WO2011009011A1 - Cadre intégré pour assistance d'opérateur de véhicule sur la base d'une prédiction de trajectoire et d'une évaluation de menace - Google Patents

Cadre intégré pour assistance d'opérateur de véhicule sur la base d'une prédiction de trajectoire et d'une évaluation de menace Download PDF

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Publication number
WO2011009011A1
WO2011009011A1 PCT/US2010/042203 US2010042203W WO2011009011A1 WO 2011009011 A1 WO2011009011 A1 WO 2011009011A1 US 2010042203 W US2010042203 W US 2010042203W WO 2011009011 A1 WO2011009011 A1 WO 2011009011A1
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Prior art keywords
threat
vehicle
control
optimal
operator
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PCT/US2010/042203
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English (en)
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Sterling J. Anderson
Steven C. Peters
Karl D. Iagnemma
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Massachusetts Institute Of Technology
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Priority claimed from US12/711,935 external-priority patent/US20100228427A1/en
Application filed by Massachusetts Institute Of Technology filed Critical Massachusetts Institute Of Technology
Priority to US13/254,761 priority Critical patent/US8437890B2/en
Publication of WO2011009011A1 publication Critical patent/WO2011009011A1/fr
Priority to US13/859,203 priority patent/US8744648B2/en

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    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B9/00—Simulators for teaching or training purposes
    • G09B9/02—Simulators for teaching or training purposes for teaching control of vehicles or other craft
    • G09B9/04—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass
    • G09B19/16—Control of vehicles or other craft
    • G09B19/167—Control of land vehicles
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B9/00—Simulators for teaching or training purposes
    • G09B9/02—Simulators for teaching or training purposes for teaching control of vehicles or other craft
    • G09B9/04—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles
    • G09B9/048—Simulators for teaching or training purposes for teaching control of vehicles or other craft for teaching control of land vehicles a model being viewed and manoeuvred from a remote point

Definitions

  • inventions described herein relate to a novel, corridor-based framework that performs threat assessment and provides varying degrees of mutually consistent automated operator assistance as a threat response in human-machine systems, such as locally or remotely-operated passenger vehicles, transport vehicles, agricultural machinery, fork lift trucks, aerial vehicles, robots, or surgical tools.
  • This framework explicitly considers human and machine dynamics without presuming operator intentions or limiting the
  • avoidance maneuver and its associated threat assessment
  • It provides a unified framework that allows for various modes and levels of mutually consistent operator assistance, from operator warning to stability control to passive intervention, to active semi-autonomous control, and finally, to autonomous machine operation.
  • Automotive active safety systems are concerned with preventing accidents through the introduction of various computer-controlled actuation methods to inform, improve, or override a human operator's steering and/or braking
  • Active safety systems currently in existence include yaw stability control, roll stability control, traction control, and antilock braking, among others. While these systems reduce accident frequency, their path-based and largely reactive nature limits their ability to: 1) accurately assess the threat posed by a given scenario and 2 ) adequately determine when and how to intervene to assist the driver. This dependence on a specific trajectory (amidst a myriad of options available to the operator) reduces the accuracy and significance of the threat assessment and leads to controllers that selectively replace (rather than assist) the driver in order to follow an automation-designated path.
  • inventions described and claimed herein relate, primarily, to the unified nature of the framework, and its flexibility in being able to accurately assess threat, and then participate in one or more of a wide variety of mutually consistent operator assistance modes, of varying levels of operator autonomy, from complete autonomy, to no autonomy.
  • Inventions described and claimed in the Threat assessment application relate primarily to threat assessment aspects of this novel framework. Both have similar disclosures.
  • sensing systems such as radar, LIDAR, cameras, inertial measurement units and GPS localization systems are used to detect, classify, and track the position of objects and the drivable road surface in the host vehicle's vicinity as well as measure vehicle states. Once these potential hazards have been identified, localized and their motion has been
  • a threat metric is used to quantify the threat they pose to the host vehicle, together with the threat of
  • threat assessment technology is designed to then trigger and/or implement countermeasures to reduce the threat. These countermeasures can be passive or active. The effectiveness of threat assessment metrics depends on their ability to correctly identify hazards and accurately assess the threat that potential hazards pose to the host vehicle.
  • Time-based threat measures project time to collision (TTC) based on current speeds, positions,
  • Distance-based metrics are generally calculated using
  • acceleration-based metrics assess the threat of a given maneuver based on the minimum (and often assumed
  • At least one known method of assessing threat relates to a vehicle that is intended to navigate along a path.
  • the path may be predetermined, or calculated, based on data, such as information about obstacles and a path followed by a track, such as a roadway in the case of a road vehicle, such as an automobile.
