Methods for collecting traffic data

Abstract

The invention relates to a method for acquiring traffic data of a defined area (1) using several acquiring devices (2) and evaluating the acquired traffic data, wherein each acquiring device (2) is designed as a mobile, autonomous and location-independent acquiring device (2), a criticality of the defined area (1) is determined based on current, historical and / or predicted data, when a defined criticality value is exceeded, the acquiring devices (2) are automatically moved from a current position to the defined area (1) to acquire traffic data, the traffic data is acquired within the defined area (1) by means of the acquiring devices (2), and the acquired traffic data is evaluated and / or aggregated in real time.An accident probability is determined based on the evaluated and / or aggregated traffic data, and if a defined accident probability is exceeded, the evaluated and / or aggregated traffic data is transmitted to a vehicle (3) located within a defined radius around the defined area (1).

Description

[0001] The invention relates to a method for recording traffic data of a defined area using several recording devices.

[0002] A system for real-time traffic monitoring is known from IN202541040082 A.

[0003] The invention is based on the objective of providing a novel method for recording traffic data of a defined area using several recording devices.

[0004] The problem is solved according to the invention by a method with the features specified in claim 1.

[0005] Advantageous embodiments of the invention are the subject of the dependent claims.

[0006] In the method for acquiring traffic data from a defined area using multiple acquiring devices and evaluating the acquired traffic data, each acquiring device is designed as a mobile, autonomous, and location-independent acquiring device according to the invention. Based on current, historical, and / or predicted data, the criticality of the defined area is determined. If a defined criticality value is exceeded, the acquiring devices are automatically moved from their current position to the defined area to acquire further data.

[0007] Traffic data within the defined area is collected by means of the detection devices. This data is then analyzed and / or aggregated in real time. Based on this analyzed and / or aggregated data, an accident probability is determined for the defined area. If this probability is exceeded, the analyzed and / or aggregated traffic data is transmitted to a vehicle located within a defined radius of the defined area.

[0008] The defined area could be, for example, a district, a street, a section of road, an intersection, a roundabout, or the like.

[0009] The mobile, autonomous, and location-independent detection devices can, for example, be configured as a sensor swarm. In particular, each detection device can be configured as a drone.

[0010] Current traffic data can be, for example, up-to-date data from a traffic authority and / or a map service regarding current traffic volume. Historical data can be traffic data recorded and stored at an earlier time. Predicted data can be data generated, for example, based on a prediction using weather conditions, external events, or historical traffic data.

[0011] The determined criticality refers, for example, to a driving condition of at least one vehicle traveling through the area.

[0012] The method according to the invention contributes to innovation through the use of state-of-the-art sensor technology and real-time communication. Safety for all road users can be increased by the method according to the invention, as it provides more comprehensive knowledge about the current traffic situation, optimizes traffic flow, and helps prevent accidents.

[0013] Exemplary embodiments of the invention are explained in more detail below with reference to a drawing.

[0014] This shows: Fig. 1. Schematic view of an intersection area with several detection devices in a bird's-eye view.

[0015] Fig.Figure 1 shows a defined area 1 with high traffic volume and several detection devices 2 for carrying out an embodiment of the method according to the invention. In this embodiment, the defined area 1 is an intersection 1.1. However, the defined area 1 can also be a district, a street, a section of road, a roundabout, or the like.

[0016] In the area of ​​intersection 1.1 and on the streets 1.2 adjacent to intersection 1.1, there are several vehicles 3. Each street 1.2 has several lanes 1.2.1. In the area of ​​intersection 1.1, there is an accident scene 7. Due to an accident that occurred at accident scene 7 and the high volume of traffic, traffic is backed up in the area of ​​intersection 1.1.

[0017] The detection devices 2 located in the area of ​​intersection 1.1 are designed as mobile, autonomous, and location-independent detection devices 2, in particular as drones 2.1. Each drone 2.1 has sensor technology 4 for recording traffic data. In particular, each drone 2.1 has a camera 4.1. Each drone 2.1 monitors a detection area 5, whereby the detection areas 5 of the drones 2.1 partially overlap. The detection areas 5 of the drones 2.1 completely or almost completely cover the defined area 1.

[0018] At least one of the Drones 2.1 is connected to a Backend 6 in terms of data technology.

[0019] In the illustrated embodiment, one drone 2.1 is configured as a master drone 2.1.1, which is data-linked to the backend 6 and is located centrally above the intersection 1.1. The four other drones 2.1 are configured as slave drones 2.1.2 and are each located above a road 1.2 adjacent to the intersection 1.1.

[0020] Based on current, historical and / or predicted data, the criticality of the defined area 1 is determined. Current data could include, for example, information from a traffic authority and / or a map service about current traffic volume.

