SE1950962A1 - Method and control arrangement for vehicle motion planning and control algorithms - Google Patents

Abstract

Method (600) and control arrangement (120) for motion planning of an autonomous vehicle (100) passing an underneath segment (110). The method (600) comprises the steps of: determining (601) geographical coordinates of the vehicle (100) when approaching the underneath segment (110), or a road / lane/ street/ parking identifier of the underneath segment (110) when the vehicle (100) is approaching; obtaining (603) pre-computed procedures associated with the underneath segment (110) of the provided (602) geographical coordinates or road / lane/ street identifier; and planning (604) motions of the vehicle (100) during passage of the underneath segment (110), based on the obtained (603) pre-computed procedures.

Description

METHOD AND CONTROL ARRANGEMENT FOR VEHICLE MOTION PLANNING ANDCONTROL ALGORITHMS TECHNICAL FIELD This document discloses a control arrangement and a method. More particularly, a controlarrangement and a method are provided, for motion planning of an autonomous vehiclepassing an underneath segment, such as a road section, a parking lot, or similar driveable surface.

BACKGROUND Autonomous systems, of which automated vehicles are a part of, often follow a sense-plan-control architecture. Sensors on the vehicle are determining state of the environment of thevehicle. The Planning module is responsible for finding a feasible sequence of vehicle statesfor a vehicle to follow. This sequence could be either a multi-dimensional path (e.g., a se-quence of vehicle states in space), or a trajectory (that is, a path to which a speed profile isassociated). Paths and trajectories generated by the planning module define how the vehicle moves in the environment.

The controller is then responsible to convert this output of the planning module into actuationcommands. lt is worth noting that the controller could be part of the planning module or itcould be a separate module.

The Control module is responsible for making the autonomous vehicle follow the sequenceof states planned by the Planning module. To achieve this, the control module directly con-trols the actuators of the vehicle, such as the steering, brake systems, engine and gearbox.

The algorithms used to solve the tasks associated with the Planning and Control modulesare often computationally intensive. This problem is exacerbated by the fact that often thecomputational power available in autonomous vehicles is scarce and the time allocated to calculate new motions is quite short (typically between 50 to 500 ms).

Document WO2018091373 describes a method for motion planning of a vehicle. This docu-ment specifies a solution where initial guesses for a motion planner solution are stored in acloud/ memory and sent to the vehicle while driving. These initial guesses speed up themotion planning algorithms, by providing them with a possibly good approximation.

Many times, this initial solution/ guess needs to be further optimised. However, the documentdoes not disclose or suggest how a further optimisation may be made and/ or how time orcomputational resources of the vehicle could be saved.

Document WO2017214581 describes the problem of computational heavy calculations of avehicle and suggests storage of a planned graph in a memory for later usage. By makingpre-computation of several motions, which can then be stitched together in order to form amotion plan, the computational speed may be increased.

However, there are limitations to this method, as a very large amount of computer memorywould be required in order to store the number of pre-computations for all roads that oneneeds to drive past during a voyage.

Document US20180164822 describes a method for reducing computational costs in associ-ation with route planning of an autonomous vehicle. Vehicle motion plans are generated onboard. The document describes a few possible options on how to speed up computationaltimes, such as re-using solutions computed in the previous planning cycle and performingdecoupled lateral and longitudinal planning.

However, the disclosed method is likely to only provide a very limited saving of computational time. lt appears that further development is required for reducing computational time during motion planning of an autonomous vehicle.

SUMMARY lt is therefore an object of this invention to solve at least some of the above problems and improve trajectory planning of an autonomous vehicle.

According to a first aspect of the invention, this objective is achieved by a method for motionplanning of an autonomous vehicle passing an underneath segment such as a road section,a passage, a parking lot or similar surface. The method comprises determining geographicalcoordinates of the vehicle when approaching the underneath segment, or a road/ lane/ street/parking identifier of the underneath segment when the vehicle is approaching. The methodalso comprises obtaining pre-computed procedures associated with the underneath segmentof the provided geographical coordinates or road/ lane/ street/ parking identifier. ln addition,the method further comprises planning motions of the vehicle during passage of the under-neath segment, based on the obtained pre-computed procedures.

