Abstract: Micro-authorization of remote assistance for an autonomous vehicle is described herein. A constraint that inhibits propulsion by a mechanical system of the autonomous vehicle is activated by a computing system of the autonomous vehicle, wherein a signal that identifies the activated constraint is transmitted from the autonomous vehicle to a remote computing system. The remote computing system generates instructions to deactivate the activated constraint. A return signal is transmitted from the remote computing system that specifies instructions to deactivate the constraint and a distance to desirably advance the autonomous vehicle. The computing system of the autonomous vehicle deactivates the constraint and the mechanical system is controlled to advance the autonomous vehicle when signal latency is less than a predetermined threshold duration of time.

Abstract: Micro-authorization of remote assistance for an autonomous vehicle is described herein. A constraint that inhibits propulsion by a mechanical system of the autonomous vehicle is activated by a computing system of the autonomous vehicle, wherein a signal that identifies the activated constraint is transmitted from the autonomous vehicle to a remote computing system. The remote computing system generates instructions to deactivate the activated constraint. A return signal is transmitted from the remote computing system that specifies instructions to deactivate the constraint and a distance to desirably advance the autonomous vehicle. The computing system of the autonomous vehicle deactivates the constraint and the mechanical system is controlled to advance the autonomous vehicle when signal latency is less than a predetermined threshold duration of time.

Abstract: An autonomous vehicle configured to perform a lane change maneuver is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, radar, sonar, infrared, and GPS. The autonomous vehicle additionally includes a computing system that executes instructions on a lane change detection system and a control system. The lane change detection system includes a region of interest module and an object trajectory module for determining whether the autonomous vehicle will collide with another object if the autonomous vehicle maneuvers into an adjacent lane. An instruction module of the lane change detection system is further used to facilitate operational control of a mechanical system of the autonomous vehicle, such as an engine or a steering system.

Abstract: An autonomous vehicle configured to perform a lane change maneuver is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, radar, sonar, infrared, and GPS. The autonomous vehicle additionally includes a computing system that executes instructions on a lane change detection system and a control system. The lane change detection system includes a region of interest module and an object trajectory module for determining whether the autonomous vehicle will collide with another object if the autonomous vehicle maneuvers into an adjacent lane. An instruction module of the lane change detection system is further used to facilitate operational control of a mechanical system of the autonomous vehicle, such as an engine or a steering system.

Abstract: Micro-authorization of remote assistance for an autonomous vehicle is described herein. A constraint that inhibits propulsion by a mechanical system of the autonomous vehicle is activated by a computing system of the autonomous vehicle, wherein a signal that identifies the activated constraint is transmitted from the autonomous vehicle to a remote computing system. The remote computing system generates instructions to deactivate the activated constraint. A return signal is transmitted from the remote computing system that specifies instructions to deactivate the constraint and a distance to desirably advance the autonomous vehicle. The computing system of the autonomous vehicle deactivates the constraint and the mechanical system is controlled to advance the autonomous vehicle when signal latency is less than a predetermined threshold duration of time.

Abstract: Micro-authorization of remote assistance for an autonomous vehicle is described herein. A constraint that inhibits propulsion by a mechanical system of the autonomous vehicle is activated by a computing system of the autonomous vehicle, wherein a signal that identifies the activated constraint is transmitted from the autonomous vehicle to a remote computing system. The remote computing system generates instructions to deactivate the activated constraint. A return signal is transmitted from the remote computing system that specifies instructions to deactivate the constraint and a distance to desirably advance the autonomous vehicle. The computing system of the autonomous vehicle deactivates the constraint and the mechanical system is controlled to advance the autonomous vehicle when signal latency is less than a predetermined threshold duration of time.

Abstract: Micro-authorization of remote assistance for an autonomous vehicle is described herein. A constraint that inhibits propulsion by a mechanical system of the autonomous vehicle is activated by a computing system of the autonomous vehicle, wherein a signal that identifies the activated constraint is transmitted from the autonomous vehicle to a remote computing system. The remote computing system generates instructions to deactivate the activated constraint. A return signal is transmitted from the remote computing system that specifies instructions to deactivate the constraint and a distance to desirably advance the autonomous vehicle. The computing system of the autonomous vehicle deactivates the constraint and the mechanical system is controlled to advance the autonomous vehicle when signal latency is less than a predetermined threshold duration of time.

