Patent Publication Number: US-2023150548-A1

Title: Method for steering a vehicle and apparatus therefor

Description:
TECHNICAL FIELD 
     The present invention relates to a method for steering a vehicle along a path in a driveway and around obstacles between a starting position into a target position as well as an apparatus therefor. 
     PRIOR ART 
     WO 2015/028316 A1 discloses a method for steering a vehicle along a path and around obstacles between a starting position into an end position. The path comprises a plurality of linear path parts which are defined by a step size and a steering angle. The method according to WO 2015/028316 A1 comprises the steps of a) determining a maximum steering angle range and a maximum and a minimum step size range; b) determining the current distance to the intended end position and a target angle as well as the angle difference between the current vehicle angle and the target angle; c) carrying out an optimization method in order to determine a path part by minimizing the value of a cost function assigned to the path part, d) determining the new position by adding the determined path part to the current position; and finally e) repeating the steps b) to d) until the end position is reached with sufficient accuracy. The cost function comprises the current distance to the end position and the current angle difference with respect to the target angle as optimization variables which are weighted independently of one another, and the maximum steering angle range, the maximum and minimum step size ranges and a collision check are provided as boundary conditions. 
     This prior art document describes an optimization step-by-step and it is not disclosed how the obstacle avoidance is realized besides naming a function without further reference. Furthermore, the method only relies on path parts without taking into account the factor time. 
     US 2012/101654 A1 discloses a similar method and mentions in [ 0032 ] relating to a figure that the method according to said document also takes into account both the amount of time to complete a path trajectory to a destination as well as the distance traveled as factors similar to collision avoidance and legal restraints. 
     Other methods to control the automatic displacement of a vehicle are shown in DE 10 2015 222 934 with an automatic valet parking, where a vehicle shall automatically be guided from a start position to a target position without colliding with static or dynamic (i.e. moving) obstacles. 
     WO 2017/041943 discloses a method to guide a car safely from a starting position to an end position by successively computing pieces of movement in the range visible to sensors provided in the car. 
     In DE 10 2014 224073, a server to operate a parking facility is disclosed that communicates with cars and sends the drivable space in form of maps to the cars. 
     U.S. Pat. No. 9,969,386 B1 discloses a method for steering a vehicle along the path in a driveway with the features of the preamble of claim  1 . 
     U.S. Pat. No. 9,227,632 B1 discloses a system and a method for providing path planning for evasive steering maneuver and generation, wherein continuity assumptions are employed in the algorithm for a smooth path and include the start position and the orientation angle of the desired path aligned with the current vehicle position and the vehicle moving direction, where a lane change path should finish at the center of the destination lane and align with the lane in the first and second order derivatives. 
     WO 2016/198139 discloses a parking facility system comprising at least one parking space and used by at least a first group of manually operated traffic participants and a second group of automatically operated traffic participants. The method uses a central control device for determining, for each traffic participant of the first and second groups of traffic participants, operation of each traffic participant in a common route planning in the parking environment, wherein instruction information, via a communication link, is transmitted to the traffic participants, wherein the instruction information is used for outputting information or automatically controlling each traffic participant. 
     SUMMARY OF THE INVENTION 
     For maneuvering ground vehicles autonomously, the control unit responsible for the operation of the vehicle requires the computation of a guidance trajectory that is collision-free and that can be safely followed by the vehicle or vehicle configuration by using its low-level control functions. Such trajectory computation needs to be carried out in a reasonable time, in the region of a fraction of a second, to be useful for automatic maneuvering of vehicles. The problem to be solved by the present invention may be regarded as an improved trajectory determination. 
     In the context of this invention, a trajectory is to be understood as a collection of time-stamped or path-length-stamped segments that describe the future configuration (position, heading, etc.) of a vehicle, its dynamic states including for example, but not limited to, velocity and acceleration, as well as vehicle inputs such as jerk or change of yaw rate. The segments can be zero-order hold segments (piecewise constant between times), or can assume a certain functional shape (e.g. piece-wise linear or cubic polynomial etc.). 
     In the context of this invention, a vehicle or vehicle configuration is to be understood as a ground vehicle (such as a car, a bus, a lorry, a tractor or a trike), with or without one or multiple trailers. The vehicle may be steered via its front or rear axle, may have multiple steered axles, or can be steered by some other means such as, for example, torque vectoring. 
     This invention is related to an improved method providing guidance trajectories with the purpose of safely maneuvering one or multiple ground vehicles or vehicle configurations simultaneously in tight environments between a start and target configuration. In the case of a plurality of ground vehicles, there are adapted target configurations changing over time, when one or more ground vehicles are reaching an end position, being part of the target configuration, and when one or more new ground vehicles are added ad hoc to the present configuration and modify the target configuration of the system. Furthermore, it is an object of the invention to include the relative position of a plurality of moving vehicles over time. 
     The method according to the invention provides joint lateral and longitudinal planning, that is, the forward and backward movement of a vehicle along a path is jointly planned with the steering. This avoids dynamical infeasibilities and reduces the mismatch between planned and driven trajectory, enhancing safe operation of the autonomous vehicle over state-of-the-art methods. 
