Methods for collecting safety-critical information

Abstract

The invention relates to a method for collecting safety-critical information about the environment of an at least partially automated motor vehicle, comprising the following steps: Receiving environmental signals that represent the environment of the motor vehicle as detected by means of environmental sensors, Creating an environmental model based on environmental signals, Determining safety-critical information from the environment model that is relevant for at least partially automated driving, Sending the identified security-critical information to a remote server. The invention further relates to a method for collecting safety-critical information about a respective environment of at least partially automated motor vehicles, a device, a server, a system, a computer program and a machine-readable storage medium.

Description

[0001] The invention relates to a method and a device for collecting safety-critical information about the environment of an at least partially automated motor vehicle, a server and a system for collecting safety-critical information about the respective environment of at least partially automated motor vehicles, a computer program and a machine-readable storage medium. State of the art

[0002] The patent application DE 10 2020 120 128 A1 discloses a method for tracking objects based on a signal received from a sensor.

[0003] Publication DE 11 2021 000 422 T5 of the international application with publication number WO 2021 / 189009 discloses a prediction of future trajectories in environments with multiple actuators for autonomous machine applications.

[0004] Disclosure document US 2021 / 0342599 A1 discloses a method for locating a vehicle. Disclosure of the invention

[0005] The object underlying the invention is to provide a concept for collecting safety-critical information about the environment of one or more at least partially automated motor vehicles.

[0006] This object is achieved by means of the respective subject matter of the independent claims. Advantageous embodiments of the invention are the subject matter of the respective dependent subclaims.

[0007] Following an initial assessment, a procedure for collecting safety-critical information about the environment of an at least partially automated motor vehicle is provided, comprising the following steps: Receiving environmental signals that represent the environment of the motor vehicle as detected by means of environmental sensors, Creating an environmental model based on environmental signals, Determining safety-critical information from the environment model that is relevant for at least partially automated driving, Sending the identified security-critical information to a remote server.

[0008] A second aspect is described: a procedure for collecting safety-critical information about the respective environment of at least partially automated motor vehicles is provided, comprising the following steps: Receiving safety-critical information about the respective environment of at least partially automated motor vehicles and Storing the received security-critical information in a database.

[0009] A third aspect involves providing a device for collecting safety-critical information about the environment of an at least partially automated motor vehicle, comprising: an input which is set up to receive environmental signals representing the environment of the motor vehicle as detected by means of environmental sensors, a processor device which is configured to create an environment model based on the environment signals and to determine safety-critical information from the environment model which is relevant for safety for at least partially automated driving, and a communication device that is set up to send the identified security-critical information to a remote server.

[0010] A fourth aspect involves providing a server for collecting safety-critical information about the respective environment of at least partially automated motor vehicles, including: a communication device which is set up to receive safety-critical information about the respective environment of at least partially automated motor vehicles, and a processor device which is configured to store the received security-critical information in a database.

[0011] A fifth aspect involves providing a system for collecting safety-critical information about the respective environment of at least partially automated motor vehicles, including: the server according to the fourth aspect and a database that is set up to store the received security-critical information.

[0012] According to a sixth aspect, a computer program is provided, comprising instructions which, when executed by a computer, cause it to perform a procedure according to the first and / or the second aspect.

[0013] According to a seventh aspect, a machine-readable storage medium is provided on which the computer program according to the sixth aspect is stored.

[0014] The invention is based on the understanding that the above problem is solved by identifying safety-critical information relevant to the safety of the vehicle's at least partially automated driving from an environment model used by the vehicle for its at least partially automated driving. This information has previously been discarded and not used further. The core of the concept described here is to not discard this information, but rather to send it to a remote server. This server can then process the information further, for example, by storing it, comparing it with older information, aggregating it with older information, and creating or updating a digital road map based on this data.Such information and / or the digital road map can thus be made available to a large number of at least partially automated motor vehicles, so that they can, for example, plan and carry out their own at least partially automated journey based on this information and / or the digital road map.

[0015] This results, for example, in the technical advantage that such at least partially automated journeys can be carried out more safely.

[0016] In the context of at least partially automated driving, the vehicle already generates an environmental model based on which it drives, at least partially automatically. In addition to this use of the environmental model, it is also used to determine safety-critical information. The environmental model is thus used efficiently, allowing for the efficient collection of safety-critical information.

