Patent Description:
The use of autonomous devices that work closely with humans (e.g., robots, autonomous vehicles, etc.) is increasing.

Autonomous devices may work as autonomous agents which can make decisions independently, and move around in many cases in the presence of other agents, objects and humans. Autonomous devices may use Artificial Intelligence (AI) algorithms to perform different tasks, such as path planning, object recognition, localization, etc. Research on safety issues that originate from deployment of autonomous AI-based agents has become a hot-topic due to the improving abilities of such systems. Different safety standards are provided for this purpose, such as International Standards Organization (ISO) <NUM> for vehicles, ISO <NUM>-<NUM>, ISO <NUM>-<NUM> for robots and ISO/TS <NUM>:<NUM> for collaborative robots. However, in most cases (particularly for collaborative robots) these standards are not fully matured and safety is still an issue. <CIT> relates to a software architecture that generates signals for controlling a self-driving or "autonomous" vehicle. <CIT> relates to systems and methods for autonomous or semi-autonomous vehicle control, including data analysis, route determination, and automatic adjustment of autonomous operation features.

The invention is set forth in the independent claims <NUM> and <NUM> and in the dependent claims <NUM> to <NUM>. According to some embodiments of inventive concepts, a method performed by a risk management node may be provided. The risk management node may determine state parameters from a representation of an environment that includes at least one object, an autonomous device, and a set of safety zones for the autonomous device relative to the at least one object. The risk management node may further determine a reward value for the autonomous device based on evaluating a risk of a hazard with the least one object based on the determined state parameters and current location and current speed of the autonomous device relative to a safety zone from the set of safety zones. The risk management node may further determine a control parameter for controlling action of the autonomous device based on the determined reward value. The risk management node may further initiate sending the control parameter to the autonomous device to control action of the autonomous device. The control parameter may be dynamically adapted to reduce the risk of hazard with the at least one object based on reinforcement learning feedback from the reward value.

According to some other embodiments of inventive concepts, a risk management node may be provided. The risk management node may include at least one processor, and at least one memory connected to the at least one processor to perform operations. The operations may include determining state parameters from a representation of an environment that includes at least one object, an autonomous device, and a set of safety zones for the autonomous device relative to the at least one object. The operations may further include determining a reward value for the autonomous device based on evaluating a risk of a hazard with the least one object based on the determined state parameters and current location and current speed of the autonomous device relative to a safety zone from the set of safety zones. The operations may further include determining a control parameter for controlling action of the autonomous device based on the determined reward value. Further, the operations may include initiating sending the control parameter to the autonomous device to control action of the autonomous device. The control parameter may be dynamically adapted to reduce the risk of hazard with the at least one object based on reinforcement learning feedback from the reward value.

According to some embodiments, a computer program may be provided that includes instructions which, when executed on at least one processor, cause the at least one processor to carry out methods performed by the risk management node.

According to some embodiments, a computer program product may be provided that includes a non-transitory computer readable medium storing instructions that, when executed on at least one processor, cause the at least one processor to carry out methods performed by the risk node.

Other systems, computer program products, and methods according to embodiments will be or become apparent to one with skill in the art upon review of the following drawings and detailed description. It is intended that all such additional systems, computer program products, and methods be included within this description and protected by the accompanying claims.

Operational advantages that may be provided by one or more embodiments may include enabling development of a more robust system that enhances safety of operation of autonomous devices while dynamically adapting to machine learned experiences during operation of the autonomous devices. A further advantage may provide for a reinforcement learning-based solution for risk management of autonomous devices that may be adaptive in nature using multi-layered safety zones and semantic information of an environment.

Various embodiments will be described more fully hereinafter with reference to the accompanying drawings. Other embodiments may take many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. Like numbers refer to like elements throughout the detailed description.

As used herein, an autonomous device (also referred to as a robot or an agent) may include any autonomous device including, but not limited to, any mobile robot platform such as research robots, automated ground vehicle (AGV), autonomous vehicle (AV), service robots, mobile agents, and collaborative robots where humans and robots share the environment without having boundaries (e.g., in human-robot collaboration (HRC) operations. HRC collaboration may refer to an environment where humans and robots work closely to accomplish a task and share the work space).

