• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
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    • 专利摘要:
    19 Summary The invention relates to a system for regulating an autonomous vehicle in a traffic system comprising a plurality of autonomous vehicles. The system analyzes external information according to predetermined rules and generates analysis signals to the vehicle which are given different priority depending on the analysis performed and the result of the analysis. A weighted analysis signal Sx is determined based on the content of the analysis signals and their prioritization. The vehicle can then adapt its control to the weighted analysis signal S. The invention also relates to a method for regulating an autonomous vehicle in a traffic system comprising a plurality of autonomous vehicles. (Figure 2)
    公开号:SE1350329A1
    申请号:SE1350329
    申请日:2013-03-19
    公开日:2014-09-20
    发明作者:Jon Andersson；Joseph Ah-King；Tom Nyström
    申请人:Scania Cv Ab；
    IPC主号:
    专利说明:

    Method and systems for controlling autonomous vehicles Field of the invention The present invention relates to technology for handling various situations in traffic systems which comprise a plurality of autonomous vehicles.
    Back to the invention A vehicle that can be driven without a driver on the ground is called an unmanned ground vehicle (UGV). There are two types of hazardous ground vehicles, those that are remotely controlled and those that are autonomous.
    A remote-controlled UGV is a vehicle that is regulated by a human operator via a communication link. All actions are determined by the operator based on either direct visual observation or the use of sensors such as digital video cameras. A simple example of a remote-controlled UGV is a remote-controlled toy car.
    There is a wide variety of remote controlled vehicles in use today. These vehicles are often used in dangerous situations and environments that are unsuitable for people to live in, for example to disarm bombs and in case of dangerous chemical emissions. Remotely controlled hazardous vehicles are also used in connection with Monitoring assignments 20 and the like.
    By an autonomous vehicle is meant hdr a vehicle that is capable of navigating and maneuvering without human control. The vehicle uses sensors to gain understanding of the surroundings. Sensor data is then used by control algorithms to determine what is the next step for the vehicle to take into account an overall goal for the vehicle, for example to pick up and load goods at different positions. More specifically, an autonomous vehicle must be able to read the surroundings well enough to be able to carry out the task assigned to it, for example "move the boulders from place A to place B via the mine passage C". The autonomous vehicle needs to plan and follow a road to the selected destination while detecting and avoiding obstacles on the road. In addition, the autonomous vehicle must carry out its task as quickly as possible without making any mistakes. Autonomous vehicles 2 have, among other things, been developed so that they can be used in dangerous environments, for example in the father defense and war industry and in the mining industry, both above ground and underground. If people or ordinary, manually controlled vehicles approach the work area of the autonomous vehicles, they normally cause a break in work due to safety concerns. When the work area is free again, the autonomous vehicles can be ordered to resume work.
    The autonomous vehicle uses information regarding the road, the surroundings and other aspects that affect the progress made to automatically regulate the throttle, braking and steering. An accurate assessment and identification of the planned progress is necessary to assess whether a road is passable and is necessary to be able to salt a person's assessment on a successful salt when it is necessary to drive the vehicle. Pedestrian behaviors can be complex and when driving a normal driver-controlled vehicle, the driver makes hundreds of observations per minute and adjusts the operation of the vehicle based on the perceived vagrancy behaviors to find, for example, a passable vagabond object that may be on the road. In order to be able to replace the human perceptual capacity with an autonomous system, this meant, among other things, being able to perceive objects on an exact salt and being able to effectively control the vehicle so that one steers past these objects. The technical methods used to identify an object in connection with the vehicle include using one or more cameras and radar to create images of the surroundings. Avon laser technology is used, both scanning lasers and fixed lasers, capable of detecting objects and grinding distance. These bends are often LIDAR (Light Detection and Ranging) or LADAR (Laser Detection and Ranging). In addition, the vehicle is equipped with various sensors, including the ability to detect speed and accelerations in different directions. Positioning systems and other wireless technology can also be used to determine if the vehicle is approaching, for example, an intersection, a narrowing of the road, and / or other vehicles.