According to a second aspect of the invention, this objective is achieved by a control ar-rangement. The control arrangement aims at motion planning of an autonomous vehiclepassing an underneath segment. The control arrangement is configured to determine geo-graphical coordinates of the vehicle when approaching the underneath segment, or a road/lane/ street/ parking identifier of the underneath segment when the vehicle is approaching.Further, the control arrangement is configured to obtain pre-computed procedures associ-ated with the underneath segment of the provided geographical coordinates or road/ lane/street/ parking identifier. The control arrangement is additionally configured to plan motionsof the vehicle during passage of the underneath segment, based on the obtained pre-com-puted procedures.

The pre-computed procedures associated with the underneath segment may comprise forexample pre-computed trajectories, pre-computed paths, pre-computed Iattices, pre-com-puted gradients, pre-computed sampling biases, pre-computed obstacle avoidance sugges-tions, and/ or pre-computed acceleration/ braking suggestions, for example.

Thanks to the pre-computation and storage of one or more procedures, the required timeperiod for performing motion planning and/ or calculation of various control algorithms maybe reduced. Hereby the computational effort required in the task of autonomous drivingonboard the vehicle is reduced.

Other advantages and additional novel features will become apparent from the subsequentdetailed description.

FIGURES Embodiments of the invention will now be described in further detail with reference to theaccompanying figures, in which: Figure 1 illustrates an embodiment of a vehicle according to a side view; Figure 2 illustrates an embodiment of a lattice and three highlighted motion primitivesof a vehicle according to an embodiment; Figure 3 illustrates an embodiment of a lattice created by computing several motionprimitives that can connect the set of discretised states along the road accord-ing to an embodiment; Figure 4 illustrates an embodiment of a planned trajectory of a vehicle according to an embodiment; Figure 5 illustrates a vehicle interior according to an embodiment; Figure 6 is a flow chart illustrating an embodiment of the method; Figure 7 illustrates a system according to an embodiment.

DETAILED DESCRIPTION Embodiments of the invention described herein are defined as a control arrangement and amethod in a control arrangement, which may be put into practice in the embodiments de-scribed below. These embodiments may, however, be exemplified and realised in many dif-ferent forms and are not to be limited to the examples set forth herein; rather, these illustra-tive examples of embodiments are provided so that this disclosure will be thorough and com-plete.

Still other objects and features may become apparent from the following detailed description,considered in conjunction with the accompanying drawings. lt is to be understood, however,that the drawings are designed solely for purposes of illustration and not as a definition ofthe limits of the herein disclosed embodiments, for which reference is to be made to theappended claims. Further, the drawings are not necessarily drawn to scale and, unless oth-en/vise indicated, they are merely intended to conceptually illustrate the structures and pro-cedures described herein.

Figure 1 illustrates a vehicle 100, driving in a driving direction 105 on an underneath seg-ment 110. The vehicle 100 may comprise a means for transportation in broad sense such ase.g. a truck, a car, a motorcycle, a trailer, a bus, a bike, a train, a tram, an aircraft, a water-craft, an unmanned underwater vehicle, a drone, a humanoid service robot, a spacecraft, orother similar manned or unmanned means of conveyance running e.g. on wheels, water, air, or similar media.

The vehicle 100 may be autonomous in some embodiments.

The underneath segment 110 may for example be a road section, a parking lot, a drivinglane, a highway, or similar driveable surface of a vehicle.

The vehicle 100 comprises a control arrangement 120. The control arrangement 120 may inturn comprise e.g. one or several Electronic Control Units (ECUs), typically a plurality ofinteracting ECUs. The control arrangement 120 may comprise a digital computer that con-trols one or more electrical systems, or electrical sub systems, of the vehicle 100, based one.g. information read from sensors placed at various parts and in different components of thevehicle 100, or possibly even outside the vehicle 100. ECU is a generic term that often is used in automotive electronics, for any embedded system that controls one or more of theelectrical system or sub systems in the vehicle 100. The control arrangement 120 may beparticularly configured to implement height estimation and distance measurements based onsensor input and to perform parameter comparisons and make decisions based on the out-come of the made comparison.

The vehicle 100 typically has one or several forwardly, laterally and/ or backwards directedsensors 130, in order to collect environmental information for performing the autonomous driving.

