Patent Description:
Autonomous driving requires perception and prediction of the surrounding environment, including other actors on the road. This aids in decreasing the likelihood that an autonomous vehicle (AV) will collide with potential actors and objects along a trajectory of the AV. However, no perception or prediction system is perfect.

Issues that may happen in perception include not fully detecting objects due to a limited range or field of view (FOV) of the sensors, not fully detecting objects because the objects are occluded by other objects, oversegmentation and/or undersegmentation of objects. (i.e., incorrectly identifying one actor as multiple, or multiple separate actors as one actor), poor estimation of object position and derivatives (i.e., velocity, acceleration, etc.), and poor estimation of the bounding shape of an object, and incorrect classification of object type (e.g. classifying a cyclist as a pedestrian, etc.). Additionally, issues that may happen in prediction include forming an incorrect inference of actor intents or goals. (e.g. "Does a pedestrian intend to enter a crosswalk or remain on the sidewalk?", "Does a car intend to remain parked or proceed down the lane?", etc.) and incorrectly forecasting one or more actor trajectories (e.g. "Will the actor accelerate or maintain current speed?", "Which lane will the actor enter at the intersection?", etc.), among others.

These issues can be intermittent. For example, the perception model and/or the prediction model may be correct during one planning cycle but experience issues during another planning cycle. If nothing is done to account for these intermittent issues in motion planning, the AV will be indecisive.

In order to address this issue, some systems apply hysteresis in their decision making, causing the system to bias towards choosing the same action as was chosen in one or more previous planning cycles. However, hysteresis in decision making alone is not an ideal solution, since knowing which maneuver was selected on a previous cycle provides insufficient context to assess the utility of various maneuvers on subsequent cycles, or to plan trajectories for those maneuvers.

<CIT> discloses systems and methods that utilize multi-task machine-learned models for object intention determination in autonomous driving applications. A computing system receives sensor data obtained relative to an autonomous vehicle and maps data as-sociated with a surrounding geographic environment of the autonomous vehicle. The sensor data and map data are provided as input to a machine-learned intent model. The computing system receives a jointly determined prediction from the machine-learned intent model for multiple outputs including at least one detection output indicative of one or more objects detected within the surrounding environment of the autonomous vehicle, a first corresponding forecasting output descriptive of a trajectory indicative of an expected path of the one or more objects towards a goal location, and/or a second corresponding forecasting output descriptive of a discrete behavior intention determined from a predefined group of possible behavior intentions.

For at least these reasons, systems and methods for performing perception and prediction analysis on one or more objects while supplying relevant context is needed.

Advantageous embodiments are described in the dependent claims, the following description and the drawings. According to present invention, a method of operating an autonomous vehicle is provided. The method includes, by a perception system of the autonomous vehicle, detecting one or more objects in an environment of the autonomous vehicle. The method further includes, by a prediction system of the autonomous vehicle, predicting a first set of predicted object trajectories comprising one or more trajectories for each of the detected one or more objects, generating a plurality of candidate autonomous vehicle trajectories for the autonomous vehicle, scoring each of the candidate autonomous vehicle trajectories according to a cost function, using the scoring to select a final autonomous vehicle trajectory for execution, determining which of the predicted object trajectories affected the final autonomous vehicle trajectory and which did not do so, adding the predicted object trajectories that affected the final autonomous vehicle trajectory to a persisted prediction cache, and excluding from the persisted prediction cache any predicted object trajectories that did not affect the final autonomous vehicle trajectory. The generating includes applying to a motion planning model one or more of persisted predicted object trajectories that are stored in the persisted prediction cache, and it may also apply one or more of the predicted object trajectories of the first set to the model. The method further includes, by a motion planning system of the autonomous vehicle, executing the final autonomous vehicle trajectory to cause the autonomous vehicle to move along the final autonomous vehicle trajectory.

According to various embodiments, for each object in the one or more objects, the second set of persisted predicted object trajectories is only considered if no predictions for the object exist in the first set of predicted object trajectories.

