WO2024110919A1 - Système de commande électronique pour véhicule à moteur - Google Patents

Système de commande électronique pour véhicule à moteur Download PDF

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Publication number
WO2024110919A1
WO2024110919A1 PCT/IB2023/061843 IB2023061843W WO2024110919A1 WO 2024110919 A1 WO2024110919 A1 WO 2024110919A1 IB 2023061843 W IB2023061843 W IB 2023061843W WO 2024110919 A1 WO2024110919 A1 WO 2024110919A1
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driving path
motor
vehicle
planned
designed
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PCT/IB2023/061843
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English (en)
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Michele Basso
Antonio ACERNESE
Giulio BORRELLO
Luca LORUSSO
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C.R.F. Societa' Consortile Per Azioni
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Publication of WO2024110919A1 publication Critical patent/WO2024110919A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/06—Automatic manoeuvring for parking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D15/00—Steering not otherwise provided for
    • B62D15/02—Steering position indicators ; Steering position determination; Steering aids
    • B62D15/027—Parking aids, e.g. instruction means
    • B62D15/0285—Parking performed automatically
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • B60W2050/0033—Single-track, 2D vehicle model, i.e. two-wheel bicycle model
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
    • B60W2420/408—Radar; Laser, e.g. lidar
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/20—Static objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems
    • B60W2710/207—Steering angle of wheels
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • B60W2720/106—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/14—Yaw

Definitions

  • the invention finds application in any type of road motor vehicles, regardless of whether it is used for the transportation of people, such as a car, a bus, a camper, etc., or for the transportation of goods, such as an industrial vehicle (truck, B-train, trailer truck, etc.) or a light or medium-heavy commercial vehicle (light van, van, pick-up trucks, etc.).
  • an industrial vehicle truck, B-train, trailer truck, etc.
  • a light or medium-heavy commercial vehicle light van, van, pick-up trucks, etc.
  • the term “trajectory” will be used to indicate the state of a motor-vehicle, defined as the set of temporal trends as position, orientation, and speed, which define the desired states of the motor-vehicle motion, over a period of time, to distinguish it from the term “path”, which is generally used to indicate a sequence of positions of a motor-vehicle, without worrying about speed or higher-order terms.
  • path which is generally used to indicate a sequence of positions of a motor-vehicle, without worrying about speed or higher-order terms.
  • trajectory planning represents the motor-vehicle motion references design.
  • Low-speed trajectory planning can involve a large variety of different scenarios, including structured and unstructured environments, pedestrians, cyclists, etc.
  • real-time planning is crucial to the dexterity of autonomous motor vehicles when traversing environments with unknown obstacles.
  • a huge number of different motor-vehicle trajectory planning approaches have been proposed, which can be approximately classified into three macro-categories: heuristic-based methods, geometric-based methods, and methods based on optimal control techniques.
  • Heuristic-based approaches usually apply artificial intelligence techniques, such as machine learning methods, search-based methods and random sampling methods.
  • Geometric-based methods are suitable mainly for low speed applications such as automated parking but, at higher speeds, these can’t consider the dynamic behaviour of motor-vehicle and therefore its stability.
  • Most of the geometric-based and heuristic-based methods generate paths instead of trajectories.
  • To obtain a trajectory some speed profile could be used to convert the computed path into a trajectory.
  • Optimal control-based methods use optimal control techniques such as MPC (Model Predictive Control) and NLP (Non-Linear Programming) in order to generate the trajectory.
  • MPC Model Predictive Control
  • NLP Non-Linear Programming
  • optimization techniques are used to find the appropriate control input sequence, i.e., steering wheel angle and motor-vehicle longitudinal acceleration, that drives the motor-vehicle to the desired end-point.
  • the behaviour of the system in term of system states to the given sequence of control actions is computed by a model-based prediction.
  • These methods enable the direct definition of trajectories instead of paths.
  • a real-time, MPC-based, low-speed trajectory planning is proposed in WO 2021/079338 A1 to the present Applicant.
  • WO 2021/079338 A1 discloses an automotive electronic dynamics control system for a motor-vehicle equipped with an automotive automated driving system designed to cause the motor-vehicle to perform low-speed manoeuvres in automated driving and comprising an automotive sensory system designed to sense motor-vehicle-related quantities, and automotive actuators comprising an Electric Power Steering, a Braking System and a Powertrain (PT).