The sensor 130 may comprise e.g. a camera, a stereo camera, an infrared camera, a videocamera, a radar, a lidar, an ultrasound device, a time-of-flight camera, or similar device, indifferent embodiments. ln some embodiments, the vehicle 100 may comprise a plurality ofsensors 130 which may be of the same, or different kinds, such as e.g. a radar and a camera;a lidar and a radar, etc.

The sensor 130 may for example detect various indicators of the ahead road shape, such asroad lines, road limitations, the position of an ahead vehicle, etc.

Autonomous driving applications require that the planned trajectories have a high degree ofsmoothness. According to embodiments herein, successfully calculated paths or trajectoriesof the vehicle 100, or features and methods, or partial computations of a motion planner orcontroller that are common or equal across different, vehicles and/ or road traffic situationsat specific geographic locations are stored in a database. The database may be vehicle ex- ternal and accessible via wireless communication.

Communication may be made over a wireless communication interface, such as e.g. Vehicle-to-Vehicle (V2V) communication, or Vehicle-to-Infrastructure (V2l) communication. The com-mon term Vehicle-to-Everything (V2X) is sometimes used. ln some embodiments, the communication between the vehicle 100 and the database maybe performed via V2V communication, e.g. based on Dedicated Short-Range Communica-tions (DSRC) devices. DSFIC works in 5.9 GHz band with bandwidth of 75 I\/lHz and approx-imate range of 1000 m in some embodiments.

The wireless communication may be made according to any IEEE standard for wireless ve-hicular communication like e.g. a special mode of operation of IEEE 802.11 for vehicularnetworks called Wireless Access in Vehicular Environments (WAVE). IEEE 802.11p is an extension to 802.11 Wireless LAN medium access layer (MAC) and physical layer (PHY)specification.

Such wireless communication interface may comprise, or at least be inspired by wirelesscommunication technology such as Wi-Fi, Ethernet, Wireless Local Area Network (WLAN),3GPP LTE, LTE-Advanced, or similar, just to mention some few options, via a wireless com- munication network.

The database may then provide this information to any other autonomous vehicle that ispassing the same specific road segment. This will result in the other vehicle having to per-form less computations (specifically not performing the computations associated with theoutput in the database). The information could be stored in a map or be transmitted to thevehicle 100 while it is driving.

The stored information in the vehicle map and/ or database could also be sent to a back-endalong with the vehicle configuration parameters for the vehicle 100 that calculated the suc-cessful paths or trajectories. lt can thereby be provided to other vehicles, if they wish, whenthey download new map updates, in some embodiments.

Computational power is a scarce resource in autonomous vehicles, and the disclosed solu-tion reduces computational times in safety critical components of Motion Planning of an au-tonomous vehicle 100. These benefits can be shared across e.g. all passing vehicles; vehi-cles of a particular brand; vehicles having the same owner; vehicles subscribing to a partic-ular service, etc. Hereby, computational time is saved, enabling usage of less sophisticated(i.e. less expensive) computational resources onboard the vehicle 100. By spending lesscomputational efforts on routine calculations, computational power is saved for handling e.g.exceptional emergency situations that may occur. Hereby, traffic safety is enhanced, whileeconomical efforts are reduced.

Figure 2 illustrates an example of a lattice and five highlighted motion primitives, for planninga vehicle route of the vehicle 100. This may be referred to as Lattice planning. Pre-computedlattices may be an example of pre-computed procedures.

Lattice planners are a known solution, for the problem of finding a feasible sequence of statesthat takes the vehicle 100 from its current location to a goal location, while respecting thedynamic constraints.

A lattice is a discretisation of the planning space in which the vehicle 100 can move. Byselecting a finite set of states in the environment, and connecting these states to each otherusing motion primitives, a graph of possible movement combinations may be created. Thena search can be performed on this graph in order to find solutions to the Planning problem.

The process of creation of a lattice may comprise computing upwards of thousands of motionprimitives, and then performing a search on these motion primitives. The creation of motionprimitives is a very computationally intensive process, due to the extremely high number ofprimitives computed.

However multiple vehicles can use the same lattice, given that they are located on the samestretch of the underneath segment/ road 110. Thus, it is possible to precompute lattices andassociate them to certain sections of the underneath segment 110. A lattice for a given un-derneath segment 110 may be used once the vehicle 100 is travelling on the underneathsegment 110. This lattice can be stored in a database and sent to vehicles approaching theparticular underneath segment 110 via Cloud-based solutions, or V2X technologies.