     The method according to the invention provides joint computation of configuration space path and dynamical trajectory. WO 2015/028316 A1 provides a method computing a geometrical path. Since the vehicle has to move to reach the target as outlined in the prior art, in a second step the velocity/acceleration/steering schedule is computed. The present invention increases safety and efficiency since the joint computation ensures that the trajectory is feasible for the vehicle and its control systems, thus reducing the number of potential collisions and decreasing the area required for maneuvering. 
     A further beneficial consequence of the advantages conferred by the above method element is an improved ability to provide trajectories in tight environments respecting the actual physical capabilities of the car. This includes on one hand a calculation of the path which will be taken by the vehicle, i.e. the kinetic envelope of the vehicle, which can include the implications and restrictions due to the car and its usual parameters such as the technically feasible turning radius, but on the other hand also comfort considerations—in the sense that, for example, the technically feasible turning radius is not the turning radius which shall be used but a larger one—so that this influences the path and its curvatures. Therefore, the method according to the invention combines these two geometrical restrictions within the evaluation of a path. Another example for a comfort setting are the maximally allowed accelerations as well as the initial gear selection to be used at the start of the maneuver, which can be either pre-defined or the method choses the best gear automatically. 
     The method works independently of pre-defined situations such as parallel parking, perpendicular parking etc., i.e. it is suitable for fully autonomous operation of vehicles, and e.g. it can be applied with a remote control of the vehicle. 
     The method is fully configurable to the vehicle by a simple set of geometrical and physical parameters of the vehicle (and potential trailers). 
     The method provides atomic elements as an interface to be agnostic to particular sensors and environmental model. Simple building blocks are provided that build up the map of obstacles and/or free/drivable space. The map will dynamically develop over time when moving objects such as obstacles, e.g. other vehicles or pedestrians, are included. This relates to the combination of the abovementioned geometrical and vehicle-related conditions with the dimension of the travel of the path in time and possible collisions due to other moving obstacles as e.g. vehicles and pedestrians. The simple building blocks consist of basic convex geometrical shapes, e.g. points, polytopes, half-spaces, circles/balls, ellipsoids, cones etc. A specific obstacle may be represented by the union of these basic shapes to model non-convex shapes. For moving obstacles, the interface provides the possibility to input trajectories of paths or time evolutions of the objects in space. Another interface layer reads in non-convex shapes and segments them into a union of convex objects and passes this to the first interface. 
     According to the invention, the method computes a trajectory from start to target configuration in one shot and is as such faster than prior art solutions. In the present context the trajectory is computed from a start state to a target state which are also addressed as configurations. 
     There may be a specified tolerance on the target position within predefined allowable tolerances on the desired target configuration including position or angle tolerances. 
     A structure-exploiting nonlinear programming solver tailored to the specific mathematical structure and properties is used to achieve the runtimes necessary for embedded, on-vehicle implementation. 
     The method computes a trajectory that, when applied to a vehicle by means of a feedback control system, maneuvers a vehicle or vehicle configurations (such as vehicles with trailers) in a tight or in an open environment. It is based on 
     a. environmental information such as the number of obstacles that need to be avoided, their location and their size, and/or the size and location of the drivable surface where the car can be located, 
     b. information on the current configuration/state of the vehicle (for example position, velocity, yaw angle etc.), 
     c. desired target configuration, 
     d. kinematic parameters of the vehicle or vehicle configuration such as length and width, front and rear overhang, wheel base, as well as physical capabilities of the steering system, etc. If one or multiple trailers are present, these parameters are given for all trailers and the configuration, and 
     e. comfort constraints, for example acceleration and jerk that the vehicle shall obey if at all possible. 
     In order to determine a drivable path and inputs for the vehicle, the method uses 
     (i) a distance computation method (DCM), comprising the steps of 
     1. determining a distance measure of the vehicle to an obstacle, including a measure of penetration depth in case the vehicle would penetrate obstacles, 
     2. returning a positive value if the vehicle does not intersect with the obstacle, and a negative value if it does intersect (collide), 
     3. furthermore returning sensitivities (derivatives) of the distance (and depth) with respect to the configuration of the vehicle and other variables under consideration, e.g. time, etc., 
     4. furthermore determining the distance to moving obstacles for which predictions about the obstacles configuration in the future are available (e.g. another car crossing a certain area), as well as sensitivities in relation to time and configuration of the controlled vehicle&#39;s configuration. 
     (ii) a vehicle model (VM) that describes, by physical equations of motion, the motion of the vehicle or vehicle configuration in a world coordinate frame. 
