Patent Application
20250121849
The subject matter described herein relates, in general, to control barrier functions, and, more particularly, to implementing predictive risk-bounded control barrier functions.
Vehicles with autonomous driving assistance features may utilize methods such as control barrier functions to try and ensure that vehicle control inputs do not result in the vehicle entering an unsafe state of operation (e.g., colliding with another vehicle). Current development of control barrier functions in a stochastic setting has leaned heavily on martingale theory for both discrete-time and continuous-time stochastic processes.
In certain illustrative embodiments, a system is described which includes a processor and memory communicably coupled to the processor. The memory stores machine-readable instructions that, when executed by the processor, cause the processor to predict a state of a vehicle, plan a trajectory based on the state of the vehicle, determine a present control input based on the trajectory, apply a predictive risk-aware control barrier function (“PRA-CBF”) to determine a final control input, and apply the final control input to the vehicle. In determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input.
In yet another illustrative embodiment, a non-transitory computer-readable medium is described which includes instructions that, when executed by one or more processors, cause the one or more processors to predict a state of a vehicle, plan a trajectory based on the state of the vehicle, determine a present control input based on the trajectory, apply a PRA-CBF to determine a final control input, and apply the final control input of the vehicle. In determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input.
In another illustrative embodiments, a method is described that comprises A method, predicting a state of a vehicle, planning a trajectory based on the state of the vehicle, determining a present control input based on the trajectory, applying a PRA-CBF to determine a final control input, and applying the final control input to the vehicle. In determining the final control input, the PRA-CBF takes a predicted control input into account when assessing risks associated with the present control input.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate various systems, methods, and other embodiments of the disclosure. It will be appreciated that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one embodiment of the boundaries. In some embodiments, one element may be designed as multiple elements or multiple elements may be designed as one element. In some embodiments, an element shown as an internal component of another element may be implemented as an external component and vice versa. Furthermore, elements may not be drawn to scale.
Systems, methods, and other embodiments associated with implementing predictive risk-bounded control barrier functions in vehicles are disclosed herein. The present disclosure is applicable to a variety of vehicle systems including, for example, Advance Driver Assistance or Automated Driving systems. The present disclosure constitutes an approach for risk-aware, autonomous control design of a vehicle system (e.g., truck tractor-trailer system) using model predictive control (“MPC”) and predictive risk-aware control barrier functions (“PRA-CBFs”), i.e., MPC+PRA-CBFs. For example, the truck-trailer system (“TTS”) is notoriously difficult to control, even for experienced human drivers in ideal road conditions. Moreover, the presence of other vehicles on the road as well as complicating factors such as inclement weather, deteriorating roadways, etc., renders the environment in which a TTS must operate both dynamic and uncertain. As such, it is necessary for any controller to have a formal, mathematical understanding of the risk incurred by taking its computed actions, and to be able to bound that risk according to some design tolerance. Accordingly, illustrative embodiments of the present disclosure introduce frameworks for accomplishing such tasks for the TTS modeled as a stochastic differential equation (“SDE”) by using predictive, risk-aware control barrier functions, the effects of which certify using predicted future trajectories that the probability of the TTS becoming unsafe over a time interval remains bounded by a user-specified value.
There are disadvantages to conventional approaches. For example, MPC may exhibit strong closed-loop performance by solving an optimal control problem over some finite time interval, but it does not provide a straightforward way to characterize the risk associated with the TTS throughout this time period. On the other hand, conventional risk-aware control barrier functions do not take future (predicted) control actions into account when assessing the risk associated with any present control action. In the TTS, where the truck and trailer centers of mass may be accelerating in different directions at different time instances, it is critical to be able to assess this risk over the predicted future trajectories rather than at any instantaneous time instance.
The illustrative embodiments described herein provide a solution to these disadvantages. In the disclosed embodiments, by using the solution to the MPC problem as a nominal path for the PRA-CBF, the risks of the TTS over future time intervals are more accurately estimated and thus contained within the acceptable levels. Additionally, if ever the predicted risk is in jeopardy of exceeding the tolerable threshold, the PRA-CBF of the present disclosure admits an affine (linear) inequality condition and, thus, may be included as a constraint in a quadratic program-based control law, the solution to which represents the set of control actions which both satisfy the PRA-CBF condition and minimally deviate from the MPC solution.
