EP3716245A1 - Verfahren und steuerungsvorrichtung zum senden von informationen an umgebende fahrzeuge - Google Patents

Verfahren und steuerungsvorrichtung zum senden von informationen an umgebende fahrzeuge Download PDF

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Publication number
EP3716245A1
EP3716245A1 EP19165492.0A EP19165492A EP3716245A1 EP 3716245 A1 EP3716245 A1 EP 3716245A1 EP 19165492 A EP19165492 A EP 19165492A EP 3716245 A1 EP3716245 A1 EP 3716245A1
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EP
European Patent Office
Prior art keywords
vehicle
movement
predefined
surrounding
control device
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP19165492.0A
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English (en)
French (fr)
Inventor
Tomas Andersson
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Zenuity AB
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Zenuity AB
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Publication date
Application filed by Zenuity AB filed Critical Zenuity AB
Priority to EP19165492.0A priority Critical patent/EP3716245A1/de
Priority to CN202010228179.1A priority patent/CN111754762B/zh
Publication of EP3716245A1 publication Critical patent/EP3716245A1/de
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/161—Decentralised systems, e.g. inter-vehicle communication
    • G08G1/163—Decentralised systems, e.g. inter-vehicle communication involving continuous checking
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/056—Detecting movement of traffic to be counted or controlled with provision for distinguishing direction of travel
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0965—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages responding to signals from another vehicle, e.g. emergency vehicle
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096783—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a roadside individual element
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096791—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is another vehicle
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/161—Decentralised systems, e.g. inter-vehicle communication
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/164—Centralised systems, e.g. external to vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Definitions

  • the present disclosure relates to a control device and a method for informing surrounding vehicles of deviant behaviour and a non-transitory computer-readable storage medium related thereto.
  • the present disclosure further relates to a vehicle and an infrastructure device comprising such a control device.
  • ADAS advanced driver assistance systems
  • AD autonomous drive
  • ADAS features or functions include lane departure warning systems, lane centring, lane keeping aid, pilot assist, lane change assistance, parking sensors, pedestrian protection systems, blind spot monitors, adaptive cruise control (ACC), anti-lock braking systems, and so forth.
  • ACC adaptive cruise control
  • Perception systems can in the present context be understood as systems responsible for acquiring raw sensor data from on vehicle sensors such as cameras, LIDAR, and RADAR, and converting this raw data into scene understanding for the vehicle.
  • control device a vehicle comprising such a control device, an infrastructure device comprising such a control device, a method, and a non-transitory computer-readable storage medium as defined in the appended claims.
  • vehicle comprising such a control device
  • infrastructure device comprising such a control device
  • method a non-transitory computer-readable storage medium as defined in the appended claims.
  • exemplary is in the present context to be understood as serving as an instance, example or illustration.
  • a control device for informing surrounding vehicles of deviant behaviour.
  • the control device comprises at least one processor, at least one memory, at least one communication interface, and at least one sensor interface.
  • the at least one processor is configured to execute instructions stored in the memory to perform a method for informing surrounding vehicles of deviant behaviour. More specifically, the at least one processor is configured to receive signals from a perception system, the signals comprising information about a movement of at least one surrounding vehicle located in a surrounding environment, the information about a movement comprising at least one movement parameter.
  • the at least one processor is further configured to receive signals comprising information about a current road scenario, compare the movement of the at least one surrounding vehicle with a predefined behaviour model, wherein the predefined behaviour model comprises at least one predefined movement parameter threshold for the current road scenario, and send a signal to the at least one surrounding vehicle based on the comparison.
  • the proposed control device it is possible to utilize and ego-vehicle's perception system in order to improve reliability and add redundancy to other surrounding vehicles' perception systems.
  • a safety system may not be aware of the fact that the vehicle is about the pass over a road boundary or that it is inadvertently rolling/moving backwards, which is when the proposed control device ensures that the vehicle is made aware the current situation in order to take the necessary actions to mitigate any risks.
  • the at least one processor is configured to determine a current state of the at least one surrounding vehicle based on the received signals from the perception system, and wherein the sent signal to the at least one surrounding vehicle is further based on the determined current state of the at least one surrounding vehicle.