One example of an autonomous device is an AV that may need to keep a safe distance between the AV and obstacles in real-time. One approach may be to use a safety zone (or safety bubble) around the AV during movement of the AV. For example, some safety standards for AVs may define or provide guidance about the size of the safety bubble. The safety bubble may be a virtual space cushion which separates the AV from potential hazards such as other moving vehicles, parked vehicles, trees, road barriers, etc. The empty space may give the AV time to see, react to, and avoid problems that may arise on the road around the AV.

In some approaches, risk management may be employed to aid autonomous device navigation in an environment (e.g., avoiding collisions). According to ISO <NUM>, risk management may include several steps, such as risk analysis and risk mitigation. In risk analysis, objects that surround an autonomous device may be identified and a risk level for the objects may be evaluated. In risk mitigation, autonomous device controls for lowering the risk may be calculated.

In some approaches, risk management may be implemented using different AI algorithms such as fuzzy logic, reinforcement learning (RL), etc. In the context of RL, safety processes may be important for enabling continued learning. A technical challenge for RL, however, may be to transpose a gap that may exist between simulated training and real-world actuation. Therefore, attempts to deploy or productify autonomous AI agents may need strict safety processes, e.g. smart manufacturing. In an HRC collaborative environment these aspects may be particularly aggravated.

Some safety standards may address collaborative tasks performed between a fixed robot (e.g., robot arms) and a human.

A potential disadvantage of approaches discussed above may be that safety is addressed at some level but the methods are not sufficient to address safety in a HRC scenario or other scenario with no or limited boundaries between an autonomous device and humans. A further potential disadvantage may be that risk management may be addressed through reactive navigation that is not prepared to deal with unforeseen situations.

Additional potential limitations with such approaches may include:.

Certain aspects of the present disclosure and their embodiments may provide solutions to these and/or other challenges. <FIG> illustrates an autonomous device <NUM> (also referred to as an agent or a robot) in an environment <NUM> that includes a RL-based risk management node <NUM> for controlling actions of autonomous device <NUM> in accordance with various embodiments. The autonomous device <NUM> also may include a scene graph generator <NUM>, a risk analysis circuit105, a trajectory planner module <NUM>, and a robot controller circuit <NUM>. Although risk management node <NUM> is shown onboard autonomous device <NUM>, risk management node <NUM> may be a node located in a radio access network or wireless network that is in communication with autonomous device <NUM>. As used herein, RL may include any machine learning where an agent takes actions in an environment, which may be interpreted into a reward and a representation of at least one state parameter, which are fedback into the agent including, but not limited to, deep deterministic policy gradient, asynchronous actor-critic algorithm, Q-learning with normalized advantage functions, trust region policy optimization, proximal policy optimization, etc..

Certain embodiments may provide one or more of the following technical advantages. A potential advantage of various embodiments may include that risk management node <NUM> incorporates the current environment <NUM> around autonomous device and at least one state parameter of autonomous device <NUM> in an improved manner in contrast to, for example, using a predefined speed for a safety bubble. State parameters of autonomous device <NUM> (also referred to herein as state or states) may include one or more of the distance between autonomous device <NUM> and an obstacle; maximum stopping distance of autonomous device <NUM> based on current speed and weight of autonomous device <NUM>; current direction of autonomous device <NUM>; current speed of autonomous device <NUM>; current location of autonomous device <NUM>; distance of at least one obstacle from a safety zone in a set of safety zones for autonomous device <NUM>; direction of the at least one object relative to a surface of autonomous device <NUM>; a risk value for the at least one object based on a classification of the at least one object; etc. Thus, autonomous device may avoid collision with an obstacle by reducing speed of autonomous device <NUM> or autonomous device <NUM> braking in less distance to the obstacle. Brake intensity and braking distance of autonomous device may be dependent on its current speed and payload (that is, the weight of autonomous device <NUM> itself and the weight that it carries) which is dynamic. Thus, controlling speed of autonomous device <NUM> based on current context may be important for a dynamic environment <NUM>.