We will now discuss the problem faced and overcome by the present disclosure. The problem under consideration is the following: given the SDE model of the TTS, a set of state constraints (e.g., obstacle/collision avoidance, speed limit, truck-trailer jackknife angle, etc.), and a finite operating time interval, design a control law such that the risk of the TTS violating any constraint over the time interval is bounded according to some acceptable risk threshold.
Risk-aware control design is an active area of research with application to many autonomous systems. For generic systems, it has become popular to design controllers such that, with respect to a probability distribution of outcomes, a given risk metric specification (e.g., expected value, value-at-risk (“VaR”), conditional value-at-risk (“CVaR”), etc.) is satisfied over the operating time. Some recent work has even used control barrier functions to encode the satisfaction of these risk specifications. However, the existing approaches are prone to myopic tendencies in that they do not provide any assessment of risk over a prediction interval. Other approaches require sampling from future predicted trajectories in order to generate an estimate of the risk associated with future trajectories, and therefore tend to provide guarantees of bounded risk only as the number of samples grows very large.
For systems modeled as stochastic differential equations, several works employ the use of stochastic control barrier functions to bound the probability of constraint violation over a finite time interval. These approaches have been shown to lead to conservative system behavior, however, which tends to impact the ability of the system to meet other performance specifications, such as goal-reaching or trajectory tracking.
Specific to the TTS, some conventional approaches seek to design MPC-CBF control laws for guaranteed safety under the assumption of a perfect system model and perfect state measurements. Not only are these assumptions unrealistic in practice, but the CBFs used to filter out potentially unsafe MPC-generated inputs do not account for the prediction of future state trajectories, which may lead to the same myopic behavior as when no MPC is used in the first place. In addition, the use of present-focused CBFs is more likely to result in constraints that cannot be jointly satisfied due to the geometry of the TTS. By including prediction in embodiments of the present disclosure, we can ascertain whether the constraints will in fact be satisfied throughout the predicted maneuver—thus providing a solution over the disadvantages of conventional approaches.
As described herein, embodiments of the present disclosure provide a number of advantages over conventional approaches. First, in contrast to sampling-based approaches, which rely on large numbers of sampled trajectories to obtain probabilistic bounds on risk, the PRA-CBF approach of the present disclosure is a formal method for certifying risk in system behavior and provides a certificate that the risk threshold will be always satisfied.
Second, while conventional risk-aware control barrier functions have been shown to reduce conservatism associated with risk-aware control design, they nevertheless are prone to producing myopic system behavior (system behavior prioritizes the instantaneous rate of change of the values of the constraint functions over their evolution across a time interval) in the sense that the present judgement of risk may be significantly higher than the actual risk incurred along a proposed future trajectory. The use of the PRA-CBFs of the present disclosure to encode a prediction of future states and control actions mitigates this problem, and results in the controller tolerating actions that are less conservative in nature while still abiding by the specified risk tolerance.
Third, conventional approaches to CBF-based TTS control simply layer a CBF filter on top of the nominal MPC policy. While providing formal guarantees of safety under strict assumptions, the trajectories predicted by the MPC are completely decoupled from the CBF filter, which considers only the present state in its judgement of what constitutes an admissible control input. In practice, this often means that even trajectories that remain safe over the prediction time interval are deemed either i) inadmissible by the CBF filter, which adds unnecessary conservatism to the system, or ii) infeasible altogether, at which point the controller ceases to reliably operate. By using MPC as a nominal/predicted path, embodiments of the present MPC+PRA-CBF approach are equipped to judge the effect of the proposed control actions on the risk incurred by the TTS over the full future time interval, which leads to more accurate risk assessment and reduces conservatism.
Referring to
The vehicle 100 also includes various elements. It will be understood that in various embodiments it may not be necessary for the vehicle 100 to have all of the elements shown in
Some of the possible elements of the vehicle 100 are shown in
With reference to
The PRA-CBF system 170 as illustrated in
With reference to
Accordingly, the detection module 220, in one embodiment, controls the respective sensors to provide the data inputs in the form of the sensor data 250. Additionally, while the detection module 220 is discussed as controlling the various sensors to provide the sensor data 250, in one or more embodiments, the detection module 220 can employ other techniques to acquire the sensor data 250 that are either active or passive. For example, the detection module 220 may passively acquired the sensor data 250 from a stream of electronic information provided by the various sensors to further components within the vehicle 100. Moreover, the detection module 220 can undertake various approaches to fuse data from multiple sensors when providing the sensor data 250 and/or from sensor data acquired over a wireless communication link (e.g., v2v) from one or more of the surrounding vehicles. Thus, the sensor data 250, in one embodiment, represents a combination of data acquired from multiple sensors.