According to various embodiments, excluding from the persisted prediction cache any predicted object trajectories that did not affect the final autonomous vehicle trajectory comprises adding all of the predicted object trajectories to the cache, and then removing from the cache any predicted object trajectories that did not affect the final autonomous vehicle trajectory.

According to various embodiments, excluding from the persisted prediction cache any predicted object trajectories that did not affect the final autonomous vehicle trajectory comprises adding to the cache only the predicted object trajectories that affected the final autonomous vehicle trajectory, and not any predicted object trajectories that did not affect the final autonomous vehicle trajectory.

According to various embodiments, the method further includes comparing an age of each persisted predicted object trajectory in the persisted prediction cache against an age limit.

According to various embodiments, the method further includes excluding each persisted predicted object trajectory in the persisted prediction cache that has an age greater than the age limit.

According to various embodiments, the method further includes determining an object type for each object in the one or more objects.

According to another aspect of the present invention, a system for operating an autonomous vehicle is provided. The system includes an autonomous vehicle, one or more sensors coupled to the autonomous vehicle configured to detect one or more objects in an environment of the autonomous vehicle; and a computing device coupled to the autonomous vehicle. The computing device includes a processor and memory. The memory includes instructions that, when executed by the processor, cause the processor to predict a first set of predicted object trajectories comprising one or more trajectories for each of the detected one or more objects, generate a plurality of candidate autonomous vehicle trajectories for the autonomous vehicle, score each of the candidate autonomous vehicle trajectories according to a cost function, use the scoring to select a final autonomous vehicle trajectory for execution, determine which of the predicted object trajectories affected the final autonomous vehicle trajectory and which did not do so, add the predicted object trajectories that affected the final autonomous vehicle trajectory to a persisted prediction cache, exclude from the persisted prediction cache any predicted object trajectories that did not affect the final autonomous vehicle trajectory, and, using a motion planning system of the autonomous vehicle, execute the final autonomous vehicle trajectory to cause the autonomous vehicle to move along the final autonomous vehicle trajectory.

According to various embodiments, the instructions, when executed by the processor, are further configured to cause the processor to compare an age of each persisted predicted object trajectory in the persisted prediction cache against an age limit.

According to various embodiments, the instructions, when executed by the processor, are further configured to cause the processor to exclude each persisted predicted object trajectory in the persisted prediction cache that has an age greater than the age limit.

According to various embodiments, the instructions, when executed by the processor, are further configured to cause the processor to determine an object type for each object in the one or more objects.

As used in this document, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. As used in this document, the term "comprising" means "including, but not limited to. " Definitions for additional terms that are relevant to this document are included at the end of this Detailed Description.

An "electronic device" or a "computing device" refers to a device that includes a processor and memory. Each device may have its own processor and/or memory, or the processor and/or memory may be shared with other devices as in a virtual machine or container arrangement. The memory will contain or receive programming instructions that, when executed by the processor, cause the electronic device to perform one or more operations according to the programming instructions.

The terms "memory," "memory device," "computer-readable storage medium," "data store," "data storage facility" and the like each refer to a non-transitory device on which computer-readable data, programming instructions or both are stored. Except where specifically stated otherwise, the terms "memory," "memory device," "computer-readable storage medium," "data store," "data storage facility" and the like are intended to include single device embodiments, embodiments in which multiple memory devices together or collectively store a set of data or instructions, as well as individual sectors within such devices.

The terms "processor" and "processing device" refer to a hardware component of an electronic device that is configured to execute programming instructions. Except where specifically stated otherwise, the singular term "processor" or "processing device" is intended to include both single-processing device embodiments and embodiments in which multiple processing devices together or collectively perform a process.

The term "module" refers to a set of computer-readable programming instructions, as executed by a processor, that cause the processor to perform a specified function.

The term "vehicle" refers to any moving form of conveyance that is capable of carrying either one or more human occupants and/or cargo and is powered by any form of energy. The term "vehicle" includes, but is not limited to, cars, trucks, vans, trains, autonomous vehicles, aircraft, aerial drones and the like. An "autonomous vehicle" (AV) is a vehicle having a processor, programming instructions and drivetrain components that are controllable by the processor without requiring a human operator. An AV may be fully autonomous in that it does not require a human operator for most or all driving conditions and functions, or it may be semi-autonomous in that a human operator may be required in certain conditions or for certain operations, or that a human operator may override the vehicle's autonomous system and may take control of the vehicle.