  • a current state of a vehicle may for example be derived from analysing images acquired by one or more cameras associated with the perception system. In these images brake lights, hazard lights, turning indicators, emergency vehicle lighting, type of vehicle, and other vehicle features can be identified in order to determine a current state (e.g. about to turn left/right, braking, about to move backwards, emergency dispatch, bus traveling in a bus lane, etc.). By further determining a current state of the surrounding vehicle(s), unnecessary transmission of signals and alerts can be avoided.
  • the predefined behaviour model comprises a lateral movement speed threshold.
  • a threshold value For example if the lateral movement of the surrounding vehicle is below a threshold value, it can be interpreted as that the vehicle is inadvertently drifting out of lane, as compared to a faster lateral movement which can be understood as a lane-change.
  • an alert is sent to that vehicle in order to notify a user/driver/operator that the vehicle is about to exit the lane wherefore the user/driver/operator of that vehicle can take action accordingly.
  • the vehicle operating system can be notified in order to subsequently perform a systems check as one or more sensors may be malfunctioning and thereby causing the lane drift.
  • the processor(s) of the control device can be configured to always send a signal to a surrounding vehicle informing it of any lane change that is not preceded by an activation of a turn indicator. The receiving surrounding vehicle can then choose to act upon the received information or not.
  • the predefined behaviour model comprises an expected direction of travel.
  • this can be construed as a rollback determination and alert.
  • the control device is adapted to detect if a vehicle in the surrounding area appears to inadvertently be rolling in an opposite direction of traffic (e.g. during a full stop in a steep hill).
  • Not all vehicles are equipped with internal rollback detection/prevention systems, wherefore accidents may occur in for example road gradients or if the wrong gear is inadvertently selected. Accordingly, by enabling receiving of alerts when a rollback of a vehicle is detected, at the very least it provides for an increased redundancy of that vehicle's own rollback detection system.
  • a vehicle comprising a control device according any one of the embodiments disclosed herein.
  • the vehicle may for example be any form of surface vehicle such as e.g. a car, a truck, a bus, or similar.
  • the vehicle may be autonomous or semi-autonomous (i.e. equipped with advanced driver-assistance systems (ADAS)).
  • ADAS advanced driver-assistance systems
  • an infrastructure device comprising a control device according to any one of the embodiments disclosed herein.
  • the infrastructure device may for example be an overhead Radio Frequency Identification (RFID) reader or camera, a traffic light, a lane marker, a streetlight, a sign, or a parking meter.
  • RFID Radio Frequency Identification
  • a method for informing surrounding vehicles of deviant behaviour comprises receiving signals from a perception system, the signals comprising information about a movement of at least one surrounding vehicle located in a surrounding environment, the information about a movement comprising at least one movement parameter, receiving signals comprising information about a current road scenario, comparing the movement of the at least one surrounding vehicle with a predefined behaviour model, wherein the predefined behaviour model comprises at least one predefined movement parameter threshold for the current road scenario and sending a signal to the at least one surrounding vehicle based on the comparison.
  • the method further comprises determining a current state of the at least one surrounding vehicle based on the received signals from the perception system, and wherein the sent signal to the at least one surrounding vehicle is further based on the determined current state of the at least one surrounding vehicle.
  • a non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a vehicle control system, the one or more programs comprising instructions for performing the method according to any one of the embodiments disclosed herein.
  • Fig. 1 shows a vehicle 1 (may also be referred to as an ego-vehicle) in a road scenario where another vehicle 2 has stopped in front of a crosswalk 5.
  • the first vehicle 1 has a control device 10 for informing surrounding vehicles of deviant behaviour in accordance with an exemplary embodiment of the present invention.
  • the control device 10 has a processor (may also be referred to as control circuitry) 11, a memory unit 12, a communication interface 13, and a sensor interface 14.
  • the processor is configured to receive signals from a perception system of the vehicle 1.
  • a perception system is in the present context to be understood as a module responsible for acquiring raw sensor data from on sensors such as cameras, LIDARs and RADARs, ultra sound sensors, and converting this raw data into scene understanding.