In addition to locations of surrounding vehicles, the sensor data 250 may also include, for example, information about lane markings, and so on. Moreover, the detection module 220, in one embodiment, controls the sensors to acquire the sensor data 250 about an area that encompasses 360 degrees about the vehicle 100 in order to provide a comprehensive assessment of the surrounding environment. Of course, in alternative embodiments, the detection module 220 may acquire the sensor data about a forward direction alone when, for example, the vehicle 100 is not equipped with further sensors to include additional regions about the vehicle and/or the additional regions are not scanned due to other reasons (e.g., unnecessary due to known current conditions).
Moreover, in one embodiment, the PRA-CBF system 170 includes the database 240. The database 240 is, in one embodiment, an electronic data structure stored in the memory 210 or another data store and that is configured with routines that can be executed by the processor 110 for analyzing stored data, providing stored data, organizing stored data, and so on. Thus, in one embodiment, the database 240 stores data used by the modules 220 and 230 in executing various functions. In one embodiment, the database 240 includes the sensor data 250 along with, for example, metadata that characterize various aspects of the sensor data 250. For example, the metadata can include location coordinates (e.g., longitude and latitude), relative map coordinates or tile identifiers, time/date stamps from when the separate sensor data 250 was generated, and so on.
In one embodiment, the command module 230 generally includes instructions that function to control the processor 110 or collection of processors in the cloud-computing environment 300 for implementing the illustrative predictive risk-bounded control barrier functions described herein. With regard to implementing predictive risk-bounded control barrier functions, a class of tractor-trailer dynamical systems may be represented by the following non-linear, control-affine, stochastic differential equation (SDE):
and where xt denotes the state xt∈⊆
n, which consists of lateral position x, longitudinal position y, longitudinal velocity of the rear truck axle v, longitudinal acceleration of the rear truck axle a, orientation of the truck body with respect to the horizontal θ1, orientation of the trailer body with respect to the horizontal θ2, and steering angle δ. In addition, ƒ denotes the control-independent drift function ƒ:
n→
n, g denotes the control-independent drift function g:
n→
n×m, ut denotes the control input vector ut∈
⊆
m at time t consisting of longitudinal rear axle jerk j and rate of change of steering angle ω, σ denotes a positive semi-definite, diagonal diffusion function σ:
n→
n×q whose elements depend on the level of stochastic noise assumed in the system, and wt denotes a standard q-dimensional Wiener process (also known as Brownian motion) defined over the complete probability space (Ω,
, P) for sample space Ω, σ-algebra
over Ω, and probability measure P:
→[0, 1].
In the embodiments disclosed herein, one key feature of the present disclosure is the re-formulation of control barrier functions as a novel PRA-CBF directly using the MPC as a predicted trajectory for future risk assessment.
Then, still with reference to
where a: n×(N+1)×
m×N→
and b:
n×(N+1)×
m×N→Rm depend on the chosen CBF, the minimum predicted value of which over the considered time interval is given by μ, and where α:
→
is a class K function, v∈
is a system-dependent robustness parameter, and ρ∈(0,1) is the (designed) risk specification, such that the satisfaction of Eq. 2 results in the probability of the system becoming unsafe being bounded from above by rho, a design specification. By layering this PRA-CBF-QP as a filter for the nominal MPC policy and then using the PRA-CBF to evaluate the risk of these predicted trajectories, the controller of the present disclosure is able to provide a more accurate assessment of the risk incurred by the system and ensure that it remains at an acceptable level. Any nominal control inputs not satisfying the above condition will be modified by the QP in order to meet the specification. The solution to the QP (u*) is then digitally transmitted to the plant, which, in simulation, is the ‘ground truth’ model given by the SDE in Eq. 1 and is integrated forward in time using numerical integration techniques to produce the state x for subsequent timesteps, and which, for a physical TTS, is the exact mathematical expression that captures how the state of the physical system changes based on transmitting u* to the various system actuators (e.g., gas/brake pedals, steering wheel, etc.).
In experimentation of the present disclosure, the efficacy of the proposed controller was demonstrated on three distinct TTS scenarios: 1) an adaptive cruise control (“ACC”) problem for highway driving, 2) circumventing a roundabout, and 3) executing a right-turn in the presence of both static and dynamic obstacles.