  • the electronic dynamics control system is disclosed to be designed to implement a Driving Path Planner designed to receive data representative of static obstacles in the surroundings of the motor vehicle and representing static space constraints to the motion of the motor vehicle, and compute, based on the received data, a planned driving path for the motor vehicle during a low-speed manoeuvre performed in automated driving.
  • the electronic dynamics control system is disclosed to be designed to further implement a Model Predictive Control (MPC)-based Trajectory Planner and Controller designed to receive from the Driving Path Planner data representative of the planned driving path and from the automotive sensory system data representative of positions and orientations of the motor vehicle and of dynamic obstacles in the surroundings of the motor vehicle and representing dynamic space constraints to the motion of the motor vehicle, and compute, based on the received data, a planned lateral trajectory and a planned longitudinal trajectory for the motor vehicle during the low-speed manoeuvre performed in automated driving.
  • MPC Model Predictive Control
  • the electronic dynamics control system is disclosed to be designed to further implement a Motion Controller designed to receive from the Trajectory Planner and Controller data representative of the planned lateral and longitudinal trajectories, and compute commands for the Electric Power Steering based on the planned lateral trajectory, and for the Braking System and the Powertrain based on the planned longitudinal trajectory.
  • the Driving Path Planner is disclosed to be designed to compute the planned driving path within an obstacle-free driving corridor, within which the motor vehicle may be driven and made up of a series of driving path segments each with a length and an orientation referenced in an inertial reference frame.
  • the MPC-based Trajectory Planner and Controller is disclosed to comprises an MPC-based Lateral Trajectory Planner and Controller designed to compute the planned lateral trajectory as a series of steering requests ( ⁇ ) referenced in a motor vehicle reference frame, and an MPC-based Longitudinal Trajectory Planner and Controller designed to compute the planned longitudinal trajectory as a series of longitudinal acceleration requests.
  • the Lateral Trajectory Planner and Controller is disclosed to be designed to further compute the planned lateral trajectory based on a linearized Model which exhibits a representation singularity whenever the relative orientation of a couple of successive driving path segments of the planned driving path is equal to or higher than a given amount, and to dynamically modify relative orientation of the motor- vehicle reference frame and the inertial reference frame along the planned driving path to cause the relative orientations of all of the couples of successive driving path segments of the planned driving path to be lower than the given amount.
  • the Lateral Trajectory Planner and Controller is disclosed to be designed to compute the orientation of the motor-vehicle reference frame relative to the inertial reference frame based on, in particular as a (linear) interpolation of, the orientations of the driving path segment currently driven by the motor vehicle and of one or more of the next driving path segments.
  • S UBJECT - MATTER AND S UMMARY OF THE I NVENTION The aim of the present invention is to provide an improved MPC-based, low- speed trajectory planning with dynamic obstacle avoidance that is able to generate a dynamically feasible, comfortable, and customizable trajectory that allows motor- vehicles to perform low-speed manoeuvres in automated driving.
  • FIG. 1 shows a block diagram of an automotive automated driving system to perform low-speed manoeuvres.
  • Figure 2 shows an automotive sensory platform of the an automotive automated driving system.
  • Figure 3 shows inertial and motor-vehicle reference frames for lateral control.
  • Figure 4 shows a general block diagram of trajectory planning and control for low-speed manoeuvres.
  • Figure 5 shows a general block diagram of longitudinal control setup.
  • Figure 6 shows a flowchart of an algorithm of a rotation angle optimization.
  • Figures 7 and 8 comparatively show choices of orientation according to the state of the art and to an embodiment described in the present disclosure, respectively.
  • Figures 9 and 10 comparatively show ad hoc free-corridor constructed according to the state of the art and to an embodiment of the present disclosure, respectively.
  • DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION The present invention will now be described in detail with reference to the attached figures to allow a person skilled in the art to make and use it.
  • Various modifications to the described embodiments will be immediately apparent to the persons skilled in the art and the generic principles described can be applied to other embodiments and applications without departing from the protective scope of the present invention, as defined in the attached claims. Therefore, the present invention should not be considered limited to the described and illustrated embodiments, but it must be accorded the widest protective scope in accordance with the described and claimed characteristics.
  • Figure 1 shows a block diagram of an automotive electronic automated driving system 1 of a motor vehicle 2 and designed to cause the motor vehicle 2 to perform low-speed manoeuvres in automated driving.