Abstract: Systems and method are provided for controlling a vehicle. In various embodiments, a method of path planning for a vehicle includes receiving sensor data relating to an environment associated with the vehicle; defining a region of interest for the vehicle, based on the sensor data; defining a graph comprising a plurality of nodes, each of the plurality of nodes comprising a state of the vehicle and an associated cost, based on a cost function as applied to the state of the vehicle, at one of a plurality of points in time; and performing, via a processor, a search of the graph, based on the associated costs of each node of the graph, to determine a selected path for the vehicle through the region of interest that minimizes a total cost via the graph.

Abstract: An autonomous vehicle configured to perform a lane change maneuver is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, radar, sonar, infrared, and GPS. The autonomous vehicle additionally includes a computing system that executes instructions on a lane change detection system and a control system. The lane change detection system includes a region of interest module and an object trajectory module for determining whether the autonomous vehicle will collide with another object if the autonomous vehicle maneuvers into an adjacent lane. An instruction module of the lane change detection system is further used to facilitate operational control of a mechanical system of the autonomous vehicle, such as an engine or a steering system.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes calculating, via a router of a vehicle system that accesses road map data, at least one route to a destination based on the road map data, thereby producing route solution data. The vehicle system enters a remote assistance mode in response to remote assistance decision data received from a blockage arbiter of the vehicle system. In the remote assistance mode, the method includes determining, via the router, at least one road segment of the road map data that is permitted to be blacklisted, thereby producing permitted blacklist data. The method includes transmitting the permitted blacklist data and the route solution data, via a vehicle communications module of the vehicle system, to a remote vehicle assistance system. The method includes updating, via the router, the road map data to exclude at least one blacklisted road segment defined by the permitted blacklist data.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method of path planning includes receiving sensor data relating to an environment associated with a vehicle, and defining, with a processor, a region of interest and an intended path of the vehicle based on the sensor data. The method further includes determining a set of predicted object paths of one or more objects likely to intersect the region of interest; determining, with a processor, a first candidate path that minimizes a first cost function applied to a spatiotemporal decision-point graph constructed based on the predicted object paths; determining, with a processor, a second candidate path that minimizes a second cost function applied to a state lattice graph constructed based on the predicted object paths; and determining a selected path from the first and second candidate paths based on a set of selection criteria.

Abstract: Systems and method are provided for controlling a vehicle. In various embodiments, a method of path planning for a vehicle includes receiving sensor data relating to an environment associated with the vehicle; defining a region of interest for the vehicle, based on the sensor data; defining a graph comprising a plurality of nodes, each of the plurality of nodes comprising a state of the vehicle and an associated cost, based on a cost function as applied to the state of the vehicle, at one of a plurality of points in time; and performing, via a processor, a search of the graph, based on the associated costs of each node of the graph, to determine a selected path for the vehicle through the region of interest that minimizes a total cost via the graph.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes defining a region of interest and an intended path of the vehicle based on sensor data, and determining a set of predicted paths of one or more objects likely to intersect the region of interest within a planning horizon. The method further includes defining, within a spatiotemporal path space associated with the region of interest and the planning horizon, a set of obstacle regions corresponding to the set of predicted paths. Decision points for each of the obstacle regions are determined, and a directed graph is defined based on the plurality of decision points and a cost function applied to a set of path segments interconnecting the decision points. The directed graph is then searched to determine a selected path.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes calculating, via a router of a vehicle system that accesses road map data, at least one route to a destination based on the road map data, thereby producing route solution data. The vehicle system enters a remote assistance mode in response to remote assistance decision data received from a blockage arbiter of the vehicle system. In the remote assistance mode, the method includes determining, via the router, at least one road segment of the road map data that is permitted to be blacklisted, thereby producing permitted blacklist data. The method includes transmitting the permitted blacklist data and the route solution data, via a vehicle communications module of the vehicle system, to a remote vehicle assistance system. The method includes updating, via the router, the road map data to exclude at least one blacklisted road segment defined by the permitted blacklist data.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: identifying, by a processor, at least one keep clear zone having a beginning and an ending within a roadway; determining, by a processor, if a speed of the vehicle is expected to be below a threshold when a position of the vehicle is expected to be within the keep clear zone; creating, by a processor, a stop point associated with the keep clear zone based on the determining; generating, by a processor, advice to a path planner based on the stop point; generating, by a processor, a path plan based on the stop point; and controlling, by a processor, the vehicle based on the path plan.