As can be seen in
For the roundabout scenario, a set of comparative studies were undertook to highlight the effect of the PRA-CBF-QP filter on top of the nominal MPC control law: i) no CBF filter, ii) PRA-CBF filter for collision avoidance only on the truck, but not the trailer, and iii) PRA-CBF filter for collision avoidance for both the truck and trailer.
For the right-turn scenario, cases where the TTS was asked to execute a right-turn in the presence of both a static and dynamic obstacle were simulated.
For the case of a dynamic obstacle, we considered both fast-and slow-moving obstacles.
In the illustrative embodiments disclosed herein, command module 230 may contain a variety of models for modeling movement, state estimations or other actions of vehicle 100 or other vehicles. Such models are discussed in co-pending U.S. patent application Ser. No. 18/108,453 entitled “SYSTEMS AND METHODS FOR RISK-BOUNDED CONTROL BARRIER FUNCTIONS,” filed on Feb. 10, 2023, naming Black et al. as inventors, which is hereby incorporated by reference in its entirety.
Referring back to
In some embodiments, command module 230 may generate nominal control inputs for vehicle 100 or other vehicles and devices based on a trajectory. For example, command module 230 based on a trajectory may generate nominal control inputs in terms of steering angle, acceleration, braking, and so on. As another example, command module 230 based on a trajectory may generate nominal control inputs actuating one or more actuators of a medical or robotic device.
With reference to
The cloud server 310 is shown as including a processor 315 that may be a part of the RB-CBF system 170 through network 305 via communication unit 335. In one embodiment, the cloud server 310 includes a memory 320 that stores a communication module 325. The memory 320 is a random-access memory (RAM), read-only memory (ROM), a hard-disk drive, a flash memory, or other suitable memory for storing the module 325. The module 325 is, for example, computer-readable instructions that when executed by the processor 315 cause the processor 315 to perform the various functions disclosed herein. Moreover, in one embodiment, cloud server 310 includes the database 330. The database 330 is, in one embodiment, an electronic data structure stored in the memory 320 or another data store and that is configured with routines that can be executed by the processor 315 for analyzing stored data, providing stored data, organizing stored data, and so on.
The infrastructure device 340 is shown as including a processor 345 that may be a part of the PRA-CBF system 170 through network 305 via communication unit 370. In one embodiment, the infrastructure device 340 includes a memory 350 that stores a communication module 355. The memory 350 is a random-access memory (RAM), read-only memory (ROM), a hard-disk drive, a flash memory, or other suitable memory for storing the module 355. The module 355 is, for example, computer-readable instructions that when executed by the processor 345 cause the processor 345 to perform the various functions disclosed herein. Moreover, in one embodiment, infrastructure device 340 includes the database 360. The database 360 is, in one embodiment, an electronic data structure stored in the memory 350 or another data store and that is configured with routines that can be executed by the processor 345 for analyzing stored data, providing stored data, organizing stored data, and so on.
Accordingly, in addition to information obtained from sensor data 250, PRA-CBF system 170 may obtain information from cloud servers (e.g., cloud server 310), infrastructure devices (e.g., infrastructure device 340), other vehicles (e.g., vehicle 380), and any other systems connected to network 305. For example, command module 230 may receive external parameters (e.g., environmental parameters), vehicle parameters, contextual parameters, or other parameters via network 305. As another example, command module 230 may receive additional information, such as state estimations, trajectories, control inputs, and so on, such as for instance as they relate to other vehicles and devices, via network 305.
It should be appreciated that the command module 230 in combination with a prediction model 270 can form a computational model such as a machine learning logic, deep learning logic, a neural network model, or another similar approach. In one embodiment, the prediction model 270 is a statistical model such as a regression model that determines estimates of the state of a vehicle based on sensor data 250, map data 116, or other sources of information as described herein. Accordingly, the model 270 can be a polynomial regression (e.g., least weighted polynomial regression), least squares or another suitable approach.
Moreover, in alternative arrangements, the prediction model 270 is a probabilistic approach such as a hidden Markov model. In either case, the command module 230, when implemented as a neural network model or another model, in one embodiment, electronically accepts the sensor data 250 as an input, which may also include evaluation metrics. Accordingly, the command module 230 in concert with the prediction model 270 produce various determinations/assessments as an electronic output that characterizes the noted aspect as, for example, a single electronic value. Moreover, in further aspects, the PRA-CBF system 170 can collect the noted data, log responses, and use the data and responses to subsequently further train the model 270.
