EP4270352A1 - Contrôle d'un état futur de trafic sur un segment de route - Google Patents

Contrôle d'un état futur de trafic sur un segment de route Download PDF

Info

Publication number
EP4270352A1
EP4270352A1 EP22169701.4A EP22169701A EP4270352A1 EP 4270352 A1 EP4270352 A1 EP 4270352A1 EP 22169701 A EP22169701 A EP 22169701A EP 4270352 A1 EP4270352 A1 EP 4270352A1
Authority
EP
European Patent Office
Prior art keywords
vehicle
vehicles
traffic state
future traffic
subset
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
EP22169701.4A
Other languages
German (de)
English (en)
Inventor
Carl LINDBERG
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zenseact AB
Original Assignee
Zenseact AB
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Zenseact AB filed Critical Zenseact AB
Priority to EP22169701.4A priority Critical patent/EP4270352A1/fr
Publication of EP4270352A1 publication Critical patent/EP4270352A1/fr
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • 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
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow 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
    • G08G1/096708—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
    • 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
    • G08G1/096733—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place
    • G08G1/096741—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place where the source of the transmitted information selects which information to transmit to each 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
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096775—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 central station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

Definitions

  • the present disclosure relates to controlling a future traffic state on a road segment of a geographical region. More specifically, various embodiments of the present disclosure relate to systems and methods for controlling a future traffic state on a road segment of a geographical region based on a current traffic state in the geographical region involving a plurality of vehicles in the current traffic state.
  • ADAS driver-assistance systems
  • ACC adaptive cruise control
  • forward collision warning etc.
  • AD Autonomous Driving
  • ADAS and AD will herein be referred to under the common term Automated Driving System (ADS) corresponding to all of the different levels of automation as for example defined by the SAE J3016 levels (0 - 5) of driving automation, and in particular for level 4 and 5.
  • ADS Automated Driving System
  • An ADS may be construed as a complex combination of various components that can be defined as systems where perception, decision making, and operation of the vehicle are performed by electronics and machinery instead of a human driver, and as introduction of automation into road traffic. This includes handling of the vehicle, destination, as well as awareness of surroundings. While the automated system has control over the vehicle, it allows the human operator to leave all or at least some responsibilities to the system.
  • An ADS commonly combines a variety of sensors to perceive the vehicle's surroundings, such as e.g. radar, LIDAR, sonar, camera, navigation system e.g. GPS, odometer and/or inertial measurement units (IMUs), upon which advanced control systems may interpret sensory information to identify appropriate navigation paths, as well as obstacles, free-space areas, and/or relevant signage.
  • Traffic congestion is a well-known phenomenon for anyone who lives in a bigger city. Besides the fact that it causes often significantly longer travel times for the road users, congestion is also an indirect cause of traffic accidents. Therefore, having an adequate understanding of the traffic conditions imposed on the vehicles travelling on roads is essentially helpful to alleviate some unnecessary road incidents as well as improving the experience of travelling between the start and destination points.
  • ADS features and the detection and perception capabilities of today's modern vehicles an attractive opportunity presents itself to enable intelligent interaction with the traffic infrastructure, with the other vehicles on the road, with several external communication networks, or with high definition maps providing depth information of roads. This in turn provides for acquiring an extensive amount of data of the surroundings of the vehicle, as well as road conditions, weather conditions, and the like to produce accurate information on the traffic situation on road segments and even large geographical regions on which the vehicles are set to travel.
  • a method for controlling a future traffic state on one or more road segment(s) of a geographical region comprising obtaining vehicle data from at least one subset of vehicles among a plurality of vehicles in the current traffic state, the vehicle data comprising velocity data and position data of one or more vehicle(s) comprised in the at least one subset of vehicles.
  • the method further comprises determining, based on the obtained vehicle data, the future traffic state on the one or more road segment(s) within a predetermined time period ensuing the current traffic state.
  • the method comprises determining a plurality of alternative future traffic states based on a plurality of predetermined vehicle behavior criteria configured to influence the determined future traffic state on the one or more road segment(s). Additionally, the method comprises selecting a predetermined vehicle behavior, comprised in the plurality of the predetermined vehicle behavior criteria, resulting in an augmented future traffic state among the alternative future traffic states and being representative of a most desired future traffic state on the one or more road segment(s). The method further comprises communicating the selected predetermined vehicle behavior to one or more vehicle(s) comprised in the at least one subset of vehicles among the plurality of vehicles in the current traffic state.