  • the path is a curve of simple
  • the system determines that danger has arisen, and the system generates a threat signal.
  • the threat of actual danger is potentially low, because vehicles driven by a human operator typically operate within a field of safe travel, or a corridor, rather than along a relatively arbitrary line, such as the centerline of a roadway.
  • This dependence on a specific trajectory reduces the accuracy and significance of the threat assessment and leads to controllers that selectively replace (rather than assist) the driver in order to follow an automation-designated path.
  • a path is a simple geometric curve in two-dimensional space, along which a vehicle may travel.
  • a path has a width of essentially zero.
  • a trajectory is a physically-achievable and time-parameterized sequence of vehicle states (such as
  • trajectory includes the velocity of a vehicle as an element. It may also be thought of as having time as a parameter of the path, which may then establish velocity at different locations.
  • a corridor may be considered to be the space between two curves in two-dimensional space. Travel anywhere within the corridor is considered to be safe.
  • a region is a concept that is defined in connection with inventions hereof, and it will be defined below.
  • a Model Predictive Controller is an optimal control method typically used to generate an optimal set of control inputs (spanning through a future time horizon) required to track a desired path while minimizing a user-defined objective function.
  • MPC Model Predictive Controller
  • a human driver typically operates a vehicle within a safe range of vehicle states. For example, the driver
  • a human operator operates within an tridimensional region of state space, rather than along a simple, zero, or nearly-zero width curve of a physical trajectory.
  • Some known threat assessors exist as a separate system, not integrated with other systems of the device analysis and control apparatus. These systems may base the assessment of threat on a device state exceeding a relatively arbitrary threshold. Further, these threat assessors
  • rudimentary threat assessment metrics based on, for example, the current deviation of the device from a predetermined optimal path. It would be desirable to be able to take
  • a further object would be to be able to
  • Still another object would be to be able to assess threat on a realistic corridor, rather than an unrealistic single path.
  • Another object would be to assess threat with an apparatus that is integrated with other systems of system analysis and control, which are also used to control the device or system, rather than assessing threat on a device state exceeding an arbitrary threshold.
  • Still another object would be to use the assessment of threat to generate an operator assistance signal to assist the operator in safely operating the device or replace the operator as necessary to ensure safe operation of the device.
  • Fig. 1 is a block diagram illustrating basic
  • Fig. 2 graphically shows an example of various potential intervention laws based on threat metric
  • Fig. 3a showing the different levels of intervention
  • Fig. 3b showing the relative locations of the host vehicle and the environment
  • FIG. 4 shows, in flowchart form, basic steps of a method of threat assessment and semi-autonomous control, with possible considerations at each step;
  • Figs. 5a, 5b and 5c show, graphically, a simulated test illustrating system response when driver fails to
  • Figs. 6a, 6b and 6c show, graphically, a simulated test illustrating system response to an erroneous driver swerve, as shown schematically in Fig. 6a, with steering angle shown in Fig. 6b and control authority K shown in Fig. 6c, where K represents the proportion of control authority given to autonomous system, with the driver allowed the remaining (1-K);
  • Figs. 7a, 7b and 7c show, graphically, a simulated test illustrating system response when driver fails to
  • FIG. 8 shows, in flow chart form, steps of a method of an invention hereof for assessing threat, with additional considerations associated with some of the steps;
  • FIG. 9a shows, schematically, an illustration of a simple path tracking control set up of the prior art
  • Fig. 10 is a schematic representation in block diagram form illustrating how a representative system of the prior art that can lead to poor, mutually inconsistent
  • Fig. 11 is a schematic representation in block diagram form showing an array of functions performed in a unified, mutually consistent manner by inventions hereof.
  • inventions described in the above-referenced Semi- Autonomous applications, and the Comprehensive Provisional patent application relate, among other things, to a unified framework for performing threat assessment and semi-autonomous vehicle navigation and control while allowing for adaptable and configurable intervention laws and configurable control inputs .
  • Inventions described in the Threat Assessment application relate to methods and apparati for identifying and quantifying threat being experienced by a system that can be modeled, such as a vehicle, such as a road vehicle, such as an automobile.
  • a model of the device with a model of the environment and the current state of the device and the environment are used to iteratively generate a
  • sequence of optimal device control inputs that, when applied to a model of the device, generate an optimal device
  • This optimal trajectory and the sequence of device control inputs that generates it is used to generate a threat assessment metric.
  • An appropriate type and level of operator assistance is generated based on this threat assessment.
  • Operator assistance modes include warnings, decision support, operator feedback, vehicle stability
  • each assistance mode is mutually consistent because they are generated using the same optimal trajectory.
