receiving input data, predicting an occupancy sets of the actor using reachability analysis and the input data, determining an occupancy corridor constraints of the actor using a machine learning model and the input data, and determining an occupancy corridor of the actor using the occupancy corridor constraints of the actor using the machine learning model and the occupancy sets of the actor using a reachability analysis. Moreover, the method includes controlling the movement of a host vehicle based on the occupancy corridor of the actor.

Abstract: A system for a vehicle includes sensors that sense surroundings of the vehicle. A perception module generates a map of the surroundings of the vehicle. The map comprises objects around the vehicle. An occlusion computing module computes occluded regions in the map. The occluded regions are regions around the vehicle that are occluded by one or more of the objects around the vehicle. A filtering module filters none, or one or more of the occluded regions from the map. The map comprises a plurality of filtered occluded regions after the filtering. A scoring module scores the filtered occluded regions based on importance of the filtered occluded regions to a trajectory of the vehicle. A motion planning module modifies the trajectory of the vehicle based on importance scores of the filtered occluded regions. A propulsion module propels the vehicle according to the modified trajectory.

Abstract: A system and method for trajectory planning selects a trajectory from a plurality of candidate trajectories to direct an autonomous vehicle. The system includes a controller in communication with a global positioning system (GPS) and at least one sensor. The GPS receives a plurality of global scene information. The at least one sensor collects local scene information. The controller is programmed to receive global scene information and local scene information, identify the location of the occluding obstacle. Additionally, the controller is programmed to determine an occluded portion of the roadway, identify a location of the scene actors on the roadway, and calculate a contracted occluded portion within the occluded portion. Furthermore, the system generates candidate trajectories for the autonomous vehicle to travel along, score the candidate trajectories, and select one of the candidate trajectories based on the assigned score.

Abstract: A vehicle trajectory planning system includes a perception system of a host vehicle collecting information from multiple sources and communicating with a computer. A fusion module fuses scene information from a map and perception items identified by the perception system. A behavior planning module receives an output of the fusion module and produces a host vehicle baseline trajectory. A trajectory and motion planning module receives an output of the fusion module in parallel with the behavior planning module. The trajectory and motion planning module determines a reference trajectory and operation corridor for a host vehicle. A disturbance and reachability refiner module receives an output of the trajectory and motion planning module including the reference trajectory and operation corridor. An algorithm is applied to adjust and re-plan the host vehicle baseline trajectory to be robust to a range of inclement weather conditions.

Abstract: A system and method for trajectory planning selects a trajectory from a plurality of candidate trajectories to direct an autonomous vehicle. The system includes a controller in communication with a global positioning system (GPS) and at least one sensor. The GPS receives a plurality of global scene information. The at least one sensor collects local scene information. The controller is programmed to receive global scene information and local scene information, identify the location of the occluding obstacle. Additionally, the controller is programmed to determine an occluded portion of the roadway, identify a location of the scene actors on the roadway, and calculate a contracted occluded portion within the occluded portion. Furthermore, the system generates candidate trajectories for the autonomous vehicle to travel along, score the candidate trajectories, and select one of the candidate trajectories based on the assigned score.

Abstract: A method of adaptively tuning parameters in action planning for automated driving of a vehicle to a destination is provided. The method comprises receiving sensor data from a vehicle sensor, lane data of a road plan to the destination, and a plurality of first hyperparameters in a reinforcement learning agent at an initial state and an initial timestamp. The reinforcement learning agent has a planning policy. The method comprises adjusting the plurality of first hyperparameters via the planning policy having at least one first activation function to define an output defining a plurality of second hyperparameters. The method comprises determining a baseline trajectory action based on a trajectory reward value at a final state and a final timestamp. The method comprises modifying the baseline trajectory action between the initial state and the final state to define a refined trajectory action and controlling the vehicle based on the refined trajectory action.

Abstract: A vehicle trajectory planning system includes a perception system of a host vehicle collecting information from multiple sources and communicating with a computer. A fusion module fuses scene information from a map and perception items identified by the perception system. A behavior planning module receives an output of the fusion module and produces a host vehicle baseline trajectory. A trajectory and motion planning module receives an output of the fusion module in parallel with the behavior planning module. The trajectory and motion planning module determines a reference trajectory and operation corridor for a host vehicle. A disturbance and reachability refiner module receives an output of the trajectory and motion planning module including the reference trajectory and operation corridor. An algorithm is applied to adjust and re-plan the host vehicle baseline trajectory to be robust to a range of inclement weather conditions.