[0017] By having a large number of vehicles each determine safety-critical information that was relevant to their own at least partially automated driving and send this information to the remote server, a large amount of safety-critical information can be efficiently collected, which can then be used meaningfully on the server side, for example to improve the safety of at least partially automated driving of motor vehicles in the future.

[0018] Safety-critical information, as defined in this description, is in particular information that was relevant to the safety of at least partially automated driving of a motor vehicle. Safety-critical information, as defined in this description, is in particular information that was relevant to the safety of occupants and (other) road users.

[0019] In one embodiment of the method according to the first aspect, it is provided that when a traffic violation is detected by a road user, the road user is classified as safety-critical information, and a result of the classification is sent to the remote server as further safety-critical information.

[0020] This results, for example, in the technical advantage that particularly suitable safety-critical information can be collected.

[0021] One result of the classification specifies, in particular, the type of road user. Examples of such types are: pedestrian, cyclist, driver, motor vehicle, truck, passenger car.

[0022] In one embodiment of the method according to the first aspect, it is provided that when a traffic rule violation by a road user is determined, a degree of traffic rule violation is determined as further safety-critical information.

[0023] This results, for example, in the technical advantage of improved planning of the at least partially automated motor vehicle, which can also be referred to as an ego-motor vehicle.

[0024] If it is known that traffic violations frequently occur at certain locations, corresponding behavior can be anticipated or avoided.

[0025] Example: Traffic situation: a confusing T-junction with a speed limit on the perpendicular lanes. Road users are not adhering to the maximum permitted speed. Knowing that road users are not adhering to the maximum permitted speed, the ego-vehicle cannot safely enter the intersection. Therefore, the ego-vehicle can adjust its own behavior and, for example, cautiously approach the intersection.

[0026] In one embodiment of the method according to the first aspect, it is provided that the degree of traffic rule violation specifies a duration of the traffic rule violation and / or a speed of the road user during the traffic rule violation and / or a trajectory of the road user during the traffic rule violation.

[0027] This results, for example, in the technical advantage that the traffic violation can be efficiently characterized.

[0028] In one embodiment of the method according to the first aspect, it is provided that when determining a changed traffic pattern as safety-critical information, a trajectory driven at least partially automatically by the motor vehicle due to the changed traffic pattern is sent to the remote server as further safety-critical information.

[0029] This results, for example, in the technical advantage that particularly suitable safety-critical information can be collected.

[0030] In one embodiment of the method according to the first aspect, it is provided that when determining a changed traffic pattern as safety-critical information, a driving corridor traversed at least partially automatically by the motor vehicle due to the changed traffic pattern is sent to the remote server as further safety-critical information.

[0031] This results, for example, in the technical advantage that particularly suitable safety-critical information can be collected.

[0032] In one embodiment of the method according to the second aspect, it is provided that the received safety-critical information is checked for plausibility, whereby implausible safety-critical information is compared with correspondingly older implausible safety-critical information, and based on the comparison it is determined that the correspondingly received safety-critical information is indeed plausible.

[0033] This results, for example, in the technical advantage that an older plausibility check can be efficiently verified. Furthermore, it results in the technical advantage that any change occurring in the vicinity of the vehicle(s) can be efficiently detected. Such a change includes, for example: the establishment or removal of a construction site, a change in lane guidance, or a lane closure.

[0034] In one embodiment of the method according to the second aspect, it is provided that the received safety-critical information is checked for plausibility, whereby only the plausible safety-critical information is aggregated with safety-critical information already stored in the database.

[0035] This results, for example, in the technical advantage that plausible information can be stored in the database, so that the information content of the stored data is internally consistent.

[0036] In one embodiment of the method according to the second aspect, it is provided that the received safety-critical information is aggregated and a result of the aggregation is stored as safety-critical information in the database.

[0037] This results, for example, in the technical advantage that the received security-critical information can be processed meaningfully.

[0038] In one embodiment of the method according to the second aspect, it is provided that trajectories are received as safety-critical information, wherein the aggregation includes averaging corresponding trajectories to determine an average trajectory, determining a difference between the current average trajectory and a corresponding older average trajectory, and establishing an updated average trajectory based on the comparison, such that the result of the aggregation includes the updated average trajectory.

[0039] This results, for example, in the technical advantage of performing a particularly meaningful aggregation, thus obtaining a particularly meaningful result, the updated average trajectory.