  • the perception system is a vehicle perception system.
  • the vehicle 1 may be connected to external network(s) 20 via for instance a wireless link (e.g. for retrieving map data).
  • a wireless link e.g. for retrieving map data
  • the same or some other wireless link may be used to communicate with other vehicles 2 in the vicinity of the vehicle or with local infrastructure elements.
  • Cellular communication technologies may be used for long range communication such as to external networks and if the cellular communication technology used have low latency it may also be used for communication between vehicles, vehicle to vehicle (V2V), and/or vehicle to infrastructure, V2X.
  • Examples of cellular radio technologies are GSM, GPRS, EDGE, LTE, 5G, 5G NR, and so on, also including future cellular solutions.
  • LAN Wireless Local Area
  • ETSI is working on cellular standards for vehicle communication and for instance 5G is considered as a suitable solution due to the low latency and efficient handling of high bandwidths and communication channels.
  • the received signals comprise information about a movement of a surrounding vehicle 2 located in a surrounding environment of the vehicle 1.
  • the information about the movement comprises at least one movement parameter.
  • the movement parameter can for example be a longitudinal acceleration, a longitudinal velocity, a lateral acceleration, a lateral velocity, a position, a direction of travel, a yaw acceleration, and so forth.
  • the received signals may further comprise information about a state of the surrounding vehicle 2, e.g. active brake lights, turning signals, reverse light, etc.
  • the received signals form the perception system indicate that the other vehicle 2 is moving backwards 4, towards the ego-vehicle 1.
  • the processor 11 is configured to receive signals comprising information about a current road scenario.
  • a current road scenario can for example be traveling on a highway, traveling in a dense urban area, waiting at an intersection having traffic lights, stopping in front of a crosswalk (as in the illustrated embodiment of Fig. 1 ), and so forth.
  • the signals may for example originate from the perception system of the vehicle 1, and/or from a localization system (e.g. GPS unit).
  • the processor 11 is further configured to compare the movement (based on the received movement parameter(s)) of the other vehicle 2 with a predefined behaviour model.
  • the predefined behaviour model has one or more predefined movement parameter thresholds for the identified current road scenario. Then, based on the comparison, the processor 11 is configured to send a signal to one or more surrounding vehicles 2 based on the comparison.
  • the signal may be via a communication interface 13 of the control device 10 by means of one or more antennas provided on the vehicle 1, as indicated by the schematic box 3a.
  • the vehicles 1, 2 are arranged to communicate with each other by means of wireless communication technologies via any suitable vehicle to vehicle (V2V) communication protocol as readily understood by the skilled artisan.
  • V2V vehicle to vehicle
  • the communication/antenna interface 13 may also provide the possibility to acquire sensor data directly or via dedicated sensor control circuitry in the vehicle: for instance GPS data may be acquired through the antenna interface 13, some sensors in the vehicle may communicate with the control circuitry using a local network setup, such as CAN bus, I2C, Ethernet, optical fibers, and so on.
  • the communication interface 13 may be arranged to communicate with other control functions of the vehicle and may thus be seen as control interface also; however, it separate control interface (not shown) may be provided.
  • Local communication within the vehicle may also be of a wireless type with protocols such as WiFi, LoRa, Zigbee, Bluetooth, or similar mid/short range technologies.
  • the processor 11 of the control device 10 can for example be configured to send the signal to the surrounding vehicle 2 if the identified movement of the other vehicle 2 deviates from the predefined behaviour model. For example, the processor 11 of the control device 10 may determine that the other vehicle 2 is at a standstill. This may be "expected" in the determined road scenario (i.e. approaching a cross-walk 5 that a pedestrian 30 is attempting to cross).
  • the road scenario may be determined based on signals originating from the perception system of the vehicle (detecting the pedestrian, lane markings, road signs, etc.), map data (retrieved from a remote server via an external network 20), and a geographical position of the vehicle 1 (retrieved by means of a localization system).