A further potential advantage may be that a representation of environment <NUM> may be used to input current context for a dynamic environment to a processor for performing a RL-based operations. Some embodiments may provide for a RL-based solution that may be adaptive in nature, including in case of collaborative devices. Various embodiments may provide for use of multi-layered safety zones and semantic information of an environment (e.g., in a representation of the environment, such as in a scene graph). Some embodiments may provide for increased precision and fine-grained control of operations of autonomous devices. Some embodiments may further provide for continuous refinement of operations of the autonomous device from the interaction with the environment. Some embodiments may further provide for transfer of a good performing model to other models having the same formulation.

An example of a HRC where humans and robots work closely to accomplish a task and share work space is described, for example, in <NPL> (Inam). Inam describes a safety analysis by assessing potential risk of obstacles around HRC robots. The safety analysis in Inam describes use of three safety zones around a robot. The safety zones described in Inam included different levels of safety described as:.

Various embodiments of the present disclosure include multiple layered safety-zones (e.g., three layers of safety zones). Although various embodiments are described with reference to three layered safety zones, the embodiments are not so limited and may include different numbers of multiple safety zones. In various embodiments, a representation (e.g., a scene graph) of an environment proximate an autonomous device may be used to capture information about the environment, including relationships of the autonomous device with the environment. A RL-based risk management node may process the captured information, including linking a reward function with the multiple safety zones thus directly learning from the environment.

Referring again to <FIG>, a mobile autonomous device may perform its task(s) by navigating through environment <NUM> (e.g., a warehouse). Autonomous device may follow a certain trajectory generated by a trajectory planner module <NUM> that knows a map of environment <NUM>. However, in an actual operation, autonomous device <NUM> may work together with other elements such as other devices and humans in environment <NUM>. An obstacle around the path of autonomous device may create a potential hazard, both to autonomous device <NUM> and to the obstacle. Thus, a risk management node <NUM> may be implemented to reduce potential hazards that may occur.

Referring again to <FIG>, some embodiments of the present disclosure are directed to a risk management node <NUM> for an autonomous device <NUM> that may monitor and take measurements of environment <NUM> through an exteroceptive sensor <NUM> and use the measurements to build a semantic and contextual representation of the environment, such as a scene graph. Autonomous device <NUM> may include a scene graph generator <NUM> for building the scene graph. The representation may be used by risk analysis circuit <NUM> to evaluate a risk level associated with each obstacle in environment <NUM>. Risk management node <NUM> may determine risk mitigation or reduction that can be used to calculate a control for autonomous device that may reduce the risk level(s). RL may be used in risk management node <NUM> to compute the control based on a current autonomous device state and reward.

In various embodiments, risk management node <NUM> may include a RL algorithm. The RL algorithm may include safety zones in a reward function. Risk management node <NUM> may use a representation of environment <NUM> to evaluate a current autonomous device state.

In some RL algorithm approaches, a reward function may be based on a fixed distance between the autonomous device and the closest obstacle. In contrast, in various embodiments of the present disclosure, risk management module <NUM> may be configured to determine a dynamic reward value that depends on a distance between autonomous device <NUM> and an obstacle, and additional parameters. The additional parameters may include, but are not limited to, multiple safety zones and a maximum stopping distance of autonomous device <NUM> that is based on a current speed and weight of autonomous device <NUM>. In some embodiments, when autonomous device <NUM> is located near an obstacle, autonomous device <NUM> may receive a good reward if the speed is low. This may be a potential advantage of some embodiments of the present disclosure in contrast with some RL approaches. In some RL approaches, a reward function may depend only on a distance between an obstacle and an autonomous device, which may result in the robot receiving a bad reward when the autonomous device stays/moves near the obstacle. In contrast, a potential advantage of various embodiments of the present disclosure may be that efficiency of autonomous device <NUM> may be improved because autonomous device <NUM> may receive a good reward when autonomous device <NUM> stays/moves near an object when the speed of autonomous device <NUM> is low.

