Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: A method and system for managing carrier vessel information are provided. A carrier path and shipment planning system receives carrier information associated with individual carrier vessels from a plurality of carrier information sources. Based on the carrier information the carrier path and shipment planning system processes the carrier information to form complete path scenarios. The carrier path and shipment planning system associates control rules to the complete path scenarios to determine one or more candidate paths. The carrier path and shipment planning system can then generate various interfaces for identifying selected, ranked paths, information associated with a competitive marketplace and availability information for contracting for cargo shipment. A user can then further manipulate the user interfaces for scenario planning or filtering available selections.

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 predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, we describe techniques for adjusting lateral clearance for a vehicle using a multi-dimensional envelope. A trajectory is generated for the vehicle. A multi-dimensional envelope is generated indicating a drivable region for the vehicle and containing the trajectory. One or more objects are identified located along or adjacent to the trajectory. At least one dimension of the generated multi-dimensional envelope is adjusted to adjust a lateral clearance between the vehicle and the identified one or more objects. A control module of the vehicle navigates the vehicle along the multi-dimensional envelope.

Abstract: A method and system for managing carrier vessel information are provided. A carrier path and shipment planning system receives carrier information associated with individual carrier vessels from a plurality of carrier information sources. Based on the carrier information the carrier path and shipment planning system processes the carrier information to form complete path scenarios. The carrier path and shipment planning system associates control rules to the complete path scenarios to determine one or more candidate paths. The carrier path and shipment planning system can then generate various interfaces for identifying selected, ranked paths, information associated with a competitive marketplace and availability information for contracting for cargo shipment. A user can then further manipulate the user interfaces for scenario planning or filtering available selections.

Abstract: Among other things, techniques are described for expressive vehicle systems. These techniques may include obtaining, with at least one processor, data associated with an environment, the environment comprising a vehicle and at least one object; determining an expressive maneuver including a deceleration of the vehicle such that the vehicle stops at least a first distance away from the at least one object and the vehicle reaches a peak deceleration when the vehicle is a second distance away from the at least one object; generating data associated with control of the vehicle based on the deceleration associated with the expressive maneuver; and transmitting the data associated with the control of the vehicle to cause the vehicle to decelerate based on the deceleration associated with the expressive maneuver.

Abstract: Among other things, techniques are described for expressive vehicle systems. These techniques may include obtaining, with at least one processor, data associated with an environment, the environment comprising a vehicle and at least one object; determining an expressive maneuver including a deceleration of the vehicle such that the vehicle stops at least a first distance away from the at least one object and the vehicle reaches a peak deceleration when the vehicle is a second distance away from the at least one object; generating data associated with control of the vehicle based on the deceleration associated with the expressive maneuver; and transmitting the data associated with the control of the vehicle to cause the vehicle to decelerate based on the deceleration associated with the expressive maneuver.

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, a world model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the world model.

Abstract: Enclosed are embodiments for scenario-based behavior specification and validation.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, a vehicle having autonomous driving capabilities is operated in a mixed driving mode.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: Among other things, a vehicle having autonomous driving capabilities is operated in a mixed driving mode.

Abstract: Among other things, a model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the model.

Abstract: Multiple trajectories for a vehicle are generated based on a road segment. Sensor data is received from at least one sensor. The vehicle is traveling the road segment in accordance with a first trajectory of the multiple trajectories. A potential collision is predicted between the vehicle and an object based on the sensor data and the first trajectory. A set of constraints is determined to avoid the potential collision. The set of constraints is determined based on the sensor data. A maneuver is determined for the vehicle by superimposing each constraint of the set of constraints on each other constraint of the set of constraints. The maneuver includes a second trajectory independent of the multiple trajectories. Instructions are transmitted to a control circuit of the vehicle to override the first trajectory and traverse the road segment according to the second trajectory to perform the maneuver.

Abstract: Enclosed are embodiments for a sampling-based maneuver realizer.

Abstract: Among other things, techniques are described for planning a route for an autonomous vehicle. As an example, a set of candidate constraints for a road segment to be traversed by a vehicle is obtained. A plurality of homotopies are determined, each including a different respective combination of the candidate constraints. For each homotopy, a first prediction of a motion of the vehicle is generated according to a first degree of precision, and a determination is made that the vehicle can traverse the road segment according to a subset of the homotopies. Further, a plurality of trajectories are determined according to the subset of the homotopies, including generating at least one second prediction of the motion of the vehicle according to a second degree of precision greater than the first degree of precision, and selecting one of the trajectories.