Abstract: An autonomous control system for a vehicle is provided. The control system comprises a first sensor to detect a left lane line and a right lane line of a traffic lane. The control system comprises a second sensor to detect an object located within or proximate to the traffic lane. The control system processes first image data captured by the first sensor to determine locations of the left lane line and the right lane line of the traffic lane. The system generates a first control signal based on the determined locations of the left lane line and the right lane line, wherein the first control signal causes the vehicle to follow a vehicle trajectory between the left lane line and the right lane line. The control system further processes second data captured by the second sensor to determine an object location and compute an object trajectory.

Abstract: A method for controlling an autonomous vehicle based on predicted behavior of one or more actors on a road comprises receiving input data comprising sensor data from one or more sensors of the autonomous vehicle about one or more actors on a road; generating an objective prediction based on the input data comprising a probability that a first actor will execute a driving operation; identifying one or more behavioral features associated with the driving operation; generating one or more behavioral feature predictions corresponding to the one or more behavioral features; providing the objective prediction and the one or more behavioral feature predictions to a motion planner; determining one or more responsive actions of the autonomous vehicle based on at least one of the objective prediction or the one or more behavioral feature predictions; and controlling motion of the autonomous vehicle based on the one or more responsive actions.

Abstract: A method for selecting a trajectory for a vehicle includes receiving first perception data from one or more sensors positioned on the vehicle at a first time; generating at least one nominal trajectory based on the first perception data; receiving second perception data from the one or more sensors at a second time after the first time; generating at least one fast-path trajectory based on the second perception data; selecting a trajectory from the at least one nominal trajectory and the at least one fast-path trajectory.

Abstract: An exemplary vehicle comprises: one or more sensors; one or more processors; and one or more computer readable media storing instructions that, when executed by the one or more processors, cause the vehicle to: operate in an autonomous driving mode, wherein an operation in the autonomous driving mode comprises performing one or more driving operations based on road and environmental conditions detected by the one or more sensors; detect, by the one or more sensors, a first visual fiducial; and in response to detecting the first visual fiducial, operate in a semi-autonomous follower mode, wherein an operation in the semi-autonomous follower mode comprises performing one or more driving operations based on road and environmental conditions detected by the one or more sensors and one or more behaviors of a guide vehicle.

Abstract: Systems and methods for operating a vehicle. The methods comprise: generating, by a computing device, a vehicle trajectory for the vehicle that is in motion; detecting an object within a given distance from the vehicle; generating, by the computing device, at least one possible object trajectory for the object which was detected; performing, by the computing device, a collision check to determine that there remains time to react safely to worst-case behavior by the object (the collision check being based on the vehicle trajectory and at least one possible object trajectory); and performing operations, by the computing device, to selectively cause the vehicle to perform an emergency maneuver based on results of the collision check.

Abstract: Systems and methods are described for measuring velocity of an object detected by a light detection and ranging (lidar) system. According to some aspects a method may include receiving a lidar dataset generated by the lidar system, transforming the lidar dataset into a first layer dataset and a second layer dataset, and converting the first layer dataset into a first image and the second layer dataset into a second image. The method may also include performing a feature detection operation that identifies at least one feature in the first image and the same feature in the second image, locating a first location of the feature in the first image and a second location of the feature in the second image, and generating a velocity estimate of the feature based on a difference between the first location and the second location and a difference between the different time intervals.

Abstract: Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

Abstract: Provided are systems, methods, and computer program products for controlling an autonomous vehicle (AV) to maneuver in a roadway, comprising acquiring, data associated with an actor detected on a route of the AV in the roadway for sensing a trajectory of the actor, predicting that the trajectory of the actor includes at least one characteristic that is associated with invoking a conditionally disallowed action in the AV, automatically restricting the conditionally disallowed action from a motion plan of the AV to prevent the AV from executing the conditionally disallowed action in response to detecting that one or more conditions are present in the roadway, issuing a command to control the AV on a candidate trajectory generated to prevent an option for the conditionally disallowed action.

Abstract: Systems and methods for controlling navigation of an autonomous vehicle for making an unprotected turn while traversing an intersection. The methods may include identifying a loiter pose of an autonomous vehicle for stopping at a point in an intersection before initiating an unprotected turn, initiating navigation of the autonomous vehicle to the loiter pose when a traffic signal is at a first state, determining whether the traffic signal has changed to a second state during or after navigation of the autonomous vehicle to the loiter pose, and in response to determining that the traffic signal has changed to the second state, generating a first trajectory for navigating the autonomous vehicle to execute the unprotected turn if the expected time for moving the autonomous vehicle from a current position to a position when the autonomous vehicle has fully exited an opposing conflict lane is less than a threshold time.

Abstract: Systems and methods for operating a vehicle are disclosed. The methods comprise: generating, by a computing device, a vehicle trajectory for the vehicle while the vehicle is in motion; detecting an object within a given distance from the vehicle; generating at least one possible object trajectory for the object which was detected; performing a collision check to determine whether a collision between the vehicle and the object can be avoided based on the vehicle trajectory and the at least one possible object trajectory; performing a plausibility check to determine whether the collision is plausible based on content of a map, when a determination is made in the collision check that a collision between the vehicle and the object cannot be avoided; and performing operations to selectively cause the vehicle to perform an emergency maneuver based on results of the plausibility check.