[0040] In one embodiment of the method according to the second aspect, it is provided that traffic violations are received as safety-critical information, wherein the aggregation includes determining lower and / or upper thresholds with regard to one or more traffic violations, from which or up to which a potential traffic violation is defined as an actual traffic violation, such that the result of the aggregation includes the determined thresholds.

[0041] This results, for example, in the technical advantage of performing a particularly meaningful aggregation, leading to a particularly meaningful result: threshold values. These threshold values ​​can then be sent from the server to one or more at least partially automated vehicles, enabling them to determine, based on these thresholds, whether a potential traffic violation detected by the vehicle based on the environmental model is actually a violation or not.

[0042] For aggregated trajectories, the most important factor is the two-dimensional path traveled and the degree to which this path overlaps with the roadway on which the ego vehicle intersects. This roadway can also be referred to as the ego roadway.

[0043] Example: An ego vehicle approaches an intersection where the right-of-way rule "right before left" applies. Drivers approaching from the right typically cut into the ego vehicle's lane before or after the stop line relevant to the ego vehicle.

[0044] If an aggregated trajectory clearly shows that the roadway in front of the Ego vehicle's stop line is potentially blocked, the Ego vehicle can determine a "virtual" stop line in front of the actual stop line and brake to it.

[0045] Thus, the misbehavior of others is anticipated and factored into the driving strategy of the ego-motor vehicle.

[0046] For example, the following will be discussed in advance: Fig. 7. If the Ego vehicle, referring to the paper level, approaches intersection 703 from bottom to top and intends to cross it, it would be forced to brake prematurely due to other vehicles, such as the vehicle with reference sign 711, if these frequently cut into the Ego lane.

[0047] In one embodiment of the method according to the second aspect, it is provided that, based on the safety-critical information stored in the database, a digital road map is created and / or an existing digital road map is updated and sent to one or more at least partially automated motor vehicles.

[0048] This offers the technical advantage, for example, that safety-critical information can be sent to vehicles in a particularly suitable format. Based on the digital road map, the vehicles can then drive at least semi-autonomously.

[0049] In one embodiment of the method according to the first aspect, this is a computer-implemented method.

[0050] In one embodiment of the method according to the second aspect, this is a computer-implemented method.

[0051] The procedure according to the first aspect is carried out, for example, using the device according to the third aspect.

[0052] The procedure according to the second aspect is executed, for example, by means of the server according to the fourth aspect.

[0053] The procedure according to the second aspect is carried out, for example, using the system according to the fifth aspect.

[0054] The device according to the third aspect is, for example, programmed to execute the computer program, provided that this program includes instructions which, when the computer program is executed by a computer, cause it to execute a procedure according to the first aspect.

[0055] The server according to the fourth aspect and / or the system according to the fifth aspect are, for example, programmed to execute the computer program according to the sixth aspect, provided that this program includes commands which, when executed by a computer, cause the computer to perform a procedure according to the second aspect.

[0056] Process characteristics result analogously from corresponding device characteristics and / or from corresponding server characteristics and / or from corresponding system characteristics and vice versa.

[0057] This means, in particular, that technical functionalities of the method according to the first aspect and / or according to the second aspect result from corresponding technical functionalities of the device according to the third aspect and / or of the server according to the fourth aspect and / or of the system according to the fifth aspect.

[0058] The server is, for example, part of a cloud infrastructure, meaning it is implemented within a cloud infrastructure.

[0059] The embodiments and examples described here can be combined in any way, even if this is not explicitly described.

[0060] The phrase "at least partially automated" includes the following formulations: partially automated, fully automated, highly automated and autonomous.

[0061] The invention is explained in more detail below using preferred embodiments. These show: Fig. 1 a flowchart of a method according to the first aspect, Fig. 2 a flowchart of a method according to the second aspect, Fig. 3 a device according to the third aspect, Fig. 4 a server according to the fourth aspect, Fig. 5 a system according to the fifth aspect, Fig. 6 a machine-readable storage medium according to the seventh aspect and Fig. Numbers 7-9 each depict a traffic scene.

[0062] Fig. Figure 1 shows a flowchart of a procedure for collecting safety-critical information about the environment of an at least partially automated motor vehicle, comprising the following steps: Receiving 101 environmental signals representing an environment of the motor vehicle as detected by means of environmental sensors, Creating 103 an environment model based on the environmental signals, Determining 105 safety-critical information from the environment model that is relevant for safety for at least partially automated driving, Sending 107 the determined safety-critical information to a remote server.