     (iii) a path planner (PP), which uses DCM and VM to plan a collision-free initial path from initial position to target position. The global path planner employs a search method on a discrete set of the configuration space for obtaining paths. The discretization can be achieved by gridding, sampling, or dividing the space into cells of the configuration space of the vehicle to explore the drivable surface. A graph of discrete points or cells is connected through arcs of movement, which the VM provides. These arcs can be arbitrarily complicated; however, they are typically of simple geometry such as straight lines, curves, clothoids etc. that can easily be tracked with low-level feedback controllers such as PID, LQR, or MPC. The search method uses cost functions for rating the transition from a certain configuration to another to determine the best path from start to target position. Infeasible configurations, i.e. configurations that are not admissible due to e.g. collision with obstacles, are assigned an infinite cost, which avoids that the method selects them. Multiple costs measuring different criteria can be combined into a single cost by a weighted sum. Typical costs are length of movement, distance from the target position or the number of direction changes. 
     (iv) a path improver (PI), which smooths the trajectory obtained by PP by means of numerical optimization. The optimization scheme makes use of DCM and VM to find a collision-free trajectory close to the one obtained by PP, but fulfilling dynamical constraints such as acceleration and steering rate limits, which the PP path does not necessarily fulfil, and which plans lateral and longitudinal movement of the vehicle in a joint optimization problem or by means of separate optimization problems. The PI is based on gradient information and uses first- or second-order numerical methods for continuous optimization to compute one trajectory from start to target and fulfilling all constraints. 
     The PI of the present invention takes into account obstacles, static obstacles and moving obstacles which can appear after having started the movement on the path. The gradient information and first- or second-order numerical methods used by the PI according to the present invention are based on a polynomial function of third or higher degree, preferably of fifth degree, for the instant path, whereas U.S. Pat. No. 9,227,632 B1 only uses the derivatives of said order to obtain a continuous function of trajectory portions between lane boundaries. 
     The method of the present invention uses the solution of a differential equation system and derivatives of first and second order. 
     The method can be applied in an apparatus that is implemented or installed on-board of the vehicle to be controlled, communicating with local vehicle sensors and low-level vehicle systems that realize the required control inputs, i.e. follow the given guidance trajectory. 
     Such an apparatus can comprise a distance computation unit configured to determine distances and sensitivities of distances of obstacles, a path planning unit configured to plan a collision-free initial path from an initial position to a target position and a path improving unit configured to plan lateral and longitudinal movement of the vehicle in a joint optimization problem or by means of separate optimization problems. 
     An apparatus for steering a vehicle along a path in a driveway with the features of the previous paragraph can also be provided for a general steering method. The path in a driveway can comprise a specific length and can end with speed 0 or be calculated for ongoing extended paths. 
     The method according to the invention preferably takes into account uncertainty information on the obstacle positions, sizes, and plans robustly against these uncertainties to minimize the effect of sensor and parameter uncertainty on the planned trajectories or movement. This can, inter alia, be done, in that the method artificially increases the size of obstacles by an appropriate amount to deal with uncertainty or model mismatch. 
     The method can take into account the range of sensors, and restricts the movement of the vehicle to only as far as it is safe to go. Such other dynamical constraints of the vehicle can be taken from the group encompassing intervals of acceleration, intervals of deceleration, predetermined speed interval, and steering rate of the vehicle. The least costly path can then comprises at least one stop portion for a predetermined time interval to avoid collisions or bottlenecks on the predetermined path. Additionally or as alternative at least one time dilatation portion can be provided having a scaled-down velocity for the predetermined path. Finally, additionally or as alternative at least a time shortening portion can be provided having a scaled-up velocity for a determined path with the proviso that the acceleration and deceleration does not exceed the predetermined limits or constraints of the vehicle. Predetermined limits are usually provided for the comfort of the user in the vehicle or limits on the driveway imposed by the authorities. 
     Information about detected obstacles over time, especially when the vehicle is already moving, can be supplied from an on-board sensor fusion system which combines inputs from arbitrary sensors on- and off-board the vehicle. Such information about detected obstacles can be directly supplied by on-board sensors such as cameras, RADAR, LIDAR or ultrasound. 
     Information about persistent static obstacles to be avoided is supplied from a digital map available to the vehicle. 
     It is preferred that the method can use predictions of future configurations of dynamic obstacles by means of extrapolation methods based on the currently detected configuration of the dynamic obstacle. A dynamic obstacle is an obstacle which is not static, i.e. which moves at the same time or at least partially at the same time when the controlled vehicle is moving and possibly intersects the current or one of the intended paths of the controlled vehicle. Predictions of future configurations of dynamic obstacles can be provided by an external prediction system. The PI of the present invention provides, within the DCM as explained below, uses the distance function including appearing static and moving obstacles to provide an improved path and velocity, wherein U.S. Pat. No. 9,969,386 only provides an optimal velocity on a previously calculated path. The method according to the present invention, while varying path and velocity (and thus also change acceleration over time) can reduce the number of direction changes from e.g. 5 down to 3 with an collision free optimized path with higher comfort for the passenger in the vehicle, wherein U.S. Pat. No. 9,969,386 cannot influence on the number of direction changes and path length. 
     The method can also be applied to infrastructure-based parking. There, it can also use off-board information such as mapping data to perform the computations. Further facility-related information can be obtained from off-board sensors, e.g. cameras, LIDARs etc., which are monitoring the maneuvering space via a communication interface in order to perform the computations. Preferably the method then runs off-board in the cloud or in the infrastructure and communicates to the vehicle, via a wireless communication link, the planned guidance trajectories according to the method for one vehicle in real-time. 