  • the automated driving system 1 comprises: - automotive systems, of which only those involved in the implementation of the present invention will be described below, and comprising, inter alia, an automotive sensory system or platform 3 designed to detect motor-vehicle-related quantities comprising, by way of example, wheel angle, steering wheel angle, yaw rate, longitudinal and lateral acceleration, position, etc., and automotive actuators 4 comprising, inter alia, Electric Power Steering (EPS) 5, Braking System Module (BSM) 6, and PoWerTrain (PWT) 7; and - an automotive electronic control unit (ECU) 8 designed to communicate, via an automotive on-board communication network 9, such as a high-speed CAN, also known as C-CAN, FlexRAy or others, with the automotive sensory platform 3 and the automotive actuators 4, directly or indirectly, i.e., via dedicated automotive electronic control units, and to store and execute an automated driving software comprising software instructions which, when executed, cause the ECU 8 to become configured to communicate and cooperate with the with the automotive sensory platform 3 and the automotive
  • the automotive sensory platform 3 may comprise traditional normal production ESC inertial Active Chassis sensors comprising longitudinal and lateral acceleration sensors, yaw rate sensors, and environment sensors including a (dual antenna) GNSS receiver, one or different forward-looking stereo cameras, a normal production forward-looking camera, one or different lidar sensors, one or more radar sensors, and a number of ultrasonic sensors.
  • EPS 5 comprises an electric motor operatively coupled to either a steering gear or a steering column and electrically controlled by the ECU 8 based on angular position and torque of the steering column sensed by the automotive sensory system or platform 3 to apply assistive steering torque and, resultingly, provide different amounts of assistance depending on driving conditions.
  • a normal production EPS driven by a traditional Park Assist HWTO (Hand Wheel Torque Overlay) interface on the C-CAN 6 has been modified in terms of maximum motor-vehicle speed to result in the EPS providing not only assistance to the motor-vehicle driver but also a mechatronic unit to steer the motor-vehicle in a stand-still condition.
  • BSM 6 represents a functional interface to realize acceleration and deceleration actions on the motor-vehicle 2.
  • the ECU 8 provides on the C-CAN 9 a functional channel able to be a gateway of acceleration / positive torque to the powertrain ECU (ECM) and the way to decelerate the motor-vehicle 2 by brakes.
  • the normal production parking longitudinal interface has been modified in order to achieve technical targets of present invention.
  • Dual Dry Clutch Transmission (DDCT) ECU software has been modified in order to manage the longitudinal manoeuvres from/to 0/15 km/h around to 0 Nm of engine torque in a comfortable way.
  • MOTOR-VEHICLE MODEL The Applicant has experienced that well-known dynamic models currently used for Trajectory Planning and Control for low-speed manoeuvres are undefined or ill-conditioned at low speeds. As shown to be effective in Polack et al., The Kinematic Bicycle Model: a Consistent Model for Planning Feasible Trajectories for Autonomous Vehicles, IEEE Intelligent Vehicles Symposium (IV), 2017, and Kong et al.
  • Kinematic and dynamic vehicle models for autonomous driving control design in 2015 IEEE Intelligent Vehicles Symposium 2015, pp.1094-1099, the model used for low speed applications (0 ⁇ 20 km/h) is the kinematic bicycle model.
  • Lateral Motor-Vehicle Model With regard to the lateral motor-vehicle model, the 4-DoF (Degree of Freedom) kinematic bicycle model is one of the simplest and well-conditioned models used in motion planning to approximate and plan the motion of a motor vehicle in the context of autonomous driving.
  • Figure 3 shows the inertial and motor-vehicle reference frames for lateral control, in which the left and right wheels are approximated with two single wheels at points A and B, respectively for the front and the rear axles.
  • the steering angles of the front and rear wheels are represented by ⁇ f ⁇ R and ⁇ r ⁇ R, respectively.
  • ⁇ ⁇ ⁇ ⁇ ⁇ (1)
  • ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ (2)
  • ⁇ ⁇ (4)
  • x ⁇ R and y ⁇ R are the Cartesian coordinates of the motor- vehicle’s rear wheel
  • v : [v min , v max ] ⁇ R
  • a : [a min , a max ] ⁇ R denote the velocity and longitudinal acceleration, respectively.
  • the described kinematic model is nonlinear.
  • An approach to efficiently solve the trajectory planning problem is to use a “divide et impera” approach.