The term "actor" refers to a moving or moveable object that the AV detects in its environment. The term "actor" includes, but is not limited to, vehicles, pedestrians, cyclists, and/or other objects which can move into the AV's path.

When used in the context of AV motion planning, the term "trajectory" refers to the plan that the AV's motion planning system will generate, and which the AV's motion control system will follow when controlling the AV's motion. A trajectory includes the AV's planned position and orientation at multiple points in time over a time horizon, as well as the AV's planned steering wheel angle and angle rate over the same time horizon. An AV's motion control system will consume the trajectory and send commands to the AV's steering controller, brake controller, throttle controller and/or other motion control subsystem to move the AV along a planned path.

When used in the context of actor motion prediction, a "trajectory" of an actor that a vehicle's perception or prediction systems may generate refers to the predicted path that the actor will follow over a time horizon, along with the predicted speed of the actor and/or position of the actor along the path at various points along the time horizon.

In this document, when terms such as "first" and "second" are used to modify a noun, such use is simply intended to distinguish one item from another, and it is not intended to require a sequential order unless specifically stated. In addition, terms of relative position such as "vertical" and "horizontal", or "front" and "rear", when used, are intended to be relative to each other and need not be absolute, and only refer to one possible position of the device associated with those terms depending on the device's orientation.

Referring now to <FIG>, to illustrate some of the problems with traditional perception and prediction models, an autonomous vehicle (AV) <NUM> waiting to make an unprotected right turn onto a road <NUM>, and perceiving an oncoming vehicle <NUM> on the left using traditional perception and prediction models, is illustratively depicted over four timesteps (t0, t1, t2, and t3). As shown in <FIG>, the traditional perception and prediction models inaccurately perceive and predict the oncoming vehicle <NUM> over the four timesteps.

As shown in <FIG>, at timestep t0, the AV <NUM> is waiting to turn onto road <NUM> and the oncoming vehicle <NUM> is detected and, according to a predicted trajectory <NUM> and velocity <NUM>, is predicted to enter the AV's <NUM> desired lane on the road <NUM>. However, as shown in <FIG>, at timestep t1, the oncoming vehicle is oversegmented and incorrectly detected and perceived as multiple static objects <NUM>, <NUM>, <NUM> having no predicted trajectories or velocity.

As shown in <FIG>, at timestep t2, the oncoming vehicle <NUM> is again correctly detected, but the velocity <NUM> is underestimated because it is newly tracked, predicting a new trajectory <NUM>. According to this new predicted trajectory <NUM>, the oncoming vehicle <NUM> does not reach the AV's <NUM> lane during the AV's <NUM> turn, incorrectly predicting that the AV <NUM> would not collide with the oncoming vehicle <NUM> during the turn. As shown in <FIG>, at timestep t3, the velocity <NUM> of the oncoming vehicle <NUM> is once again correctly estimated with a new predicted trajectory <NUM>.

If, at timestep t0, the motion planning system of the AV <NUM> had decided to cause the AV <NUM> to remain stopped and wait for the oncoming vehicle <NUM> to clear the intersection, and if the motion planning system did nothing to mitigate the perception and prediction issues described above, the motion planning system might have decided to cause the AV <NUM> to proceed at timesteps t1 and t2, when the oncoming vehicle <NUM> was not detected, or its velocity was underestimated, then decided to cause the AV <NUM> to stop again at timestep t3. In some examples, the motion planning system of the AV <NUM> may have decided to bias towards stopping based on the decision at timestep t0, but, without context, the decision to remain stopped may be latched for too long, even after the oncoming vehicle <NUM> clears the intersection.

If, at timestep t1, the motion planning system of the AV <NUM> decided to proceed and aggressively accelerate ahead of the oncoming vehicle, the perception and prediction issues may have resulted in the AV <NUM> colliding with the oncoming vehicle <NUM>. For at least these reasons, systems and methods for performing perception and prediction analysis on one or more objects while supplying relevant context is needed.