    When using autonomous vehicles, the human ability to follow both traffic rules and traffic culture must also be emulated by the vehicle's control system. A driver of a conventional vehicle, for example, usually instinctively avoids a collision before it reaches the speed limits. Today's autonomous vehicle's perception of traffic is normally limited to "stopping if someone comes near or enters my work area". In order to be able to take into account many different parameters, the autonomous vehicle must know which parameter or which parameter is most important.
    US-8103438-B2 describes a method and a system for automatically controlling traffic in a work area. Manning vehicles are assigned different priorities depending on, for example, the road they drive or how heavy they are. In the event of a conflict, the priorities of the vehicles are compared, and the vehicle with lower priority may give way to the vehicle with higher priority.
    US-7979174-B2 discloses automatic speed planning and control of autonomous vehicles. Speed planning is based on a number of restrictions with different priorities, for example, it is a higher priority to avoid a collision than to follow speed restrictions.
    In order for an entire transport system consisting of many autonomous vehicles mixed with, for example, manually controlled vehicles and pedestrians to be able to work together for a long time, improved methods are needed to take into account many different parameters and tasks while the autonomous vehicles reach their most efficient levels.
    The object of the invention is thus to provide an improved method for assisting an autonomous vehicle in making decisions as the vehicle must take into account a number of different actions.
    SUMMARY OF THE INVENTION According to one aspect of the invention, the object is achieved by a system for regulating an autonomous vehicle in a traffic system comprising a plurality of autonomous vehicles according to the first independent claim. The system analyzes external information according to predetermined rules and generates analysis signals to the vehicle which are given different 4 priorities depending on which analysis has been performed and the result of the analysis. A weighted analysis signal S, determined based on the content of the analysis signals and their prioritization. The vehicle can adapt its regulation to the interleaved analysis signal S.
    Through the system, the transports in the system can always be carried out on the most efficient salt, not only by avoiding collisions and following traffic rules, but by continuously ensuring that all parts of the transport system cooperate against the stated goals. The autonomous vehicle knows in each situation how it should act so that its action must be safe and efficient for the entire traffic system.
    According to another aspect, the object is achieved by a method father to regulate an autonomous vehicle in a traffic system comprising a plurality of autonomous vehicles.
    According to a third aspect, the object is achieved with a computer program product comprising computer program instructions for forming a computer system to perform the steps of the method.
    The vehicles described herein are dangerously autonomous, but according to one embodiment may also be partially manually steerable. Each vehicle knows where the other vehicles are and what they are doing through communication between vehicles and between vehicles and control center. According to one embodiment, an autonomous vehicle can also detect other, unconnected road users moving in the traffic area and notify the control center and the other vehicles.
    Preferred embodiments are defined by the dependent claims.
    Brief description of the figure Figure 1 illustrates a traffic system with a plurality of autonomous vehicles.
    Figure 2 shows a system for controlling an autonomous vehicle in a traffic system according to an embodiment of the invention.
    Figure 3 shows a flow chart of a method according to an embodiment of the invention.
    Detailed Description of Preferred Embodiments of the Invention Figure 1 schematically shows three autonomous vehicles 2, 3 and 4 traveling along a wagon. The arrows in the autonomous vehicles 2, 3, 4 show their respective correction. The autonomous vehicles 2, 3, 4 can communicate with a control center 1 via eg V21 communication (Vehicle-to-Infrastructure) 5 and / or with each other via eg V2V communication (Vehicle-to-Vehicle) 6. This communication is wireless and can be done, for example, via WLAN (Wireless Local Area Network) protocol IEEE 802.11, for example IEEE 802.11p. However, other wireless means of communication are also conceivable. The command center 1 organizes the autonomous vehicles 2, 3, 4 and gives them assignments to perform. When an autonomous vehicle completes an assignment, the vehicle can independently ensure that the assignment is performed. An assignment can, for example, consist of an instruction to pick up goods at a goods collection point A. The vehicle then has the capacity to determine its current position, determine a vague frail the current position to the goods collection point A, and get there. During the journey, the vehicle must also have the capacity to sway for obstacles, handle other autonomous vehicles that may have a more important task and must be given representation. The vehicle can also be given a new assignment during the ongoing assignment, which must be given higher priority than the ongoing assignment. In a manned vehicle, the driver makes these decisions continuously while driving. An autonomous vehicle needs to have predetermined rules for how it should prioritize in various transactions in order to be able to steer itself on a salt that is the most efficient for the entire traffic system.