  • the original model can be divided into longitudinal and lateral dynamics, respectively, to obtain two independent yet simpler sub-models.
  • a nonlinear state and input transformation can be applied to the lateral model, namely the time-state control form (T-SCF) disclosed by Sampei, M.
  • T-SCF time-state control form
  • the dynamics control system 10 may be entirely implemented by the ECU 8 or its implementation may be distributed among different ECUs, according to a proprietary logical architecture that the automotive manufacturer will decide to adopt. For ease of description, in the following the dynamics control system 10 will be described to be entirely implemented by the ECU 8, without thereby this implying any loss of generality.
  • the dynamics control system 10 is designed to implement a Driving Path Planner 11 designed to receive data representative of static obstacles, such as roads, buildings, etc., in the surroundings of the motor vehicle 2 and representing, in the form of, e.g., a high definition road map or a binary occupancy map, static space constraints to the motion of the motor vehicle 2, and to compute a planned driving path for the motor vehicle 2 during a low-speed manoeuvres performed in automated driving based on the positions of the static obstacles in the surroundings of the motor vehicle 2.
  • the Driving Path Planner 11 is designed to compute the planned driving path within an obstacle-free or collision-free driving corridor, within which the motor vehicle 2 may be driven in automated driving without colliding with any of said static obstacles.
  • the planned driving path is made up of waypoints which do not collide with any static objects in the environment and form a series of driving path segments each defined by respective start and end waypoints, an orientation and a driving direction referenced in an inertial (or absolute) reference frame.
  • inertial or absolute
  • the dynamics control system 10 is further designed to implement a Model Predictive Control (MPC)-based Trajectory Planner and Controller 12 designed to receive from the Driving Path Planner 11 data representative of the planned driving path of the motor vehicle 2 and to compute, based thereon, a planned lateral trajectory and a planned longitudinal trajectory of the motor vehicle 2 as a series of high-level vehicle requests for steering wheel angle and acceleration, as described in more detail in the following.
  • MPC-based Trajectory Planner and Controller 12 comprises two distinct MPC-based Trajectory Planners and Controllers, a Lateral Trajectory Planner and Controller 12a designed to plan and control the Lateral dynamics, and a Longitudinal Trajectory Planner and Controller 12b designed to plan and control the Longitudinal dynamics.
  • the Lateral Trajectory Planner and Controller 12a is designed to compute the planned lateral trajectory as a series of steering requests ⁇ along the planned driving path in a motor-vehicle reference frame
  • the Longitudinal Trajectory Planner and Controller 12b is designed to compute the planned longitudinal trajectory as a series of longitudinal acceleration requests a of the motor vehicle 2 along the planned driving path.
  • Model Predictive Control has been developed considerably over the last two decades. The main advantage of the MPC is the fact that it allows the current timeslot to be optimized while taking future time-slots into account. This is achieved by optimizing a finite time-horizon, but only implementing the first time-slot. MPC can manage future reference profiles and constraints and anticipate control actions accordingly.
  • the dynamics control system 10 is further designed to implement a Motion Controller 13 designed to receive from the Trajectory Planner and Controller 12 data representative of the planned lateral a longitudinal trajectories and to compute appropriate commands for the automated driving system 1, as described in more detail in the following.
  • MPC-based Lateral Trajectory Planner and Controller Given the motor-vehicle dynamic model expressed by equation (5), the MPC- based Lateral Trajectory Planner and Controller 12a is designed to control the motor- vehicle lateral dynamics to the optimal path according to motor-vehicle constraints on the steering angle ⁇ min and ⁇ max and environmental constraints on the obstacle-free driving corridor.
  • the lateral control problem can be formulated as an MPC problem as follows: (8) subject to the following constraints: Ymink ⁇ z2(k) ⁇ Ymaxk ⁇ k ⁇ (0,HLat] tan( ⁇ min) ⁇ ⁇ ( z3, ⁇ 2) ⁇ tan( ⁇ max) where Hlat ⁇ N is the prediction horizon, and yrefk ⁇ R Hlat+1 is the centerline of the left and right free-spaces are obtained by discretizing by ⁇ s the segments that connect the path planning waypoints. The combination of these two vectors is generally referred to as the free corridor of the Trajectory Planning.
  • Equation (8) shows the formulation of the Trajectory Planning as an MPC- based problem that focuses only on the lateral control of the motor vehicle. Since it is designed as a linear optimization problem, it turns out that the solution can be computed efficiently and guarantees a deviation from the reference path that shrinks to zero over the time-steps.