Referring now to <FIG>, a system <NUM> for perceiving and predicting trajectories of one or more objects <NUM> using a persistent object prediction model is illustratively depicted, in accordance with various embodiments of the present invention.

It is common for the environment around an AV <NUM> to be complicated due to, for example, various types of lighting, objects, etc. For example, the environment around the AV <NUM> may include one or more of objects <NUM>. These objects <NUM> may be stationary or in motion and may be, or may become, in the path of one or more trajectories of the AV <NUM>.

As shown in <FIG>, multiple objects <NUM> are in the environment of the AV <NUM> and visible from the AV <NUM>. In order to determine a position and/or trajectory for each of these objects <NUM>, a perception and prediction module, including a perception system and a prediction system, of the AV <NUM> must analyze each of the objects <NUM>.

According to various embodiments, the system <NUM> includes a vehicle <NUM>. The vehicle <NUM> is traveling on a road <NUM>. It is noted, however, that any suitable path for the vehicle <NUM> may be implemented.

The perception and prediction module of the AV <NUM> may include one or more computing devices <NUM> configured to receive sensor data pertaining to each of the objects <NUM>. The sensor data is generated by one or more sensors <NUM>. The sensors <NUM> may include, for example, one or more image capturing devices (e.g., cameras), one or more RADAR systems, one or more LIDAR systems, and/or one or more other suitable sensor types. The computing device <NUM> may be in electronic communication with the one or more sensors <NUM>. The one or more sensors <NUM> may be positioned at various positions of the AV <NUM> such as, for example, the front, rear, and/or sides of the AV <NUM> and/or any other suitable position or positions. The sensors <NUM> may include one or more pairs of stereo cameras. According to various embodiments, the AV <NUM> may include a plurality of sensors <NUM> encircling the AV <NUM>.

The AV <NUM> may include a geographic location system configured to determine a location and orientation of the vehicle <NUM> and/or one or more of the objects <NUM>. The geographic location system may include a Global Positioning System device. It is noted, however, that other forms of geographic location may additionally, or alternatively, be used.

The vehicle <NUM> may further include a transceiver <NUM> configured to send and receive digital information from a remote server <NUM> via a wired and/or wireless connection such as, for example, through the cloud <NUM>, wherein the vehicle <NUM> and the remote server <NUM> are in electronic communication with each other. The computing device <NUM> may include a processor <NUM>. The processor <NUM> may be configured to receive, using the transceiver <NUM>, information pertaining to features of the environment at the location of the vehicle <NUM>, and use the information and the orientation of the vehicle <NUM> to identify the one or more objects <NUM>. It is noted that the processor <NUM> may be a standalone processor <NUM>, the vehicle's <NUM> processor <NUM>, and/or the remote server's <NUM> processor <NUM>. Data processed by the processor <NUM> may be data received from the vehicle <NUM>, received from the remote server <NUM>, and/or a combination of data received from the vehicle <NUM> and the remote server <NUM>. According to various embodiments, the computing device <NUM> may include one or more digital storage devices <NUM> and some or all of the digital information may be stored locally at the vehicle <NUM>.

Each of the sensors <NUM> is configured to sense and generate data pertaining to each of the objects <NUM>. The processor <NUM> is configured to analyze the sensor <NUM> data in order to detect each of the objects <NUM> and determine, for each object <NUM>, a type of object (e.g., vehicle, pedestrian, bicycle, and/or another other suitable type of object), whether the object <NUM> is in motion, and, if the object <NUM> is in motion, a velocity and trajectory of the object <NUM>.

The one or more computing devices <NUM> may include the perception and prediction module and AV <NUM> motion planning module.

Referring now to <FIG>, a flowchart of a method <NUM> for perceiving and predicting trajectories of one or more objects using a persistent object prediction model, is illustratively depicted, in accordance with various embodiments of the present invention.

According to various embodiments, the present method <NUM> aids in decreasing the effects of perception and prediction inaccuracies by selectively persisting predicted objects and predicted trajectories of the predicted objects.