    Figure 2 illustrates a system 16 according to an embodiment of the invention for controlling an autonomous vehicle in a traffic system comprising a plurality of autonomous vehicles. The autonomous vehicle may, for example, be one of the autonomous vehicles shown in Figure 1 and referred to as 2, 3 or 4. The system 16 may be located entirely in the autonomous vehicle or in the control center 1, or partly in the vehicle and partly in the control center. The system 16 will now be explained 6 with reference to Figure 2. The system 16 comprises a track unit 7 which is adapted to receive a mission signal Su indicating a mission for the autonomous vehicle, the mission comprising destination information galling at least one destination of the vehicle. The assignment will preferably be Iran's command center 1. The assignment may, for example, include destination information in the form of a destination in GPS coordinates. The path unit 7 is further adapted to determine at least in part a path along which the vehicle is to drive to reach said destination based on at least the destination information, and to generate a path signal SB indicating the path. The path unit 7 can, for example, receive map information from an external map unit 15 via a map signal Sm, and position information from a position determining unit 18 via a position signal SG. This can be done by satellite positioning (Global Navigation Satellite System, often abbreviated to GNSS) in cases where the system 16 is used outdoors. GNSS is a collective name for a group of world-wide navigation systems that use signals from a constellation of satellites and pseudo-satellites to enable position input for a receiver. The American GPS system is the most well-known GNSS system, but in addition there are the Russian GLONASS and the future European Galileo. The position of the vehicle can also be determined by monitoring the signal strength from several access points for wireless networks (WiFi) nearby. Another way to determine the position is to enter the number of wheel revolutions and, with the help of the circumference of the wheels, determine how far the vehicle has traveled. Together with knowledge of the vehicle's direction, the vehicle's position in relation to a map can be determined. In this way, you can always know where the vehicle is.
    The system 16 further comprises a plurality of analysis units 8, 9, 10, 11 which are adapted to receive external information 13 along the path. This external information 13 is shown schematically with an arrow 13 to the system 16, and can for example be additional assignments from the control unit 1, information from sensors in the autonomous vehicle, information via V2V from other vehicles, information via V2I from eg traffic lights, speed signs, etc. The analysis units 8, 9, 10, 11 are adapted to analyze the external 7 information 13 at least according to predetermined rules and to determine and generate analysis signals S1, S2, S3, S4 for the analysis units 8, 9, 10, 11 based on the results of the analyzes.
    According to one embodiment, the analysis units 8, 9, 10, 11 comprise a collision unit 8, a navigation unit 9, a collaboration unit 10 and / or a mission unit 11. An analysis unit 8, 9, 10, 11 may be adapted to receive external information 13 in the form of sensor signals from various sensors in the autonomous vehicle, for example camera, laser (eg LIDAR or LADAR), radar, speed sensors, acceleration sensors, and information about other vehicles or obstacles via V2V and / or V21 communication. The external information 13 may also include a new assignment for the vehicle, or other information from the control center 1. This external information 13 can then be used by the various analysis units 8, 9, 10, 11 on different salts. Most recently, the various analysis units 8, 9, 10, 11 will be explained in more detail.
    The collision unit 8 is adapted to use the external information 13 to anticipate a risk of collision with another vehicle or object along the path indicated by the path signal Sg. According to one embodiment, the collision unit 8 is adapted to analyze the external information 13 based on rules for the risk of a collision with one's own vehicle. On such salt, the risk of collision can be continuously evaluated. The external information 13 is thus analyzed according to predetermined rules and an analysis signal Si is determined for the collision unit 8 based on the result of the analysis. Analysis signal Si indicates, for example, whether there is a risk of a collision. The analysis signal Si may, according to one embodiment, also include steering instructions indicating how the vehicle is to be steered to avoid the obstacle, for example a slow speed, turning instructions, stops, or a new lane for the vehicle. If there is no risk of collision, the analysis signal Si indicates according to an embodiment Above this.
