System and procedure for lane control of autonomous vehicles

Abstract

A system and method for lane control of a fleet (18) of autonomous vehicles (16) in a road network with multi-lane roads is characterized in that it comprises a backend server (10) wirelessly connected to the fleet vehicles (16), which contains location-specific levels of automation (L1..L5) for each lane (26a, 26b, 26c) on multi-lane roads (24), wherein the fleet vehicles (16) moving on such a road (24) are designed and configured to determine environmental data of the fleet vehicle (16) for the current vehicle lane (26a, 26b, 26c) and vehicle position and to transmit this data to the backend server (10), wherein the backend server (10) uses at least this environmental data to determine a level of automation (L1...L5) determines and stores, and the backend server (10) is further trained and configured to transmit the levels of automation (L1...L5) to each fleet vehicle (16) for a given section of track (24) for the available lanes (26a, 26b, 26c), and each fleet vehicle (16) includes a trajectory computer (22) which is trained and configured to determine and follow a lane trajectory (27) from the transmitted levels of automation (L1...L5) of the lanes (26a, 26b, 26c) of the section of track (24), taking advantage of the highest possible level of automation.

Description

[0001] The invention relates to a system for lane control of autonomous vehicles in a road network with multi-lane roads, as well as a corresponding method and an autonomous motor vehicle trained for this purpose.

[0002] US patent 2022 / 0178708 discloses a method for controlling an autonomous vehicle based on environmental information, in particular the road width.

[0003] There are currently five levels of automation for autonomous vehicles.

[0004] In the so-called automation level 1 (assisted driving), the driver must observe the traffic and control his vehicle, with support from automatic distance control systems and lane keeping assistants to maintain distances to vehicles ahead and lane boundaries.

[0005] In automation level 2 (partially automated driving), adaptive cruise control and lane keeping assist systems are combined with overtaking and parking assist systems in a single unit that moves the vehicle under defined conditions. However, the driver must constantly monitor the traffic.

[0006] At automation level 3 (highly automated driving), the vehicle can be controlled independently and without human intervention for a limited period and under suitable conditions, allowing the driver to take their attention away from the road, for example, to the rear seats or to read. However, if the system detects a problem and notifies the driver, the driver must immediately regain control of the vehicle.

[0007] At automation level 4 (fully automated driving), the vehicle is able to drive longer distances fully automatically, allowing a driver to read or sleep, because the vehicle is able to independently reach a safe state (especially a standstill) in case of problems.

[0008] Finally, at automation level 5 (autonomous driving) there is no longer a driver, but all vehicle occupants are passengers, so that the vehicle can be moved in all driving situations even without occupants.

[0009] The degree of automation achievable in practice, at which a vehicle can be operated, currently depends on the vehicle's surroundings. For example, a narrow road or one without lane markings allows for a lower degree of automation than a wide road with lane markings. Similarly, environmental factors such as weather and lighting conditions play a role in determining the possible degree of automation.

[0010] The object of the invention is to provide a system for lane control of autonomous vehicles in a road network with multi-lane roads.

[0011] The invention is defined by the features of the independent claims. Advantageous further developments and embodiments are the subject of the dependent claims.

[0012] The problem is solved according to claim 1 by a system comprising a backend server wirelessly connected to the fleet vehicles, which contains location-specific automation levels for each lane on multi-lane roads, wherein the fleet vehicles moving on such a road are designed and configured to determine environmental data of the fleet vehicle for the current vehicle lane and position and to transmit this data to the backend server, wherein the backend server determines and stores at least one automation level for each vehicle lane and position from this environmental data, and the backend server is further designed and configured to transmit the automation levels for the available lanes to each fleet vehicle for a given road segment. Furthermore, each fleet vehicle comprises a trajectory computer that is designed and configured to...From the transmitted automation levels of the lanes of the route section, a lane trajectory is to be determined and followed, utilizing the highest possible degree of automation.

[0013] According to the invention, each fleet vehicle continuously measures a series of unchanging, static environmental data and dynamic, i.e., only valid at the present moment, changing environmental data for the currently used lane. This data serves to determine a maximum permissible level of automation for the current position, lane, and time, a process continuously performed by the backend server. In addition to the environmental data obtained from a fleet vehicle, further data, in particular historical data from other vehicles that previously traveled the same route and lane, and officially prescribed restrictions (for example, that driving with an automation level greater than 3 is not permitted on a section of the route) can be included in the calculation of the automation level. The automation levels calculated in this way are stored in the backend server.