Another potential disadvantage of some approaches may be that an autonomous device may have a predefined speed where the autonomous device may handle the situation similarly (e.g., avoiding the obstacle) regardless of the obstacle's type and potential risk. In contrast, a potential advantage of various embodiments of the present disclosure may be that risk management node <NUM> may output control parameters to control the speed and direction of autonomous device <NUM> from current context of the environment. For example, risk management node <NUM> may output two mutually exclusive control parameters, e.g., a speed scale for a left wheel of autonomous device <NUM> and a speed scale for a right wheel of autonomous device <NUM>, so that the speed and direction of movement of autonomous device <NUM> are controlled. Determining a control parameter for controlling action of autonomous device <NUM> (such as speed and direction of movement) based on a reward value determined by risk management node <NUM> may provide advantages over some approaches that use a predefined speed. With a predefined speed, an autonomous device may only reduce speed without changing the direction that the autonomous device is travelling (e.g., the autonomous device still approaches the obstacle with a slower speed).

Although a control parameter to control speed of a wheel(s) of autonomous device <NUM> is used in some examples, the output of risk management node <NUM> may be a control parameter for controlling movement of any actuator that operates to move all or part of autonomous device <NUM> including, but not limited to, a wheel(s) or a joint(s) of autonomous device <NUM>.

In various embodiments of inventive concepts, a method may be performed by a risk management node <NUM> that may use information from a scene graph and reinforcement learning for risk mitigation or reduction for autonomous devices (e.g., robots). Autonomous device (s) <NUM> may be equipped with at least one exteroceptive sensor <NUM> (e.g., camera, lidar, etc.) to perform measurements of an environment <NUM> proximate robot(s) <NUM>.

The measurements may be sent to scene graph generator <NUM> which may include a computer vision system that extracts objects from the sensor data and builds a semantic representation of the environment. Objects from the scene graph may be analyzed and evaluated by risk analysis circuit <NUM> for their corresponding risk level. The scene graph and the risk levels may be sent to risk management node <NUM>. Risk management node <NUM> may include one or more processors (as described in more detail below) which may execute a RL algorithm to calculate a current state of autonomous device <NUM> and a reward. A current state of autonomous device <NUM> may include, but is not limited to, one or more of the distance between autonomous device <NUM> and an obstacle; maximum stopping distance of autonomous device <NUM> based on current speed and weight of autonomous device <NUM>; current direction of autonomous device <NUM>; current speed of autonomous device <NUM>; current location of autonomous device <NUM>; distance of at least one obstacle from a safety zone in a set of safety zones for autonomous device <NUM>; direction of the at least one object relative to a surface of autonomous device <NUM>; a risk value for the at least one object based on a classification of the at least one object; etc. Risk management node <NUM> may formulate the state and reward to minimize or reduce a potential risk. For example, the at least one processor of risk management node <NUM> may execute a RL algorithm to calculate a scale of wheel speeds for autonomous device <NUM> for reducing a potential risk.

Meanwhile, at least one processor of trajectory planner module <NUM> of autonomous device <NUM> may compute a path and a velocity that autonomous device <NUM> may follow to reach a certain object/target. At least one processor of robot controller <NUM> may combine the speed scale and the trajectory to compute movements that autonomous device <NUM> may perform in environment <NUM>. Interaction with environment <NUM> may be performed in a continuous loop until autonomous device <NUM> achieves a certain target.

As discussed above, a representation of environment <NUM> may be included in a scene graph. A scene graph is a graph structure that may be effective for representing physical and contextual relations between objects and scenes. See e.g., <NPL>. A potential advantage of a scene graph may be its level of interpretability by both machines and humans. A scene graph also may store information about an object's properties such as size, distance from the observer, type, velocity, etc..

In some embodiments of the present disclosure, a scene graph may represent environment <NUM>. The scene graph may include information about an object's properties. Information about an object's properties may be used as an input to risk management node <NUM> and risk analysis circuit <NUM>.