The lattice does not become invalidated due to the presence of obstacles or other vehicleson the underneath segment 110, the lattice depends substantially on the geometry of theunderneath segment 110, which typically is static in nature.

Figure 3 presents an underneath segment 110 such as e.g. a road section, and the latticeassociated with it. The lattice may be computed once, be kept stored in a database associ-ated with the particular underneath segment 110 and then sent out to approaching vehicles100 that are traversing through this underneath segment 110. The vehicle 100 may then usethis lattice to plan a vehicle path, which is successful in avoiding obstacles.

Figure 4 shows an example in which a vehicle 100 makes use of the provided method toreduce its computations times when performing Motion Planning. The vehicle 100 deter-mines and broadcasts its location along the underneath segment 110. ln a database 410,pre-computed procedures such as for example a road lattice of the underneath segment 110may be stored, associated with the location/ coordinates. This road lattice of the underneathsegment 110 is extracted and provided to the vehicle 100, which then receives the latticecorresponding to the current underneath segment 110 and may use it for route planning.

Hereby, the need for performing computations on the own onboard computers of the vehicle100 is reduced.

Figure 5 illustrates an example of a scenario as previously illustrated in Figure 4 as it maybe perceived by a hypothetical driver of the vehicle 100, keeping in mind that the vehicle 100 may be an autonomous vehicle without a driver.

The vehicle 100 may comprise a control arrangement 120, for trajectory planning of an au-tonomous vehicle 100.

Further the vehicle 100 may comprise various actuators 540 or adjustment devices such as,or functionally corresponding to a driving wheel for making |atera| adjustments; and/ or abrake, a clutch pedal, an accelerator and/ or gearbox for making a longitudinal adjustmentof the vehicle position, as instructed by the control arrangement 120 in order to follow adetermined trajectory.

Thus, the actuators 540 or adjustment devices may be configured to adjust the position ofthe vehicle 100 on the underneath segment 110 according to the determined trajectory andthereby follow the ahead underneath segment 110.

The actuators/ adjustment devices 540 in the illustration have a design adapted for driverintervention. ln other embodiments however, the same or corresponding functionalities may be applied in the autonomous vehicle 100, possibly having a distinct design.

The geographical position of the vehicle 100 may in some embodiments wherein reliablemaps are obtainable be determined by a positioning unit 510 in the vehicle 100, which maybe based on a satellite navigation system such as the Navigation Signal Timing and Fianging(Navstar) Global Positioning System (GPS), Differential GPS (DGPS), Galileo, GLONASS,or the like; and a database 520 comprising map data.

The geographical position of the positioning unit 510, (and thereby also of the vehicle 100)may be made continuously with a certain predetermined or configurable time intervals ac- cording to various embodiments.

Positioning by satellite navigation is based on distance measurement using triangulationfrom a number of satellites 530a, 530b, 530c, 530d. ln this example, four satellites 530a,530b, 530c, 530d are depicted, but this is merely an example. More than four satellites 530a,530b, 530c, 530d may be used for enhancing the precision, or for creating redundancy. Thesatellites 530a, 530b, 530c, 530d continuously transmit information about time and date (forexample, in coded form), identity (which satellite 530a, 530b, 530c, 530d that broadcasts),status, and where the satellite 530a, 530b, 530c, 530d are situated at any given time. The GPS satellites 530a, 530b, 530c, 530d send information encoded with different codes, forexample, but not necessarily based on Code Division Multiple Access (CDMA). This allowsinformation from an individual satellite 530a, 530b, 530c, 530d to be distinguished from theothers' information, based on a unique code for each respective satellite 530a, 530b, 530c,530d. This information can then be transmitted to be received by the appropriately adaptedpositioning device comprised in the vehicle 100.

Distance measurement can according to some embodiments comprise measuring the differ-ence in the time it takes for each respective satellite signal transmitted by the respectivesatellites 530a, 530b, 530c, 530d to reach the positioning unit 510. As the radio signals travelat the speed of light, the distance to the respective satellite 530a, 530b, 530c, 530d may becomputed by measuring the signal propagation time.

