Classifications

• • 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
  • B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
• • 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
  • B60W30/10—Path keeping
  • B60W30/12—Lane keeping
• • 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
  • B60W50/0097—Predicting future conditions
• • 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

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, 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 as 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.
• SUBJECT-MATTER AND SUMMARY OF THE INVENTION 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.
• an automotive electronic dynamics control system for an autonomous motor vehicle is provided, as claimed in the appended claims.
• Figure 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 the rotation angle optimization according to the present invention.
• Figures 7 and 8 comparatively show choices of orientation according to the state of the art and to the present invention, respectively.
• Figures 9 and 10 comparatively show ad hoc free-corridor constructed according to the state of the art and to the present invention, respectively.
• 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)
• ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ (3)
• ⁇ ⁇ ⁇ (4)
• x ⁇ R and y ⁇ R are the Cartesian coordinates of the motor- vehicle’s rear wheel
• v : [v min , v max ] ⁇ R
• 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.
• 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 as an obstacle-free or collision-free driving corridor or path within which the motor vehicle 2 may be driven and made up of waypoints that do not collide with any static objects in the environment and that 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) reference frame several solutions have been proposed to solve the path planning problem, although most of them are limited to simulated scenarios and have not been applied to real prototypes. Within them, searches through graphs are very popular for their simplicity and effectiveness, e.g., by combining Voronoi decomposition diagram with path search algorithms like A*, or through explorations, e.g., RRT*.
• 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 track 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: subject to the following constraints: tan( ⁇ min) ⁇ ⁇ ( z3, ⁇ 2) ⁇ tan( ⁇ max) where H lat ⁇ N is the prediction horizon, and y refk ⁇ R Hlat+1 is the centerline of the left and right free-spaces Y maxk ⁇ R Hlat+1 and Y mink ⁇ R Hlat+1 .
• T-SCF time-state control form
• Figure 6 shows a flowchart of the rotation angle ⁇ Rot optimization according to the present invention.
• 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 ⁇ new
• 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: 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 i k containing the current position of the motor-vehicle, ⁇ 1,0 ⁇ is the distance between the end waypoint of the driving path segment i k 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 ⁇ 2 ⁇ are the first two orientations of ⁇ OUT w.r.t.
• Rotation angle ⁇ Rot optimization Require: XSEG,YSEG, ⁇ SEG,iMAX,ik Ensure: ⁇ Rotk while j ⁇ i MAX do ⁇ new ⁇ ComputeAngle(Xi,Yi,Xj+1,Yj+1) while
• Figures 7 and 8 comparatively show the choices of segment orientation according to WO 2021/079338 A1 to the present Applicant and to the present invention, 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 present invention solves the problem of the singularities without limiting the number of segments to take into account.
• Figures 9 and 10 comparatively show the ad hoc free-corridors constructed according to according to WO 2021/079338 A1 to the present Applicant and to the present invention, respectively, where XSEG and YSEG are vectors containing the waypoints (xi, yi) provided by the higher level path planning module.
• XSEG and YSEG are vectors containing the waypoints (xi, yi) provided by the higher level path planning module.
• ⁇ SEG is the vector of orientations of the segments derived from these waypoints
• i k is the segment i in which the motor vehicle is located at that time.
• i MAX 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.
• ⁇ 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 is the saturation angle w.r.t. the current orientation ⁇ 0 ⁇ , and its value depends on the particular application.
• 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.
• the longitudinal dynamics expressed by equation (6) can be discretized leveraging the Tustin rule, obtaining the following discrete-time longitudinal model: 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 H Long is the prediction horizon, jmin and jmax 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. However, low level controllers are needed to transform this control inputs into physical signals for the actuators.
• 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. Further details about a model-based development and related practical considerations may be found in Raffone E.
• a motor-vehicle is faced in which the motor-vehicle interfaces, for longitudinal dynamic control, are: - engine-torque request: delivered to Engine Control (Treqs) - deceleration request: delivered to Brakeing System (areqs)
• the longitudinal acceleration control is implemented by means of the structure shown in Figure 5.
• the command a x is split in two channels, one for each interface.
• the Braking System includes a deceleration closed loop that allows to accept directly deceleration command (areqs) and to generate a braking torque Tbra.
• the Engine Control performs a closed-loop control based on an estimation of applied engine torque T Eng .
• T Eng To match the available interface it has been implemented an inverse vehicle model, to convert ax in Treqs, which considers the current motor-vehicle configuration (e.g., gear ratio, inertia, friction) and mainly exogenous input (e.g. road slope, drag forces).

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

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• 2022
  • 2022-10-28 IT IT102022000022308A patent/IT202200022308A1/it unknown
• 2023
  • 2023-10-24 WO PCT/IB2023/060715 patent/WO2024089595A1/en not_active Ceased
  • 2023-10-24 EP EP23798521.3A patent/EP4608699A1/de active Pending

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