  • the present inventor has realized that by utilizing vehicle data of the one or more vehicle(s) comprised in the at least one subset of vehicles, systems and methods can be provided which output predictions of near future traffic states such as traffic congestion on one or more road segment in real time. Further, the systems and methods of the present disclosure provide a feedback process through which active intervention instructions are transmitted to one or more of the vehicle(s) comprised in the at least one subset of vehicles, enabling influencing and controlling of the future traffic state in the geographical region.
  • the method may further comprise determining the future traffic state on the one or more road segment(s) within the predetermined time period ensuing the current traffic state by means of generating a Markov chain model based on the velocity and position data of one or more vehicle(s) comprised in the at least one subset of vehicles.
  • the method may further comprise determining the plurality of alternative future traffic states based on the plurality of predetermined vehicle behavior criteria for the subset of vehicles by means of the generated Markov chain model, the predetermined vehicle behavior criteria being a function of the velocity and position data of one or more vehicle(s) comprised in the at least one subset of vehicles.
  • the step of communicating the selected predetermined vehicle behavior may further comprise transmitting a signal to one or more vehicle(s) comprised in the at least one subset of vehicles, the signal comprising an instruction to adopt the selected predetermined vehicle behavior by the one or more vehicle(s) comprised in the at least one subset of vehicles.
  • the vehicle data may comprise real-time vehicle data in the current traffic state.
  • the predetermined vehicle behavior criteria may comprise any one of an adjusted velocity of the vehicle, an adjusted distance of the vehicle to an external vehicle ahead, and an updated route selection for the vehicle.
  • the predetermined time period for determining the future traffic state on the one or more road segment(s) may be determined based on the velocity and the position data of one or more vehicle(s) in the at least one subset of vehicles.
  • the future traffic state on the one or more road segment(s) may comprise a future traffic congestion state on the one or more road segment(s) and the most desired future traffic state on the one or more road segment(s) may comprise a resolved future traffic congestion state.
  • the one or more vehicle(s) comprised in the at least one subset of vehicles may be equipped with an automated driving system, ADS, feature.
  • a (non-transitory) computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a processing system, the one or more programs comprising instructions for performing the method according to any one of the embodiments of the method of the present disclosure.
  • a computer program product comprising instructions which, when the program is executed by one or more processors of a processing system, causes the processing system to carry out the method according to any one of the embodiments of the method disclosed herein.
  • a system for controlling a future traffic state on one or more road segment(s) of a geographical region, based on a current traffic state in the geographical region comprising processing circuitry configured to obtain vehicle data from at least one subset of vehicles among a plurality of vehicles in the current traffic state, the vehicle data comprising velocity data and position data of one or more vehicle(s) comprised in the at least one subset of vehicles.
  • the processing circuitry is further configured to determine, based on the obtained vehicle data, the future traffic state on the one or more road segment(s) within a predetermined time period ensuing the current traffic state and to determine a plurality of alternative future traffic states based on a plurality of predetermined vehicle behavior criteria configured to influence the determined future traffic state on the one or more road segment(s). Further, the processing circuitry is configured to select a predetermined vehicle behavior, comprised in the plurality of the predetermined vehicle behavior criteria, resulting in an augmented future traffic state among the alternative future traffic states and being representative of a most desired future traffic state on the one or more road segment(s). The processing circuitry is further configured to communicate the selected predetermined vehicle behavior to one or more vehicle(s) comprised in the at least one subset of vehicles among the plurality of vehicles in the current traffic state.
  • a remote server comprising the system for controlling a future traffic state on one or more road segment(s) of a geographical region, based on a current traffic state in the geographical region according to any one of the embodiments of the fourth aspect disclosed herein.
  • a cloud environment comprising one or more remote servers according to any one of the embodiments of the fifth aspect disclosed herein.
  • Fig. 1a and Fig. 1b illustrate schematic perspective top views of a geographical region 200 and one or more geographical sub-regions 200a-f comprised in the geographical region 200.
  • Each geographical sub-region comprises road networks having a plurality of road segments 24 on which a plurality of vehicles 100 are in traffic.
  • the geographical sub-regions 200a-f may for instance be a part of an urban traffic infrastructure comprising large urban road networks within the territories of a large city as well as outside the boundaries of the large city such as rural and suburban areas in connection to the urban road networks. These areas and road networks are collectively referred to as the geographical region 200 in the present context.
  • the roads may be any type of road e.g. highways with carriageways, motorways, freeways or expressways.
  • the roads may also be country roads or any other carriageways with one or more lanes wherein the plurality of vehicles 100 will be travelling on.
  • Each road in the road networks may comprise road segments 24 e.g. intersections, roundabouts, various stretches of road, etc. as shown in Fig. 1a .