     A further preferred application is to combine the method in use case for multiple vehicles. Such a method coordinates and maneuvers multiple vehicles, wherein each single path or trajectory is determined according to the method as described above, where multiple trajectories are computed for multiple vehicles simultaneously. Therefore, the DCM, as mentioned above, includes distance computations to other vehicles, and PP and PI are executed to solve the joint problem of maneuvering multiple vehicles in tight, confined spaces. PP and PI are executed such that all cars are taken into consideration and the path is planned for all vehicles at the same time. It may also be that PP is done for each car separately and then PI does the planning for all cars together. 
     A prioritization of vehicles can be performed, which allows to sequentially execute the method of the invention (with DCM adjusted to take into account other vehicles) in descending order of priority. 
     In a post-processing step, a time dilatation or shift is applied in order to create collision-free trajectories out of trajectories computed independently from each other, without taking into consideration other vehicles. 
     The method according to the invention finds a further application within planning of parking facilities to detect problematic driving situations in a simulation mode. Such a method analyzes an environment represented in a digital map with respect to the effectively drivable space. This method uses trajectory computations based on the method as mentioned above to determine what configurations in a given map are reachable by a given vehicle. Such a method can use computer simulations to suggest improvements to the map, e.g. to increase the effectively drivable space or to reach identical configurations in a smaller space by removing unreachable space from the map. Similarly, such a method can use computer simulations to identify bottlenecks and certain parking or packing patterns well suited for efficient operation of a facility. 
     The step of determining distance and determining sensitivities of distances of obstacles can comprise the input of a number of vehicles in a joint optimization problem or by means of separate optimization problems. 
     Such connected vehicles can comprise user-driven vehicles and apparatus-driven vehicles. Then the apparatus is preferably a central computer connected with a wireless connection to all connected vehicles and further comprises a drive command transmitter and is configured to deliver driving signals to any apparatus-driven connected vehicle having a corresponding receiver and connected car control unit to control the driving path of said apparatus-driven connected vehicle from a start position to a target position and is configured to deliver driving commands to any user-driven connected vehicle to be displayed to guide the user on the driving path of said user-driven connected vehicle from a start position to a target position. Then user-driven connected vehicles are informed, e.g. via a HUD display what is expected from them as next maneuver whereas apparatus-driven vehicles do not need any hardware/software update beside a car control unit to control the driving path of said apparatus-driven connected vehicle which means control over acceleration, deceleration, steering and gear if not automatically adjusted. 
     The method uses sensitivities with respect to the vehicle configuration. This relates in other words to derived values of the vehicle configuration in the mathematical sense. 
     A parking facility system comprises an apparatus as mentioned above, wherein a plurality of perception sensors are configured to monitor the driveway and to deliver entry values for a distance computation unit of said apparatus and further comprises a drive command transmitter, wherein the apparatus is configured to deliver driving signals to any vehicle presented at an entry point of the parking facility system and having a corresponding receiver and connected car control unit to control the driving path of said vehicle from a start position to a target position. Here, user-driven connected vehicles are informed what trajectory (and speed) is expected from them as next maneuvers to a specific parking place, whereas apparatus-driven vehicles are directly controlled within their driving path to be directed to such a specific parking place. The system comprises the advantage to be able to provide a collision free trajectory calculation for all connected vehicles (and considers not-connected vehicles as moving obstacles), allowing to use automatic parking for “dumb” connected vehicles (without driver) simply based on their remote control based on wireless transmitted control signals, whereas driven vehicles receive driving instructions, wherein deviations of such human drivers from the intended path in terms of velocity and position are taking into account in the PI module. It is also possible that vehicles with users are controlled as connected vehicles without driver to allow the user to take any belongings from the car when it arrives at its final parking position. 
     Further embodiments of the invention are laid down in the dependent claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Preferred embodiments of the invention are described in the following with reference to the drawings, which are for the purpose of illustrating the present preferred embodiments of the invention and not for the purpose of limiting the same. In the drawings, 
         FIG.  1    shows a schematical map of an operation which is pulling a vehicle out of a parking space, carrying out the method according to an embodiment of the invention; 
         FIG.  2    shows a schematical map of an operation which is parking a vehicle in a designated parking space, carrying out the method according to an embodiment of the invention; 
         FIG.  3    shows a block diagram of an apparatus for carrying out a method according to the invention; 
         FIG.  4    shows a flow chart describing the method for the path improver PI; 
         FIG.  5    shows a diagram of an automatic parking system using the apparatus according to  FIG.  3    and the method according to invention; and 
         FIG.  6    shows a diagram of a collision resolution module implementing the method of the apparatus according to the invention. 
     