The positions of the satellites 530a, 530b, 530c, 530d are known, as they continuously aremonitored by approximately 15-30 ground stations located mainly along and near the earth'sequator. Thereby the geographical position, i.e. latitude and longitude, of the vehicle 100may be calculated by determining the distance to at least three satellites 530a, 530b, 530c,530d through triangulation. For determination of altitude, signals from four satellites 530a,530b, 530c, 530d may be used according to some embodiments.

The geographical position of the vehicle 100 may alternatively be determined, e.g. by havingtransponders positioned at known positions around the route and a dedicated sensor in thevehicle 100, for recognising the transponders and thereby determining the position; by de-tecting and recognising WiFi networks (WiFi networks along the route may be mapped withcertain respective geographical positions in a database); by receiving a Bluetooth beaconingsignal, associated with a geographical position, or other signal signatures of wireless signalssuch as e.g. by triangulation of signals emitted by a plurality of fixed base stations with knowngeographical positions.

The various entities on-board the vehicle 100 may communicate with each other via e.g. awired or wireless communication bus. The communication bus may comprise e.g. a Control-ler Area Network (CAN) bus, a Media Oriented Systems Transport (MOST) bus, or similar.However, the communication may alternatively be made over a wireless connection com-prising, or at least be inspired by any of the previously discussed wireless communicationtechnologies.

Figure 6 illustrates an example of a method 600 according to an embodiment. The flow chartin Figure 5 shows the method 600 in a control arrangement 120, for motion planning of an autonomous vehicle 100 passing an underneath segment 110 such as a road section, apassage, a parking lot or similar surface.

The control arrangement 120 may be situated in a vehicle external structure, such as a con-trol tower. Alternatively, in some embodiments, the control arrangement 120 may be situated in an autonomous vehicle 100. ln order to correctly be able to plan the motions of the vehicle 100, the method 600 maycomprise a number of steps 601-605. Further, the described steps 601-605 may be per-formed in a somewhat different chronological order than the numbering suggests. Somemethod steps may also be performed in a somewhat different manner. The method 600 maycomprise the subsequent steps: Step 601 comprises determining geographical coordinates of the vehicle 100 when ap-proaching the underneath segment 110, or a road/ lane/ street/ parking identifier of the un-derneath segment 110 when the vehicle 100 is approaching.

The geographical coordinates of the vehicle 100 may be determined by a positioning unit510 onboard the vehicle 100. Alternatively, a road/ lane/ street/ parking identifier may beextracted from a road map and/ or captured by a sensor onboard the vehicle 100. ln yetsome alternative embodiments, a road-side sensor, associated with the road/ lane/ street/parking identifier of the underneath segment 110, may identify the approach of the vehicle100. ln some embodiments, an onboard sensor 130 may capture an image of a road sign com-prising the road/ lane/ street/ parking identifier in some embodiments. The sensor 130 mayalternatively capture an image of the underneath segment 110, which may be identified byimage recognition/ computer vision and object recognition.

The sensors 130 may comprise or be connected to a control arrangement 120 configuredfor image recognition/ computer vision and landmark recognition. Computer vision is a tech-nical field comprising methods for acquiring, processing, analysing, and understanding im-ages and, in general, high-dimensional data from the real world in order to produce numericalor symbolic information. A theme in the development of this field has been to duplicate theabilities of human vision by electronically perceiving and understanding an image. Under-standing in this context means the transformation of visual images (the input of retina) intodescriptions of world that can interface with other thought processes and elicit appropriateaction. This image understanding can be seen as the disentangling of symbolic information 11 from image data using models constructed with the aid of geometry, physics, statistics, andlearning theory. Computer vision may also be described as the enterprise of automating andintegrating a wide range of processes and representations for vision perception. Hereby, therelevant underneath segment 110 may be identified.

Step 602, which may be performed only in some embodiments, comprises providing thedetermined 601 geographical coordinates or road/ lane/ street/ parking identifier to a data-base 410 comprising pre-computed procedures of the underneath segment 110.

The pre-computed procedures of underneath segments 110 may comprise any, i.e. one ormore of: pre-computed trajectories, pre-computed paths, pre-computed lattices, pre-com-puted gradients, pre-computed sampling biases and/ or pre-computed obstacle avoidancesuggestions, and/ or pre-computed acceleration/ braking suggestions or similar feature.