[0014] Other fleet vehicles perform the same procedure at other times and also for other roads and other lanes of the same road, so that for a multi-lane road, a sequence of automation levels is stored in the backend server for each lane.

[0015] When a fleet vehicle follows a route specified by the navigation system and continuously transmits environmental data to the backend server, the system receives a sequence of calculated automation levels for the available lanes in the section of road immediately ahead (preferably 1-5 minutes). Based on this data (automation levels) and taking into account the vehicle's own design-related maximum permissible automation level, the fleet vehicle plans a trajectory with the aim of achieving the highest possible permissible automation level by changing lanes. The fleet vehicle then transmits the actually used automation level, along with the environmental data, to the backend server.

[0016] By defining the automation levels on a lane-specific basis in a backend server, it is determined what a fleet vehicle can do at any given time.

[0017] This allows automated fleet vehicles to plan a lane trajectory more accurately, thereby increasing driving comfort for the drivers.

[0018] According to an advantageous embodiment of the invention, the environmental data includes static environmental data, in particular the lane width, lane markings, and road surface. With a lane width of less than 2.2 m, automated driving is not possible due to the minimum lateral distances that must be maintained between vehicles. The absence of lane markings reduces the level of automation by one level, as orientation by the lane markings is not possible. Road surface conditions such as speed bumps and poor pavement – ​​measured, for example, by acceleration sensors – also lead to a lower level of automation.

[0019] According to an advantageous embodiment of the invention, the environmental data includes dynamic environmental data, in particular brightness, weather conditions, traffic density, and a vehicle ahead. In daylight (e.g., a brightness of 1000 lux or more) and without precipitation (e.g., less than 0.2 mm / h), the level of automation increases by one level. A vehicle ahead at a time interval of preferably 1.8 to 2.2 seconds further increases the level of automation on the lane.

[0020] According to an advantageous embodiment of the invention, the environmental data includes the current vehicle speed. The lower the vehicle speed, the higher the degree of automation. Thus, in a traffic jam (speed less than 50 km / h), a higher degree of automation can be achieved than in free-flowing traffic.

[0021] According to an advantageous embodiment of the invention, historical data on the degree of automation can be stored in the backend server. In particular, the lane determination device performs a change in the determined degree of automation based on historical data.

[0022] Historical lane-specific automation levels at a particular location are a characteristic of the road infrastructure that does not change rapidly. For example, if a section of road has been traversed by a number of fleet vehicles during a recent period (e.g., 100 times in the last week at automation level 5) and there were no interruptions, i.e., no downgrades in automation level, then it will remain at that automation level.

[0023] Locations of downgrades in automation levels on the lanes are recorded and used to detect problematic locations and redirect other vehicles. For example, lanes can be avoided if other vehicles have experienced problems at that location (e.g., if there have been three attempts by vehicles to autonomously navigate a section with automation level 5 in the last hour, and all attempts failed). According to a beneficial further development, the automation level for this section is initially reduced to level 4 for subsequent vehicles. Once per hour, a vehicle is permitted to proceed with level 5; if there are no failures during this attempt, then subsequent vehicles are also allowed to pass through this section with automation level 5.

[0024] According to a second aspect of the invention, an autonomous motor vehicle is proposed which is wirelessly connected to the backend server and which includes a trajectory computer which is designed and configured to determine and follow a lane trajectory from the transmitted automation levels of the lanes of the route section, utilizing the highest possible degree of automation.

[0025] According to a third aspect of the invention, a method for controlling the lane of an autonomous fleet vehicle using the system described above is proposed, wherein - the fleet vehicle determines the current vehicle lane and position and transmits it to the backend server, - the backend server uses at least this environment data for each vehicle lane and -position determines and stores a degree of automation, - the backend server transmits the automation levels to each fleet vehicle for a given route segment and the available lanes, - Each fleet vehicle determines and follows a lane trajectory based on the transmitted automation levels of the lanes of the route section, utilizing the highest possible level of automation.

[0026] Further advantages, features, and details will become apparent from the following description, in which – possibly with reference to the drawings – at least one embodiment is described in detail. Identical, similar, and / or functionally equivalent parts are identified by the same reference numerals.

[0027] This shows: Fig. 1 a schematic representation of the system, Fig. 2 a flowchart of the method according to the invention, Fig. 3 a section of track to be travelled by a fleet vehicle.