According to various embodiments, the computing device of the AV includes a perception and prediction module, including a perception system and a prediction system, and a motion planning system. At <NUM>, one or more objects in an environment of the AV are detected by the perception system. The perception system analyzes data generated by one or more sensors. According to various embodiments, the one or more sensors are coupled to the AV. At <NUM>, the perception system determines an object type (e.g. vehicle, pedestrian, cyclist, etc.) for each object. Each of the predicted objects is provided to the motion planning system with a unique identifier that is consistent, cycle-to-cycle, and includes the predicted trajectories for the one or more objects. At <NUM>, the prediction system, using position and motion data from the sensors, predicts a first set of predicted object trajectories, comprising one or more predicted trajectories for each detected object.

At <NUM>, for each of a plurality of planning cycles, the motion planning system of the AV generates a plurality of possible candidate trajectories of the AV. In the generation at <NUM>, the motion planning model may take as input at least some of the predicted object trajectories in the first set as well as persisted predicted object trajectories that are stored in a second set in a persisted prediction cache. Methods by which a motion planning system may receive information (such as predicted trajectories of other objects near the AV) and use that information to generate and score trajectories are well known, for example as described in: (a) <CIT>, and (b) <NPL>). At <NUM>, each of the candidate AV trajectories is scored according to a cost function. Th cost function may take as input object projections and other context, such as measures of how close the AV and other object come to each other when following their respective trajectories, or how sudden the candidate trajectory causes the AV to jerk or decelerate. (In each case shorter distances, and higher levels of jerking or deceleration mean higher costs). Such methods, or other now or hereafter known methods for generating trajectories, may be used. According to various embodiments, persisted predictions for an object in the second set of predicted object trajectories are only considered if no predictions for the object exist in the first set of predicted object trajectories. Based on and using the scores of the candidate AV trajectories, a final AV trajectory, for execution, is selected, at <NUM>. The process above will repeat for multiple persisted predicted object trajectories that are stored in a second set in a persisted prediction cache.

At <NUM>, the first set of predicted object trajectories are compared against the final AV trajectory to determine which of the predicted object trajectories affect the final AV trajectory and which of the predicted object trajectories do not affect the final AV trajectory. At <NUM>, any objects and newly predicted object trajectories that are determined to affect the final AV trajectory are added to the persisted prediction cache.

According to various embodiments, other criteria may be considered when determining whether to cache an object in the persisted prediction cache. This criteria may include, for example, the proximity of the object to the AV, the proximity of the object to blind spots of the AV, whether or not the object is classified as a vulnerable road user (e.g., a pedestrian, a cyclist, etc.), and/or other suitable criteria. For example, the object may be a pedestrian who may or may not be entering a crosswalk.

According to various embodiments, criteria to determine if a predicted object trajectory affects the final AV trajectory may include, but is not limited to, determining if the predicted object trajectory affects the AV's decision of whether or not to perform a maneuver (e.g., proceeding through an unprotected intersection, proceeding through a crosswalk, performing a lane change, etc.), determining whether the AV took, has considered taking, or is considering taking a longitudinal action for the object (e.g., tracking behind the object), determining whether the AV took, has considered taking, or is considering taking lateral action for the object (e.g., veering around the object), and/or any other suitable criteria.

At <NUM>, any predicted objects and object trajectories that are determined not to affect the final AV trajectory are excluded from the persisted prediction cache. According to various embodiments, if an object is determined to have a predicted trajectory that affects the final AV trajectory, and a previous predicted trajectory for that object already exists in the persisted prediction cache, then the most recent predicted object trajectory for that object replaces the previous predicted object trajectory for that object in the persisted prediction cache.

According to some embodiments, excluding any predicted object trajectories that did not affect the final AV trajectory from the persisted prediction cache includes adding all of the predicted object trajectories to the cache, and then removing from the cache any predicted object trajectories that did not affect the final AV trajectory. According to other embodiments, excluding any predicted object trajectories that did not affect the final AV trajectory from the persisted prediction cache includes adding to the cache only the predicted object trajectories that affected the final AV trajectory, and not any predicted object trajectories that did not affect the final AV trajectory.