  • identifying and quantifying threat will be referred to as assessing threat, or sometimes,
  • the methods and apparati for assessing threat are of the same type as can be used in the unified framework for semi-autonomous vehicle navigation and control, discussed in the applications mentioned above. Such methods and apparati can be used for assessing threat in any system that can be modeled.
  • the inventions described herein do not rely on a subjective prediction of a path intended by a human operator or a specific path proposed by an automatic path planning algorithm. Instead, these inventions base threat assessment and operator assistance decisions on dynamic properties and known constraints inherent to the vehicle and the environment. These inventions warn of deviation from a physically-constrained and dynamically- feasible region in N space (that includes a two-dimensional corridor in physical space) rather than an arbitrarily- calculated path, which is often less meaningful since it often does not represent a true, or even achievable, much less optimal route. Further, by predictively simulating the vehicle dynamics over a time horizon, which may be finite or infinite, its threat assessment and intervention operations explicitly and pre-emptively consider the combined effects of vehicle dynamics, stability constraints, and terrain interaction on maneuver severity.
  • a region is an N-dimensional analogue of a corridor.
  • a region is an n-dimensional area defined in the state space of the device model over a time horizon. This region is bounded by corridor constraints (which apply to vehicle position states), together with other state
  • constraints such as those imposed on vehicle states such as yaw angle, yaw rate, velocity, wheel sideslip angle, etc.).
  • MPC planners of inventions hereof are not required to utilize a single
  • planners described herein generate a course of motion that need not follow a predefined path but may instead generate its own optimal trajectory (and the control inputs necessary to achieve it) at successive time steps.
  • Threat assessment inventions described herein may be used with any system that can be modeled, including vehicles, such as terrestrial, nautical, and aerial vehicles,
  • the explanation is for illustrative purposes only, and that the inventions described herein can be used to identify and quantify (i.e., assess) threat in all systems that can be modeled.
  • the systems may be physical, such as devices and chemical processes. They may also be non-physical, such as economic systems.
  • the system model will include states (such as pitch angle for an aerial vehicle, solvent concentration for a chemical system, or asset prices for a financial system) for which a desired value or range of values exists and to which constraints (such as stall limits for an aerial vehicle, saturation for a chemical process, or price caps for a financial system) may apply.
  • Sensory information would include data related to nearby vehicles, pedestrians, road edges, and other salient features to assess accident threat.
  • navigation systems ideally operate only during instances of significant threat: it should give a driver full control of the vehicle in low threat situations but apply appropriate levels of computer-controlled actuator effort during high threat situations.
  • An active navigation system can therefore be termed semi-autonomous, since it must allow for human- controlled, computer-controlled, and shared human/computer vehicle operation.
  • Such a system should be as unobtrusive to the driver as possible (i.e. it should intervene only as much as is minimally required to avoid an impending accident).
  • Fig. 1 shows, schematically, in block diagram form, a basic framework operation.
  • a model Predictive Control (MPC) vehicle navigation block 110 starts with a model 112 of the environment, a model 114 of the device, in this case, a vehicle, and the vehicle's current state, including the position. It generates an optimal vehicle trajectory from the current position through a time horizon. The trajectory is optimal with respect to a predefined, configurable set of criteria. It also generates a corresponding optimal set of control input commands necessary to execute an optimal trajectory within the corridor and ensure that the vehicle 102 operates within safe driving limits (defined by a constraint-bounded region in the state space).
  • the environment model can be based on a priori known information (e.g. from maps) and/or information gathered by real time sensors, such as on-vehicle sensors 104 (e.g.
  • V2V sensors vehicle to vehicle
  • information 106 related to the environmental and potential environmental hazards, such as position of road edges, lane boundaries, holes, slopes, static obstacles (e.g. trees, road-side signs), and dynamic obstacles (e.g. other vehicles, pedestrians).
  • the vehicle model is user-defined and can be of varying complexity and fidelity.
  • the real-time sensors may also be mounted in the environment and communicate with the control system on the vehicle.
  • control inputs can be associated with one or multiple actuators, such as active steering, active braking, and others.
  • the predicted vehicle trajectory and associated control inputs may be generated via constrained optimal control, which leverages efficient optimization methods and constraint-handling
  • trajectory is used herein to mean a sequence of vehicle states, including its position, velocity, sideslip and yaw angles, etc.
  • the predicted vehicle trajectory and predicted control inputs are analyzed by a threat assessor 108 to quantify the threat to the vehicle by computing a configurable metric, such as the maximum lateral acceleration, sideslip angle, or roll angle over the trajectory, the minimum proximity to obstacles, or other metrics.
  • a configurable metric such as the maximum lateral acceleration, sideslip angle, or roll angle over the trajectory, the minimum proximity to obstacles, or other metrics.