  • the MPC-based Lateral Trajectory Planner and Controller 12a is designed to compute and store in ⁇ OUT the last orientation of the equivalent segment created between the first waypoint and the next ones that satisfies the condition
  • ⁇ ⁇ th where ⁇ th is a user-settable threshold lower than ⁇ /2.
  • the obtained segment orientation is then used as a new reference ⁇ ref , and the process repeats until the last waypoint is reached.
  • the functions PickIndex and PickOrientation given the index i k of the segment where the motor-vehicle is located, extract the information necessary to compute the rotation angle at that time, through the formula: (9) where: dk is the Euclidean distance at time-step k between the current position of the motor-vehicle (the motor-vehicle rear center axle) and the end waypoint of the driving path segment ik containing the current position of the motor-vehicle, ⁇ 1,0 ⁇ is the distance between the end waypoint of the driving path segment ik containing the current position of the motor-vehicle and the end waypoint of the next driving path segment following the one containing the current position of the motor- vehicle, and ⁇ 1 ⁇ and ⁇ 2 ⁇ are the first two orientations of ⁇ OUT w.r.t.
  • Rotation angle ⁇ Rot optimization Require: XSEG,YSEG, ⁇ SEG,iMAX,ik ⁇ OUT (0) ⁇ ⁇ SEG (0) IOUT(0) ⁇ 0 ⁇ ref ⁇ ⁇ SEG(0) n ⁇ 1,i ⁇ 1,j ⁇ i + 1 while j ⁇ i MAX do ⁇ new ⁇ ComputeAngle(X i ,Y i ,X j+1 ,Y j+1 ) while
  • ⁇ ⁇ th do ⁇ new ⁇ ComputeAngle(Xi,Yi,Xj+1,Yj+1) j ⁇ j + 1 end while ⁇ OUT ⁇ new through (9)
  • X SEG and Y SEG are vectors containing the waypoints (x i , y i ) provided by the higher level path planning module
  • ⁇ SEG is the vector of orientations of the segments derived from these waypoints
  • ik is the segment i in which the motor vehicle is located at that time.
  • iMAX represents a stopping iteration condition, that can for example take into account the end of the trajectory or a change in the direction of motion.
  • the Lateral Trajectory Planner and Controller 12a is designed to compute the rotation angles ⁇ ⁇ ⁇ ⁇ of which the motor- vehicle reference frame is to be rotated relative to the inertial reference frame along the planned driving path by: - computing an equivalent driving path which approximates/simplifies the planned driving path in terms of waypoints and orientation while maintaining the same destination and is made up of a series of equivalent driving path segments, wherein: ⁇ an equivalent driving path segment is computed to extend from a start waypoint of a planned driving path segment and an end waypoint of either the same or a different planned driving path segment along the planned driving path; ⁇ an equivalent driving path segment is therefore defined by (the coordinates xi, yi) of the start and end waypoints of the planned driving path segment(s) between which the equivalent driving path segment extends, an equivalent orientation ⁇ and a driving direction referenced in the inertial reference frame; and ⁇ the end waypoint of an equivalent driving path segment is computed as the end waypoint of the last planned driving path
  • Figures 7 and 8 comparatively show the choices of segment orientation according to WO 2021/079338 A1 to the present Applicant and to the rotation angle ⁇ Rot optimization disclosed above, respectively.
  • the light grey waypoints represent the waypoints delimiting the planned driving path segments;
  • the light grey dashed lines, denoted with reference letter A represent the planned driving segments;
  • the dark grey dashed lines, denoted with reference letter B represent the planned driving segments exploitable in the MPC horizon;
  • the darker grey dashed lines, denoted with reference letter C represent equivalent driving path segments that establish ⁇ 0, ⁇ 1 and ⁇ 2 and the light grey cone forms the angle ⁇ th.
  • the rotation angle ⁇ Rot optimization solves the problem of the singularities without limiting the number of segments to take into account.
  • the proposed approach provides two main benefits, i.e., i) it maximizes the prediction horizon that the MPC can use, and ii) it allows arbitrary dense path as inputs, with a significant improvement in the trajectory accuracy.
  • the rotation angle ⁇ Rot resulting from the proposed algorithm is bounded by construction, for the sake of practical applications, it could be convenient to apply an additional saturation to equation (9) to increase comfort. It can be expressed as follows: where ⁇ SAT i ... 0 ⁇ , and its value depends on the particular application.