To construct a scene graph, measurements of environment <NUM> may be processed through an object detection method and the object properties may be extracted. <FIG> illustrates a process of scene graph construction. Referring to <FIG>, scene graph generator <NUM> may include an object detection module <NUM> and a graph generator module <NUM>. Object detection module <NUM> may detect objects in the field of view of autonomous device <NUM>. Object detection module <NUM> may extract properties of one or more objects in environment <NUM>. Graph generator module <NUM> may organize information from object detector module <NUM> in a semantic and contextual way.

A structure of a scene graph may be formed by nodes that may represent the objects that are in the field of view of autonomous device <NUM>, and the edges may represent a semantic relationship between these objects. An example of a scene graph structure <NUM> dynamically generated by an autonomous device <NUM> in or proximate a warehouse environment <NUM> is illustrated in <FIG>.

Referring to <FIG>, warehouse <NUM> is a root node of scene graph structure <NUM>. Floor <NUM> is a child node of warehouse node <NUM>. Floor <NUM> is an element that connects objects in the scene. Objects detected by autonomous device <NUM> are depicted below the floor node <NUM> and an edge of floor node <NUM> is labeled with "on", which represents the placement of two exemplary objects on floor <NUM>. Human <NUM> and shelf <NUM> are two objects depicted as grandchildren nodes "on" floor node <NUM>. Additional objects detected by autonomous device <NUM> are depicted below shelf node <NUM> and an edge of shelf node <NUM> is labeled with "on", which represents the placement of two exemplary products on shelf <NUM>. Product <NUM> and product <NUM> are depicted as great grandchildren nodes "on" shelf node <NUM>. With scene graph structure <NUM>, risk management node <NUM> may use the contextual information provided by scene graph structure <NUM> for risk assessment and to generate control parameters with respect to each object in scene graph structure <NUM>.

Still referring to <FIG>, each node may have property attributes (also referred to as environment parameters). For example, floor node <NUM> has a size attribute of <NUM> meters by <NUM> meters. Human node <NUM> has seven attributes: a type attribute (e.g., type <NUM> for human), a distance attribute (e.g., <NUM> meters from a surface of autonomous device <NUM>), an orientation attribute (e.g., -<NUM>° from face of human <NUM> to autonomous device <NUM>), a direction attribute (e.g., <NUM>° from a font surface of autonomous device <NUM> to human <NUM>), a velocity attribute (e.g., velocity of human <NUM> is <NUM> meters per second), a size attribute in the x direction (e.g., <NUM> meters), and a size attribute in the y direction (e.g., <NUM> meters). Type attribute of objects may include, but is not limited to, three types (<NUM> for a static object, <NUM> for a dynamic object, and <NUM> for a human).

In various embodiments, risk management node <NUM> may use at least one processor to execute a RL-based risk mitigation algorithm by taking information (e.g., environment parameters) input from a representation of environment <NUM>, such as scene graph structure <NUM>, and converting the attributes to discrete states for each property attribute. Output of risk management node <NUM> may be, but is not limited to, a speed scale for each actuator of autonomous device <NUM>.

<FIG> is a block diagram illustrating elements of a risk management node <NUM> (also referred to as a node) that is configured according to various embodiments. Risk management node <NUM> may be located onboard autonomous device <NUM> or may be located in a network that is in radio or wireless communication with autonomous device <NUM>. As shown, the risk management node <NUM> includes at least one processor circuit <NUM> (also referred to as a processor), at least one memory circuit <NUM> (also referred to as memory), and a control interface <NUM> (e.g., a wired control interface and/or wireless control interface) configured to communicate with autonomous device <NUM>. Risk management node <NUM> may be configured as a node in a radio access or wireless network, and may contain a RF front end with one or more power amplifiers that transmit and receive through antennas of an antenna array. The at least one memory <NUM> stores computer readable program code that when executed by the at least one processor <NUM> causes the processor <NUM> to perform operations according to embodiments disclosed herein.

Still referring to <FIG>, risk management node <NUM> may calculate one or more states <NUM> for reinforcement learning by discretizing information extracted from a scene graph structure by scene graph parser <NUM>. For example:.