    The navigation unit 9 can use the external information 13 to ensure that the vehicle does not violate any traffic rules and / or to ensure that the vehicle finds the 8th narrowest turn to its mission under the vehicle's CEO. Idngs lane indicated by the lane signal SB. The traffic rules may be different depending on the environment in which the traffic system is located. For example, there may be different traffic rules in a mine and in ordinary, civilian traffic. According to one embodiment, the navigation unit 9 is adapted to analyze the external information 13 based on traffic rules and / or to find the narrowest path to achieve the task. Traffic rules can, for example, mean a maximum number of vehicles on a carriageway, or maximum and minimum speeds for the autonomous vehicle. The navigation unit 9 can receive map information from the map unit 15 via a map signal Sm, and position information from a position determining unit 18 via a position signal SG, which is shown as dashed lines in Figure 2, in order to determine the narrowest path to perform the task. By combining requirements to follow traffic rules and to drive the nearest road, you can achieve efficient driving in accordance with traffic rules. The navigation unit 9 is adapted to determine and generate an analysis signal S2 for the navigation unit 9 based on the result of the analysis. The analysis signal S2 may, for example, indicate that the predetermined path indicated by the path signal SB cannot be followed due to the traffic rules, or that it is not the narrowest road. According to one embodiment, the navigation unit 9 is adapted to determine a new path that follows the traffic rules and / or is the closest route to performing the task. The analysis signal S2 can then indicate this. If there is no change in the length of the track based on traffic rules and / or the vehicle has already reached the nearest road, the analysis signal S2 according to one embodiment indicates this.
    The interoperability unit 10 can use the external information 13 to ensure that the autonomous vehicle interacts with other vehicles in the traffic system in a way that is efficient for the entire traffic system. According to one embodiment, the interaction unit 10 is adapted to analyze the external information 13 based on interaction rules with other road users. In collaboration, both the individual autonomous vehicles and the control center 1 take into account the efficiency of the entire traffic system. What efficiency means can differ from traffic system to traffic system and can be chosen by the traffic system's human supervisors. If two different heavy vehicles meet at a bottleneck, for example a tunnel or mine with only 9 a lane, and the heavier one is on the way up, it may be more efficient for the heavier vehicle to be left in front of the lighter vehicle that is on the way down. The interaction unit 10 can then be adapted to compare parameters from the different vehicles with each other, for example weight parameters. If a lone autonomous vehicle meets a vehicle roof, it may be more efficient for the lone autonomous vehicle to stop even if it is heavier, but not if it means that it will not be able to start again after the stop. In the same situations, some of the vehicles may instead slow down in good time to avoid conflict altogether. The interaction unit 10 is then adapted to determine and generate an analysis signal S3 for the interaction unit 10 based on the result of the analysis.
    The analysis signal S3 can, for example, indicate that collaboration needs to take place and / or which collaboration needs to take place. If there is no need for cooperation, the analysis signal S3 according to an embodiment indicates this.
    According to one embodiment, the external information 13 may comprise an external traffic management decision. An external traffic management decision can, for example, be a decision for an autonomous vehicle to get out of a mine after completing an assignment because an accident has occurred. The traffic management decision then meant a new assignment - to get out of the mine to a predetermined place. According to one embodiment, the assignment unit 11 is then adapted to analyze the external information 13 based on rules for external traffic management decisions. The assignment unit 11 is then adapted to determine and generate an analysis signal S4 for the assignment unit 11 based on the result of the analysis. The analysis signal S4 can then include the information that a new assignment has been received and, for example, 2-destination information.
    In special cases where there are no clear rules for how the vehicles should act in the situation that has arisen, for example how two vehicles should interact, the system 16 can ask a command center 1, possibly including a human supervisor, for a row to reach a decision. According to one embodiment, at least one of the analysis units 8, 9, 10, 11 is adapted to send a request signal 131 indicating a request to a control center 1 related to the external information 13. The request is then processed in the control center 1 and a decision is made. The decision can, for example, be made by a human supervisor or operator. The analysis unit 8, 9, 10, 11 is then adapted to receive a decision signal 132 indicating the decision from the control center 1, and to analyze the external information 13 based on the decision from the control center 1. On such salt can also respond or complex situations in the system 16 are handled .
