Abstract: Systems and methods for generating a trajectory of a vehicle are disclosed. The methods include generating a spatial domain speed profile for a path represented as a sequence of poses of a vehicle between a first point and a second point. Generation of the spatial domain speed profile uses a longitudinal problem while excluding a derate interval on the path when speed of the vehicle cannot exceed a threshold. The methods further include generating a derate profile using the spatial domain speed profile and the derate interval, and transforming the derate interval to a temporal derate interval. The temporal derate interval may include time steps at which the speed of the vehicle cannot exceed the threshold while traversing the path, and may be used as an input to the longitudinal problem for generating a trajectory of the vehicle for navigating between the first point and the second point.

Abstract: Disclosed herein are systems, methods, and computer program products for vehicle path planning. The methods comprise: estimating a current state of a vehicle based on sensor data; generating a control error representing a difference between the estimated current state of the vehicle and a desired state of the vehicle as described by a previously published trajectory; comparing the control error to a threshold value; generating a first plan for the vehicle using an open-loop path planning approach when the control error is below the threshold value or a second plan for the vehicle using a closed-loop path planning approach when the control error is above the threshold value; and causing the vehicle to execute the first or second plan.

Abstract: Methods of refining a planned trajectory of an autonomous vehicle are disclose. For multiple cycles as the vehicle moves along the trajectory, the vehicle will perceive nearby objects. The vehicle will use the perceived object data to calculate a set of candidate updated trajectories. The motion planning system will measure a discrepancy between each candidate updated trajectory and the current trajectory by: (i) determining waypoints along each trajectory; (ii) determining distances between at least some of the waypoints; and (iii) using the distances to measure the discrepancy between the updated trajectory and the current trajectory. The system will use the discrepancy to select, from the set of candidate updated trajectories, a final updated trajectory for the vehicle to follow.

Abstract: Disclosed herein are systems, methods, and computer program products for vehicle path planning. The methods comprise: estimating a current state of a vehicle based on sensor data; generating a control error representing a difference between the estimated current state of the vehicle and a desired state of the vehicle as described by a previously published trajectory; comparing the control error to a threshold value; generating a first plan for the vehicle using an open-loop path planning approach when the control error is below the threshold value or a second plan for the vehicle using a closed-loop path planning approach when the control error is above the threshold value; and causing the vehicle to execute the first or second plan.

Abstract: Disclosed herein are systems, methods, and computer program products for generating a reference path for an autonomous vehicle. The methods comprising: receiving, by a computing device, a reference trajectory for the autonomous vehicle; generating, by the computing device, a first alternative trajectory for the autonomous vehicle based on an objective identified by the reference trajectory; comparing, by the computing device, the first alternative trajectory to the reference trajectory or a previously generated alternative trajectory; selecting, by the computing device, whether to generate a second alternative trajectory based on a result of said comparing; and generating, by the computing device, the second alternative trajectory for the autonomous vehicle in response to said selecting.

Abstract: Systems and methods for generating a trajectory of a vehicle are disclosed. The methods include generating a spatial domain speed profile for a path represented as a sequence of poses of a vehicle between a first point and a second point. Generation of the spatial domain speed profile uses a longitudinal problem while excluding a derate interval on the path when speed of the vehicle cannot exceed a threshold. The methods further include generating a derate profile using the spatial domain speed profile and the derate interval, and transforming the derate interval to a temporal derate interval. The temporal derate interval may include time steps at which the speed of the vehicle cannot exceed the threshold while traversing the path, and may be used as an input to the longitudinal problem for generating a trajectory of the vehicle for navigating between the first point and the second point.

Abstract: A system of linearizing a trajectory of an autonomous vehicle about a reference path includes a computing device and a computer-readable storage medium.

Abstract: Methods of refining a planned trajectory of an autonomous vehicle are disclose. For multiple cycles as the vehicle moves along the trajectory, the vehicle will perceive nearby objects. The vehicle will use the perceived object data to calculate a set of candidate updated trajectories. The motion planning system will measure a discrepancy between each candidate updated trajectory and the current trajectory by: (i) determining waypoints along each trajectory; (ii) determining distances between at least some of the waypoints; and (iii) using the distances to measure the discrepancy between the updated trajectory and the current trajectory. The system will use the discrepancy to select, from the set of candidate updated trajectories, a final updated trajectory for the vehicle to follow.

Abstract: Devices, systems, and methods are provided for counter-steering penalization. A device may analyze, by one or more processors, lane geometry associated with one or more lanes at a geographic location. The device may identify one or more corners in the lane geometry. The device may select one or more desired maximum steering angles of a steering wheel of an autonomous vehicle. The device may select weight values associated with the one or more desired maximum steering angles. The device may execute a path planning optimization based on the one or more desired maximum steering angles and the weight values.

Abstract: A system includes a computing device of an autonomous vehicle and a computer-readable storage medium that includes one or more programming instructions. The system identifies one or more lead objects located in front of the autonomous vehicle, and, for each of the one or more lead objects that are identified, determines an action type associated with the lead object which is used to generate a longitudinal plan for the autonomous vehicle.

Abstract: Systems and methods for determining one or more juke events are provided. The method includes generating, for an initial trajectory cycle, an initial planned trajectory of an autonomous vehicle, using a motion planning module, and generating, for a subsequent trajectory cycle, a subsequent planned trajectory of the autonomous vehicle, using the motion planning module. Each of the initial planned trajectory and the subsequent planned trajectory includes a series of planned steering wheel angles over a period of time and a steering wheel angle rate of change over the period of time. The method further includes identifying one or more first juke event qualifiers and one or more second juke event qualifiers, and identifying one or more juke events, wherein each juke event correlates to a time interval at which a first juke event qualifier and a second juke event qualifier occur within a threshold length of time from each other.

Abstract: At the start of a path planning cycle for an autonomous vehicle, the system identifies a current plan associated with the autonomous vehicle, a speed plan that defines one or more velocities over time for the autonomous vehicle during the path planning cycle, and a current state of the autonomous vehicle. The current plan includes a spatial plan that defines a proposed trajectory for the autonomous vehicle during the path planning cycle. The current state defines one or more dynamic states of the autonomous vehicle. The system generates a sequence of predicted states of the autonomous vehicle over a prediction horizon period, identifies a predicted state from the sequence that corresponds to a publishing time of an updated plan for the autonomous vehicle, generates the updated plan, and causes the autonomous vehicle to execute the updated plan.

Abstract: A system of linearizing a trajectory of an autonomous vehicle about a reference path includes a computing device and a computer-readable storage medium.

Abstract: An autonomous mode controller of an autonomous vehicle according to various rider-selectable comfort configurations. When the controller identifies a comfort configuration, it will access a configuration data set that corresponds to the selected comfort configuration. The controller also will receive sensor data from one or more autonomous driving sensors. In response to the received sensor data, the controller will generate an instruction for operation of a steering, braking, powertrain or other subsystem for operation of the autonomous vehicle. The instruction while includes values that correspond to the sensed data and to one or more parameters of the configuration data set. The subsystem will then cause the autonomous vehicle to move according to the instruction.

Abstract: An autonomous mode controller of an autonomous vehicle according to various rider-selectable comfort configurations. When the controller identifies a comfort configuration, it will access a configuration data set that corresponds to the selected comfort configuration. The controller also will receive sensor data from one or more autonomous driving sensors. In response to the received sensor data, the controller will generate an instruction for operation of a steering, braking, powertrain or other subsystem for operation of the autonomous vehicle. The instruction while includes values that correspond to the sensed data and to one or more parameters of the configuration data set. The subsystem will then cause the autonomous vehicle to move according to the instruction.