  • Threat may be more formally considered to be a hierarchical combination of obstacle avoidance, stability-critical states, inputs, etc, based on a model of the vehicle.
  • the control authority exerted by the system can then be determined as a function of this generated threat: generally speaking, if the threat metric value is low, the control system intervention is low (i.e. the driver commands the vehicle with little or no computer-controlled intervention); if the threat metric value is high, the control system intervention is high.
  • the form of the intervention law modulating this control system authority is configurable and can differ for different actuators (i.e. a vehicle with both active steering and braking can have
  • intervention law can also be defined to adapt to driver performance based on an assessment of driver skill, and/or to include considerations for driver preference, environmental conditions, previous threat metric values, previous control inputs, and other factors.
  • Fig. 2 shows, schematically, examples of various potential
  • intervention laws showing, from top to bottom, linear, smooth and threshold-shaped intervention laws that depend only on predicted threat.
  • the vertical axis represents the degree of control authority given to the active navigation and control system while the horizontal axis represents the predicted threat, with cause for intervention increasing from left to right.
  • the control system begins to assume control authority to preempt an unsafe maneuver.
  • the controller's authority phases out. In this manner, the system can be said to be both predictive and semi-autonomous .
  • intervention law can be designed such that it assumes full authority by the time the predicted safe trajectory reaches the limit of any pre-defined critical vehicle states. This corresponds to a situation where only an optimal set of inputs would result in a safe vehicle trajectory.
  • FIG. 3 shows schematically an obstacle avoidance scenario illustrating different stages of intervention for an inattentive driver. Initially, the host automobile 302 is at location 1. No obstacles are in view, and the optimal
  • the predicted trajectory is a straight path.
  • the predicted threat is at a low level, indicated by the vertical line designated 1, near to the left hand side, which represents low threat.
  • the vehicle 302 advances along the roadway to the location 2, it comes nearer to a truck 304, whose velocity is either zero, or much less than that of the host vehicle 302.
  • the sensors sense the proximity of the obstacle vehicle 304, and generate a threat metric that is larger, as at the vertical line designated 2, near the right hand limit of the threat scale.
  • the optimal predicted trajectory assumes a curved shape, to avoid the obstacle 304. Simultaneously, the level of
  • intervention K increases (as shown by each of the three different intervention laws) so that, in a semi-autonomous system, the controller would take more and more control, the nearer to the obstacle the host vehicle 302 comes.
  • the controller may take different countermeasures, such as initiating a warning, priming brakes, seatbelts, or airbags, and/or engaging active systems, etc.
  • Fig. 4 shows, schematically, in flow chart form, a basic flow of steps performed by a controller of an invention described in the Semi-Autonomous applications, above, with possible considerations at each step.
  • An initial step 402 generates an optimal set of control inputs and corresponding vehicle trajectory by forward simulation. Considerations 402a for this step include, for example, (but are not limited to) the vehicle dynamics, current state of the vehicle and environment, terrain and environmental disturbances, available actuation, trajectory objectives, safety limits, and driver inputs.
  • a next step is to assess 404 the predicted threat to the vehicle.
  • General considerations 404a for this step include characteristics of the optimal path and associated control input, safety limits and driver inputs. The method of threat assessment is discussed below in more detail in connection with Fig. 8.
  • a next step is to generate D06 control authority gains, with a major consideration D06a at this stage being the desired intervention characteristic.
  • the next step D08 is to implement the scaled control for the current time.
  • FIGs. 5a, 5b and 5c show, graphically, the results of a simulated test illustrating system response when a driver fails to navigate a curve in the road, shown by in Fig. 5a by a pair of lines.
  • the trajectory that the driver would have followed without assistance is shown dashed. Note that it leaves the roadway. With assistance, it is shown solid black and remains within the roadway.
  • K represents proportion of control authority given to the autonomous system, with the driver allowed the remaining (1-K).
  • the middle graph, Fig. 5b shows the steer inputs, with the dashed line corresponding to the driver and the solid curve
  • Fig. 5c shows the control authority given to the autonomous system, in this case, steering, with the degree varying with distance (x) along the horizontal scale.
  • Figs. 6a, 6b and 6c show, graphically, the results of a simulated test illustrating the system response to an erroneous driver swerve.
  • K represents proportion of control authority given to autonomous system, with the driver allowed the remaining (1-K).
  • the same line types as above correspond to the driver without assistance (gray dashed) and with assistance (solid line).
  • the safe roadway corridor is shown in Fig. 5a by a pair of light solid lines in the upper graph. Distance is shown along the horizontal scale. The assisted trajectory remains within the safe roadway.
  • Figs. 7a, 7b and 7c show, graphically, a simulated test illustrating system response when a driver fails to anticipate/avoid an obstacle, similar to the scenario
  • K represents the proportion of control authority given to autonomous system.