  • Fig. 1b several geographical sub-regions 200a-f are shown with interconnected traffic routes 241 amongst the geographical sub-regions 200a-f forming an urban road network in the geographical region 200.
  • the fleet vehicles 110 travelling in the current state of traffic in the geographical sub-region 200a are shown in hatched shaded patterns in Fig. 1a for ease of identification. It should be clear that any other vehicles than the illustrated vehicles 110 being hatched shaded, may be comprised in the fleet vehicles 110, and the selection of the example vehicles in Fig. 1a is merely for the sake of assisting the reader.
  • Each vehicle 110 comprised in the subset of vehicles may be provided with a driver support function, which in the present context may be understood as an Autonomous Driving (AD) feature or an Advanced Driver Assistance Feature (ADAS), both of which are herein encompassed under the term an Automated Driving System (ADS), or an ADS feature.
  • Each vehicle 110 may also be provided with means for wireless communication compatible with various short-range or long-range wireless communication protocols as further explained with reference to Fig. 4 .
  • the vehicles 110 may be any type of vehicle such as cars, motorcycles, cargo trucks, busses, smart bicycles, autonomous driving delivery vehicles, etc.
  • the ADS feature may e.g. control one or more functions of the vehicles 110 such as acceleration, steering, route planning and braking of the vehicle 110.
  • Each vehicle 110 may further comprise a vehicle control system 10 which comprises control circuitry 11 configured to obtain data comprising information about the surrounding environment of the vehicle 110. Accordingly, each vehicle 110 in the at least one subset of vehicles may also comprise sensing capabilities e.g. at least one on-board sensor device which may be a part of a vehicle perception system or module 6 comprising sensor devices 6a-6c such as the ones shown in the vehicle of Fig. 4 .
  • the vehicle 110 may also comprise a localization system 5 configured to monitor a geographical position and heading of the vehicle, and may in the form of a Global Navigation Satellite System (GNSS), such as a GPS. However, the localization system may alternatively be realized as a Real Time Kinematics (RTK) GPS in order to improve accuracy.
  • GNSS Global Navigation Satellite System
  • RTK Real Time Kinematics
  • the localization system may further comprise inertial measurement units (IMUs).
  • IMUs inertial measurement units
  • the vehicle control system 10 of the vehicle 110 may thus be configured to obtain vehicle data associated with a position and/or velocity and/or acceleration of the vehicle 110. Accordingly, in several aspects and embodiments the vehicle data may comprise a position, velocity and heading of each vehicle 110 comprised in the at least one subset of vehicles traveling on one or more road segments 24 of the geographical sub-regions 200a-f.
  • the present inventor has realized that by utilizing vehicle data of the fleet vehicles, systems and methods can be provided which output predictions of near future traffic states such as traffic congestion on one or more road segment 24 in real time. Further, the systems and methods of the present disclosure provide a feedback process through which intervention instructions are transmitted to one or more of the fleet vehicles, enabling influencing and controlling of the future traffic state in the geographical region 200.
  • a traffic management model 31 i.e. a traffic prediction and control model is constructed based on the obtained vehicle data by means of Markov chain theory in finite state and discrete time steps.
  • the Markov chain model 31 can be parameterized in real time, thanks to the large amounts of data continuously generated by the fleet vehicles 110.
  • the generated Markov chain model 31 is then executed "into the future" i.e. in discrete time steps for predicting future outcomes of the traffic in the geographical region 200.
  • the generated Markov model can be automatically parameterized in real time, which provides a great advantage in terms of its application to traffic prediction and control.
  • the generated Markov chain model enables dynamic calculations of a plurality of alternative traffic scenarios on a road segment 24 as a function of the vehicle data.
  • effects of modifying variables such as vehicle velocity and position associated with each vehicle 110 on a future state of traffic in the geographical region 200 can be anticipated by the model 31. This in turn improves understanding of the traffic state and handling traffic problems in the road networks, as well as adapting the traffic management in real time, and consequently alleviating traffic issues such as instances of traffic congestion on road segments 24 effectively.
  • a traffic management system (TMS) 30 comprising the traffic management model 31 is provided which is configured to predict and control a future state of traffic on one or more road segments by obtaining the vehicle data of vehicle(s) 110 of the fleet of vehicles from the vehicle control system 10 of each vehicle 110.
  • the TMS 30 is configured to determine, based on the obtained vehicle data, the future traffic state on one or more road segments 24 of a geographical region 200 within a predetermined time period ensuing the current traffic state.