[0028] Fig. Figure 1 shows a schematic representation of the system according to the invention. This system comprises a backend server 10, which includes a data storage device 12 and an automation level computer 14. A number of fleet vehicles 16 of the fleet 18 are in constant mobile communication with the backend server 10. The term "fleet vehicles" 16 refers only to those vehicles that are connected to the system according to the invention, i.e., that are in contact with the backend server 10, preferably via a mobile communication connection.

[0029] The fleet vehicles 16 each possess an individual, design-related maximum permissible degree of automation. The fleet vehicles 16 are equipped with a number of sensors 20, such as cameras, radars, and lidars, to capture both static environmental data, such as lane width, lane markings, and road surface conditions, and dynamic environmental data, in particular brightness, weather conditions, traffic density, and the presence of a vehicle ahead. The fleet vehicles 16 also include a trajectory computer 22, which is designed and configured to transmit the sensor data 20 and the currently set degree of automation to the backend server 10. All fleet vehicles 16 perform the same function.

[0030] In Fig.Figure 2 illustrates the process flow. In a first step 100, the trajectory computer 22 of a fleet vehicle 16 determines the sensor data from its sensors 20 and transmits this data wirelessly to the backend server 10 in step 101. In addition to the sensor data, the data includes information about a route, the current automation level set by the fleet vehicle 16, position data (e.g., GPS data), and the time.

[0031] In step 102, the backend server 10 receives the data transmitted by the fleet vehicle 16 and stores it in the data storage 12. In step 103, the automation degree calculator 14 of the backend server 10 calculates the automation degree for a given route segment 24 based on the historical or other data received from the fleet vehicle 16 and any historical or other data stored in the data storage 12. Fig.3) and for each lane 26a, 26b, 26c of track section 24, a sequence of automation level sections 28a-e of lane 26a, automation level sections 30a-f of lane 26b, and automation level sections 32a-e of lane 26c. In step 104, the automation level computer 14 transmits this data set to the trajectory computer 22 of the fleet vehicle 16. The following scheme (Table 1) is preferably used to determine the automation level: Table 1: Scheme for determining the degree of automation Street width Page markers Traffic conditions Automatic degree <2,2m Yes Free traffic >50 km / h L1 <2,2m Yes Traffic jam <50km / h L2 <2,2m No Free traffic >50 km / h L1 <2,2m No Traffic jam <50km / h L1 2.2m <x<2,5m Yes Free traffic >50 km / h L3 2.2m <x<2,5m Yes Traffic jam <50km / h L4 2.2m <x<2,5m No Free traffic >50 km / h L2 2.2m <x<2,5m No Traffic jam <50km / h L3 >2,5m Yes Free traffic >50 km / h L4 >2,5m Yes Traffic jam <50km / h L5 >2,5m No Free traffic >50 km / h L3 >2,5m No Traffic jam <50km / h L4

[0032] In step 105, the automation degree calculator 14 receives this data set and calculates a lane trajectory 27 from it, taking into account the design-related maximum permissible automation degree of the fleet vehicle 16, and controls the fleet vehicle 16 accordingly in step 106.

[0033] In Fig.Figure 3 shows a track section 24 with three lanes 26a, 26b, 26c, with the fleet vehicle 16 located at the left edge of track section 24. Each of the lanes 26a, 26b, 26c is divided into a sequence of automation-level sections 28a-e, 30a-f, 32a-e. For example, the automation-level section 28a of the far left lane 26a has an automation level of 3 (L3), the following automation-level section 28b has an automation level of 4 (L4), the following automation-level section 28c has an automation level of 5 (L5), and so on. The first automation-level section 30a of the middle lane 26b has an automation level of 2 (L2) in the example shown, and the first automation-level section 32a of the right lane 26c has an automation level of 1 (L1). It is assumed that the maximum permissible level of automation of the fleet vehicle under consideration is 16 degrees L4.

[0034] The lane trajectory 27 determined in step 105 begins in the example shown at the left edge of the left lane 26a, because the automation level section 28a there, with level 3 (L3), is higher than the automation level sections 30a and 32a of the middle lane 26a and the right lane 26c. The lane trajectory 27 initially remains on the left lane 26a and passes through the automation level sections 28a, 28b, and 28c there. Although a new automation level section 32b with an even higher level L5 occurs approximately in the middle of automation level section 28b on the right lane 26c, since this example assumes that the fleet vehicle under consideration has a maximum automation level of only L4, the lane trajectory 27 remains on the left lane 26a.Only at the end of automation level section 28c would the following automation level section 28d of the left lane 26a only have level 1 (L1), while the middle lane 26b in automation level section 30d has level 3 (L3) and the right lane in automation level section 32d even has level 4 (L4).