Still referring to <FIG>, reward calculation will now be discussed. Reward is feedback for a RL system to measure action of the RL system to a certain condition. In various embodiments of the present disclosure, feedback for the autonomous device may be based on collision, location of obstacles, and movement of the autonomous device. Autonomous device <NUM> may determine a reward based on the current location of autonomous device <NUM> relative to one safety zone of the multiple safety zones (e.g., safe zone, warning zone, or critical zone). For example, a reward value can be formulated as follows:.

A purpose of the reward calculation may be to calculate the reward value to try to minimize or reduce the risk of hazards to/from an object proximate autonomous device <NUM>. Risk of hazards may include, but is not limited to, collision between autonomous device <NUM> and an object. Thus, if autonomous device <NUM> is rewarded positively, the action taken by autonomous device <NUM> may reduce the probability of collision with the object.

In various embodiments, an objective of the autonomous device may be to not maximize reducing the distance to the obstacle as autonomous device <NUM> keeps in a trajectory toward the goal/object (e.g., the distance between autonomous device <NUM> and product <NUM> on shelf <NUM> in warehouse <NUM>). A potential advantage of various embodiments is that the reward calculation may be calculated from a scene graph structure which can introduce detailed information regarding the environment in contrast to if a reward was calculated using just raw sensor information.

Still referring to <FIG>, in various embodiments, the output of risk management node <NUM> may be a speed scale for each actuator of autonomous device <NUM> from a calculate action module <NUM>. For example, the output scale may be a value, such as <NUM>, <NUM>, <NUM>, <NUM> meters per second. The speed scale may be applied in robot controller circuit <NUM> to a trajectory of autonomous device <NUM> from trajectory planning module <NUM>.

A sequence of operations that may performed by autonomous device <NUM>, including operations that may be performed by risk management node <NUM>, are illustrated in <FIG>. In various embodiments, actors may include environment <NUM>, which may include humans and other objects (such as other devices), and autonomous device <NUM>. Autonomous device <NUM> may be an autonomous agent that interacts with environment <NUM> and may perform actions that minimize or reduce risk of hazards with objects in environment <NUM>. Referring to <FIG>, autonomous device <NUM> may measure data <NUM> with sensors of autonomous device <NUM>. Scene graph generator <NUM> may convert the measurements to generate a scene graph structure <NUM> of environment <NUM>. Scene graph structure <NUM> may be used as an input to risk analysis circuit <NUM> to calculate a risk level <NUM> of each object in the scene graph structure. The scene graph structure and the risk levels of the objects may be input to risk management node <NUM> for calculating a state <NUM>, calculating a reward <NUM>, and calculating a control parameter <NUM>, which in turn may be modeled on top of reinforcement learning. Autonomous device <NUM> states and rewards may be calculated and suitable actions of autonomous device <NUM> may be obtained to try to maximize the reward (or in other words, to try to minimize or reduce the risk of hazards). The obtained action (e.g., speed scale) may be combined <NUM> with an output of trajectory planning module <NUM> and controls may be sent to autonomous device <NUM> to control interactions of autonomous device <NUM> with environment <NUM>. The sequence diagram of operations of <FIG> may be repeated in a loop for each object in the environment and/or for the same object in environment <NUM> until a target/goal for the object is achieved.

Operations of risk management node <NUM> (implemented using the structure of the block diagram of <FIG>) will now be discussed with reference to the flow charts of <FIG> according to some embodiments of inventive concepts. For example, modules may be stored in at least one memory <NUM> of <FIG>, and these modules may provide instructions so that when the instructions of a modules are executed by at least one processor <NUM>, at least one processor <NUM> performs respective operations of the flow charts.

Referring initially to <FIG>, operations can be performed by a risk management node (e.g., <NUM>) for controlling actions of an autonomous device (e.g., <NUM> in <FIG>). The operations include determining <NUM> state parameters from a representation of an environment that includes at least one object, an autonomous device, and a set of safety zones for the autonomous device relative to the at least one object. The operations further include determining <NUM> a reward value for the autonomous device based on evaluating a risk of a hazard with the at least one object based on the determined state parameters and current location and current speed of the autonomous device relative to a safety zone from the set of safety zones. The operations further include determining <NUM> a control parameter for controlling action of the autonomous device based on the determined reward value. The operations further include initiating <NUM> sending the control parameter to a controller of the autonomous device to control action of the autonomous device. The control parameter is dynamically adapted to reduce the risk of hazard with the at least one object based on reinforcement learning feedback from the reward value.