  • the obstacle is simulated by a jog in the light line that represents one edge of the safe roadway. The only inputs used in this simulation are, again, steering of the driver and the autonomous system.
  • Threat assessment techniques disclosed herein, and uses within a system as described herein provide improved modularity and adaptability when compared to previous methods and apparati. Aspects of this improved modularity are
  • the underlying control framework can accommodate multiple actuation modes and vehicle models, allowing for ready application of the system to various vehicle types and actuator configurations.
  • the system's intervention law is also readily adapted (i.e. it can change over time based on an assessment of driver skill, driver preference, environmental conditions, previous threat metric values, previous control inputs, and other factors). These adaptations can be performed either statically or dynamically.
  • Gauging threat using metrics based on predicted vehicle state evolution within a region/corridor is novel. This includes threat assessment based on a trajectory that remains within that corridor, along with semi-autonomous control necessary to keep the vehicle within the safe
  • corridor it is generally meant a portion of physical space, such as the width of a roadway, or a roadway and adjoining break down lane.
  • region it is generally meant a region in N-space, in which two of these N dimensions may be the two spatial dimensions of the roadway's width and length, and other dimensions may be states and/or control inputs of the vehicle, such as sideslip angle, yaw angle, velocity, steering angle, etc. This leads to significant difference in performance of the two general approaches.
  • the simple path-based approach of the prior art is suited only to warn or selectively replace a human
  • the corridor/region approach described herein may warn, supplement, or act in conjunction with a human operator, as well as replace.
  • FIG. 8 a representative process for assessing threat is described. The steps about to be
  • Considerations 802a that are taken in conjunction with this step may include, but are not limited to: vehicle dynamics and constraints, trajectory objectives (controller objective function), terrain and environmental disturbances and environmental information, such as: obstacle locations, terrain properties (slope, roughness, friction coefficient, etc.) and other disturbances (such as wind) . It is through these considerations of vehicle dynamics and constraints and obstacle locations, that the corridor aspects of this method of threat assessment enters. Trajectory objectives more broadly describes the penalties applied to violating constraints and/or approaching unstable states and shapes what an optimal solution looks like within the
  • the model predictive controller generates an optimal trajectory, which constitutes an optimal sequence of inputs and the corresponding set of optimal vehicle states over a time horizon. These outputs are generated by the MPC unit, based on analysis of the models of the vehicle and the
  • model predictive control over a time horizon, as discussed in more detail below.
  • threat assessment methods disclosed herein operate on an optimal (sometimes referred to herein and the literature as best case, or benchmark)
  • a desired path gives a rather inflexible and in some cases, arbitrary goal state to which the driver will be forced to adhere or risk setting off warning indicators or controller intervention
  • the optimal trajectory gives an indication of the threat posed to the vehicle and a backup plan in case the human operator doesn't keep the vehicle within the
  • the optimal trajectory, of predicted states and predicted vehicle control inputs, over a time horizon, is next coalesced 804 to generate a scalar prediction metric.
  • Considerations 804a that contribute to the step of generating a scalar metric may include but are not limited to: past, present and/or predicted states, inputs and objective function costs.
  • Various norms discussed below may be used to combine trajectory components into this scalar.
  • Past present and/or predicted operator inputs and performance may also, but need not be taken into account.
  • the result of the coalescing step 804 is a scalar prediction metric.
  • This scalar can be non-dimensionalized 806 by normalizing it against known or approximated physical limits and/or predetermined desired thresholds to obtain a threat assessment at the current time. Suitable candidates for such thresholds include, but are not limited to maximum sideslip angle before loss of control, maximum load transfer before wheel liftoff, maximum lateral acceleration before skidding, maximum longitudinal acceleration before skidding, maximum total acceleration before skidding, maximum steer angle before actuator saturation, maximum available
  • Considerations 806a that may be taken into account include, but are not limited to known physical limits on vehicle dynamics (e.g. friction saturation limits, rollover thresholds, etc), desired intervention characteristics, operator performance and operator preference.
  • the result of the non-dimensionalizing step 806 is a threat assessment, which can then be used for one or more of a variety of mutually consistent threat response functions, depending upon the system configuration.
  • the threat assessment can be used to generate a warning, which may be perceptible by any human sense, including audible, visual, haptic and
  • the threat assessment can also, or alternatively, be used to trigger assistance, which may be passive, or active, as discussed above, and, if active, to varying
  • the threat assessment may even be used to modulate other system or vehicle characteristics.
  • Examples of passive assistance in the context of vehicular control include, but are not limited to: resistance torques on steering wheel, traction control, anti-lock braking systems.
