Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a vehicle includes: one or more onboard sensing devices, one or more onboard actuators, and a controller that, by a processor, obtains sensor data from the one or more sensing devices, identifies an assistance event based at least in part on the sensor data, determines an automated response to the assistance event based at least in part on the sensor data and an autonomous assistance policy, and autonomously operates the one or more actuators onboard the vehicle in accordance with the automated response. In exemplary embodiments, the autonomous assistance policy is determined using inverse reinforcement learning to optimize a reward function and mimic a remote human assistor.

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: In one embodiment, a method for controlling movement of an autonomous vehicle around a stationary vehicle includes obtaining data, via one or more sensors, pertaining to the stationary vehicle; making a plurality of initial determinations pertaining to the stationary vehicle, via a processor, based on the data; determining whether the stationary vehicle is double parked, via the processor, based on the plurality of initial determinations; and facilitating movement of the autonomous vehicle around the stationary vehicle, via instructions provided by the processor, if it is determined that the stationary vehicle is double parked.

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 vehicle includes: one or more onboard sensing devices, one or more onboard actuators, and a controller that, by a processor, obtains sensor data from the one or more sensing devices, identifies an assistance event based at least in part on the sensor data, determines an automated response to the assistance event based at least in part on the sensor data and an autonomous assistance policy, and autonomously operates the one or more actuators onboard the vehicle in accordance with the automated response. In exemplary embodiments, the autonomous assistance policy is determined using inverse reinforcement learning to optimize a reward function and mimic a remote human assistor.

Abstract: Systems and method are provided for assessing a target vehicle in proximity to a host vehicle. In one example, a method includes identifying the target vehicle in proximity to the host vehicle; obtaining sensor data, from one or more sensors onboard the host vehicle, pertaining to one or more characteristics of the target vehicle; and assessing a state of the target vehicle, via a processor, via a dynamic Bayesian network, using the sensor data as inputs for the dynamic Bayesian network.

Abstract: Systems and method are provided for assessing a target vehicle in proximity to a host vehicle. In one example, a method includes identifying the target vehicle in proximity to the host vehicle; obtaining sensor data, from one or more sensors onboard the host vehicle, pertaining to one or more characteristics of the target vehicle; and determining whether the target vehicle is an active traffic participant, via a processor, using the sensor data in connection with a mathematical classification system.