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: 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: The technology involves evaluating components of an autonomous vehicle that can be meaningfully evaluated over a single operational iteration. One or more snapshots of single iteration scenarios can be tested quickly and efficiently, either on vehicle or via a back-end system. Each snapshot corresponds to a particular point in time when a given component runs. Each snapshot contains a serialized set of inputs necessary to evaluate the particular component. These inputs comprise the minimal amount of information needed to accurately and faithfully recreate what the component did or does. Each snapshot is triggered at the particular point in time based on one or more criteria associated with either a driving scenario or a component of the vehicle during autonomous driving. A serialized snapshot may be retrieved from storage and deserialized, so that the system may evaluate the state of the component at the particular point in time.

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: 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: 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: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

Abstract: The technology relates to assigning customer service requests for a fleet of autonomous vehicles providing trip services. For instance, a message having contents including information about a trip the vehicle is currently servicing may be received from a computing device of an autonomous vehicle of the fleet of vehicles. A request for customer service for the vehicle may be generated based on the contents of the message. A priority of the request may be determined based on the contents of the message. The request may be inserted into a queue based on the priority. The request may be assigned to a customer service representative based on a place of the request in the queue.

Abstract: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

Abstract: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

Abstract: Techniques are described herein for leveraging an online semantic processor to generate a finite state machine to be delivered to and implemented on a resource-constrained device. In various implementations, a user request may be received by a personal assistant module. Data indicative of the user request may be uploaded, e.g., by the personal assistant module, to an online semantic processor. Data may be received, e.g., by the personal assistant module, from the online semantic processor. The received data may represent a state machine having a plurality of candidate states of the personal assistant module. Each candidate state may be potentially responsive to the request. Resources local to the resource-constrained device may be analyzed to ascertain signal(s) unavailable to the online semantic processor. The personal assistant module may then transition to a given state of the plurality of candidate states. The given state may be selected based on the signal(s).

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for suggesting voice actions. The methods, systems, and apparatus include actions of receiving an utterance spoken by a user, wherein the utterance (i) includes a reference to an entity, and (ii) does not include a reference to any particular voice action. Additional actions include determining a set of voice actions that are characterized as appropriate to be performed in connection with the entity and determining a subset of the voice actions based at least on user profile data associated with the user. Further actions include prompting the user to select a voice action from among the voice actions of the subset and receiving data identifying a selected voice action. Additional actions include in response to receiving the data, generating a suggested voice command for performing the selected voice action in relation to the entity.