  • the processor 11 determines that the other vehicle 2 is moving backwards towards the vehicle 1 (as indicated by the bolded arrow 4). This reverse movement is not expected in the present road scenario in the behaviour model. Thus, the processor 11 is configured to alert and inform the other vehicle 2 about the reverse movement as it appears to be involuntary. In more detail, the processor 11 is configured to send a signal comprising information about the movement of the other vehicle 2. By providing information about the "rollback" movement to the other vehicle 2, the control device 10 can either temporarily act as the other vehicle's 2 rollback detection and prevention system, or alternative the confidence of that vehicle's 2 own rollback detection and prevention system can be increased.
  • the predefined behaviour model comprises an expected direction of travel for a determined (current) road scenario.
  • the behaviour model may comprise an expected direction of travel for each lane in the current road scenario so that no unnecessary messages are sent to vehicles traveling in the left lane in the illustrated two-way road segment of Fig. 1 .
  • this does not exclude the processor 11 from being configured to send signals to vehicles traveling in the opposite lane if it deviates from the predefined behaviour model in other aspects (e.g. the processor determines that the other vehicle is at risk with colliding with the pedestrian 30).
  • control device 10 is adapted to detect if a vehicle 2 in the surrounding area appears to inadvertently be rolling in an opposite direction of traffic (e.g. during a full stop in a steep hill).
  • the predefined behaviour model may for example comprise a self-learning model based on machine learning and for example realized as an artificial neural network.
  • the self-learning model may be trained to identify a current road scenario based on the input signals received from the perception system, map data and/or the localization system of the vehicle, and to output associated movement threshold parameters associated with the identified road scenario.
  • the self-learning model may be any type of trained deep neural network (DNN), such as e.g. a convolutional neural network (CNN).
  • DNN deep neural network
  • a deep neural network is the present context to be understood as an artificial neural network (ANN) with multiple layers between the input and output layers.
  • the DNN is trained to find the correct mathematical manipulation to turn the input into the output, whether it be a linear relationship or a nonlinear relationship.
  • Fig. 2 is another schematic perspective view illustration of a vehicle 1 comprising a control device 10 according to another exemplary embodiment of the present invention.
  • the ego-vehicle 1 is traveling on a highway behind another vehicle 2. Traveling on a highway means that both of the illustrated lanes have a common traveling direction.
  • the components and features of the control device 10 of the ego-vehicle have already been discussed in detailed in the foregoing with reference to Fig. 1 , these common aspects are considered to be readily understood by the skilled reason and will therefore for the sake of brevity and conciseness not be repeated.
  • the control device 10 has a processor 11 configured to receive signals from a perception system of the vehicle 1, and from the received signals, to determine a movement of one or more surrounding vehicles. Moreover, the processor 11 may further determine a state of the other vehicle, based on signals originating from the perception system. The aforementioned signals may for example indicate active brake lights, turn indicators, etc., wherefore the processor 11 can be configured to determine if the vehicle 2 is decelerating, about to turn, and so forth. Moreover, the processor 11 is configured to receive signals comprising information about a current road scenario (e.g. determining that the ego-vehicle 1 is traveling on a highway based on GPS data).
  • a current road scenario e.g. determining that the ego-vehicle 1 is traveling on a highway based on GPS data.
  • the determined movement of the surrounding vehicle 11 is then compared to a predefined behaviour model that comprises one or more movement parameter thresholds.
  • the predefined behaviour model may comprise a lateral movement speed threshold for a determined road scenario.
  • the predefined behaviour model may expect a surrounding vehicle to stay within a lane unless a turn indicator is active, or that a very slow lateral movement is an early indicator indicative of inadvertent drifting off of the road.
  • the processor 11 identifies that the surrounding vehicle 2 is swerving slightly, and has started to drift out of the road as indicated by the bolded arrow.
  • the processor 11 is configured to determine that the surrounding vehicle 2 is about to drive off the road. This can e.g. be due to malfunctioning safety systems in the surrounding vehicle 2 (e.g. erroneous road edge interventions, erroneously calibrated sensors, and so forth).
  • the processor 11 is further configured to send a signal to a control system of the other vehicle 2, in order to inform a user/driver or a control system of the vehicle 2 of the movement, and thereby of the divergent/unexpected behaviour of the other vehicle.