  • Examples of active assistance include, but are not limited to adaptive cruise control, yaw stability assistance Electronic Stability Control (ESC) lane keeping assistance, obstacle avoidance. Modulation of other system characteristics may include but are not limited to: seatbelt pretensioning, brake priming, suspension modifications (active suspensions, suspension stiffness modifications, etc.).
  • Examples of semi- autonomous (a form of active assistance) assistance include, but are not limited to partial control of the steering and braking commands sent to the vehicle.
  • Examples of autonomous assistance include, but are not limited to, full control of one or more vehicle actuator such as wheel steering angles, braking torques, acceleration torques, etc.
  • Model Predictive (MPC) (or receding horizon) Control is a family of finite-horizon optimal control schemes that iteratively minimizes a performance objective defined for a forward- simulated plant model subject to performance and input
  • MPC uses a model of the plant to predict future vehicle state evolution and optimize a set of plant control inputs such that this prediction satisfies constraints and minimizes a user-defined objective function.
  • Model predictive control has a number of significant properties that make it particularly well suited to threat assessment for use with, for instance, autonomous and semi- autonomous vehicle navigation problems. Its ability to
  • MPC can be configured to according to inventions disclosed herein, to plan its own, optimal, path given a set of
  • the path thus planned through the (pre-delineated) safe operating environment potentially offers a number of advantages over alternative trajectory planning methods; not only is it explicitly aware of vehicle dynamics, measured disturbances, and actuator limitations, but the constraint-satisfying trajectory plan it generates is feasible, since it is obtained from an already- generated set of control inputs. It is also optimal, with respect to some performance metric such as minimum lateral acceleration over a future time horizon, minimum wheel slip, etc. In the semi-autonomous framework described below, this optimal prediction can serve not only as a optimal trajectory plan, but also as an effective threat assessor.
  • the first control element of this input sequence is implemented at the current time and the process is repeated at subsequent time steps. A state sequence spanning the same time period is also generated. No known MPC process uses elements of either of these sequences, after it has identified the single control input element for the current time. Only that single, current control input element is used.
  • inventions disclosed herein use these subsequent elements of one or both the state and control sequences to assess threat to which the device is (or may in the future be) exposed.
  • controller inputs also enables generation of a metric for analyzing the threat posed to the device by a given scenario.
  • This metric is comparable to and in many situations more useful/accurate than existing metrics because it is based solely on known or approximated physical limits of the
  • threshold threat values may trigger driver warnings at critical/desired threat thresholds.
  • threat assessment may be used to determine when and how strongly to intervene. The latter application is a topic of the Semi-Autonomous applications. This disclosure focuses on the design and development of the threat assessment metric itself .
  • a corridor-based trajectory-planning method may be used, based on constrained optimal control. When the objective function and constraints are defined as described below, the vehicle path calculated at each time step by the MPC
  • controller is assumed to be the best case or safest path within a corridor through the environment. Some key metrics from this prediction may be used to assess the instantaneous threat posed to the vehicle.
  • y k Cx, + D v v, with x, y, u, and v representing states, outputs, inputs, and disturbances of the system respectively, a quadratic
  • the vector V allows for variable constraint softening over the prediction horizon, p, when ⁇ is included in the objective function.
  • trajectory-tracking setup of a prior art controller and threat assessor, through a hazard-containing environment may then be illustrated by Fig. 9a.
  • Fig. 9a shows a vehicle 902a that seeks to avoid an obstacle of another vehicle 904a and a pedestrian 906a.
  • a path based controller and threat assessor would attempt to follow a desired single track path y des . This path may safely navigate the hazards, but it is unnecessarily restrictive.
  • y y max and y y mln represent the upper and lower limits on the vehicle lateral position (y) as illustrated in Fig. 9b.
  • the driveable corridor 908b is between these limiting curves. These limits exclude more than simply off-road/out-of-lane regions from the navigable corridor — they also extend to stationary and/or moving hazards in the roadway such as debris, pedestrians 906b or other vehicles 904b. Thus, a hazard in the roadway looks to the controller like a constriction in the corridor as illustrated by the arrows C in Fig. 9b.
  • the host vehicle 902b is permitted to travel anywhere within the corridor 908b.
  • Constraints can be softened by including the
  • the MPC objective function can be configured to force the constrained optimal solutions to satisfy corridor constraints before minimizing front wheel sideslip.
  • This hierarchy of objectives is achieved by setting constraint violation weights (p ⁇ ) significantly higher than the competing minimization weight (R aa ) on front slip. Then when constraints are not active, front wheel sideslip — and the corresponding threat — remains low.