  • Figures 9 and 10 comparatively show the ad hoc free-corridors constructed according to WO 2021/079338 A1 to the present Applicant and to the rotation angle ⁇ Rot optimization disclosed above, respectively, where X SEG and Y SEG shown in Figure 10 are vectors containing the waypoints (xi, yi) provided by the higher level path planning module, i.e. the Driving Path Planner 11.
  • a free-corridor is the area inside which the vehicle has to be during the maneuver.
  • Each of the free-corridors of Figures 9 and 10 is depicted as delimited between two broken lines, which approximate a curve to be travelled by the motor vehicle 2.
  • the outer broken lines (right broken line) is defined by the two vectors X min and Y min and the inner broken line (left broken line) is defined by the vectors Xmax and Ymax.
  • the waypoints contained in vectors XSEG and YSEG are enclosed in the free-corridor.
  • Vector ⁇ SEG is the one containing the orientations of the segments derived from these waypoints.
  • Vectors Ymin and Ymax represent the bounds of the free-corridor for the lateral dynamics of the motor vehicle 2 while vectors X min and X max represent the boundaries of the free-corridor for the longitudinal dynamics of the motor vehicle 2.
  • the proper creation of vectors Ymin and Ymax is crucial in the performance of the lateral MPC.
  • the output of the Driving Path Planner 11 is a static path P comprising a plurality of waypoints.
  • the waypoints of the path P are first pre-processed in order to be effectively used to create a drivable trajectory.
  • the MPC-based Trajectory Planner and Controller 12 is configured to firstly apply a resampling to path P to reduce the number of the waypoints so as to take only the those necessary, based on a trade-off between accuracy in approximation of the path and dimensions of the vector that will then be used by the MPC.
  • the outputs of this step are vectors XSEG, YSEG and ⁇ SEG.
  • the MPC-based Trajectory Planner and Controller 12 computes the free-corridor.
  • the Lateral Trajectory Planner and Controller 12a is designed to compute the free-corridor by computing, for each waypoint of the path P, a pair of corridor segments, i.e. left and right segments, which are parallel to the segment associated to said waypoint, i.e. the segment starting with said waypoint, and are located at a same user-settable distance from said segment on opposite sides of it.
  • the computing of the pair corridor segments comprises the following steps: - for each waypoint of the path P, computing a line perpendicular to the segment associated to the waypoint, i.e.
  • the computing of the free corridor comprises the following further steps: - finding the intersection point between two consecutive corridor segments; and - saving the resulting intersection points in the vectors Y min and Y max , particularly the intersection points generated by the consecutive corridor segments on the right of the path P are saved in vector Ymin and the intersection points generated by the consecutive corridor segments on the right of the path P are saved in vector Y max .
  • the intersection points collected in the vectors Y min and Y max defines the two (left and right) boundaries of the free corridor along the lateral direction.
  • the free-corridor determined as disclosed above is a static corridor with respect to the motor-vehicle reference frame. Therefore the free-corridor will be roto- translated with respect to the inertial reference frame, said roto-translation comprises a rotation according to the rotation angle ⁇ Rot.
  • MPC-based Longitudinal Trajectory Planner and Controller Like the MPC-based Lateral Trajectory Planner and Controller 12a, also for MPC-based Longitudinal Trajectory Planner and Controller 12b the control technique is the linear MPC. This allows speed reference and obstacle tracking to be managed in a single integrated approach.
  • the longitudinal dynamics expressed by equation (6) can be discretized leveraging the Tustin rule, obtaining the following discrete-time longitudinal model: (10) where: ⁇ is the longitudinal position of the motor-vehicle from the start of the planned driving path, ⁇ is the longitudinal speed, and ⁇ t ⁇ R is the discretization step w.r.t. time.
  • the MPC-based Trajectory Planning problem for the longitudinal control can be then formulated as: subject to the following constraints: where HLong is the prediction horizon, j min and j max are minimum and maximum allowed jerks, and vrefk is the motor vehicle speed reference.
  • ⁇ max k min( ⁇ Goal , ⁇ Corridor , ⁇ Obstacle ) (13)
  • the stop condition takes into account the goal position ( ⁇ Goal ), dynamic objects ( ⁇ Obstacle) and the availability of a sufficiently large road ( ⁇ Corridor ) and takes the minimum of those stop conditions.