    The system 16 further comprises a result unit 12 which is adapted to receive analysis signals S1, S2, S3, S4. The result unit 12 is adapted to relate a priority to at least one analysis signal S1, S2, S3, S4 based on which analysis unit 8, 9, 10, 11 they come from and their contents. If the analysis signal 51 does not indicate any risk of collision, this analysis signal will not be given priority. The same grid for the analysis signal S2, and if this analysis signal indicates that no change needs to take place, the analysis signal S2 does not receive priority. If the analysis signal S3 does not indicate any need for cooperation, the analysis signal S3 flag will not be given priority. If the analysis signal S4 does not indicate a new assignment, this flag does not take priority. If none of the analysis signals indicates any need for change from the current trajectory, the vehicle follows a specific trajectory according to one embodiment, for example SB. According to one embodiment, the analysis signal S1 from the collision unit 8 is ranked highest, followed by the analysis signal S3 from the interaction unit 10 and then the analysis signal S2 from the navigation unit 9 and finally the analysis signal S4 from the mission unit 11. risk of collision. However, the exemplified prioritization may be different. The result unit 12 is further adapted to determine a weighted analysis signal S, based on the content of the analysis signals and their possible priorities. In order to determine a combined analysis signal Sx, the results unit is adapted to take into account the possibility for the vehicle to, for example, avoid a collision by driving past an obstacle, cooperating with other vehicles, following traffic rules and receiving a new assignment, without violating any results. from flagon other analysis unit 8, 9, 10, 11. This analysis is caused by continuously comparing the contents of the different analysis signals S1-S4. The result unit 11 12 is thus adapted to determine whether the vehicle can act according to the analysis signal S1-S4 which has the highest priority, without coming into conflict with any of the results from the other analysis units 8, 9, 10, 11 which have lower priority.
    If, for example, all two vehicles are facing a narrow tunnel, and the vehicle that has the lowest priority from the point of view of the transport system finds that it has to get through the tunnel before the oncoming higher priority vehicle arrives at the tunnel, then the lower priority the vehicle on it and drive on. This can be indicated, for example, in the analysis signal S3 that cooperation does not have to take place if the vehicle with the lowest priority reaches a certain speed or reaches the tunnel within a certain time, etc. Just before the tunnel, however, the collision unit 8 discovers an obstacle, which according to rules for risk of collision with the own vehicle gives an analysis signal S1 which indicates a risk of collision. Getting around the obstacle is possible, but the extra time it will take means that the oncoming vehicle will meanwhile reach the tunnel. The result unit 12 is then adapted to analyze whether the lower priority vehicle can get past the obstacle, but breathe reach the tunnel within the particular time, and to determine a weighted analysis signal Sx which indicates the result of the analysis. In this case, the lower priority vehicle cannot get around the obstacle and breathe the tunnel in time, resulting in an interleaved analysis signal that includes instructions to the vehicle to stop and overtake the oncoming vehicle before it can pass the obstacle.
    The result unit 12 is then adapted to transmit the interleaved analysis signal Sx to a control system 17 in the autonomous vehicle, after which the vehicle 2 adjusts its control in accordance with the interleaved analysis signal S. In this case, the autonomous vehicle can prioritize in different situations so that the entire traffic system efficiently as possible. The analysis signal S, according to an embodiment, may also comprise control parameters which the control system 17 can control according to.
    The described units may be incorporated in a processor unit comprising one or more processors and associated computer memory 19. In the computer memory 12 19 instructions may be stored to cause the processor or processors to perform the steps described.
    The invention also relates to a method for controlling an autonomous vehicle in a traffic system comprising a plurality of autonomous vehicles, the method will be explained with reference to the flow chart in Figure 3. The method comprises a first step A1) to receive an assignment for the autonomous the vehicle, the assignment including destination information galling at least one destination for the vehicle. The assignment can, for example, come from a command center 1.