    
    
     DESCRIPTION OF PREFERRED EMBODIMENTS 
       FIG.  1    shows a schematical map of an operation pulling a vehicle  11  out of a parking space  21  carrying out the method according to an embodiment of the invention. The map of  FIG.  1    is implemented in a 2D coordinate system. If a parking garage with several levels is implemented, the map will be implemented within a 3D coordinate system, including height. A further reason to include the height, even in a one-level parking environment as 3D information, is the maximum height in parking spaces due to installation of e.g. air venting ducts etc. in a parking level. 
     Vehicle  11  shows its initial position in parking space  21 . The vehicle  11  has four corners,  101 ,  102 ,  103 ,  104 . The corners  101  to  104  are connected to a box symbolising the vehicle  11 . More corners can be used to more accurately represent the actual vehicle shape. 
     The parking place  21  is a cross parking place, wherein a wall or obstacle  31  is provided in front of the box/car  11 . Behind the box/car  11  there is a drivable space or driveway  50 , essentially in parallel to the front wall  31 . A rear wall  32  is provided on the other side of the driveway  50  as further obstacle in order not to come into contact with the car. The driveway  50  is per definitionem any allowed drive surface for a vehicle  12 . 
     When the car  11 , automatically driven by the method as described here within, leaves its parking space  21 , the way of the corner points  101 ,  102 ,  103  and  104  are moving along the lines  111 ,  112 ,  113 ,  114  (also named corner movement lines), respectively. The calculation takes into consideration the form of the box of car  11  so that the steering deflection does not bring box/car  11  into contact with a corner  42  of the parking space, a possible obstacle, be that a column of the building structure of the parking lot or a point near the corner of a car positioned adjacently to the car  11  in parking space  41 . Of course, the large box of parking space  41  can be replaced during calculation through an existing car positioned there and its actual shape. 
     As can be seen from  FIG.  1   , the driveway  50  is too narrow, so that the corner  124  of the car is stopped just in front of rear wall  32 . The steering angle is adapted to show into a different direction, following the lines  131  and  132  to new intermediate corner points, i.e. points  141  and  142  connected in this ways to the former mentioned points  121  to  124 . The path followed here ended near the border line of parking space  41 , wherein a last reversal takes place after which the car travels to its final target position with the box car shown with reference numeral  12  and reference numerals for its corners  161 ,  162 ,  163  and  164 . 
     Car  12  is considered to be in a target position because an end position would mean that car  12  is leaving the control zone of the present application space. 
     The difference between the prior art and path according to  FIG.  1    is that the entire path way is calculated based on constraints explicitly considered in the optimizer to not touch the obstacles  31 ,  32  and  41 . It uses the speed and acceleration information and takes into account moving obstacles as persons or other vehicles or cars, which are not necessarily driven or even influenced by the present method. 
       FIG.  2    shows a schematical map of an operation parking a vehicle  11  in a designated parking space as car target position  12 , which is the end position here, carrying out the method according to an embodiment of the invention; similar features have—as afore—received similar reference numerals. There is a driveway  50 , a back wall  32 , whereas the front wall does not exist as such and is replaced in the drawing by a double line  131  which symbolises a soft wall, i.e. an obstacle which might be engaged. Other cars  13  are already in parking positions and there is another option for a parking spot just on the front right side of the car  11  in its initial position. The method according to the invention calculates the cost function for either place and considers parking position  12 ′ as the preferred position. This is also due to the fact that the method observes the moving car  14 , starting from its initial position  14  via the intermediate position  14 ′ and ending at position  14 ″ at the time that the car  12  is in its end position  12 ′. The path  14 - 14 ′- 14 ″ is on one hand stored in the program of the controlled car  11  as a most probable event and is on the other hand constantly monitored via recognition of the movement of the car  14 . In the event. It is not shown in  FIG.  2    that, had the car  14  been leaving a parking place nearer to the possible end position  12 ′, then the method according the invention would have chosen the parking place  12 ″ as preferred end position  12 ″ with a lower cost in time and imponderableness connected with a moving car  14 . 
       FIG.  3    shows a block diagram of an apparatus for carrying out a method according to the invention. The apparatus comprises a plurality of different hardware components. The perception component  200  comprises a sensor fusion element  210  having at least one or preferably a plurality of interfaces to gather information of the environment within which the car  12  is moving. These interfaces are connected to sensors such as a speed sensor  201  of the car  12  to sense the speed of the moving car, a radar sensor  202  to detect size, shape and velocity of obstacles, a vision sensor  203  to detect obstacles, especially obstacles not known and moving obstacles as cars  14  or pedestrians, as well as to identify elements of the driveable surface and their limits such as curbs or floor markings, a LIDAR sensor  204  to detect distances to obstacles and speed of moving obstacles, an inertial navigation sensor  205 , usually comprising accelerometers and gyroscopes to detect position, orientation and speed of a movement including accelerations of the vehicle as well as a global positioning system sensor  206 , usually applied to detect position and velocity and, if in movement, orientation of the vehicle. The sensor fusion element  210  further comprises interfaces with vehicle internal sensors such as steering angle of the wheels and further vehicle property related sensors. 
