Abstract: Among other things, planning a motion of a machine having moving capabilities is based on strategic guidelines derived from various basic principles, such as laws, ethics, preferences, driving experiences, and road environments.

Abstract: Among other things, techniques are described for implementing occlusion representations over road features. In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include: obtaining area information of at least one area of interest for a vehicle, identifying occlusion data associated with the at least one area of interest, determining occlusion information associated with the at least one area of interest based on the area information and the occlusion data, providing the occlusion information for planning a route for the vehicle, and operating the vehicle in accordance with the planned route. The occlusion data includes data associated with the occlusion information, and the occlusion information has a smaller data size than the occlusion data.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: Among other things, techniques are described for determining precedence order at a multiway stop. In embodiments, identifications are assigned to tracks, and young tracks are compared to stale tracks. A young track matches a stale track based on one or more factors. An identification of the young track is reassigned to an identification of the stale track, wherein the young track is determined to match the stale track based on the one or more factors. An earliest time of appearance of agents is determined based on identifications and in view of perception obscured areas. A precedence order for navigating through the intersection is determined based on local rules, the identifications, and the earliest time of appearance of agents, and the vehicle proceeds through the multiway stop intersection in accordance with the precedence order.

Abstract: Among other things, we describe techniques for operation of a vehicle based on measured load characteristics and/or passenger comfort. One or more sensors of the vehicle can measure passenger data and/or load data of the vehicle. The passenger data and/or load data of the vehicle can be used by the vehicle to determine how to navigate within the surrounding environment.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: Among other things, planning a motion of a machine having moving capabilities is based on strategic guidelines derived from various basic principles, such as laws, ethics, preferences, driving experiences, and road environments.

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: Provided are methods for gridlock prevention, which can include obtaining sensor data, obtaining an intersection parameter, and determining a constraint. Some methods described also include generating trajectories and providing data associated with a selected trajectory, such as for operation of an autonomous vehicle along the trajectory. Systems and computer program products are also provided.

Abstract: Embodiments for operational envelope detection (OED) with situational assessment are disclosed. In some embodiments, a method comprises: determining at least one current or future constraint for a trajectory of a vehicle in an environment that is associated with the vehicle becoming immobile for an extended period of time; determining a stopping-reason for immobility of the vehicle based on determining the at least one current or future constraint for the trajectory of the vehicle in the environment; identifying a timeout threshold based on the stopping-reason, wherein the timeout threshold is an amount of time a planning system of the vehicle will wait before initiating at least one remedial action to address the immobility; identifying that the timeout threshold is satisfied; and initiating the at least one remedial action for the vehicle based on the identifying that the timeout threshold is satisfied.

Abstract: Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

Abstract: Among other things, techniques are described for implementing occlusion representations over road features. In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include: obtaining area information of at least one area of interest for a vehicle, identifying occlusion data associated with the at least one area of interest, determining occlusion information associated with the at least one area of interest based on the area information and the occlusion data, providing the occlusion information for planning a route for the vehicle, and operating the vehicle in accordance with the planned route. The occlusion data includes data associated with the occlusion information, and the occlusion information has a smaller data size than the occlusion data.

Abstract: Among other things, we describe techniques for operation of a vehicle based on measured load characteristics and/or passenger comfort. One or more sensors of the vehicle can measure passenger data and/or load data of the vehicle. The passenger data and/or load data of the vehicle can be used by the vehicle to determine how to navigate within the surrounding environment.

Abstract: Among other things, techniques are described for implementing occlusion representations over road features. In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include: obtaining area information of at least one area of interest for a vehicle, identifying occlusion data associated with the at least one area of interest, determining occlusion information associated with the at least one area of interest based on the area information and the occlusion data, providing the occlusion information for planning a route for the vehicle, and operating the vehicle in accordance with the planned route. The occlusion data includes data associated with the occlusion information, and the occlusion information has a smaller data size than the occlusion data.

Abstract: Among other things, planning a motion of a machine having moving capabilities is based on strategic guidelines derived from various basic principles, such as laws, ethics, preferences, driving experiences, and road environments.

Abstract: Among other things, techniques are described for determining precedence order at a multiway stop. In embodiments, identifications are assigned to tracks, and young tracks are compared to stale tracks. A young track matches a stale track based on one or more factors. An identification of the young track is reassigned to an identification of the stale track, wherein the young track is determined to match the stale track based on the one or more factors. An earliest time of appearance of agents is determined based on identifications and in view of perception obscured areas. A precedence order for navigating through the intersection is determined based on local rules, the identifications, and the earliest time of appearance of agents, and the vehicle proceeds through the multiway stop intersection in accordance with the precedence order.

Abstract: Provided are methods for predicting motion of hypothetical agents, which can include receiving sensor data, generating a segmentation mask indicative of at least one occluded area, generating at least one hypothetical agent trajectory, determining at least one agent generation point, determining whether a threshold distance from the at least one agent generation point to the vehicle is satisfied, generating at least one agent, planning a path of the vehicle and controlling the vehicle according to the planned path. Systems and computer program products are also provided.

Abstract: Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

Abstract: Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.