[0063] Fig. Figure 2 shows a flowchart of a procedure for collecting safety-critical information about a given environment of at least partially automated motor vehicles, comprising the following steps: Receiving 201 safety-critical information about a respective environment of at least partially automated motor vehicles and Store 203 of the received security-critical information in a database.

[0064] Sending and / or receiving, as described, means, for example, sending and / or receiving via one or more communication networks. A communication network is, for example, a wireless communication network or a wired communication network. A wireless communication network is, for example, a WLAN network or a mobile network. A wired communication network is, for example, an Ethernet communication network.

[0065] Fig. Figure 3 shows a device 301 for collecting safety-critical information about the environment of an at least partially automated motor vehicle, comprising: an input 303, which is configured to receive environmental signals representing the environment of the motor vehicle as detected by means of environmental sensors, a processor unit 305, which is configured to create an environment model based on the environment signals and to determine safety-critical information from the environment model that is relevant for safety in at least partially automated driving, and a communication device 307, which is configured to send the identified security-critical information to a remote server.

[0066] Environmental sensors, as described above, include, for example, one or more environmental sensors. These sensors are found, for example, in motor vehicles.

[0067] An environmental sensor as described is, for example, one of the following environmental sensors: radar sensor, LiDAR sensor, image sensor, in particular image sensor of a video camera, ultrasonic sensor, infrared sensor and magnetic field sensor.

[0068] Fig. Figure 4 shows a 401 server for collecting safety-critical information about the respective environment of at least partially automated motor vehicles, including: a communication device 403, which is set up to receive safety-critical information about a respective environment of at least partially automated motor vehicles, and a processor unit 405, which is configured to store the received safety-critical information in a database.

[0069] A communication device, as described above, includes, for example, one or more communication interfaces. A communication interface is, for example, a wireless communication interface or a wired communication interface. A wireless communication interface is, for example, a mobile communication interface or a WLAN interface. A wired communication interface is, for example, an Ethernet interface.

[0070] A processor system as described includes, for example, one or more processors.

[0071] Fig. Figure 5 shows a System 501 for collecting safety-critical information about the respective environment of at least partially automated motor vehicles, including: the server 401 of Fig. 4 and a 503 database which is set up to store the received security-critical information.

[0072] Fig. Figure 6 shows a machine-readable storage medium 601 on which a computer program 603 is stored. The computer program 603 comprises instructions which, when executed by a computer, cause the computer program 603 to perform a procedure according to the first aspect and / or according to the second aspect.

[0073] Fig. Figures 7 to 9 each depict a traffic scene.

[0074] This shows Fig. 7 a first traffic scene 701.

[0075] The first traffic scene 701 includes an intersection 703. At intersection 703, a first road 705 and a second road 707 intersect. The first road 705 runs horizontally relative to the plane of the paper. The second road 707 runs vertically relative to the plane of the paper.

[0076] Three vehicles are shown as examples: a first vehicle 709, a second vehicle 711, and a third vehicle 713. The first vehicle travels from bottom to top in the direction of travel relative to the plane of the paper and is already beyond the intersection 703 on the second road 707, relative to its direction of travel.

[0077] The second vehicle, 711, came from the first street, 705, and turned downhill onto the second street, 707, relative to the paper level. The third vehicle, 713, is traveling from left to right, relative to the paper level, towards intersection 703 and intends to cross it.

[0078] Several trajectories are shown, some of which are marked with reference number 715. These trajectories represent the paths of motor vehicles that have crossed or passed through intersection 703. These trajectories can be determined based on environmental models created by at least partially automated motor vehicles during at least partially automated driving to pass through or cross intersection 703.

[0079] For example, the three vehicles 709, 711, and 713 are at least partially automated vehicles and each includes environmental sensors that allow them to perceive their surroundings. Based on this environmental perception, each vehicle creates an environmental model. These models describe the environment from the perspective of each individual vehicle. Therefore, the trajectories driven by vehicles 709, 711, and 713 can be determined by the other vehicles and, for example, sent as safety-critical information to a remote server.

[0080] This can aggregate the multitude of trajectories, for example by averaging them, as described in the description.