     Obstacles such as walls  31 ,  32  or columns  33  (as shown in  FIG.  1    and  FIG.  2   ) can be detected inline or previously stored in the system, especially in a construction system such as a map editor of the environment as a parking lot. External Maps  211  for longer navigation planning can be added as well in order to be connected with a navigation system  212  which is also connected with the behavioural planning unit  213 , which in turn also receives the results of the sensor fusion element  210 . A prediction generator  215  is also connected with the sensor fusion element  210  in order to receive data from other moving elements of the environment. Such other moving elements can be vehicles, such as vehicle  14 , of the visible or ascertainable environment around vehicle  12 . It is also possible to include further vehicles outside the directly ascertainable environment via external sensors. Such external sensors may be fixed sensors of e.g. a parking lot, providing e.g. information on free parking places or movement data of other vehicles which are sensed by sensors located around the parking lot. In principle it is also possible that several vehicles, e.g.  12  and  14 , are exchanging and sharing information about still (such as caddies etc.) and moving (such as pedestrians etc.) obstacles in the area where the vehicles are moving. 
     The behavioural planning unit  213 , the prediction generator  215  as well as the motion planning module  217  are part of the motion control element  220  of the apparatus executing the different method steps of the features of the invention. 
     The motion planning module  217  realizes the short-term dynamical path planning with obstacle avoidance. It is connected with the sensor fusion element  210  to obtain input information on own vehicle  12  data as well as information relating to obstacles  31 ,  32 ,  33 ,  13 ,  14  etc. of the environment, directly visible or via external sensors. This includes static permanent obstacles (walls  31 ,  32 , columns  33 ), static temporary obstacles (such as vehicles  13 , abandoned objects in the driveway  50 ), as well as moving temporary obstacles (such as vehicles  14  and pedestrians). The driving behavioural planning unit  213  comprises high level information relating to where to park (parking place with the reference numeral  12  or  12 ″ in  FIG.  2   ), how to drive (with comfort or sport threshold limits for acceleration and speed), external driving information such as speed limits, speed bumps as input for the motion planning module  217 . Moving vehicles  14  or standing vehicles  13  where e.g. lights are indicating imminent movement, often have predictable movement parameters, including acceleration, speed, direction and therefore a path over time. This information is pre-calculated within the prediction generator to influence the cost decision function within the motion planning module  217 . 
     The motion planning module  217  outputs detailed motion plan information such as position, steering, acceleration, jerk, turn rate and further car-related parameters which can be used within a following car-related car actuation element  240  provided by a vehicle actuation system  218 . This vehicle actuation system  218  is implemented within a vehicle to be controlled and connected to the various control elements, such as steering control with the steering wheel, and the speed changing control information with the brake system or the accelerator. 
       FIG.  4    shows a flow chart describing the method for the path improver PI as part of the overall method. The method starts with an initial path inquiry step  500 . Such an initial existing path may be known from a previous visit of the car in the specific environment, from a visit of a vehicle of the same type, or from a parking facility where the optimized path is known and stored. The initial path could also be from a computation that was done a few moments (fractions of a second, maybe seconds) earlier. In that time, the situation might have changed, which makes a re-computation necessary. 
     If such an initial guess is available, the method directly goes to the resampler step  502 . If such information is not available at the beginning of the method, the path planning  501  takes place. Said path planning  501  considers the constraints and uses a distance computation method (DCM) together with a vehicle model (VM) that describes, by physical equations of motion, the motion of the vehicle or vehicle configuration in a world coordinate frame. Said elements are used to plan a collision-free initial path from initial position to target position using a global search method over a discretized state space (see above). Such initial path can also be the result of the initial guess. 
     Based on this planned path based on static obstacles the result is used as input for the path improver function (PI), which smooths the trajectory obtained by path planning or the initial guess by means of numerical optimization. 
     The path improver method is shown with the sub-method steps on the right side of  FIG.  4   . 
     The initialization step  510  starts with the initial collision-free trajectory and uses the known vehicle model (VM) module  512  to fulfill dynamical constraints. It does this by computing the errors in the equations of motion. It also provides derivatives of those errors to be able to improve on them. The distance computation method for obstacles (DCMO) module  513  ensures that a collision-free trajectory is found by computing the distances between the obstacles and the vehicle when travelling along the trajectory, and derivatives thereof. The distance computation to the reference trajectory (DCRT) module  514  selects from all dynamically feasible, collision-free trajectories the one that comes closest to the reference path (either the initial guess  500  or the output of PP 501) by computing the distance between the current trajectory and the reference trajectory and derivatives thereof. VM  512 , DCMO  513  and DCRT  514  compute first-order derivatives with respect to the currently planned configuration of the vehicle and with respect to the currently planned time and might also provide second-order derivatives. 
     This result is then submitted to a quality check decision  515  with a comparison on a predefined quality metric. In particular, the computed errors and distances and the respective derivatives are used to determine if the current trajectory can be further improved. If the quality check is positive, meaning that a locally optimal, collision free and dynamically feasible trajectory fulfilling all constraints has been found, then the sub-method ends  520  and returns the path and control parameter back to the main process. 
     If the quality check  515  is negative, a quality metric improving search direction  516  is computed by a first- or second-order numerical method which determines by how much each element of the currently planned trajectory should be changed to come closer to an optimal trajectory. For this, search direction  516  uses the errors and distances and the respective derivatives or sensitivities computed before by VM  512 , DCMO  513  and DCRT  514 . 
     Once said search direction  516  has been found, a step module  517  determines the amount by which the current trajectory shall be updated along the search direction, possibly optimizing the quality metrics used in the optimality check  515 . This leads to a trajectory update  518  based on the non-sufficient previous trajectory plus the modifications based on search direction  516  and step length  517 . 