Pre-computed procedures comprising pre-computed trajectories may speed-up Motion Plan-ning and Control Algorithms of many kinds. Pre-computed procedures comprising pre-com-puted lattices may speed-up Motion Planning and Control Algorithms which are based onlattices. Pre-computed procedures comprising pre-computed gradients may speed-up Mo-tion Planning and Control Algorithms based on numerical optimisation. Pre-computed pro-cedures comprising pre-computed sampling biases may speed-up Motion Planning and Con-trol Algorithms based on Rapidly exploring Random Tree (RRT) approaches. Pre-computedprocedures comprising pre-computed obstacle avoidance suggestions such as e.g. passinga route obstacle on the left/ right side, etc.

Step 603 comprises obtaining pre-computed procedures associated with the road segment110 of the provided 602 geographical coordinates or road/ Iane/ street/ parking identifier.

The pre-computed procedures may according to some embodiments be obtained from thedatabase 410. ln some embodiments, the pre-computed procedures of the underneath segment 110 maybe stored in, and obtained from, a vehicle map.

Step 604 comprises planning motions of the vehicle 100 during passage of the underneathsegment 110, based on the obtained 603 pre-computed procedures.

The motion planning may be made, based on the pre-computed procedures, adapted to input 12 from vehicle sensors 130 concerning for example obstacles detected on the underneath seg-ment 1 10.

By using already made routine computations, computational efforts on-board the vehicle 100is saved, leading to shorter computational time during motion planning.

Step 605, which may be performed only in some embodiments, comprises providing feed-back to the database 410 concerning successfulness of the obtained 603 pre-computed pro-cedures during the planning 604 of the vehicle path 310.

Hereby, the control arrangement 120 may be notified concerning a difference between theunderneath segment 110 and the pre-computed procedures, e.g. due to changes of the un-derneath segment 110 by a road work or other adjustment of the underneath segment 110;or an error in the pre-computed procedures etc. Thus, enhanced reliability of the pre-com-puted procedures is achieved. ln some embodiments, when realising that the 603 pre-computed procedures are invalid, thecontrol arrangement 120 may compute alternative procedures to use itself and sending theseback to the database 410, which can then update the 603 pre-computed procedures to theprocedures.

Figure 7 illustrates a system 700 for motion planning of an autonomous vehicle 100.

The system 700 comprises a control arrangement 120, which may be comprised in a vehicleexternal structure, for example a road-side entity. Alternatively, the control arrangement 120may be comprised in a vehicle 100.

The control arrangement 120 may be configured for performing the described method 600according to at least some of the previously described method steps 601-605. The controlarrangement 120 is configured to determine geographical coordinates of the vehicle 100when approaching the underneath segment 110, or a road/ lane/ street/ parking identifier ofthe underneath segment 110 when the vehicle 100 is approaching. Further, the control ar-rangement 120 is also configured to obtain pre-computed procedures associated with theunderneath segment 110 of the provided geographical coordinates or road/ lane/ street/parking identifier. The control arrangement 120 is further configured to plan motions of thevehicle 100 during passage of the underneath segment 110, based on the obtained pre-computed procedures. 13 According to some embodiments, the control arrangement 120 may be further configured toprovide the determined geographical coordinates, or road/ lane/ street/ parking identifier, toa database 410 comprising pre-computed procedures of underneath segments 1 10. The pre-computed procedures may be obtained from the database 410.

The pre-computed procedures of the underneath segment 110 may be stored in a vehiclemap; and the pre-computed procedures may be obtained from the vehicle map.

Furthermore, the control arrangement 120 may be configured to provide feedback to thedatabase 410 concerning successfulness of the obtained pre-computed procedures duringthe planning of the vehicle path 310.

The pre-computed procedures of underneath segments 110 may comprise any of: pre-com-puted trajectories, pre-computed paths, pre-computed lattices, pre-computed gradients, pre-computed sampling biases and/ or pre-computed obstacle avoidance suggestions, and/ orpre-computed acceleration/ braking suggestions.