Additional aspects of implementing risk-bounded control barrier functions will be discussed in relation to
At 1002, command module 230 predicts the state of a vehicle. For example, command module 230 may predict the state of a vehicle based on modelling the movement or other actions of the vehicle. In some embodiments, command module 230 may predict the state of each vehicle in terms of its location, velocity, acceleration, heading, or other characteristics. In some embodiments, command module may predicts a state in three or more dimensions (e.g., x, y, z; quaternion) or with respect to a non-Cartesian coordinate system (e.g., polar coordinate system). In some embodiments, command module 230 may use extended Kalman filtering in performing state estimation.
At 1004, command module 230 plans a trajectory based on the state of the vehicle. For example, command module 230 may plan a trajectory based on start-to-goal motion planning. In some embodiments, command module 230 may plan a trajectory based on obstacles, features, landmarks, road signs, traffic controls, lane markings or other road boundary indicators, weather conditions, traffic conditions, or other characteristics. In some embodiments, command module 230 may plan a trajectory based on the characteristics relevant to an industrial environment, such as build surface, etc. In some embodiments, command module 230 may rely on automated driving assistance via automated driving module(s) 160 to determine a trajectory for vehicle 100 or other vehicles.
At 1006, command module 230 determines a present control input based on the trajectory. For example, command module 230 based on a trajectory may generate present control inputs in terms of steering angle, acceleration, braking, and so on. In some embodiments, command module 230 may rely on automated driving assistance via automated driving module(s) 160 to determine a nominal control input based on the trajectory.
At 1008, command module 230 applies the PRA-CBF methods described herein to determine a final control input. In determining the final control input, using the PRA-CBF, the command module takes the predicted control input into account when assessing risks associated with the present control input. Here, as described, the PRA-CBF is applied as a filter for the MPC policy, thus allowing the system controller to provide an accurate assessment of the risk incurred by the system and ensure it remains at an acceptable level.
At 1010, command module 230 applies the final control input to the vehicle. In some embodiments, command module 230 may apply the final control input by sending an instruction to a vehicle, device, or a mechanical system component therein to implement the final control input.
In one or more embodiments, the vehicle 100 is an autonomous vehicle. As used herein, “autonomous vehicle” refers to a vehicle that operates in an autonomous mode. “Autonomous mode” refers to navigating and/or maneuvering the vehicle 100 along a travel route using one or more computing systems to control the vehicle 100 with minimal or no input from a human driver. In one or more embodiments, the vehicle 100 is highly automated or completely automated. In one embodiment, the vehicle 100 is configured with one or more semi-autonomous operational modes in which one or more computing systems perform a portion of the navigation and/or maneuvering of the vehicle along a travel route, and a vehicle operator (i.e., driver) provides inputs to the vehicle to perform a portion of the navigation and/or maneuvering of the vehicle 100 along a travel route.
The vehicle 100 can include one or more processors 110. In one or more arrangements, the processor(s) 110 can be a main processor of the vehicle 100. For instance, the processor(s) 110 can be an electronic control unit (ECU). The vehicle 100 can include one or more data stores 115 for storing one or more types of data. The data store 115 can include volatile and/or non-volatile memory. Examples of suitable data stores 115 include RAM (Random Access Memory), flash memory, ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), registers, magnetic disks, optical disks, hard drives, or any other suitable storage medium, or any combination thereof. The data store 115 can be a component of the processor(s) 110, or the data store 115 can be operatively connected to the processor(s) 110 for use thereby. The term “operatively or communicably coupled/connected,” as used throughout this description, can include direct or indirect connections, including connections without direct physical contact.
In one or more arrangements, the one or more data stores 115 can include map data 116. The map data 116 can include maps of one or more geographic areas. In some instances, the map data 116 can include information or data on roads, traffic control devices, road markings, structures, features, and/or landmarks in the one or more geographic areas. The map data 116 can be in any suitable form. In some instances, the map data 116 can include aerial views of an area. In some instances, the map data 116 can include ground views of an area, including 360-degree ground views. The map data 116 can include measurements, dimensions, distances, and/or information for one or more items included in the map data 116 and/or relative to other items included in the map data 116. The map data 116 can include a digital map with information about road geometry. The map data 116 can be high quality and/or highly detailed.