Abstract: Systems and methods are provided for navigating a vehicle to veer around a lane obstruction safely into a neighboring lane. The system may plan a trajectory around the obstructed lane. Over a temporal horizon, the system determines temporal margins by measuring an amount of time between a predicted state of a moving actor in the neighboring lane and a predicted state of the vehicle. The system identifies a minimum temporal margin of the temporal margins and determines whether the minimum temporal margin is equal to or larger than a required temporal buffer. If the minimum temporal margin is equal to or larger than the required temporal buffer, the system generates a motion control signal to cause the vehicle to follow the trajectory to veer around the obstruction into the neighboring lane. Otherwise, the system generates a motion control signal to cause the vehicle to reduce speed or stop.

Abstract: A method of determining a trajectory for an autonomous vehicle is disclosed. An ego-vehicle may detect a moving actor in an environment. To choose between candidate trajectories for the ego-vehicle, the system will consider the cost of each candidate trajectory to the moving actor. The system will use the candidate trajectory costs for the candidate trajectories to select one of the candidate trajectories via which to move the ego-vehicle. An autonomous vehicle system of the ego-vehicle may then move the ego-vehicle in the environment along the selected trajectory.

Abstract: Systems and methods are described for measuring velocity of an object detected by a light detection and ranging (lidar) system. According to some aspects a method may include receiving a lidar dataset generated by the lidar system, transforming the lidar dataset into a first layer dataset and a second layer dataset, and converting the first layer dataset into a first image and the second layer dataset into a second image. The method may also include performing a feature detection operation that identifies at least one feature in the first image and the same feature in the second image, locating a first location of the feature in the first image and a second location of the feature in the second image, and generating a velocity estimate of the feature based on a difference between the first location and the second location and a difference between the different time intervals.

Abstract: Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

Abstract: Systems and methods for controlling navigation of an autonomous vehicle through an intersection are disclosed. The methods include determining a loiter pose of an autonomous vehicle for stopping at a point within the intersection before initiating an unprotected turn for traversing the intersection. One or more distinct classes of trajectories are then identified, each of which is associated with multiple trajectories that take the same combination of discrete actions with respect to the loiter pose. A constraint set for each of the one or more distinct classes of trajectories is then be computed based on the loiter pose, and a candidate trajectory is determined for each of the one or more distinct classes based on the corresponding constraint set. A trajectory for the autonomous vehicle for executing the unprotected turn for traversing the intersection is selected from amongst the candidate trajectories.

Abstract: Systems and methods for controlling navigation of an autonomous vehicle for making an unprotected turn while traversing an intersection. The methods may include identifying a loiter pose of an autonomous vehicle for stopping at a point in an intersection before initiating an unprotected turn, initiating navigation of the autonomous vehicle to the loiter pose when a traffic signal is at a first state, determining whether the traffic signal has changed to a second state during or after navigation of the autonomous vehicle to the loiter pose, and in response to determining that the traffic signal has changed to the second state, generating a first trajectory for navigating the autonomous vehicle to execute the unprotected turn if the expected time for moving the autonomous vehicle from a current position to a position when the autonomous vehicle has fully exited an opposing conflict lane is less than a threshold time.

Abstract: Systems and methods are provided for navigating a vehicle to veer around a lane obstruction safely into a neighboring lane. The system may plan a trajectory around the obstructed lane. Over a temporal horizon, the system determines temporal margins by measuring an amount of time between a predicted state of a moving actor in the neighboring lane and a predicted state of the vehicle. The system identifies a minimum temporal margin of the temporal margins and determines whether the minimum temporal margin is equal to or larger than a required temporal buffer. If the minimum temporal margin is equal to or larger than the required temporal buffer, the system generates a motion control signal to cause the vehicle to follow the trajectory to veer around the obstruction into the neighboring lane. Otherwise, the system generates a motion control signal to cause the vehicle to reduce speed or stop.

Abstract: Systems and methods for operating a vehicle are disclosed. The methods comprise: generating, by a computing device, a vehicle trajectory for the vehicle while the vehicle is in motion; detecting an object within a given distance from the vehicle; generating at least one possible object trajectory for the object which was detected; performing a collision check to determine whether a collision between the vehicle and the object can be avoided based on the vehicle trajectory and the at least one possible object trajectory; performing a plausibility check to determine whether the collision is plausible based on content of a map, when a determination is made in the collision check that a collision between the vehicle and the object cannot be avoided; and performing operations to selectively cause the vehicle to perform an emergency maneuver based on results of the plausibility check.

Abstract: A method of determining a trajectory for an autonomous vehicle is disclosed. An ego-vehicle may detect a moving actor in an environment. To choose between candidate trajectories for the ego-vehicle, the system will consider the cost of each candidate trajectory to the moving actor. The system will use the candidate trajectory costs for the candidate trajectories to select one of the candidate trajectories via which to move the ego-vehicle. An autonomous vehicle system of the ego-vehicle may then move the ego-vehicle in the environment along the selected trajectory.

Abstract: Systems and methods for operating a vehicle. The methods comprise: generating, by a computing device, a vehicle trajectory for the vehicle that is in motion; detecting an object within a given distance from the vehicle; generating, by the computing device, at least one possible object trajectory for the object which was detected; using, by the computing device, the vehicle trajectory and the at least one possible object trajectory to determine whether there is an undesirable level of risk that a collision will occur between the vehicle and the object; modifying, by the computing device, the vehicle trajectory when a determination is made that there is an undesirable level of risk that the collision will occur; and automatically causing the vehicle to move according to the modified vehicle trajectory.