The system 700 may also comprise one or several sensors 130 of the vehicle 100. Thesensor 130 may be directed towards the front of the vehicle 100, in the driving direction 105;or in any other arbitrary direction. The sensor 130 may comprise e.g. a camera, a stereocamera, an infrared camera, a video camera, a radar, a lidar, an ultrasound device, a time-of-flight camera, or similar device, in different embodiments. ln some embodiments, the system 700 may comprise various actuators 540 or adjustmentdevices such as, or functionally corresponding to a driving wheel for making lateral adjust-ments; and/ or a brake, a clutch pedal, an accelerator and/ or gearbox for making a longitu-dinal adjustment of the vehicle position, as instructed by the control arrangement 120 in orderto perform motion planning of the vehicle 100. ln some embodiments, the control arrangement 120 may further comprise an input unit 710,configured to obtain information via a wireless communication interface, from a wireless com- munication device of the sensor 130 and/ or the actuator 540.The control arrangement 120 may further comprise a processing circuit 720, configured forperforming various calculations and computations in order to perform the method 600, ac- cording to the previously described steps 601-605.

Such processing circuit 720 may comprise one or more instances of a processing circuit, i.e. 14 a Central Processing Unit (CPU), a processing unit, a processor, an Application SpecificIntegrated Circuit (ASIC), a microprocessor, or other processing logic that may interpret andexecute instructions. The herein utilised expression "processing circuit" may thus representa processing circuitry comprising a plurality of processing circuits, such as, e.g., any, someor all of the ones enumerated above.

Furthermore, the control arrangement 120 may comprise a memory 725 in some embodi-ments. The optional memory 725 may comprise a physical device utilised to store data orprograms, i.e., sequences of instructions, on a temporary or permanent basis. According tosome embodiments, the memory 725 may comprise integrated circuits comprising silicon-based transistors. The memory 725 may comprise e.g. a memory card, a flash memory, aUSB memory, a hard disc, or another similar volatile or non-volatile storage unit for storingdata such as e.g. ROIVI (Read-Only l\/lemory), PROIVI (Programmable Read-Only Memory),EPROIVI (Erasable PROIVI), EEPROIVI (Electrically Erasable PROIVI), etc. in different embod- iments.

The control arrangement 120 may also comprise an output unit 730, configured to provideinformation via a wireless communication interface, to a wireless communication device ofthe actuator 540.

The previously described method steps 601 -605 to be performed in the control arrangement120 may be implemented through the one or more processing circuits 720 within the controlarrangement 120, together with computer program product for performing at least some ofthe functions of the method steps 601-605. Thus, a computer program product, comprisinginstructions for performing the method steps 601-605 in the control arrangement 120 mayperform the method 600 comprising at least some of the method steps 601-605 for trajectoryplanning of an autonomous vehicle 100, when the computer program is loaded into the oneor more processing circuits 720 of the control arrangement 120. The described method steps601-605 may thus be performed by a computer algorithm, a machine executable code, anon-transitory computer-readable medium, an appropriately configured hardware or a soft-ware instruction programmed into a suitable programmable logic such as the processor inthe control arrangement 120.

The computer program product mentioned above may be provided for instance in the formof a data carrier carrying computer program code for performing at least some of the methodstep 501-505 according to some embodiments when being loaded into the one or more pro-cessors of the control arrangement 120. The data carrier may be, e.g., a hard disk, a CDROIVI disc, a memory stick, an optical storage device, a magnetic storage device or any other appropriate medium such as a disk or tape that may hold machine readable data in a non-transitory manner. The computer program product may furthermore be provided as computerprogram code on a server and downloaded to the control arrangement 120 remotely, e.g., over an Internet or an intranet connection.

The terminology used in the description of the embodiments as illustrated in the accompa-nying drawings is not intended to be limiting of the described method 600, control arrange-ment 120, computer program, system 700, and/ or vehicle 100. Various changes, substitu-tions and/ or alterations may be made, without departing from invention embodiments asdefined by the appended claims. Further, the herein described different embodiments, illus-trated in Figures 1-7 may be combined and exchanged without Iimitations in various otherembodiments, within the scope of the independent claims.