[0081] This allows, for example, the detection of whether vehicles are entering an oncoming lane when turning. Driving, even partially, in an oncoming lane constitutes a traffic violation. Such safety-critical information can therefore be stored in a digital road map, which can then be made available to at least partially automated vehicles. This allows these vehicles, when driving at least partially automated to cross or pass intersection 703, to take this safety-critical information into account and, for example, proactively reduce their speed and / or move to the right within their lane to better avoid a vehicle that is partially entering their lane while turning.

[0082] Fig. Figure 8 shows a second traffic scene 801. The second traffic scene 801 includes a road that has a zebra crossing 805. The road 803 runs horizontally with respect to the plane of the paper.

[0083] A pedestrian 807 is standing at the zebra crossing 805 and wants to cross it. A motor vehicle 809 is approaching the zebra crossing 805 from right to left (relative to the plane of the paper). Behind the motor vehicle 809, a motorcyclist 811 is also approaching the zebra crossing 805 (relative to the direction of travel of the motor vehicle 809).

[0084] With respect to the direction of travel of the motor vehicle 809, to the left of road 803 there is a first building 813 and a second building 815. In the vicinity of the first building 813 there is a second pedestrian 817. With respect to the direction of travel of the motorcyclist 811, to the right of him and to the right of the road there is a third pedestrian 819.

[0085] As a rule, pedestrians should cross road 803 via the zebra crossing 805.

[0086] However, the third pedestrian, 819, will not walk to the zebra crossing, 805, but will cross street 803 where pedestrian 819 is currently located. This constitutes a traffic violation by pedestrian 819.

[0087] Such a traffic violation can be detected by the vehicle's environmental sensors and sent as safety-critical information to a remote server. If a large number of pedestrian traffic violations are detected, the server can define a critical zone or area 821 in front of the crosswalk 805 and store information about this area 821 indicating that pedestrian traffic violations are highly likely, specifically the violation of crossing the road 803 within the critical area 821. Thus, at least partially automated vehicles can, for example, adjust their driving accordingly, slowing down before reaching the critical area 821.

[0088] Fig. Figure 9 shows a third traffic scene 901. The third traffic scene 901 includes a three-lane road 903.

[0089] Thus, road 903 includes a first lane 905, a second lane 907 and a third lane 909.

[0090] The first lane, 905, is the left lane. The second lane, 907, is the middle lane. The third lane, 909, is the right lane.

[0091] A motor vehicle 911 travels from bottom to top on the right-hand lane 909, relative to the paper plane.

[0092] Route 903 makes a left turn.

[0093] Furthermore, warning beacons 913 are located on road 903. The warning beacons 913 are arranged on road 903 in such a way that they block the left lane 905 and the right lane 909, so that the curve can only be traversed within the middle lane 907.

[0094] The vehicle 911 therefore travels along a trajectory through the curve. Four possible trajectories are shown as examples, identified by the reference symbol 915.

[0095] The present case therefore involves a modified lane guidance system, which requires the vehicle 911 to adjust its original trajectory in order to negotiate the curve. The modified trajectory could, for example, be one of the following: Fig. The trajectory corresponds to the nine drawn trajectories. This modified trajectory can be sent as safety-critical information to a remote server, which can aggregate the received trajectories, for example, average them, and store them as safety-critical information in a digital road map. This digital road map can then be made available to at least partially automated vehicles, i.e., sent to them.

[0096] This means they already know in advance how to best navigate the curve. Based on such an averaged curve, at least partially automated vehicles can, for example, plan their own at least partially automated journey accordingly.

[0097] Plausibility testing, as described, can be achieved, for example, by comparing the data with older data. Plausibility testing, as described, can also be achieved, for example, instead of or in addition to comparing older data from different sources, such as in the case of vehicle fleets.