In one or more arrangements, the map data 116 can include one or more terrain maps 117. The terrain map(s) 117 can include information about the ground, terrain, roads, surfaces, and/or other features of one or more geographic areas. The terrain map(s) 117 can include elevation data in the one or more geographic areas. The map data 116 can be high quality and/or highly detailed. The terrain map(s) 117 can define one or more ground surfaces, which can include paved roads, unpaved roads, land, and other things that define a ground surface.
In one or more arrangements, the map data 116 can include one or more static obstacle maps 118. The static obstacle map(s) 118 can include information about one or more static obstacles located within one or more geographic areas. A “static obstacle” is a physical object whose position does not change or substantially change over a period of time and/or whose size does not change or substantially change over a period of time. Examples of static obstacles include trees, buildings, curbs, fences, railings, medians, utility poles, statues, monuments, signs, benches, furniture, mailboxes, large rocks, hills. The static obstacles can be objects that extend above ground level. The one or more static obstacles included in the static obstacle map(s) 118 can have location data, size data, dimension data, material data, and/or other data associated with it. The static obstacle map(s) 118 can include measurements, dimensions, distances, and/or information for one or more static obstacles. The static obstacle map(s) 118 can be high quality and/or highly detailed. The static obstacle map(s) 118 can be updated to reflect changes within a mapped area.
The one or more data stores 115 can include sensor data 119. In this context, “sensor data” means any information about the sensors that the vehicle 100 is equipped with, including the capabilities and other information about such sensors. As will be explained below, the vehicle 100 can include the sensor system 120. The sensor data 119 can relate to one or more sensors of the sensor system 120. As an example, in one or more arrangements, the sensor data 119 can include information on one or more LIDAR sensors 124 of the sensor system 120.
In some instances, at least a portion of the map data 116 and/or the sensor data 119 can be located in one or more data stores 115 located onboard the vehicle 100. Alternatively, or in addition, at least a portion of the map data 116 and/or the sensor data 119 can be located in one or more data stores 115 that are located remotely from the vehicle 100.
As noted above, the vehicle 100 can include the sensor system 120. The sensor system 120 can include one or more sensors. “Sensor” means any device, component and/or system that can detect, and/or sense something. The one or more sensors can be configured to detect, and/or sense in real-time. As used herein, the term “real-time” means a level of processing responsiveness that a user or system senses as sufficiently immediate for a particular process or determination to be made, or that enables the processor to keep up with some external process.
In arrangements in which the sensor system 120 includes a plurality of sensors, the sensors can work independently from each other. Alternatively, two or more of the sensors can work in combination with each other. In such case, the two or more sensors can form a sensor network. The sensor system 120 and/or the one or more sensors can be operatively connected to the processor(s) 110, the data store(s) 115, and/or another element of the vehicle 100 (including any of the elements shown in
The sensor system 120 can include any suitable type of sensor. Various examples of different types of sensors will be described herein. However, it will be understood that the embodiments are not limited to the particular sensors described. The sensor system 120 can include one or more vehicle sensors 121. The vehicle sensor(s) 121 can detect, determine, and/or sense information about the vehicle 100 itself. In one or more arrangements, the vehicle sensor(s) 121 can be configured to detect, and/or sense position and orientation changes of the vehicle 100, such as, for example, based on inertial acceleration. In one or more arrangements, the vehicle sensor(s) 121 can include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead-reckoning system, a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system 147, and/or other suitable sensors. The vehicle sensor(s) 121 can be configured to detect, and/or sense one or more characteristics of the vehicle 100. In one or more arrangements, the vehicle sensor(s) 121 can include a speedometer to determine a current speed of the vehicle 100.
Alternatively, or in addition, the sensor system 120 can include one or more environment sensors 122 configured to acquire, and/or sense driving environment data. “Driving environment data” includes data or information about the external environment in which an autonomous vehicle is located or one or more portions thereof. For example, the one or more environment sensors 122 can be configured to detect, quantify and/or sense obstacles in at least a portion of the external environment of the vehicle 100 and/or information/data about such obstacles. Such obstacles may be stationary objects and/or dynamic objects. The one or more environment sensors 122 can be configured to detect, measure, quantify and/or sense other things in the external environment of the vehicle 100, such as, for example, lane markers, signs, traffic lights, traffic signs, lane lines, crosswalks, curbs proximate the vehicle 100, off-road objects, etc.
Various examples of sensors of the sensor system 120 will be described herein. The example sensors may be part of the one or more environment sensors 122 and/or the one or more vehicle sensors 121. However, it will be understood that the embodiments are not limited to the particular sensors described.