  • p ⁇ constraint violation weights
  • R aa competing minimization weight
  • Additional norms that may be used include Root Mean Square of predicted state or states over the prediction horizon, and any of the above mentioned norms, with weighting profiles, over the prediction horizon (i.e. state predictions at a chosen time, for
  • closer to the current vehicle state may be weighted more heavily in the aggregate metric than predicted states at later times or at times other than the specifically chosen time.
  • the chosen time may be other than the current time, such as a time immediately after some other event).
  • front wheel slip is directly tied to, and tends to be a good indicator of, vehicle stability and controllability by front wheel steering.
  • available surface friction places a measureable limit on how large front wheel slip angles can become before loss of control is imminent. This limit provides a useful benchmark against which threat
  • assessments can be compared to assess maneuver stability (or nearness to instability).
  • the path prediction explicitly minimizes the very metric used to assess threat. This hierarchy of objectives — remain within the corridor while minimizing front slip as much as possible — thereby provides a "best case” or minimal-threat assessment from a dynamically-feasible maneuver.
  • the controller may not completely satisfy position constraints, making a an
  • the cost-based prediction J SI is related to the predicted front wheel sideslip by
  • a crowded location presents a more severe situation than traveling along an open road.
  • the system may note this and invoke a different level of threat assessment, because of the complexity, or severity of the situation.
  • the trajectory predictor (and actuator command generator in the case of semi-autonomous assistance) minimizes the very characteristics that describe threat. These characteristics, in turn, are based on an accurate model of the vehicle whose performance, stability, and safety may be assessed against an objective, physics-based metric.
  • the threat assessor combines states, inputs, and constraint violations using a weighting function, that is of the same form and order as the weighting function used by the trajectory planner and actuator command generator.
  • underground, etc. can be used with heavy equipment, such as agricultural equipment, fork lift trucks, cranes, etc., which not only move from place to place in an environment, but also, or alternatively, change their configuration from one moment to the next, such as when a crane is extending its boom. They may be used with naval vessels. Additionally, they may be used with unmanned or remotely-operated vehicles such as unmanned aerial drones, unmanned ground robots, etc.
  • the methods and apparati may be used with surgical machines, which are guided by a human surgeon who manipulates an input device, to drive an output device, such as a scalpel, cauterization tool, stitching machine, etc.
  • the environment of the surgical tool may be considered to include the body being operated upon, instrument supports, etc. They may also be used to control chemical processes, and power plants, such as nuclear, electric, etc.
  • inventions disclosed herein are also useful with respect to devices that do not move from one point to another in an environment, and are also useful in connection with large systems, such as power plants, and also processes, such as chemical processes.
  • a system that does not move within an environment still needs to account for the influence of external processes and disturbances upon it. Although these are not obstacles per se, they still represent threats to system success. In a chemical process, such an obstacle-like threat could be an external agency that dumps chemicals at different intervals into the mixture under control, or an uncontrolled temperature variation in the environment of the plant. Regarding a
  • the term process plant is used herein and in the claims to refer to the volume of material that is being transformed and manipulated according to a chemical process, which may be in a stationary vessel, or moving along in a series of pipes and vessels, over time.
  • the environment in which the process plant operates would include the vessels and atmospheres in which the process plant exists.
  • Environmental disturbances, or hazards could include ambient temperature influences, chemical pollutants, vibrations of equipment, etc.
  • inventions disclosed herein use the coordinates of such influences, in whatever coordinate system is relevant for understanding and evaluating their effect upon the process under control.
  • the coordinates of chemical process hazards may relate to which vessel, or run of conduit a disturbance affects.
  • Autonomous operation may be local or remote.
  • Remotely operated systems typically exhibit a time lag. Because this method for threat assessment and semi- autonomous operator assistance is based on a model predictive control prediction, it is particularly well suited for
  • Unmanned Ground Vehicles UUV s
  • Unmanned Aerial Vehicles UAV s
  • Unmanned Underwater Vehicles UUVs
  • remote surgical equipment In relaying signals between the operator and the vehicle, such systems frequently experience
  • the framework described here may be configured to explicitly incorporate these time delays into the optimal trajectory prediction, thereby eliminating the need for additional modules.
  • Known systems may be illustrated with reference to Fig. 10.
  • Known systems use distinct and sometimes competing modules to assist an operator.
  • Such distinct, independent systems 1000 can include, but are not limited to: warning devices 1002, a yaw stability controller 1004; a roll
  • the roll stability controller may output a steering command that is inconsistent with a steering command generated by the lane assist
  • the inventions described here may operate as a unitary, consistent whole, 1100, in conjunction with, or independent of, the human operator.
  • the MPC constitutes a state trajectory and control input planner 1102, which generates 1102 an optimal trajectory for the device, from current position through a time horizon, together with the device control inputs necessary to follow that trajectory.