Abstract: A system for controlling an operation of a vehicle to rendezvous with a target over a finite time horizon, wherein the vehicle and the target form a multi-object celestial system. A processor to formulate passive unsafe regions as passive safety constraints. The passive unsafe regions represents regions of space around the target guaranteeing collision trajectories with the target, in an event of total thruster failure. Update a controller having a model of dynamics of the vehicle with received data, and subject the updated controller to the passive safety constraints to generate control commands that produce a collision free rendezvous trajectory which avoids unsafe regions for the specified time period, guaranteeing a collision free trajectory with respect to the target in the event of the total vehicle thruster failure, so the vehicle does not collide with the target. Output the control commands to activate or not activate thrusters of the vehicle.

Abstract: A trajectory planning system for an autonomous vehicle includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle. The one or more controllers determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver. The one or more controllers select a trajectory from the plurality of relative state trajectories for the autonomous vehicle, where the autonomous vehicle follows the trajectory while performing the maneuver.

Abstract: A trajectory planning system for an autonomous vehicle includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle. The one or more controllers approximate a set of real-time ego states of the autonomous vehicle by a function approximator, where the function approximator has been trained during a supervised learning process with the set of offline ego states as a ground-truth dataset. The one or more controllers compute a plurality of relative state trajectories for the autonomous vehicle, where the plurality of relative state trajectories avoid intersecting the set of real-time ego states of autonomous vehicle. The one or more controllers select a trajectory from the plurality of relative state trajectories for the autonomous vehicle, where the autonomous vehicle follows the trajectory while performing the maneuver.

Abstract: Drift-based rendezvous control system for controlling an operation of a spacecraft to rendezvous the spacecraft to a goal region over a finite time (FT) horizon. The system including accepting data including values of spacecraft states at a specified time period within the FT horizon. A processor at the specified time period selects a set of drift regions corresponding to a desired goal region at a location on an orbit where the target is located at the specified time period. Update a controller having a model of dynamics of the spacecraft with the accepted data. Formulate the set of drift regions as a penalty in a cost function of the updated controller. Generate control commands resulting in a real-time drift-based control policy where upon entering the drift region, the thrusters are turned off in order to minimize an amount of operation of the thrusters while rendezvousing with the desired goal region.

Abstract: Systems and methods controlling an operation of a vehicle in real time to rendezvous the vehicle with a target over a finite time horizon having multiple specified time periods. Select a set of unsafe regions from stored unsafe regions, the set of unsafe regions represents regions of space around the target in which any operation of the PSNO thrusters does not avoid collision with the target, guaranteeing collision trajectories with the target. Formulating the set of unsafe regions as safety constraints, and updating a controller having a model of dynamics of the vehicle with the accepted data. Generating control commands by subjecting the updated controller to the safety constraints to produce a rendezvous trajectory that avoids the set of unsafe regions, guaranteeing an operation of at least the PSNO thrusters, in the event of partial vehicle thruster failure results in a trajectory that does not collide with the target.

Abstract: Drift-based rendezvous control system for controlling an operation of a spacecraft to rendezvous the spacecraft to a goal region over a finite time (FT) horizon. The system including accepting data including values of spacecraft states at a specified time period within the FT horizon. A processor at the specified time period selects a set of drift regions corresponding to a desired goal region at a location on an orbit where the target is located at the specified time period. Update a controller having a model of dynamics of the spacecraft with the accepted data. Formulate the set of drift regions as a penalty in a cost function of the updated controller. Generate control commands resulting in a real-time drift-based control policy where upon entering the drift region, the thrusters are turned off in order to minimize an amount of operation of the thrusters while rendezvousing with the desired goal region.

Abstract: Systems and methods controlling an operation of a vehicle in real time to rendezvous the vehicle with a target over a finite time horizon having multiple specified time periods. Select a set of unsafe regions from stored unsafe regions, the set of unsafe regions represents regions of space around the target in which any operation of the PSNO thrusters does not avoid collision with the target, guaranteeing collision trajectories with the target. Formulating the set of unsafe regions as safety constraints, and updating a controller having a model of dynamics of the vehicle with the accepted data. Generating control commands by subjecting the updated controller to the safety constraints to produce a rendezvous trajectory that avoids the set of unsafe regions, guaranteeing an operation of at least the PSNO thrusters, in the event of partial vehicle thruster failure results in a trajectory that does not collide with the target.

Abstract: A system for controlling an operation of a vehicle to rendezvous with a target over a finite time horizon, wherein the vehicle and the target form a multi-object celestial system. A processor to formulate passive unsafe regions as passive safety constraints. The passive unsafe regions represents regions of space around the target guaranteeing collision trajectories with the target, in an event of total thruster failure. Update a controller having a model of dynamics of the vehicle with received data, and subject the updated controller to the passive safety constraints to generate control commands that produce a collision free rendezvous trajectory which avoids unsafe regions for the specified time period, guaranteeing a collision free trajectory with respect to the target in the event of the total vehicle thruster failure, so the vehicle does not collide with the target. Output the control commands to activate or not activate thrusters of the vehicle.