    The method further comprises a second step A2) to determine at least in part a path along which the vehicle is to run to reach said destination. In connection with the description of the system 16, it has been explained how a path can be determined, which also applies to the method. In a third step A3) external information 13 is received along the path. While the autonomous vehicle is driving along the designated path, the vehicle constantly receives external information 13, which may include information via camera, laser (eg LIDAR or LADAR), radar, speed sensors, acceleration sensors, and information about other vehicles or obstacles. via V2V and / or V21 communication. The external information 13 may also include a new assignment for the vehicle, or other information from the control center 1. In a fourth step A4), the external information is analyzed at least according to predetermined rules. Depending on what you want to examine, the external information 13 is analyzed according to certain rules. According to one embodiment, the analysis step A4) comprises analyzing the external information 13 based on rules for the risk of collision with one's own vehicle. In this way, the risk of the vehicle colliding with another vehicle or object can be determined. The autonomous vehicle can in later stages then be regulated to avoid the collision. According to another embodiment, the analysis step A4) comprises analyzing the external information 13 based on traffic rules and / or finding the nearest path to achieve. the mission. Different traffic systems may have different traffic rules that the autonomous vehicles must adapt to. How the nearest wagon can be determined has been described with reference to the system 16, which also applies to the method. According to another embodiment, the analysis step A4) comprises analyzing the external information 13 based on interaction rules with other road users. On such salt, an efficient grain can be achieved which is efficient for several vehicles. According to another embodiment, the analysis step A4) comprises analyzing the external information 13 based on rules for external traffic management decisions. On that salt, external traffic management decisions can be handled. The above examples of step A4) can, for example, be done in parallel. In a fifth step A5) analysis signals S1, S2, S3, S4 are determined which indicate the result of the analyzes. In a sixth step A6) a prioritization is related to at least one analysis signal S1, S2, S3, S4 based on which analysis has been performed and the content of the analysis signals. According to one embodiment, the analysis signal Si indicating the risk of collision has the highest priority, followed by the analysis signal S3 indicating the need for cooperation, then the analysis signal S2 indicating whether the predetermined path indicated by the path signal SB cannot be followed due to the traffic rules, that it into is the narrowest way. The lowest priority is then given to the analysis signal from the analysis signal S4, which can, for example, indicate a new assignment. This is based on the fact that an analysis signal that is given a priority also indicates a change for the vehicle.
    In a sixth step A6) a weighted analysis signal S is determined, based on the content of the analysis signals and their prioritization. In a seventh step A7), the interleaved analysis signal S is sent to a control system 17 in the autonomous vehicle, after which the vehicle adjusts its control in accordance with the interleaved analysis signal S.
    According to one embodiment, the analysis step A4) comprises sub-steps A41) - A43) that A41) sends a query related to the external information 13 to a control center 1, A42) receives a decision from the control center 1, and A43) analyzes the external information 13 based on the decision . In this way, you can get expert help if a complicated situation arises.
    The invention also relates to a computer program P in an autonomous vehicle 2, wherein the computer program P comprises program code for shaping the system 16 to perform the steps according to the method. Figure 2 shows the computer program P as part of the 14 computer memory 19. The computer program P is thus stored on. the computer memory 19. The computer memory 19 is connected to the units 7, 8, 9, 10, 11, 12 in the system 16, and when all or part of the computer program P is executed by one or more of the units 7, 8, 9, 10, 11, 12 , at least parts of the methods described herein are performed. The invention further comprises a computer program product comprising a program code stored on a computer readable medium for performing the method steps described herein, when the program code is run on the system 16.
    The present invention is limited to the above-described preferred embodiments. Various alternatives, modifications and equivalents can be used.
    The embodiments above shall be dello! ' is not to be construed as limiting the scope of the invention as defined by the appended claims.
    权利要求:
    Claims (15)
    [1]
    A system (16) for controlling an autonomous vehicle in a traffic system comprising a plurality of autonomous vehicles, characterized in that the system (16) comprises a track unit (7) which is adapted to: - receive a mission signal Su indicating a mission for said autonomous vehicle, the assignment comprising destination information galling at least one destination for the vehicle; - determining at least in part a path along which the vehicle is to drive to reach said destination based on at least said destination information, and generating a path signal SB indicating said path; the system (16) further comprises a plurality of analysis units (8), (9), (10), (11) adapted to: - receive external information (13) along the path; - analyzing said external information (13) at least according to predetermined rules and determining and generating analysis signals Si, S2, S3, S4 for the analysis units (8), (9), (10), (11) based on the results of the analyzes; the system (16) further comprises a result unit (12) adapted to: - receive said analysis signals S1, S2, S3, S4; Relate a priority to at least one analysis signal Si, S2, S3, S4 based on which analysis unit (8), (9), (10), (11) they come from and their contents; - determining a weighted analysis signal S, based on the content of the analysis signals and their prioritization; wherein the result unit (12) is adapted to transmit the interleaved analysis signal Sx to a control system (17) in the dot autonomous vehicle, after which the vehicle adjusts its control in accordance with the interleaved analysis signal Sx.
