Ford Global Technolgoies, LLC

Argo Ai Gmbh

Alice Kassar

Ramadev Burigsay Hukkeri

Scott Julian Varnhagen

Ghassan Atmeh

Neal Andrew Seegmiller

Schuyler H Cohen

Colen McAlister

David Breeden

Johannes Strom

Pragati Satpute

Bryan Salesky

Timothy Andrew Potts, Jr

Jing Huang

Meal Seegmiller

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.

Counter-steering penalization during vehicle turns

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.

Methods and systems for performing inter-trajectory re-linearization about an evolving reference path for an autonomous vehicle

Disclosed herein are systems, methods, and computer program products for controlling acceleration of a vehicle. The methods comprising: detecting, by a computing device, a lateral distance from a point on a trajectory of the vehicle to a first object the vehicle is expected to pass when following the trajectory, the first object being located off of and to a side of the trajectory and the point representing a future location of the vehicle while passing the first object; selecting, by the computing device, whether acceleration limiting is to be performed by the vehicle based on the lateral distance; obtaining an amount by which the acceleration of the vehicle is to be limited, when a selection is made that acceleration limiting is to be performed by the vehicle; and causing, by the computing device, the vehicle to perform operations for autonomous driving with limiting of acceleration by the obtained amount.

Systems and methods for controlling longitudinal acceleration based on lateral objects

This document discloses system, method, and computer program product embodiments for operating a vehicle. For example, the method includes: obtaining a vehicle trajectory for the vehicle and a possible object trajectory for a moving object; analyzing the vehicle trajectory and the possible object trajectory to identify a time interval in which a footprint of the vehicle overlaps laterally with a footprint of the object; generating a mover raw margin in the time interval by shifting the footprint of the vehicle laterally until contact is made with the footprint of the object; obtaining a crossing time associated with each point in the mover raw margin; and generating a mover strong boundary by laterally shifting point(s) of the mover raw margin in the direction towards the vehicle when the crossing time(s) associated with the point(s) is(are) equal to or less than a threshold time.

Systems and methods for deweighting veering margins based on crossing time

Disclosed herein are systems, methods, and computer program products for controlling acceleration of a vehicle. The methods comprising: detecting a lateral distance from a point on a trajectory of the vehicle to an object the vehicle is expected to pass when following the trajectory, the object being located off of and to a side of the trajectory and the point representing a future location of the vehicle while passing the object; comparing the lateral distance to a threshold value; selecting whether acceleration limiting is to be performed by the vehicle based on whether the lateral distance is less than the threshold value; and causing the vehicle to perform operations for autonomous driving with or without acceleration limiting based on said selecting.

This document discloses system, method, and computer program product embodiments for operating a vehicle. For example, the method includes: obtaining a vehicle trajectory and a location of an object in an environment; generating a margin comprising a first vehicle trajectory indicating a series of locations of the vehicle while contacting the object, a boundary of a drivable area or lane mark during a time interval in which the vehicle will travel past the object; generating a reachable boundary using a condition of the vehicle, a longitudinal speed profile and steering limits of the vehicle; comparing the reachable boundary and the margin; and concluding that the vehicle should steer while stopped when the reachable boundary is at any point within a threshold distance from the margin or that the vehicle should not steer while stopped when the reachable boundary is at no point within the threshold distance from the margin.

Systems and methods for determining steer while stopped behavior for a vehicle using dynamic limits

Disclosed herein are systems, methods, and computer program products for biasing a trajectory of a vehicle. The methods comprise: identifying a lane biasing interval of a lane in which the vehicle is to travel based on a lane width; obtaining a first reference path of travel for the vehicle that passes through the lane biasing interval; generating a second reference path of travel for the vehicle using a plurality of lateral offsets and the first reference path of travel for the vehicle; and adjusting the trajectory of the vehicle using the second reference path of travel. The lateral offsets are based on a lateral offset from each index point of the first reference path that resides in the lane biasing interval.

Systems and methods for biasing a trajectory of an autonomous vehicle while moving in a lane

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.

Systems and methods for controlling speed of an autonomous vehicle

Disclosed herein are systems, methods, and computer program products for controlling acceleration of a vehicle. The methods comprising: detecting a lateral distance from a point on a trajectory of the vehicle to a first object the vehicle is expected to pass when following the trajectory; selecting whether acceleration limiting is to be performed by the vehicle based on the lateral distance; obtaining a margin of the vehicle defined by a sequence of points; obtaining an amount by which the acceleration of the vehicle is to be limited based on the trajectory and the margin of the vehicle, when a selection is made that acceleration limiting is to be performed by the vehicle; and causing the vehicle to perform operations for autonomous driving with limiting of acceleration by the obtained amount.

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.

Trajectory consistency measurement for autonomous vehicle operation

A system of linearizing a trajectory of an autonomous vehicle about a reference path includes a computing device and a computer-readable storage medium. The computer-readable storage medium includes one or more programming instructions that, when executed, cause the computing device to receive a reference path for an autonomous vehicle, where the reference path defines a proposed trajectory for the autonomous vehicle in a Cartesian reference frame, identify an objective based on the received reference path, where the objective comprises a longitudinal component and a lateral component, project the objective into a curvilinear coordinate frame described by the received reference path, decouple the longitudinal component and the lateral component, linearize the lateral component about the reference path, generate a new reference path for the autonomous vehicle by fusing the linearized longitudinal component and the linearized lateral component, and map the new reference path back to the Cartesian reference frame.

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.

Rider selectable ride comfort system for autonomous vehicle

A computer is programmed to predict, based at least in part on a stored road topology for a predetermined vehicle route, a vehicle body orientation based on lateral, longitudinal, and vertical accelerations predicted for the route. The computer is programmed to, based on the predicted vehicle body orientation, adjust at least one of an orientation of the vehicle body and an orientation of an object in the vehicle as the vehicle traverses the route.

Growing community of inventors

Detroit, MI, United States of America

Alice Kassar