  • a suitable antenna arrangement 3a, 3b provided on both vehicles 1, 2.
  • the other vehicle's 2 operating system can be notified in order to perform a systems check as one or more sensor may be malfunctioning and the root cause of the unexpected behaviour.
  • Fig. 3 is a schematic perspective view of a control device 10 implemented in an infrastructure device, here in the form of a traffic light 7, according to another exemplary embodiment of the present invention.
  • an infrastructure device here in the form of a traffic light 7, according to another exemplary embodiment of the present invention.
  • RFID Radio Frequency Identification
  • Fig. 3 illustrates a road scenario in the form of a road intersection 6, where the control device is provided in an infrastructure device 7.
  • the infrastructure device 7 further has a perception system with the same or similar functionality as the ones discussed with reference to the embodiments of Fig. 1 and Fig. 2 , i.e. a perception system capable of detecting objects and features of those objects in the surrounding environment of the infrastructure device 7.
  • the perception system of the infrastructure device 7 detects the presence of two surrounding vehicles 2, 3, and the processor 11 of the control device 10 is configured to receive signals comprising information about the movement of the two surrounding vehicles 2, 3.
  • the signals may for example be received via the sensor interface 14 or the communication interface 13 as exemplified in the foregoing.
  • a first surrounding vehicle 2a is approaching the intersection 6 and is traveling towards the traffic light 7 comprising the control device 10.
  • the traffic light 7 has in the present example a red light active, i.e. approaching traffic is urged to stop.
  • the processor 11 of the control device 10 measures a longitudinal speed of the incoming vehicle 2a, and computes that the measured longitudinal speed is above a predefined threshold of the predefined behaviour model.
  • predefined behaviour model comprises a maximum speed threshold dependent on distance from the intersection 6, where vehicle traveling above this threshold are concluded to not being able to stop before the intersection 6 in a reliable manner.
  • the predefined behaviour model may have multiple longitudinal speed thresholds, e.g. a critical maximum threshold above which the approaching vehicle 2a will not be able to come to a full stop, or a maximum threshold above which the approaching vehicle 2a must make a strong deceleration that is very uncomfortable for the occupants of the vehicle 2a.
  • the processor 11 is configured to compare the movement of the first surrounding vehicle 2a, and to send a signal to the first surrounding vehicle 2a and/or to a second surrounding vehicle 2b.
  • the signal comprises information about the movement of the first surrounding vehicle 2a, and may further include an alert associated with that movement.
  • a control system or an operator of that vehicle may be informed of the approaching scenario in order to bring the vehicle 2a to a stop in front of the intersection 6.
  • the first surrounding vehicle's sensors or perception systems may be erroneous and unable to detect the stop signal (red light), wherefore the control device 10 of the infrastructure device 7 may act as a failsafe and add redundancy to the perception system of the first surrounding vehicle.
  • the processor 11 can further be configured to determine a current state of one or more surrounding vehicles based on the received signals from the perception system.
  • a vehicle state can for example be based on data received from one or more cameras associated with the perception system detecting activation of brake lights, turning indicators, hazard lights, and similar, and based on these identified feature a vehicle state can be determined such as e.g. preparing a left turn, preparing a right turn, braking, etc.
  • the processor 11 is then further configured to send the signal to one or more surrounding vehicles 2a, 2b further based on the determined current state of the surrounding vehicle(s) 2a, 2b.
  • the at least one predefined movement parameter threshold can be dependent on the determined current state of the one or more surrounding vehicles 2a, 2b.
  • the predefined behaviour model can be arranged such that if a right turning indicator/signal is active on a surrounding vehicle, then a right turn is an expected movement of that vehicle, and thresholds for movement parameters associated with a right turn (distances, lateral accelerations, etc.) are accordingly adjusted or removed. Thereby, unnecessary transmission of alerts can be avoided.
  • a rapid deceleration and/or a travel path beyond the road boundary can be expected in the predefined behaviour model wherefore parameters associated with those manoeuvres do not prompt the transmission of an alert.
  • Fig. 4 is a flow chart representation of a method for informing surrounding vehicles of deviant behaviour (of vehicles), according to an embodiment of the present invention.