  • predicting the threat posed to the vehicle given the driver's current and past performance, current and past vehicle state evolution, and environmental features/constraints. It is physically accurate because it is based on a forward-simulated vehicle model. It is predictive, because the vehicle model is simulated over a future time horizon. It is flexible because threat and intervention decisions are based on a corridor — rather than path characteristics.
  • Warning and operator feedback systems 1104 may inform any of a number of operator warning devices, including audible, visual, haptic, olfactory, etc.
  • the inventions may be configured to predictively and semi-autonomousIy act as a device stability controller 1106.
  • stability controllers that this system might replace include, but are not limited to yaw stability controllers, roll stability controllers, anti-lock brakes and traction controls.
  • this framework predicts vehicle state evolution over a projected time horizon and adjusts the current vehicle state to keep both current and predicted stability-critical states (such as yaw angle, wheel slip angles, etc.) below critical levels.
  • inventions are capable of is passive driver assistance 1108. These subsystems can apply various degrees of resistance, such as to steering wheel resistance torque overlays, braking (anti-lock brakes) traction control, yaw stability control, acceleration inhibitors, to discourage the operator from further increasing threat. (Note that there is some overlap between passive and reactive assistance systems and stability controllers . )
  • Active intervention modes 1110 go beyond providing resistive feedback and actively initiate steering and/or braking commands .
  • the inventions are well-suited to semi-autonomousIy assist the driver; utilizing sensory information related to the vehicle surroundings and a prediction of a safe vehicle trajectory through those surroundings to exert appropriate actuator effort and avoid impending accidents.
  • Sensory information would include data related to nearby vehicles, pedestrians, road edges, and other salient features to assess accident threat.
  • a second key advantage is that the invention may be configured to operate only during instances of
  • the corridor-based threat assessment upon which this intervention is based presents a key advantage over existing semi-autonomous safety systems in that using a corridor (as opposed to a path) allows intervention that does not
  • the inventions allow for human-controlled, computer-controlled, and shared human/computer vehicle operation.
  • intervention law can also be defined to adapt to driver performance based on an assessment of driver skill, and/or to include considerations for driver preference, environmental conditions, previous threat metric values, previous control inputs, and other factors.
  • the intervention law can also take on dynamics of its own, exhibiting, for example, hysteretic and higher-order behavior as a function of past, current, and predictive threat. See Fig. 1.
  • the inventions are capable of fully- autonomous vehicle control. That is, in high-threat scenarios or when desired by the human operator, this system can take full control of the vehicle, safely navigating it through the environment while avoiding both collisions and loss of control .
  • This multiplicity of highly competent options enables a unitary, integrated, mutually consistent, optimal operator assistance system. Because the modules for
  • the system can allow and direct control to flow seamlessly from a mere warning, to passive assistance, stability control, active assistance, semi-autonomous and fully autonomous, and back again, over and over again, automatically. All of the threat response activities are mutually consistent. This permits a truly, reliably user configurable semi-autonomous system. All of this flexibility flows from the use of generating an appropriate and active control input based on predicted threat.
  • the operations may be performed sequentially or simultaneously.
  • controller and threat assessor may have a driver input.

Abstract

Selon l'invention, divers types et niveaux d'assistance d'opérateur sont effectués à l'intérieur d'une structure configurable, unifiée. Un modèle du dispositif avec un modèle de l'environnement et de l'état actuel du dispositif et de l'environnement sont utilisés pour générer de manière itérative une séquence d'entrées de commande de dispositif optimales qui, lorsqu'elles sont appliquées à un modèle du dispositif, génèrent une trajectoire de dispositif optimale à travers un couloir ou une région délimité(e) par des contraintes, à l'intérieur de l'espace de l'état. Cette trajectoire optimale et la séquence d'entrées de commande de dispositif qui la génère sont utilisées pour générer une métrique d'évaluation de menace. Un type et un niveau appropriés d'assistance d'opérateur sont générés sur la base de cette évaluation de menace. Des modes d'assistance d'opérateur comprennent des avertissements, un support de décision, une rétroaction d'opérateur, une commande de stabilité de véhicule et un évitement de danger autonome ou semi-autonome. Les réponses générées par chaque mode d'assistance sont cohérentes les unes avec les autres car elles sont générées à l'aide de la même trajectoire optimale.
PCT/US2010/042203 2009-03-05 2010-07-15 Cadre intégré pour assistance d'opérateur de véhicule sur la base d'une prédiction de trajectoire et d'une évaluation de menace WO2011009011A1 (fr)

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PCT/US2010/025206 WO2010101749A1 (fr) 2009-03-05 2010-02-24 Système de navigation de véhicule semi-autonome prédictif
US12/711,935 2010-02-24
USPCT/US2010/0025206 2010-02-24

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