A method includes identifying mass distribution data of an autonomous vehicle (AV). The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes determining, based on the mass distribution data, one or more handling maneuver limits for the AV. The method further includes causing the AV to travel a route based on the one or more handling maneuver limits.

A system and method of path planning utilizing a clothoid curve to connect a start point and an end point is provided. The method includes receiving at a path planning system inputs including the start point and the end point of a desired path. The method further includes calculating clothoid parameters for a clothoid curve connecting the start point and the end point based the received start point, the received end point, and a closed-form expression, wherein the closed-form expression is generated based on a relationship between a plurality of unique center point values associated with a plurality of unique clothoid curves and the clothoid parameters associated with each of the plurality of unique clothoid curves. A clothoid curve is generated based on the determined clothoid parameters, wherein the generated clothoid curve represents a planned path from the start point to the end point.

Determining whether another entity is coordinating with an autonomous vehicle and/or to what extent the other entity's behavior is based on the autonomous vehicle may comprise determining a collaboration score and/or negotiation score based at least in part on sensor data. The collaboration score may indicate an extent to which the entity is collaborating with the autonomous vehicle to navigate (e.g., a likelihood that the entity is increasingly yielding the right of way to the autonomous vehicle based on the autonomous vehicle's actions). A negotiation score may indicate an extent to which behavior exhibited by the entity is based on actions of the autonomous vehicle (e.g., how well the autonomous vehicle and the entity are communicating with their actions).

A computer-implemented method of determining a series of control signals for controlling an autonomous vehicle to implement a planned speed change maneuver comprises: receiving from a maneuver planner a position target for the planned speed change maneuver; selecting, from a predetermined family of kinematic functions, a kinematic function for carrying out the planned speed change maneuver, each kinematic function being a first or higher order derivative of acceleration with respect to time; and using the selected kinematic function to determine a series of control signals for implementing the planned speed change maneuver; wherein the kinematic function is selected in a constrained optimization process as substantially optimizing a cost function defined for the speed change maneuver, subject to a set of hard constraints that: (i) require a final acceleration, speed and position corresponding to the selected kinematic function to satisfy, respectively, an acceleration target, a speed target and the position target, given an initial speed and acceleration of the autonomous vehicle, and (ii) impose a jerk magnitude upper limit on the selected kinematic function.

Enhancing situational awareness of an advanced driver assistance system in a host vehicle can be provided by acquiring, with an image sensor, an image data stream comprising a plurality of image frames. Analyzing A vision processor can analyze the image data stream to detect objects, shadows and/or lighting in the image frames. Recognizing A situation recognition engine can recognize at least one most probable traffic situation out of a set of predetermined traffic situations taking into account the detected objects, shadows and/or lighting. A processor can then control the host vehicle taking into account the at least one most probable traffic situation.

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

An autonomous vehicle may include an autonomous driving control apparatus including a processor that is configured to request remote control of the autonomous vehicle to a control system when the remote control of the autonomous vehicle is required, and when receiving a driving path stored during a previous remote control of the autonomous vehicle from the control system, follows and controls the received driving path.

The present invention relates to a method of cooperatively coordinating future driving maneuvers of a vehicle with fellow maneuvers of at least one fellow vehicle, wherein trajectories for the vehicle are rated with an effort value each, trajectories and fellow trajectories of the fellow vehicle are combined into tuples, the trajectory and the associated effort value of a collision-free tuple are selected as reference trajectory and reference effort value, trajectories with a lower effort value than the reference effort value are classified as demand trajectories, trajectories with higher effort value than the reference effort value are classified as alternative trajectories, and a data packet having a trajectory set consisting of the reference trajectory and the associated reference effort value as well as at least one trajectory from a group comprising the demand trajectories and the alternative trajectories as well as the respective effort values is transmitted to the fellow vehicle.

Systems and methods for vehicle motion planning based on uncertainty are provided. A method can include obtaining scene data descriptive of one or more objects within a surrounding environment of the autonomous vehicle. The method can include determining one or more subproblems based at least in part on the scene data. In some implementation, each of the one or more subproblems can correspond to at least one object within the surrounding environment of the autonomous vehicle. The method can include generating one or more branching policies based at least in part on the one or more subproblems. In some implementations, each of the one or more branching policies can include scene data associated with the autonomous vehicle and one or more objects within the surrounding environment of the autonomous vehicle. The method can include determining one or more costs associated each of the one or more branching policies. The method can include selecting a motion plan based at least in part on the one or more costs associated with each of the one or more branching policies. The method can include providing the motion plan for use in controlling a motion of the autonomous vehicle.

A vehicle control interface connects a vehicle platform including a first computer that performs travel control of a vehicle and an autonomous driving platform including a second computer that performs autonomous driving control of the vehicle. The vehicle control interface includes a control unit configured to execute: acquiring, from the second computer, a first control command including a plurality of commands for the vehicle platform; removing, from the first control command, a command that does not correspond to a predetermined kind of command by filtering the plurality of commands included in the first control command; converting the first control command, after filtering the plurality of commands, into a second control command for the first computer; and transmitting the second control command to the first computer.