Abstract: Among other things, techniques are described for receiving, from at least one sensor of a vehicle, sensor data associated with a surface along a path to be traveled by a vehicle; using a surface classifier to determine a classification of the surface based on the sensor data; determining, based on the classification of the surface, drivability properties of the surface; planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface; and controlling the vehicle based on the planned behavior.

Abstract: Provided are methods for motion planner constraint generation based on road surface hazards, which can include receiving information about an object, identifying the object as a particular road hazard, generating one or more motion constraints based on the road hazard, and controlling a vehicle based on the motion constraints. Systems and computer program products are also provided.

Abstract: Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Abstract: Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Abstract: Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Abstract: Provided are methods for generating notifications indicative of unanticipated actions. Data associated with a current trajectory of an autonomous vehicle, at least one constraint, data associated with historical data for a user using the autonomous vehicle from an expectation database, and data associated with a context that represents a relationship between the current trajectory, at least one constraint, and historical driving data is received. A model is deployed to determine, in real-time and based on the current trajectory, the at least one constraint, and the data associated with the context that a particular action by the autonomous vehicle is classified as an unanticipated autonomous vehicle action. The current trajectory, the at least one constraint, and the data associated with the context are analyzed within a predetermined range of time including a timestamp of the unanticipated autonomous vehicle action to determine a reason for occurrence of the unanticipated autonomous vehicle action.

Abstract: Among other things, techniques are described for notifying and explaining the action performed by an autonomous vehicle, including but not limited to: receiving a planned path of a vehicle, a state of the vehicle and environment data of an environment in which the vehicle is operating, receiving a deviation signal, determining whether the deviation signal was reported by a first system or a second system of the vehicle, in response selecting a first set of simulators or a second set of simulators for simulating the vehicle in the environment, simulating the vehicle in the environment using the selected first or second set of simulators, based on results of the simulating, generating a message and presenting the message to at least one occupant of the vehicle.

Abstract: Among other things, techniques are described for driver data guided spatial planning. A spatial structure is generated comprising a plurality of nodes connected by edges. At least some of the nodes and edges represent a path to navigate a vehicle from a first point to a second point. Edges of the spatial structure are labeled as useful based on a distance metric. The spatial structure is pruned by removing one or more edges from the spatial structure according to a respective label of the edges, wherein an extent of the removal is based on a predetermined graph size, a predetermined performance, or any combinations thereof to obtain a pruned graph. A path from the first point to the second point on the pruned graph is identified and the vehicle is navigated in accordance with the path from the first point to the second point on the pruned graph.

Abstract: Among other things, techniques are described for receiving, from at least one sensor of a vehicle, a sensor measurement indicative of at least one of a sound or a vibration associated with a road element; identifying the road element based on a pattern in the sensor measurement; determining, based on the road element, a vehicle behavior for the vehicle; and controlling the vehicle to operate according to vehicle behavior.

Abstract: Among other things, techniques are described for receiving, from at least one sensor of a vehicle, sensor data associated with a surface along a path to be traveled by a vehicle; using a surface classifier to determine a classification of the surface based on the sensor data; determining, based on the classification of the surface, drivability properties of the surface; planning, based on the drivability properties of the surface, a behavior of the vehicle when driving near the surface or on the surface; and controlling the vehicle based on the planned behavior.

Abstract: Among other things, techniques are described for conditional motion predictions. The techniques include generating, by a planning circuit, a set of candidate trajectories for a vehicle based on possible macro actions by the vehicle, predicting, by the planning circuit and for at least some trajectories in the set of candidate trajectories, a response by a target vehicle to the respective trajectory and a probability of the response by the target vehicle, selecting, by the planning circuit, a trajectory from the set of candidate trajectories based at least in part on the response by the target vehicle to the trajectory, the probability of the response by the target vehicle, and characteristics of the trajectory, and operating, by a control circuit, the vehicle based on the selected trajectory.

Abstract: Among other things, techniques are described for conditional motion predictions. The techniques include generating, by a planning circuit, a set of candidate trajectories for a vehicle based on possible macro actions by the vehicle, predicting, by the planning circuit and for at least some trajectories in the set of candidate trajectories, a response by a target vehicle to the respective trajectory and a probability of the response by the target vehicle, selecting, by the planning circuit, a trajectory from the set of candidate trajectories based at least in part on the response by the target vehicle to the trajectory, the probability of the response by the target vehicle, and characteristics of the trajectory, and operating, by a control circuit, the vehicle based on the selected trajectory.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a trajectory for a vehicle, the trajectory associated with a predicted path to be traveled by the vehicle. A power optimization plan is generated for the vehicle based on the trajectory. One or more power management settings are modified based on the power optimization plan.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a trajectory for a vehicle, the trajectory associated with a predicted path to be traveled by the vehicle. A power optimization plan is generated for the vehicle based on the trajectory. One or more power management settings are modified based on the power optimization plan.

Abstract: A system that avoids dead-end states during a real-time heuristic search. While transitioning from a previous state to a current state, the system may perform lookahead to populate a state-space with potential states. The system may identify safe states using a safety predicate and only select potential states that are ancestors of safe states, providing a clear path to safety when needed. In addition, the system may determine a distance-to-safety function that indicates a number of state transitions between each potential state and a nearest safe state.

Abstract: A method for transferring a communication session between a source user device and a target user device in a communication network, where the communication session is established between the source user device and a server is proposed. An identifier of a user device is determined. A session modification request is then sent from the source user device to the server, where the request includes the identifier of the target user device. A capability of the target user device to handle the communication session is then checked. Next, if the target user device is capable of handling the communication session a further communication session between the server and the target user device is established.

Abstract: A first user device establishes a video telephony session with a second user device over a best effort network. The voice quality for the video telephony session is upgraded by establishing a voice call with the second user device over a telephony network providing a quality of service guarantee for the voice call, and coupling the voice call over the telephony network with the video telephony session.

Abstract: A method for transferring a communication session between a source user device and a target user device in a communication network, where the communication session is established between the source user device and a server is proposed. An identifier of a user device is determined. A session modification request is then sent from the source user device to the server, where the request includes the identifier of the target user device. A capability of the target user device to handle the communication session is then checked. Next, if the target user device is capable of handling the communication session a further communication session between the server and the target user device is established.

Abstract: A first user device establishes a video telephony session with a second user device over a best effort network. The voice quality for the video telephony session is upgraded by establishing a voice call with the second user device over a telephony network providing a quality of service guarantee for the voice call, and coupling the voice call over the telephony network with the video telephony session.