     Said trajectory update  518  is fed back at the beginning of the sub method calculation  511  after having passed a time out check  525 . A predetermined time is allocated to provide an improved solution over the path provided by the path planner  501  or the initial guess. If this calculation time is up, then the sub-method ends here and the control is given back to the main program. Otherwise the new modified path is introduced at the beginning  511  into the loop for a further calculation. It is of course possible to provide interrupts like time check  525  routines at any stage within the functions  512 ,  513 ,  514 ,  516 ,  517 ,  518  to abort the calculation at that time and return control based on the last best preferred path and control set. 
       FIG.  5    shows a diagram of an automatic parking system using control elements of the apparatus  220  according to  FIG.  3    and the method according to the invention. The parking facility comprises a fixed sensor system  200 ′ within the facility, similar to the sensor perception components  200  of the in-vehicle system of  FIG.  3   , comprised of one or multiple sensors. The sensor system  200 ′ is connected via a streaming connection  209  with the motion control element  220 , as in  FIG.  3   . The motion control element  220  is connected with a dispatcher system  300  delivering as input vehicle characteristics  302  specific to any vehicle  12  using the system as well as map and parking target information via a further input line  301 . Both elements of information can be transmitted offline in preparation. Usually the map is only transmitted once because it&#39;s facility-dependent, but the target position is transmitted once per maneuver. Also, the map may change due to other parked cars. Car model updates are provided with new cars when such new cars are adapted for using the facility. The drive commands are issued by the motion control element  220  to the drive command transmitter  310  via the drive command line relating to the steering angle and acceleration and braking commands. This drive commands are updated (streamed) to the vehicle to create a feedback loop. Additionally, the total stop command for the engine is transmitted as well, when the final parking position or delivery position is reached. The drive command transmitter  310  uses a wireless transmitter line  311  to issue these actuation commands to the car via a reception system built into the car. It is possible to provide a number of drive command transmitters  310  within the facility providing handover possibilities between different transmitters. 
       FIG.  6    finally shows a diagram of a collision resolution module implementing a further embodiment of the method according to the invention. One application of the method according to the invention is to readily adapt the calculated solution in reaction to moving obstacles  14  which are not behaving as originally predicted by the prediction generator  215 . At the same time, the motion control element  220  calculates a path  400  of a vehicle  12  between a starting position  410  and a target position  411 , which can be an end position. The axis  402  is a schematical representation of the 2D path whereas the axis  401  is the time axis. 
     Said path  400  is calculated to follow a collision-free path in view of static obstacles such as walls  32 , columns  34  and further elements. At the same time, due to additional sensor input relating to moving obstacles as vehicles  14 , the path is optimised to allow a collision-free path in view of moving obstacles, which is achieved with path  400 . At this point, several possibilities exist which might trigger an online adaptation of the path. One is a newly moving obstacle. This can request a shifted start of the path movement shown on the right-hand diagram of  FIG.  6    with the shifted path  412 . On the other side, an originally predicted moving vehicle  14  moves slower than expected. Therefore, the controlled vehicle should not be in the later portion of the path as early as initially calculated. Therefore, the unchanged path, unchanged in view of the different legs, is executed slower than originally calculated, i.e. it is changed to a dilated path  400 ′ by means of time dilatation  413 . It is possible to leave a first portion of the path unchanged to avoid interference with other incoming new obstacles and only dilate the later portion of the path. It is also possible, in the framework of the given comfort limits, to accelerate the movement so as to pass at a predetermined portion of the path earlier and before another vehicle. In a multi-vehicle environment it is also possible to influence the paths of two or more vehicles at the same time in order to improve the overall distribution of the vehicles in the parking facility. Then, these vehicles are preferredly connected wherein connected means by radio or other especially wireless communication means. The amended movement solution is then applied to such connected vehicles. 
     The embodiment according to  FIG.  6    can be implemented using the method according to the invention but also only in connection with an apparatus of steering vehicles according to the invention. Then it is a preferred solution to have a central control unit encompassing the apparatus to provide instruction signals for at least a first group of user-driven connected vehicles and control signals for a second group of apparatus-driven connected vehicles, which are autonomously driven by the apparatus. The system then comprises the advantage to be able to provide a collision free trajectory calculation for all connected vehicles (and considers not-connected vehicles as moving obstacles). This allows to provide automatic parking for connected vehicles (without driver or with a non-interfering person at the drivers seat) simply based on a remote control based on wireless transmitted control signals to such vehicles, whereas driven vehicles receive driving instructions, wherein deviations of such human drivers from the intended path in terms of velocity and position are taking into account in the PI module. Then it is preferable that after initialization of a calculation of paths for the different vehicles, a clock function is incorporated in the different connected vehicles and the central control unit of the apparatus allowing to execute the different driving orders in the second group of vehicles or display of driving instructions to the drivers of the first group of vehicles so that in case of temporary loss of signal connection with the central control unit the pre-transferred control or display signals are executed and the correct synchronization is possible when the connection is up again. 
     