Referring to <FIG>, in at least some embodiments, the operations of determining the state parameters, determining the reward value, determining the control parameter, and initiating sending the control parameter to a controller to control action of the autonomous device may be repeated <NUM> until, for example, the autonomous device completes a task.

Referring again to <FIG>, the state parameters may be determined from the representation of the environment based on determining discrete values for information from the representation of the environment. The discrete values for information may include at least one of a current direction of the autonomous device; a current speed of the autonomous device; a current location of the autonomous device; a distance of the at least one obstacle from a safety zone in the set of safety zones for the autonomous device; a direction of the at least one object relative to a surface of the autonomous device; and a risk value for the at least one object based on a classification of the at least one object. The risk value for the at least one object based on the classification of the at least one object is input to the risk management node from a risk analysis module that assigns the risk value. The classification of the object may include, but is not limited to, an attribute parameter identifying at least one object as including (but not limited to), for example, a human, an infrastructure, another autonomous device, or a vehicle.

The state parameters of operation <NUM> may be determined from inputting to the risk management node <NUM> each of the environment paraments from the scene graph structure (e.g., from scene graph parser <NUM>) and converting each of the environment parameters to a discrete state parameter.

The set of safety zones may include a range of safety zones. Each safety zone in the range may have a different distance from the autonomous device and the autonomous device may have a different speed within each safety zone in the range of safety zones.

The reward value of operation <NUM> may include a defined numerical value based on the evaluated risk of hazard with the at least one object.

The control parameter of operation <NUM> may include a speed of at least one actuator of the autonomous device and/or an angle of at least one actuator of the autonomous device.

In some embodiments, the representation of the environment may include a scene graph structure of the at least one object and a relationship of the least one object with the autonomous device and the environment, respectively. The scene graph structure may be based on environment parameters measured by the autonomous device including, but not limited to, for example at least one of: a distance of the at least one object from a surface of the autonomous device; an orientation of a surface of the at least one object from the autonomous device; a direction of the at least one object from a surface of the autonomous device; a velocity of the at least one object; a width dimension of the at least one object; a length dimension of the at least one object; and a height dimension of the least one object.

Referring to <FIG>, in at least some embodiments, the operations may further include sending <NUM> the control parameter to a controller <NUM> for autonomous device <NUM> for application to a trajectory for autonomous device <NUM>.

The operations from the flow chart of <FIG> and <FIG> may be optional with respect to some embodiments.

Aspects of the present disclosure have been described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. These computer program instructions may be provided to a processor of a computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable instruction execution apparatus, create a mechanism for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that when executed can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions when stored in the computer readable medium produce an article of manufacture including instructions which when executed, cause a computer to implement the function/act specified in the flowchart and/or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable instruction execution apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatuses or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various aspects of the present disclosure.

Like reference numbers signify like elements throughout the description of the figures.

Claim 1:
A method performed by a risk management node (<NUM>), the method comprising:
determining (<NUM>) state parameters from a representation of an environment that includes at least one object, an autonomous device, and a set of safety zones for the autonomous device relative to the at least one object;
determining (<NUM>) a reward value for the autonomous device based on evaluating a risk of a hazard with the least one object based on the determined state parameters and current location and current speed of the autonomous device relative to a safety zone from the set of safety zones;
determining (<NUM>) a control parameter for controlling action of the autonomous device based on the determined reward value; and
initiating (<NUM>) sending the control parameter to the autonomous device to control action of the autonomous device,
wherein the control parameter is dynamically adapted to reduce the risk of hazard with the at least one object based on reinforcement learning feedback from the reward value,
characterized in that
the representation of the environment comprises a scene graph structure of the at least one object and a relationship of the least one object with the autonomous device and the environment, respectively.