As an example, in one or more arrangements, the sensor system 120 can include one or more radar sensors 123, one or more LIDAR sensors 124, one or more sonar sensors 125, and/or one or more cameras 126. In one or more arrangements, the one or more cameras 126 can be high dynamic range (HDR) cameras or infrared (IR) cameras.
The vehicle 100 can include an input system 130. An “input system” includes any device, component, system, element or arrangement or groups thereof that enable information/data to be entered into a machine. The input system 130 can receive an input from a vehicle passenger (e.g., a driver or a passenger). The vehicle 100 can include an output system 135. An “output system” includes any device, component, or arrangement or groups thereof that enable information/data to be presented to a vehicle passenger (e.g., a person, a vehicle passenger, etc.).
The vehicle 100 can include one or more vehicle systems 140. Various examples of the one or more vehicle systems 140 are shown in
The navigation system 147 can include one or more devices, applications, and/or combinations thereof, now known or later developed, configured to determine the geographic location of the vehicle 100 and/or to determine a travel route for the vehicle 100. The navigation system 147 can include one or more mapping applications to determine a travel route for the vehicle 100. The navigation system 147 can include a global positioning system, a local positioning system or a geolocation system.
The processor(s) 110, the PRA-CBF system 170, and/or the automated driving module(s) 160 can be operatively connected to communicate with the various vehicle systems 140 and/or individual components thereof. For example, returning to
The processor(s) 110, the PRA-CBF system 170, and/or the automated driving module(s) 160 can be operatively connected to communicate with the various vehicle systems 140 and/or individual components thereof. For example, returning to
The processor(s) 110, the PRA-CBF system 170, and/or the automated driving module(s) 160 may be operable to control the navigation and/or maneuvering of the vehicle 100 by controlling one or more of the vehicle systems 140 and/or components thereof. For instance, when operating in an autonomous mode, the processor(s) 110, the PRA-CBF system 170, and/or the automated driving module(s) 160 can control the direction and/or speed of the vehicle 100. The processor(s) 110, the PRA-CBF system 170, and/or the automated driving module(s) 160 can cause the vehicle 100 to accelerate (e.g., by increasing the supply of fuel provided to the engine), decelerate (e.g., by decreasing the supply of fuel to the engine and/or by applying brakes) and/or change direction (e.g., by turning the front two wheels). As used herein, “cause” or “causing” means to make, force, compel, direct, command, instruct, and/or enable an event or action to occur or at least be in a state where such event or action may occur, either in a direct or indirect manner.
The vehicle 100 can include one or more actuators 150. The actuators 150 can be any element or combination of elements operable to modify, adjust and/or alter one or more of the vehicle systems 140 or components thereof to responsive to receiving signals or other inputs from the processor(s) 110 and/or the automated driving module(s) 160. Any suitable actuator can be used. For instance, the one or more actuators 150 can include motors, pneumatic actuators, hydraulic pistons, relays, solenoids, and/or piezoelectric actuators, just to name a few possibilities.
The vehicle 100 can include one or more modules, at least some of which are described herein. The modules can be implemented as computer-readable program code that, when executed by a processor 110, implement one or more of the various processes described herein. One or more of the modules can be a component of the processor(s) 110, or one or more of the modules can be executed on and/or distributed among other processing systems to which the processor(s) 110 is operatively connected. The modules can include instructions (e.g., program logic) executable by one or more processor(s) 110. Alternatively, or in addition, one or more data store 115 may contain such instructions.
In one or more arrangements, one or more of the modules described herein can include artificial or computational intelligence elements, e.g., neural network, fuzzy logic, or other machine learning algorithms. Further, in one or more arrangements, one or more of the modules can be distributed among a plurality of the modules described herein. In one or more arrangements, two or more of the modules described herein can be combined into a single module.
The vehicle 100 can include one or more autonomous driving modules 160. The automated driving module(s) 160 can be configured to receive data from the sensor system 120 and/or any other type of system capable of capturing information relating to the vehicle 100 and/or the external environment of the vehicle 100. In one or more arrangements, the automated driving module(s) 160 can use such data to generate one or more driving scene models. The automated driving module(s) 160 can determine position and velocity of the vehicle 100. The automated driving module(s) 160 can determine the location of obstacles, obstacles, or other environmental features including traffic signs, trees, shrubs, neighboring vehicles, pedestrians, etc.