       
         
           
               
             
               
                   
               
               
                 LIST OF REFERENCE SIGNS 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
            
               
                  11 
                 vehicle, initial position 
               
               
                  12 
                 vehicle, target position 
               
               
                  12′ 
                 vehicle, target position 
               
               
                  12″ 
                 possible parking position 
               
               
                  13 
                 other cars, not moving 
               
               
                  14 
                 other car, moving, start position 
               
               
                  14′ 
                 other car, moving, intermediate position 
               
               
                  14″ 
                 other car, moving, position at target position of controlled car 
               
               
                  21 
                 parking space 
               
               
                  31 
                 front wall/obstacle 
               
               
                  31′ 
                 front line 
               
               
                  33 
                 column 
               
               
                  32 
                 rear wall/obstacle 
               
               
                  41 
                 not contemplated parking space 
               
               
                  42 
                 corner of parking space 
               
               
                  50 
                 driveway 
               
               
                 101 
                 car corner, initial position 
               
               
                 102 
                 car corner, initial position 
               
               
                 103 
                 car corner, initial position 
               
               
                 104 
                 car corner, initial position 
               
               
                 111 
                 corner movement line 
               
               
                 112 
                 corner movement line 
               
               
                 113 
                 corner movement line 
               
               
                 114 
                 corner movement line 
               
               
                 121 
                 car corner, intermediate position 
               
               
                 122 
                 car corner, intermediate position 
               
               
                 123 
                 car corner, intermediate position 
               
               
                 124 
                 car corner, intermediate position 
               
               
                 131 
                 corner movement line 
               
               
                 132 
                 corner movement line 
               
               
                 133 
                 corner movement line 
               
               
                 134 
                 corner movement line 
               
               
                 141 
                 car corner, intermediate position 
               
               
                 142 
                 car corner, intermediate position 
               
               
                 143 
                 car corner, intermediate position 
               
               
                 144 
                 car corner, intermediate position 
               
               
                 151 
                 corner movement line 
               
               
                 152 
                 corner movement line 
               
               
                 153 
                 corner movement line 
               
               
                 154 
                 corner movement line 
               
               
                 161 
                 car corner, final position 
               
               
                 162 
                 car corner, final position 
               
               
                 163 
                 car corner, final position 
               
               
                 164 
                 car corner, final position 
               
               
                 200 
                 perception component 
               
               
                 200′ 
                 sensor system 
               
               
                 201 
                 speed sensor 
               
               
                 202 
                 radar sensor 
               
               
                 203 
                 vision sensor 
               
               
                 204 
                 LIDAR sensor 
               
               
                 205 
                 inertial navigation sensor 
               
               
                 206 
                 GPS sensor 
               
               
                 209 
                 streaming connection 
               
               
                 210 
                 sensor fusion element 
               
               
                 211 
                 external maps 
               
               
                 212 
                 navigation system 
               
               
                 213 
                 behavioural planning unit 
               
               
                 215 
                 prediction generator 
               
               
                 217 
                 motion planning module 
               
               
                 218 
                 vehicle actuation system 
               
               
                 220 
                 motion control component 
               
               
                 240 
                 car actuation element 
               
               
                 300 
                 dispatcher system 
               
               
                 301 
                 information line 
               
               
                 302 
                 vehicle characteristics transmittal line 
               
               
                 310 
                 drive command transmitter 
               
               
                 311 
                 drive to car line 
               
               
                 312 
                 drive command line 
               
               
                 400 
                 schematic vehicle path 
               
               
                 401 
                 time 
               
               
                 402 
                 schematic path axis 
               
               
                 410 
                 starting position 
               
               
                 411 
                 target position 
               
               
                 500 
                 initial path inquiry 
               
               
                 501 
                 path planning (PP) 
               
               
                 502 
                 resampler step 
               
               
                 503 
                 path improver (PI) 
               
               
                 510 
                 initialization 
               
               
                 511 
                 begin of calculation 
               
               
                 512 
                 vehicle model (VM) 
               
               
                 513 
                 distance computation method for obstacles (DCMO) 
               
               
                 514 
                 distance computation to reference trajectory (DCRT) 
               
               
                 515 
                 quality check decision 
               
               
                 516 
                 search direction method 
               
               
                 517 
                 step size method 
               
               
                 518 
                 trajectory update 
               
               
                 520 
                 end of sub function 
               
               
                 525 
                 timeout check