As used herein, the term "and/ or" comprises any and all combinations of one or more of theassociated listed items. The term "or" as used herein, is to be interpreted as a mathematicalOR, i.e., as an inclusive disjunction; not as a mathematical exclusive OR (XOR), unless ex-pressly stated otherwise. ln addition, the singular forms "a", "an" and "the" are to be inter-preted as "at least one", thus also possibly comprising a plurality of entities of the same kind,unless expressly stated othen/vise. lt will be further understood that the terms "includes","comprises", "including" and/ or "comprising", specifies the presence of stated features, ac-tions, integers, steps, operations, elements, and/ or components, but do not preclude thepresence or addition of one or more other features, actions, integers, steps, operations, ele-ments, components, and/ or groups thereof. A single unit such as e.g. a processor may fulfilthe functions of several items recited in the claims. The mere fact that certain measures arerecited in mutually different dependent claims does not indicate that a combination of thesemeasures cannot be used to advantage. A computer program may be stored/ distributed ona suitable medium, such as an optical storage medium or a solid-state medium suppliedtogether with or as part of other hardware but may also be distributed in other forms such as via Internet or other wired or wireless communication system.

Claims (12)

1. A method (600) for motion planning of an autonomous vehicle (100) passing anunderneath segment (110), wherein the method (600) comprises the steps of: determining (601) geographical coordinates of the vehicle (100) when approachingthe underneath segment (110), or a road/ lane/ street/ parking identifier of the underneathsegment (110) when the vehicle (100) is approaching; obtaining (603) pre-computed procedures associated with the underneath segment(110) of the provided (602) geographical coordinates or road/ lane/ street/ parking identifier;and planning (604) motions of the vehicle (100) during passage of the underneath seg-ment (110), based on the obtained (603) pre-computed procedures.

2. The method (600) according to claim 1, further comprising the step of: providing (602) the determined (601 ) geographical coordinates or road/ lane/ street/parking identifier to a database (410) comprising pre-computed procedures of underneathsegments (110); and wherein the pre-computed procedures are obtained (603) from the da-tabase (410).

3. The method (600) according to claim 1, wherein the pre-computed procedures ofthe underneath segment (110) are stored in a vehicle map; and wherein the pre-computedprocedures are obtained (603) from the vehicle map.

4. The method (600) according to any one of claims 1-3, further comprising the stepof: providing (605) feedback to the database (410) concerning successfulness of theobtained (603) pre-computed procedures during the planning (604) of the vehicle path (310)and/ or updating the database (410) based on own vehicle computations.

5. The method (600) according to any one of claims 1-4, wherein the pre-computedprocedures of underneath segments (1 10) comprises any of: pre-computed trajectories, pre-computed paths, pre-computed lattices, pre-computed gradients, pre-computed sampling bi-ases and/ or pre-computed obstacle avoidance suggestions, and/ or pre-computed acceler-ation/ braking suggestions.

6. A control arrangement (120) for motion planning of an autonomous vehicle (100)passing an underneath segment (110), wherein the control arrangement (120) is configuredto: 17 determine geographical coordinates of the vehicle (100) when approaching the un-derneath segment (1 10), or a road/ lane/ street/ parking identifier of the underneath segment(110) when the vehicle (100) is approaching; obtain pre-computed procedures associated with the underneath segment (110) ofthe provided geographical coordinates or road/ lane/ street/ parking identifier; and plan motions of the vehicle (100) during passage of the underneath segment (110),based on the obtained pre-computed procedures.

7. The control arrangement (120) according to claim 6, further configured to providethe determined geographical coordinates, or road/ lane/ street/ parking identifier, to a data-base (410) comprising pre-computed procedures of underneath segments (110); andwherein the pre-computed procedures are obtained from the database (410).

8. The control arrangement (120) according to claim 6, wherein the pre-computed pro-cedures of the underneath segment (110) are stored in a vehicle map; and wherein the pre-computed procedures are obtained from the vehicle map.

9. The control arrangement (120) according to any one of claim 6-8, further configuredto: provide feedback to the database (410) concerning successfulness of the obtainedpre-computed procedures during the planning of the vehicle path (310) and/ or update thedatabase (410) based on own vehicle computations.

10.puted procedures of underneath segments (110) comprises any of: pre-computed trajecto- The control arrangement (120) according to any one of claim 6-9, wherein pre-com- ries, pre-computed paths, pre-computed lattices, pre-computed gradients, pre-computedsampling biases and/ or pre-computed obstacle avoidance suggestions, and/ or pre-com-puted acceleration/ braking suggestions.

11. A computer program comprising instructions which, when the computer program isexecuted by a computer, cause the computer to carry out the steps of the method (600)according to any one of claims 1-5.

12.claims 6-10. A vehicle (100) comprising a control arrangement (120) according to any one of