  • the method 100 comprises receiving 101 signals from a perception system (e.g. a control system connected to a plurality of perceptive sensors such as cameras, RADARs, LIDARs, etc.).
  • the signals comprise information about a movement of at least one surrounding vehicle located in a surrounding environment (of the perception system).
  • the information about the movement comprises one or more movement parameters (e.g. position, pose, lateral speed/acceleration, longitudinal speed/acceleration, etc.).
  • the perception system may be provided in a vehicle or in an infrastructure device.
  • the method 100 may further comprise receiving signals from a perception system, where the signals comprise information about a state of the one or more surrounding vehicles.
  • the method 100 may comprise a step of identifying 105 a current state of the one or more surrounding vehicles.
  • a state of a vehicle may in the context be about to turn left/right, braking, accelerating, and so on. The state may be determined by analysing the data received from the perception system (e.g. turn indicators active, brake lights active, and similar).
  • the method 100 further comprises receiving 102 signals comprising information about a current road scenario, i.e. identifying 102 a current road scenario.
  • the method 100 comprises determining a current road scenario such as e.g. driving on a highway, approaching an intersection where an ego-lane does not have a right of way, driving on a one-way street, and so forth.
  • the movement of the at least one surrounding vehicle is compared 103 with a predefined behaviour model.
  • the predefined behaviour model comprises at least one predefined movement parameter threshold (e.g. a threshold for position, pose, lateral speed/acceleration, longitudinal speed/acceleration, etc.) for the current road scenario.
  • a signal is sent 104 to the at least one surrounding vehicle and/or one or more other surrounding vehicles.
  • the sent 104 signal may further be based on the determined 105 current state of the surrounding vehicle.
  • the one or more movement threshold parameters are further dependent on the determined 105 current state of the vehicle. For example, if a left turning indicator is active (current state of the vehicle), the maximum acceleration/speed threshold in the left direction for that vehicle is increased or completely removed in the predefined behaviour model. In other words, if a left turn is "expected" in the predefined behaviour model, no signal is sent to that vehicle when a left turn is made.
  • a non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a vehicle control system, the one or more programs comprising instructions for performing the method according to any one of the above-discussed embodiments.
  • a cloud computing system can be configured to perform any of the methods presented herein.
  • the cloud computing system may comprise distributed cloud computing resources that jointly perform the methods presented herein under control of one or more computer program products.
  • the processor(s) 11 may be or include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory 12.
  • the device 10 has an associated memory 12, and the memory 12 may be one or more devices for storing data and/or computer code for completing or facilitating the various methods described in the present description.
  • the memory may include volatile memory or non-volatile memory.
  • the memory 12 may include database components, object code components, script components, or any other type of information structure for supporting the various activities of the present description. According to an exemplary embodiment, any distributed or local memory device may be utilized with the systems and methods of this description.
  • the memory 12 is communicably connected to the processor 11 (e.g., via a circuit or any other wired, wireless, or network connection) and includes computer code for executing one or more processes described herein.
  • parts of the described solution may be implemented either in the vehicle, in a system located external the vehicle, or in a combination of internal and external the vehicle; for instance in a server in communication with the vehicle, a so called cloud solution.
  • sensor data may be sent to an external system and that system performs the steps to compare the sensor data (movement of the other vehicle) with the predefined behaviour model.
  • the different features and steps of the embodiments may be combined in other combinations than those described.
EP19165492.0A 2019-03-27 2019-03-27 Verfahren und steuerungsvorrichtung zum senden von informationen an umgebende fahrzeuge Pending EP3716245A1 (de)

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EP19165492.0A EP3716245A1 (de) 2019-03-27 2019-03-27 Verfahren und steuerungsvorrichtung zum senden von informationen an umgebende fahrzeuge
CN202010228179.1A CN111754762B (zh) 2019-03-27 2020-03-27 用于向周围车辆发送信息的方法和控制装置

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EP19165492.0A EP3716245A1 (de) 2019-03-27 2019-03-27 Verfahren und steuerungsvorrichtung zum senden von informationen an umgebende fahrzeuge

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