According to various embodiments, each predicted object trajectory in the persisted prediction cache has a timestamp indicating an age of the predicted object trajectory. At <NUM>, an age of each predicted object trajectory in the persisted prediction cache is compared against a configured age limit. According to various embodiments, the age limit is approximately <NUM> seconds or shorter. In some embodiments, the age limit is <NUM> seconds. It is noted, however, that other suitable age limit ranges may be used, while maintaining the spirit and functionality of the present invention. At <NUM>, if the age of a predicted object trajectory in the persisted prediction cache exceeds the configured age limit, the predicted object trajectory is excluded from the persisted prediction cache.

At <NUM>, the motion planning system of the AV executes the final AV trajectory, causing the AV to move along the final AV trajectory.

Referring now to <FIG>, an autonomous vehicle (AV) <NUM> waiting to make an unprotected right turn onto a road <NUM>, and perceiving an oncoming vehicle <NUM> on the left, using the systems and methods of <FIG>, <FIG>, and <FIG>, is illustratively depicted over four timesteps (t0, t1, t2, and t3).

As shown in <FIG>, at timestep t0, the AV <NUM> is waiting to turn onto road <NUM> and the oncoming vehicle <NUM> is detected and, according to a predicted trajectory <NUM> and velocity <NUM>, is predicted to enter the AV's <NUM> desired lane on the road <NUM>. As shown in <FIG>, at timestep t1, the oncoming vehicle <NUM> is oversegmented and incorrectly detected and perceived as multiple static objects <NUM>, <NUM>, <NUM> having no predicted trajectories or velocity. However, a persisted prediction <NUM>, including the predicted trajectory <NUM> and velocity <NUM>, from t0 is still considered by the AV <NUM>.

As shown in <FIG>, at timestep t2, the oncoming vehicle <NUM> is again correctly detected, but the velocity <NUM> is underestimated because it is newly tracked, predicting a new trajectory <NUM>. According to this new predicted trajectory <NUM>, the oncoming vehicle <NUM> does not reach the AV's <NUM> lane during the AV's <NUM> turn, incorrectly predicting that the AV <NUM> would not collide with the oncoming vehicle <NUM> during the turn. However, the persisted prediction <NUM>, including the predicted trajectory <NUM> and velocity <NUM>, from t0 is still considered by the AV <NUM>.

As shown in <FIG>, at timestep t3, the velocity <NUM> of the oncoming vehicle <NUM> is once again correctly estimated with a new predicted trajectory <NUM>. The persisted prediction <NUM> is nearly coincident with the timestep t3 observed oncoming vehicle <NUM>.

If, at timestep t0, the motion planning system of the AV <NUM> had decided to have the AV <NUM> remain stopped and wait for the oncoming vehicle <NUM> to clear the intersection, the prediction <NUM> for the oncoming vehicle <NUM> would be persisted. At timesteps t1 and t2, even though predictions for the oncoming vehicle <NUM> show that the oncoming vehicle <NUM> would not enter the AV's <NUM> lane (due to oversegmentation or underestimation of velocity), the persisted prediction <NUM> from time t0 is still considered, such that the motion planning system of the AV <NUM> decides to have the AV <NUM> continue waiting. At timestep t3, the persisted prediction <NUM> is nearly coincident with the observed object prediction, such that it has no adverse effect, and the AV <NUM> can clear the intersection as soon as the oncoming vehicle <NUM> clears.

Referring now to <FIG>, an illustration of an illustrative architecture for a computing device <NUM> is provided. The computing device <NUM> of <FIG> is the same as or similar to computing device <NUM>. As such, the discussion of computing device <NUM> is sufficient for understanding the computing device <NUM> of <FIG>.

Computing device <NUM> may include more or less components than those shown in <FIG>. However, the components shown are sufficient to disclose an illustrative solution implementing the present solution. The hardware architecture of <FIG> represents one implementation of a representative computing device configured to one or more methods and means for determining object trajectories, as described herein. As such, the computing device <NUM> of <FIG> implements at least a portion of the method(s) described herein.