[0098] Safety-critical information is categorized and validated, for example, based on its criticality and / or frequency. Minor and infrequent traffic violations, i.e., breaches of traffic rules, imply simple validation. Frequent and serious traffic violations, or violations that pose a danger, such as pedestrians crossing blind corners, imply complex validation with the information being stored for a longer period without further confirmation. QUOTES CONTAINED 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] DE 10 2020 120 128 A1

[0002] DE 11 2021 000 422 T5

[0003] WO 2021 / 189009

[0003] US 2021 / 0342599 A1

[0004]

Claims

[1] Method for collecting safety-critical information about an environment of an at least partially automated motor vehicle (709, 711, 713), comprising the following steps: Receiving (101) environmental signals which represent an environment of the motor vehicle (709, 711, 713) detected by an environmental sensor system, Creating (103) an environment model based on the environment signals, Determining (105) safety-critical information from the environment model which is safety-relevant for at least partially automated driving, sending (107) the determined safety-critical information to a remote server. [2] Method according to claim 1, wherein upon detection of a traffic rule violation by a road user, the road user is classified as safety-critical information, wherein a result of the classification is sent to the remote server as further safety-critical information. [3] Method according to claim 1 or 2, wherein when determining a traffic rule violation by a road user, a traffic rule violation degree is determined as further safety-critical information. [4] Method according to claim 3, wherein the traffic violation degree indicates a duration of the traffic violation and / or a speed of the road user during the traffic violation and / or a trajectory of the road user during the traffic violation. [5] Method according to one of the preceding claims, wherein, when determining a changed traffic routing as safety-critical information, a trajectory driven at least partially automatically by the motor vehicle (709, 711, 713) due to the changed traffic routing is sent to the remote server as further safety-critical information. [6] Method according to one of the preceding claims, wherein, when determining a changed traffic routing as safety-critical information, a driving corridor travelled at least partially automatically by the motor vehicle (709, 711, 713) due to the changed traffic routing is sent to the remote server as further safety-critical information. [7] Method for collecting safety-critical information about a respective environment of at least partially automated motor vehicles (709, 711, 713), comprising the following steps: Receiving (201) safety-critical information about a respective environment of at least partially automated motor vehicles (709, 711, 713) and storing (203) the received safety-critical information in a database. [8] Method according to claim 7, wherein the received safety-critical information is checked for plausibility, wherein implausible safety-critical information is compared with correspondingly older implausible safety-critical information, wherein, based on the comparison, it is determined that the correspondingly received safety-critical information is plausible after all. [9] Method according to claim 7 or 8, wherein the received safety-critical information is checked for plausibility, wherein only the plausible safety-critical information is aggregated with safety-critical information already stored in the database. [10] Method according to one of claims 7 to 9, wherein the received safety-critical information is aggregated and a result of the aggregation is stored as safety-critical information in the database. [11] The method of claim 10, wherein trajectories are received as safety-critical information, wherein the aggregation comprises averaging corresponding trajectories to determine an average trajectory, determining a difference between the current average trajectory and a corresponding older average trajectory, and setting an updated average trajectory based on the comparison, such that the result of the aggregation comprises the updated average trajectory. [12] Method according to claim 10 or 11, wherein traffic rule violations are received as safety-critical information, wherein the aggregation comprises determining lower and / or upper threshold values ​​with respect to one or more traffic rule violations, from which or up to which a potential traffic rule violation is defined as an actual traffic rule violation, so that the result of the aggregation comprises the determined threshold values. [13] Method according to one of claims 7 to 12, wherein based on the safety-critical information stored in the database, a digital road map is created and / or an already existing digital road map is updated and sent to one or more at least partially automated motor vehicles (709, 711, 713). [14] Device (301) for collecting safety-critical information about an environment of an at least partially automated motor vehicle (709, 711, 713), comprising: an input (303) which is configured to receive environmental signals representing an environment of the motor vehicle (709, 711, 713) detected by an environmental sensor system, a processor device (305) which is configured to create an environment model based on the environment signals and to determine safety-critical information from the environment model which is safety-relevant for at least partially automated driving, and a communication device (307) which is configured to send the determined security-critical information to a remote server. [15] Server (401) for collecting safety-critical information about a respective environment of at least partially automated motor vehicles (709, 711, 713), comprising: a communication device (403) which is arranged to receive safety-critical information about a respective environment of at least partially automated motor vehicles (709, 711, 713), and a processor device (405) which is arranged to store the received safety-critical information in a database. [16] System (501) for collecting safety-critical information about a respective environment of at least partially automated motor vehicles (709, 711, 713), comprising: the server according to claim 15 and a database (503) configured to store the received safety-critical information. [17] Computer program (603) comprising instructions which, when the computer program (603) is executed by a computer, cause the computer to carry out a method according to one of claims 1 to 13. [18] Machine-readable storage medium (601) on which the computer program (603) according to claim 17 is stored.

Images