The automated driving module(s) 160 can be configured to receive, and/or determine location information for obstacles within the external environment of the vehicle 100 for use by the processor(s) 110, and/or one or more of the modules described herein to estimate position and orientation of the vehicle 100, vehicle position in global coordinates based on signals from a plurality of satellites, or any other data and/or signals that could be used to determine the current state of the vehicle 100 or determine the position of the vehicle 100 with respect to its environment for use in either creating a map or determining the position of the vehicle 100 in respect to map data.
The automated driving module(s) 160 either independently or in combination with the PRA-CBF system 170 can be configured to determine travel path(s), current autonomous driving maneuvers for the vehicle 100, future autonomous driving maneuvers and/or modifications to current autonomous driving maneuvers based on data acquired by the sensor system 120, driving scene models, and/or data from any other suitable source such as determinations from the sensor data 250. In general, the automated driving module(s) 160 may function to implement different levels of automation, including advanced driving assistance (ADAS) functions, semi-autonomous functions, and fully autonomous functions. “Driving maneuver” means one or more actions that affect the movement of a vehicle. Examples of driving maneuvers include accelerating. decelerating, braking, turning, moving in a lateral direction of the vehicle 100, changing travel lanes, merging into a travel lane, and/or reversing, just to name a few possibilities. The automated driving module(s) 160 can be configured can be configured to implement determined driving maneuvers. The automated driving module(s) 160 can cause, directly or indirectly, such autonomous driving maneuvers to be implemented. As used herein, “cause” or “causing” means to make, command, instruct, and/or enable an event or action to occur or at least be in a state where such event or action may occur, either in a direct or indirect manner. The automated driving module(s) 160 can be configured to execute various vehicle functions and/or to transmit data to, receive data from, interact with, and/or control the vehicle 100 or one or more systems thereof (e.g., one or more of vehicle systems 140).
Detailed embodiments are disclosed herein. However, it is to be understood that the disclosed embodiments are intended only as examples. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the aspects herein in virtually any appropriately detailed structure. Further, the terms and phrases used herein are not intended to be limiting but rather to provide an understandable description of possible implementations. Various embodiments are shown in the figures, but the embodiments are not limited to the illustrated structure or application.
The flowcharts 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 embodiments. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The systems, components and/or processes described above can be realized in hardware or a combination of hardware and software and can be realized in a centralized fashion in one processing system or in a distributed fashion where different elements are spread across several interconnected processing systems. Any kind of processing system or another apparatus adapted for carrying out the methods described herein is suited. A typical combination of hardware and software can be a processing system with computer-usable program code that, when being loaded and executed, controls the processing system such that it carries out the methods described herein. The systems, components and/or processes also can be embedded in a computer-readable storage, such as a computer program product or other data programs storage device, readable by a machine, tangibly embodying a program of instructions executable by the machine to perform methods and processes described herein. These elements also can be embedded in an application product which comprises all the features enabling the implementation of the methods described herein and, which when loaded in a processing system, is able to carry out these methods.
Furthermore, arrangements described herein may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied, e.g., stored, thereon. Any combination of one or more computer-readable media may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The phrase “computer-readable storage medium” means a non-transitory storage medium. A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: a portable computer diskette, a hard disk drive (HDD), a solid-state drive (SSD), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Generally, modules as used herein include routines, programs, objects, components, data structures, and so on that perform particular tasks or implement particular data types. In further aspects, a memory generally stores the noted modules. The memory associated with a module may be a buffer or cache embedded within a processor, a RAM, a ROM, a flash memory, or another suitable electronic storage medium. In still further aspects, a module as envisioned by the present disclosure is implemented as an application-specific integrated circuit (ASIC), a hardware component of a system on a chip (SoC), as a programmable logic array (PLA), or as another suitable hardware component that is embedded with a defined configuration set (e.g., instructions) for performing the disclosed functions.
Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber, cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present arrangements may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java™, Smalltalk, C++, or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
The terms “a” and “an,” as used herein, are defined as one or more than one. The term “plurality,” as used herein, is defined as two or more than two. The term “another,” as used herein, is defined as at least a second or more. The terms “including” and/or “having,” as used herein, are defined as comprising (i.e., open language). The phrase “at least one of . . . and . . . ” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. As an example, the phrase “at least one of A, B, and C” includes A only, B only, C only, or any combination thereof (e.g., AB, AC, BC, or ABC).
Aspects herein can be embodied in other forms without departing from the spirit or essential attributes thereof. Accordingly, reference should be made to the following claims, rather than to the foregoing specification, as indicating the scope hereof.