Some or all components of the computing device <NUM> can be implemented as hardware, software and/or a combination of hardware and software. The hardware includes, but is not limited to, one or more electronic circuits. The electronic circuits can include, but are not limited to, passive components (e.g., resistors and capacitors) and/or active components (e.g., amplifiers and/or microprocessors). The passive and/or active components can be adapted to, arranged to and/or programmed to perform one or more of the methodologies, procedures, or functions described herein.

As shown in <FIG>, the computing device <NUM> comprises a user interface <NUM>, a processor such as a Central Processing Unit ("CPU") <NUM>, a system bus <NUM>, a memory <NUM> connected to and accessible by other portions of computing device <NUM> through system bus <NUM>, a system interface <NUM>, and hardware entities <NUM> connected to system bus <NUM>. The user interface can include input devices and output devices, which facilitate user-software interactions for controlling operations of the computing device <NUM>. The input devices include, but are not limited to, a physical and/or touch keyboard <NUM>. The input devices can be connected to the computing device <NUM> via a wired or wireless connection (e.g., a Bluetooth® connection). The output devices include, but are not limited to, a speaker <NUM>, a display <NUM>, and/or light emitting diodes <NUM>. System interface <NUM> is configured to facilitate wired or wireless communications to and from external devices (e.g., network nodes such as access points, etc.).

At least some of the hardware entities <NUM> perform actions involving access to and use of memory <NUM>, which can be a random access memory ("RAM"), a disk drive, flash memory, a compact disc read only memory ("CD-ROM") and/or another hardware device that is capable of storing instructions and data. Hardware entities <NUM> can include a disk drive unit <NUM> comprising a computer-readable storage medium <NUM> on which is stored one or more sets of instructions <NUM> (e.g., software code) configured to implement one or more of the methodologies, procedures, or functions described herein. The instructions <NUM> can also reside, completely or at least partially, within the memory <NUM> and/or within the CPU <NUM> during execution thereof by the computing device <NUM>. The memory <NUM> and the CPU <NUM> also can constitute machine-readable media.

The terms "memory", "computer-readable medium", and "machine-readable media", as used here, interchangeably refer to a single non-transitory memory devices or multiple such devices (e.g., one or more devices storing a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions <NUM>. The terms "memory", "computer-readable medium", and "machine-readable media", as used here, also refers to any medium that is capable of storing, encoding or carrying a set of instructions <NUM> for execution by the computing device <NUM> and that cause the computing device <NUM> to perform any one or more of the methodologies of the present invention. The term "processor" refers to one or more computer processor devices that collectively operate to perform a process.

<FIG> illustrates an example system architecture <NUM> for a vehicle, such as an autonomous vehicle (e.g., AV <NUM>, as shown in <FIG> and <NUM>, as shown in <FIG>). The vehicle may include an engine or motor <NUM> and various sensors for measuring various parameters of the vehicle and/or its environment. Operational parameter sensors that are common to multiple types of vehicles include, for example: a position sensor <NUM> such as an accelerometer, gyroscope and/or inertial measurement unit; a speed sensor <NUM>; and an odometer sensor <NUM>. The vehicle also may have a clock <NUM> that the system architecture <NUM> uses to determine vehicle time during operation. The clock <NUM> may be encoded into the vehicle on-board computing device <NUM>, it may be a separate device, or multiple clocks may be available.

The vehicle also may include various sensors that, together with a processor and programming instructions, serve as the object detection system that operates to gather information about the environment in which the vehicle is traveling. These sensors may include, for example: a location sensor <NUM> such as a global positioning system (GPS) device; object detection sensors such as one or more cameras <NUM>; a LiDAR sensor system <NUM>; and/or a radar and or and/or a sonar system <NUM>. The sensors also may include environmental sensors <NUM> such as a precipitation sensor and/or ambient temperature sensor. The object detection sensors may enable the vehicle to detect objects that are within a given distance or range of the vehicle in any direction, while the environmental sensors collect data about environmental conditions within the vehicle's area of travel. The system architecture <NUM> will also include one or more cameras <NUM> for capturing images of the environment. Any or all of these sensors will capture sensor data that will enable one or more processors of the vehicle's on-board computing device <NUM> (for example, computing device <NUM> and/or <NUM>) and/or external devices to execute programming instructions that enable the computing system to classify objects in the perception data, and all such sensors, processors and instructions may be considered to be the vehicle's perception system. The vehicle also may receive state information, descriptive information or other information about devices or objects in its environment from a communication device (such as a transceiver, a beacon and/or a smart phone) via one or more wireless communication links, such as those known as vehicle-to-vehicle, vehicle-to-object or other V2X communication links. The term "V2X" refers to a communication between a vehicle and any object that the vehicle that may encounter or affect in its environment.