[0021] Historical data can be traffic data collected and stored at an earlier time, such as data from a traffic authority. Based on this historical data, for example, designated area 1 might be marked as an accident-prone area. Furthermore, temporal correlations can be recorded. For instance, there might be a high incidence of accidents in designated area 1 at a specific time of day or on a specific day of the week. An accident hotspot might be located within a designated area 1 in the morning when commuters are driving into the city, while in the evening, when commuters are leaving the city, an accident hotspot could be located elsewhere.

[0022] Predicted data can be data generated based on predictions made using weather conditions, external events, or historical traffic data. For example, predictions can be made about glare for vehicle occupants due to sunlight, increased traffic volume due to major events, poor visibility due to fog, rain, or snowfall, or similar factors. Predictions can also be based on historical data.

[0023] Criticality relates, for example, to the driving condition of at least one vehicle traveling within the defined area 1. Criticality is determined, for example, based on an average accident rate per unit of time and / or an accident rate per kilometer, a probability of an accident, and / or a sum of weighted influencing factors. The sum of weighted influencing factors can be calculated, for example, using the equation K=a0*Traffic density+a1*Weather influences+a2*Sum of historical accidents The equation is calculated, where K represents the criticality and a0, a1, and a2 describe the weighting of the respective influencing factor. Additional influencing factors can be included in the equation.

[0024] If the criticality exceeds a defined value, the recording devices 2 for collecting traffic data are automatically moved from a current position, such as a base station, to the defined area 1.

[0025] To move the detection devices 2, designed as drones 2.1, from their current position to the defined area 1, they can be transported, for example, by an unmanned aerial vehicle (UAV). The UAV can be, for example, a larger drone, with the detection devices 2 magnetically attached to the UAV and / or detachable.

[0026] Alternatively, the detection devices 2 can be transported, for example, by a traffic monitoring vehicle 3, with the traffic monitoring vehicle 3 transporting the detection devices 2 to the defined area 1 and / or picking up the detection devices 2 from the defined area 1. The detection devices 2 can be magnetically and / or mechanically attached to the roof of the vehicle 3 and can take off and / or land from the roof of the vehicle 3. Advantageously, the detection devices 2, designed as drones 2.1, fly in a defined airspace above the traffic monitoring vehicle 3. For example, the drones 2.1 fly in an air cone at a height of 5 m and / or fly along defined airways.

[0027] Alternatively, the detection devices 2 can move themselves from their current position to the defined area 1. For example, by a detection device 2 configured as a drone 2.1 flying itself to the defined area 1.

[0028] Advantageously, each detection device 2 has a sensor technology 4 by means of which the traffic data is recorded. Such sensor technology 4 can, for example, include a radar sensor, a lidar sensor, a camera 4.1, an ultrasonic camera, a thermal imaging camera and / or a stereo camera.

[0029] Each data acquisition device 2 can have a communication device by means of which wireless communication with other data acquisition devices 2 and / or vehicles 3 and / or a backend 6 is carried out. For example, communication regarding the acquired traffic data, position data, or the like can be carried out. The wireless communication can take place via a wireless local area network, such as a wireless local area network (WLAN), other radio standards, or via mobile communications or the like.

[0030] Furthermore, each detection device 2 can have a position detection device by means of which its position is detected. Position detection can be relative and / or absolute. Relative position detection can be achieved, for example, by triangulating the distance to other detection devices 2, by time-of-flight measurements of a radio technology, and / or by measuring a distance using at least one sensor of the sensor technology 4. Absolute position detection can be achieved, for example, using a Global Navigation Satellite System (GNSS).

[0031] In one possible embodiment, the master drone 2.1.1 has a global navigation satellite system for absolute position tracking. The slave drones 2.1.2 can position themselves relative to the master drone 2.1.1 using triangulation and / or time-of-flight measurement.

[0032] Furthermore, each detection device 2 can have an independent power supply by means of which the respective detection device 2 is supplied with electrical energy. The power supply of the detection devices 2 can be provided, for example, by photovoltaics, batteries and / or energy harvesting based on electrosmog and / or radio waves.

[0033] Within the defined area 1, the detection devices 2 can perform defined tasks. A task might, for example, include a command to move along a trajectory with specified location, time, duration, orientation, and / or altitude of the detection device 2. A drone 2.1 can then fly along this trajectory. The drone 2.1 can fly continuously and, for example, remain 10 m above the intersection 1.1 or road 1.2. Alternatively, the drone 2.1 can land at a suitable location, such as a traffic structure like a bridge, overhead sign gantries, a traffic light pole, or on an adjacent building within or near the defined area 1.

[0034] For detailed recording of traffic data, the flight altitude of the drone 2.1 can be reduced, for example, from 10 m to 5 m.

[0035] The trajectory of drone 2.1 advantageously lies along a defined airway. Airways can, for example, be determined by air traffic control or be permanently defined.

[0036] When collecting traffic data, each collection device 2 can, for example, generate a real-time map with data on the current traffic situation. This can include a classification into roads 1.2, lanes 1.2.1, traffic lights, traversable areas, roadside buildings, non-traversable areas, or the like. Furthermore, a classification and / or recording of the position-speed trajectory of road users such as vehicles 3, pedestrians, animals, and the like can be performed.