    [2]
    A system according to claim 1, wherein said analysis units (8), (9), (10), (11) comprise a collision unit (8), a navigation unit (9), a collaboration unit (10) and / or a mission unit (11 ). 16
    [3]
    A system according to claim 2, wherein the collision unit (8) is adapted to analyze said external information (13) based on rules for risk of collision with one's own vehicle.
    [4]
    A system according to any one of claims 2 to 3, wherein the navigation unit (9) is adapted to analyze said external information (13) based on traffic rules and / or to find the nearest route to achieve the task.
    [5]
    A system according to any one of claims 2 to 4, wherein the interaction unit (10) is adapted to analyze said external information (13) based on interaction rules with other road users.
    [6]
    A system according to any one of claims 2 to 5, wherein the assignment unit (11) is adapted to analyze said external information (13) based on rules for external traffic management decisions.
    [7]
    A system according to any one of claims 2 to 6, wherein at least one of the analysis units (8), (9), (10), (11) is adapted to - send a request signal 131 indicating a request to a control center (1). to the external information (13); - receive a decision signal 132 indicating a decision from the command center (1); - analyze said external information (13) based on said decision.
    [8]
    A method of controlling an autonomous vehicle in a traffic system 2 comprising a plurality of autonomous vehicles, the method comprising the steps of receiving an assignment for said autonomous vehicle, the assignment comprising destination information including at least one destination for the vehicle; determine at least in part a path along which the vehicle is to drive to reach said destination; receiving external information (13) along the path; 17 - analyzing said external information (13) at least according to predetermined rules; - determine analysis signals Si, S2, S3, S4 which indicate the result of the analyzes; - relate a priority to at least one analysis signal Si, S2, S3, S4 based on the analysis performed and the content of the analysis signals; determining a weighted analysis signal Sx based on the content of the analysis signals and their prioritization send the weighted analysis signal S, to a control system (17) in the autonomous vehicle, after which the vehicle adjusts its control in accordance with the weighted analysis signal Sx.
    [9]
    The method of claim 8, wherein said analyzing step comprises analyzing said external information (13) based on rules for the risk of collision with one's own vehicle.
    [10]
    A method according to any one of claims 8 to 9, wherein said analyzing step comprises analyzing said external information (13) based on traffic rules and / or the nearest road to achieve. the mission. 20
    [11]
    A method according to any one of claims 8 to 10, wherein said analyzing step comprises analyzing said external information (13) based on interaction rules with other road users. 2
    [12]
    A method according to any one of claims 8 to 11, wherein said analyzing step comprises analyzing said external information (13) based on rules for external traffic management decisions.
    [13]
    A method according to any one of claims 8 to 12, wherein said analyzing step comprises the sub-steps of - sending a request related to the external information (13) to a control center (1); 18 - receive a decision from the command center (1); - analyze said external information (13) based on said decision.
    [14]
    Computer program (P) in an autonomous vehicle, wherein said computer program (P) comprises program code for forming a system (16) to perform the steps according to any one of claims 8 to 13.
    [15]
    A computer program product comprising a program code stored on a computer readable medium for performing the method steps according to any one of claims 8 to 13, when said program code is run on a system (16). Si S2 Sx 12
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    同族专利:
    公开号 | 公开日
    DE112014001058T5|2015-12-31|
    WO2014148975A1|2014-09-25|
    SE537184C2|2015-02-24|
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    法律状态:
    优先权:
    申请号 | 申请日 | 专利标题
    SE1350329A|SE537184C2|2013-03-19|2013-03-19|Method and system for controlling autonomous vehicles|SE1350329A| SE537184C2|2013-03-19|2013-03-19|Method and system for controlling autonomous vehicles|
    DE112014001058.8T| DE112014001058T5|2013-03-19|2014-03-06|Method and system for controlling autonomous vehicles|
    PCT/SE2014/050278| WO2014148975A1|2013-03-19|2014-03-06|Method and system for control of autonomous vehicles|
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