[0035] Assuming the maximum permissible level of automation for the fleet vehicle 16 is 4 (L4), it will move to the far right lane 26c to enter automation section 32c and utilize automation level 4 there. However, if fleet vehicle 16 only has a maximum permissible level of automation 3, it could remain in the middle lane 26b because the automation level 4 available in the right lane 26c would not be usable. At the end of automation section 32c of the right lane 26c, fleet vehicle 16 will move back to the far left lane 26a because automation level 3 (L3) is available again in automation section 28e.

[0036] Although the invention has been further illustrated and explained in detail by means of preferred embodiments, the invention is not limited by the disclosed examples, and other variations can be derived from them by a person skilled in the art without departing from the scope of protection of the invention. It is therefore clear that a multitude of possible variations exist. It is also clear that the embodiments mentioned as examples are truly only examples and are not to be understood in any way as limiting, for example, the scope of protection, the possible applications, or the configuration of the invention.Rather, the preceding description and the description of the figures enable the person skilled in the art to implement the exemplary embodiments in concrete terms, whereby the person skilled in the art, with knowledge of the disclosed inventive concept, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without leaving the scope of protection defined by the claims and their legal equivalents, such as further explanations in the description. Reference symbol list: 10 backend servers 12 Data storage devices 14 Automation Degree Calculator 16 fleet vehicles 18 Fleet 20 sensors 22 trajectory calculators 24th section 26a,b,c Lane 27 Lane trajectory 28a-e Automation degree sections 30a-f Automation degree sections 32a-e Automation level sections QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] US 2022 / 0178708

[0002]

Claims

[1] Lane control system for a fleet (18) of autonomous vehicles (16) in a multi-lane road network, characterized by, that this includes a backend server (10) wirelessly connected to the fleet vehicles (16), which contains location-specific automation levels (L1..L5) for each lane (26a, 26b, 26c) on multi-lane roads (24), wherein the fleet vehicles (16) moving on such a road (24) are designed and configured to determine environmental data of the fleet vehicle (16) for the current vehicle lane (26a, 26b, 26c) and position and to transmit it to the backend server (10), wherein the backend server (10) determines and stores at least one automation level (L1...L5) for each vehicle lane (26a, 26b, 26c) and position from this environmental data, and the backend server (10) is further designed and configured to provide each fleet vehicle (16) with the available lanes (26a, 26c) for a given road section (24). 26b, 26c) the levels of automation (L1...L5) transmitted, and each fleet vehicle (16) includes a trajectory computer (22) which is trained and equipped to determine and follow a lane trajectory (27) from the transmitted levels of automation (L1...L5) of the lanes (26a, 26b, 26c) of the track section (24), taking advantage of the highest possible level of automation. [2] System according to claim 1, characterized by that the environmental data includes static environmental data, in particular lane width, side markings, road surface. [3] System according to claim 1 or 2, characterized by that the environmental data includes dynamic environmental data, in particular brightness, weather, traffic density, a vehicle driving ahead. [4] System according to claim 1 or 2, characterized by that the environmental data includes the current driving speed. [5] System according to any of the preceding claims, characterized by, that historical data on the degree of automation can be stored in the backend server (10). [6] System according to claim 5, characterized by that the lane determination device makes a change to the specified level of automation based on historical data. [7] Autonomous motor vehicle, characterized by , that this is wirelessly connected to the backend server (10) and that this includes a trajectory computer (22) which is designed and set up to determine and follow a lane trajectory (27) from the transmitted automation levels (L1...L5) of the lanes (26a, 26b, 26c) of the track section (24) using the highest possible degree of automation. [8] Method for controlling the lane of an autonomous fleet vehicle (16) using the system according to any one of claims 1 to 6, characterized by , that - the fleet vehicle (16) determines the current vehicle lane (26a, 26b, 26c) and position and transmits it to the backend server (10), - the backend server (10) determines and stores at least one level of automation (L1...L5) for each vehicle lane (26a, 26b, 26c) and position from this environment data, - the backend server (10) transmits the automation levels (L1...L5) to each fleet vehicle (16) for a specified route section (24) for the available lanes (26a, 26b, 26c), - each fleet vehicle (16) determines and follows a lane trajectory (27) from the transmitted automation levels (L1...L5) of the lanes (26a, 26b, 26c) of the track section (24), taking advantage of the highest possible level of automation.

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