[0037] The detection devices 2 can, for example, detect and identify an accident site 7 and / or lost cargo.

[0038] The traffic data collected by the individual detection devices 2 can be combined to form an aggregated real-time map. For example, the traffic data can be transmitted peer-to-peer between the detection devices 2, or all traffic data can be transmitted to the backend 6 and aggregated there. Traffic data can be transmitted directly from each detection device 2 to the backend 6, or the data can be sent from the detection devices 2 to, for example, the master drone 2.1.1. The master drone 2.1.1 then transmits the received traffic data to the backend 6. Data transmission between the master drone 2.1.1 and the backend 6 can be carried out as shown in arrow 8.

[0039] Advantageously, only a defined number of detection devices 2 have advanced sensor technology 4, such as a global navigation satellite system, and / or a data connection to the backend 6. These detection devices 2 are, for example, configured as master drones 2.1.1, wherein the detection devices 2 configured as slave drones 2.1.2 send the detected traffic data to the at least one master drone 2.1.1 and receive data from the at least one master drone 2.1.1.

[0040] Traffic data can be analyzed and aggregated in real time.

[0041] In backend 6, the probability of an accident can be determined based on the evaluated and / or aggregated traffic data. For example, the movements of road users can be predicted using current contextual information, checking whether trajectories intersect in such a way that an accident could occur.

[0042] If a defined accident probability is exceeded, the evaluated and / or aggregated traffic data can be transmitted to a vehicle 3 located within a defined radius around the defined area 1. In particular, information about the location, time, accident probability and / or road users involved in a potential accident can be sent to vehicle 3 in real time via an interface.

[0043] Information and / or warnings can be communicated to the driver of vehicle 3 visually, for example via a warning triangle and / or a graphic display of a driver assistance system, acoustically and / or haptically.

[0044] The evaluated and / or aggregated traffic data sent to vehicle 3 can be processed by the driver assistance system. The driver assistance system can, for example, adjust the driving trajectory, change the speed of vehicle 3, and / or initiate so-called Pre-Safe measures.

[0045] In one possible embodiment, an autopilot for vehicle 3 can be activated. This can be done, for example, via a teleoperation interface. By remotely controlling vehicle 3, an accident can be avoided or its severity reduced. This can be achieved, for example, by changing the speed of vehicle 3, changing lane 1, 2, 1, and / or adjusting the driving trajectory.

[0046] In other words, all objects and road users are initialized at time t0 with their current and predicted trajectories. An optimization problem is then formulated and solved with the goal of ensuring that all road users can follow their trajectories with minimal intervention and / or time loss, provided that all users drive safely, comfortably, and without accidents.

[0047] The traffic data and the probability of accidents can be transferred in backend 6 to a high-resolution real-time map, which is then read by at least one vehicle 3.

[0048] Alternatively or additionally, traffic can be influenced by stationary elements. This can be achieved, for example, by reducing the permitted maximum speed, actively illuminating the designated area 1, closing lanes 1.2.1, changing traffic light sequences, or similar measures.

[0049] If the criticality falls below the specified value, the detection devices 2 can, for example, be automatically moved to the base station for charging.

[0050] The described procedure can significantly increase the safety of all road users.

Claims

[1] Method for recording traffic data of a defined area (1) using multiple recording devices (2) and evaluating the recorded traffic data, characterized by , that - each detection device (2) is designed as a mobile, autonomous and location-free detection device (2), - the criticality of the defined area (1) is determined based on current, historical and / or predicted data, - if a specified criticality value is exceeded, the recording devices (2) for recording traffic data are automatically moved from a current position to the specified area (1), - the traffic data are recorded by means of the recording devices (2) within the defined area (1), - the recorded traffic data are evaluated and / or aggregated in real time, - an accident probability is determined based on the evaluated and / or aggregated traffic data and - if a specified accident probability is exceeded, the evaluated and / or aggregated traffic data are transmitted to a vehicle (3) located within a defined radius around the specified area (1). [2] Method according to claim 1, characterized by , that each detection device (2) - has an independent power supply by means of which the respective detection device (2) is supplied with electrical energy, - a sensor technology (4) which captures traffic data, - has a communication device by means of which wireless communication is carried out with other detection devices (2) and / or vehicles (3) and / or a backend (6), and / or - has a position detection device by means of which its position is detected. [3] Method according to any one of the preceding claims, characterized by that a real-time map is created from the evaluated and aggregated traffic data. [4] Method according to claim 3, characterized by , that the real-time map is transmitted to a vehicle (3) located within a defined radius around the specified area (1). [5] Method according to any one of the preceding claims, characterized by , that based on the traffic data transmitted to the vehicle (3) - a route of the vehicle (3) is changed, - a travel trajectory is adjusted, - the speed of the vehicle (3) is changed, - a message is issued to a driver of the vehicle (3) and / or - an autopilot is activated.

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