During operations, information is communicated from the sensors to an on-board computing device <NUM>. The on-board computing device <NUM> analyzes the data captured by the sensors and optionally controls operations of the vehicle based on results of the analysis. For example, the on-board computing device <NUM> may control braking via a brake controller <NUM>; direction via a steering controller <NUM>; speed and acceleration via a throttle controller <NUM> (in a gas-powered vehicle) or a motor speed controller <NUM> (such as a current level controller in an electric vehicle); a differential gear controller <NUM> (in vehicles with transmissions); and/or other controllers such as an auxiliary device controller <NUM>. The on-board computing device <NUM> may include an autonomous vehicle navigation controller <NUM> configured to control the navigation of the vehicle through an intersection. In some embodiments, the intersection may include traffic signal lights. In some embodiments, an intersection may include a smart node. In some embodiments, the on-board computing device <NUM> may be configured to switch modes (augmented perception mode and non-augmented perception mode) based on whether Augmented Perception Data (APD) is available if the vehicle is in-range of an intersection.

Geographic location information may be communicated from the location sensor <NUM> to the on-board computing device <NUM>, which may then access a map of the environment that corresponds to the location information to determine known fixed features of the environment such as streets, buildings, stop signs and/or stop/go signals. Captured images from the cameras <NUM> and/or object detection information captured from sensors such as a LiDAR system <NUM> is communicated from those sensors) to the on-board computing device <NUM>. The object detection information and/or captured images may be processed by the on-board computing device <NUM> to detect objects in proximity to the vehicle. In addition or alternatively, the vehicle may transmit any of the data to a remote server system for processing. Any known or to be known technique for making an object detection based on sensor data and/or captured images can be used in the embodiments disclosed in this document.

Claim 1:
A method (<NUM>) of operating an autonomous vehicle (<NUM>, <NUM>), the method (<NUM>) comprising:
detecting (<NUM>) one or more objects (<NUM>) in an environment of an autonomous vehicle (<NUM>, <NUM>);
predicting (<NUM>) a first set of predicted object trajectories comprising one or more trajectories for each of the detected one or more objects (<NUM>);
using a motion planning model to generate (<NUM>) a plurality of candidate autonomous vehicle trajectories for the autonomous vehicle (<NUM>, <NUM>), wherein at least one predicted object trajectory in the first set from a persisted prediction cache as well as persisted predicted object trajectories that are stored in a second set in the persisted prediction cache are used as an input to the motion planning model for each of the candidate autonomous vehicle trajectories, wherein the persisted predictions for the object (<NUM>) in the second set of predicted object trajectories are only considered if no predictions for the object (<NUM>) exist in the first set of predicted object trajectories;
scoring (<NUM>) each of the candidate autonomous vehicle trajectories according to a cost function;
using the scoring to select (<NUM>) a final autonomous vehicle trajectory for execution;
determining (<NUM>) which of the predicted object trajectories affected the final autonomous vehicle trajectory and which did not do so;
adding (<NUM>) the predicted object trajectories that affected the final autonomous vehicle trajectory to the persisted prediction cache;
excluding (<NUM>) from the persisted prediction cache any predicted object trajectories that did not affect the final autonomous vehicle trajectory; and
using a motion planning system of the autonomous vehicle (<NUM>, <NUM>), executing (<NUM>) the final autonomous vehicle trajectory to cause the autonomous vehicle (<NUM>, <NUM>) to move along the final autonomous vehicle trajectory.