  • Motion Controller The previously-described motor-vehicle models assume that the control inputs, i.e., the series of steering requests ⁇ and the series of longitudinal acceleration requests a, can be directly controlled.
  • the steering angle control is realized by using the EPS torque interface available on the CAN.
  • the motor-vehicle longitudinal motion is controlled by means of acceleration/deceleration requests to the braking system through normal production Adaptive Cruise Control interface.
  • Steering Control The EPS low level control loop has been designed by using a state feedback controller comprising: - a linear time invariant Kalman observer that filters/estimates the system states, useful also to estimate driver hands on the steering wheel, and - an optimal linear quadratic integral controller able to track the steering wheel angle reference from MPC-based Lateral Trajectory Planner and Controller 12a.
  • a motor-vehicle is faced in which the motor-vehicle interfaces, for longitudinal dynamic control, are: - engine-torque request: delivered to Engine Control (T reqs ) - deceleration request: delivered to Braking System (areqs)
  • T reqs Engine Control
  • deceleration request delivered to Braking System (areqs)
  • the longitudinal acceleration control is implemented by means of the structure shown in Figure 5.
  • the command ax is split in two channels, one for each interface.
  • the Braking System includes a deceleration closed loop that allows to accept directly deceleration command (a reqs ) and to generate a braking torque T bra .
  • the Engine Control performs a closed-loop control based on an estimation of applied engine torque TEng. To match the available interface it has been implemented an inverse vehicle model, to convert a x in T reqs , which considers the current motor-vehicle configuration (e.g., gear ratio, inertia, friction) and mainly exogenous input (e.g. road slope, drag forces).
  • a motor-vehicle configuration e.g., gear ratio, inertia, friction
  • exogenous input e.g. road slope, drag forces

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  • Engineering & Computer Science (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

L'invention concerne un système de commande de dynamique électronique d'automobile (10) destiné à un véhicule à moteur (2) équipé d'un système de conduite automatisée d'automobile (1) conçu pour amener le véhicule à moteur (2) à effectuer des manœuvres à faible vitesse en conduite automatisée. Le système de commande de dynamique électronique (10) est conçu pour mettre en œuvre un planificateur de trajet de conduite (11) conçu pour calculer le trajet de conduite planifié composé d'une série de segments de trajet de conduite comportant chacun des points de cheminement de début et de fin et une orientation référencée dans un référentiel inertiel. Le système de commande de dynamique électronique (10) est en outre conçu pour mettre en œuvre un système de planification et de commande de trajectoire (12) basé sur une commande prédictive de modèle (MPC) conçu pour calculer une trajectoire latérale planifiée pour le véhicule à moteur (2) pendant la manœuvre à faible vitesse effectuée en conduite automatisée. Le système de planification et de commande de trajectoire (12) est en outre conçu pour modifier de manière dynamique l'orientation relative (θ ROT ) d'un référentiel de véhicule à moteur par rapport au référentiel inertiel le long du trajet de conduite planifié de manière à ce que les orientations relatives de tous les couples de segments de trajet de conduite successifs du trajet de conduite planifié soient inférieures à la quantité donnée. Le système de planification et de commande de trajectoire (12) est en outre conçu pour calculer des angles de rotation (θ ROT ) selon lesquels le référentiel de véhicule à moteur doit tourner par rapport au référentiel inertiel le long du trajet de conduite planifié. Le système de planification et de commande de trajectoire (12) est en outre conçu pour calculer un couloir de conduite sans obstacle, à l'intérieur duquel le véhicule à moteur (2) peut être conduit en conduite automatisée sans entrer en collision avec l'un quelconque des obstacles, en calculant, pour chaque point de cheminement du trajet de conduite planifié, une paire de segments de couloir, qui sont parallèles au segment de trajet de conduite associé audit point de cheminement et qui sont situés à une même distance réglable par l'utilisateur du segment de trajet de conduite planifié sur des côtés opposés de celui-ci.
PCT/IB2023/061843 2022-11-24 2023-11-23 Système de commande électronique pour véhicule à moteur WO2024110919A1 (fr)

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WO2021079338A1 (fr) 2019-10-23 2021-04-29 C.R.F. Societa' Consortile Per Azioni Planification et commande de trajectoire de véhicule à moteur pour amener des véhicules à moteur automatisés à effectuer des manœuvres à faible vitesse en conduite automatisée

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