  • the TMS 30 may determine the future traffic state for each of the geographical sub-regions 200a-f, or in a selected number of geographical sub-regions, wherein each of these sub-regions 200a-f can be regarded as states of the Markov chain model 31, for which a future state of traffic is calculated based on the current state of traffic by means of the Markov chain model 31. This will be more elucidate with reference to Fig. 1b in the following.
  • the TMS 30 is further configured to determine a plurality of alternative future traffic states based on a plurality of predetermined vehicle behavior criteria configured to influence the determined future traffic state on the one or more road segments 24.
  • the plurality of predetermined vehicle behavior criteria in the present context is a function of the velocity and position data of one or more vehicle(s) 110 in the subset of vehicles and may comprise any one of an adjusted velocity of the vehicle 110, an adjusted distance of the vehicle 110 to an external vehicle 100, 110 ahead, and an updated route selection for the vehicle 110.
  • the predetermined vehicle behavior criteria may further comprise instructions to optimize the number of instances of a lane change by the vehicle 110, minimize an overall use of braking and idling by the vehicle 110, adjusting the ADS driving policy for "increased willingness" to let other vehicles merge into the lane on which the vehicle 110 is traveling, etc.
  • the TMS 30 is further configured to select a predetermined vehicle behavior, comprised in the plurality of the predetermined vehicle behavior criteria, resulting in an augmented i.e. improved future traffic state among the alternative future traffic states.
  • the selected augmented future traffic state is thus a representative of a most desired i.e. optimal future traffic state on the one or more road segment(s) 24 on the geographical region 200 and/or on one or more of the geographical sub-regions 200a-f.
  • the future traffic state on the one or more road segment(s) may comprise a future traffic congestion state on the one or more road segment(s) and the most desired future traffic state on the one or more road segment(s) may comprise a resolved future traffic congestion state on the one or more road segments 24.
  • resolved future traffic congestion in the present context it is to be understood as the determined future traffic congestion not taking place due to the active intervention by the TMS 30. It is clear to the person skilled in the art that a state of traffic congestion is not necessarily a full-stop traffic jam but it may comprise any intermediate traffic state leading up to such a full-stop such as slowing flow of traffic, increased time of travel for the vehicles, increased vehicle queuing, etc.
  • the TMS 30 enables influencing and controlling the future traffic state in the geographical region by accurately calculating the various future traffic scenarios as result of specific vehicle behavior.
  • the TMS 30 actively intervenes and controls the future traffic state, thus preventing the determined future traffic state such as a determined future congestion from occurring.
  • the TMS 30 is configured to communicate the selected predetermined vehicle behavior representative of the augmented or the most desirable future traffic state to one or more vehicle 110 comprised in the at least one subset of vehicles among the plurality of vehicles 100 present in the current traffic state.
  • the TMS 30 may be configured to communicate the selected predetermined vehicle behavior representative of the most desirable future traffic state to each vehicle 110 comprised in the at least one subset of vehicles.
  • determining the future traffic state on the one or more road segment(s) within the predetermined time period ensuing the current traffic state is performed by means of generating a Markov chain model 31 based on the velocity and position data of one or more vehicle(s) 110 comprised in the at least one subset of the plurality of vehicles.
  • determining the plurality of alternative future traffic states based on the plurality of predetermined vehicle behavior criteria for the subset of vehicles 110 is also performed by means of the generated Markov chain model, wherein the predetermined vehicle behavior criteria is a function of the velocity and position data of the one or more vehicle(s) comprised in the subset of vehicles.
  • the TMS 30 is configured to, when communicating the selected predetermined vehicle behavior, transmit a signal to one or more vehicle 110 comprised in the at least one subset of vehicles, the signal comprising an instruction to adopt the selected predetermined vehicle behavior by the one or more vehicles 110.
  • the signal may be transmitted to each vehicle 110 comprised in the at least one subset of vehicles.
  • each geographical sub-region may be regarded as a node in a graph model of the traffic in the urban road network 200.
  • the traffic among the geographical sub-regions 200a-f is modelled as the Markov chain model 31 and each of these geographical sub-regions 200a-f is labeled as a state of the Markov chain model.
  • Each of the geographical sub-regions 200a-f may also in turn comprise several states 200a'- f' of the Markov chain model 31 representative of the movement of the fleet vehicles 110 amongst these states 200a'- f'.
  • the systems and methods of the present disclosure model the traffic by generating a discrete-time Markov chain which is a sequence of random variables "( X n ) n ⁇ 0 ", "n” being discrete time steps, known as a stochastic process.
  • a value of the next variable i.e. a future state of the process depends only on the value of the current variable i.e. current state of the process, and not any variables in the past, thus satisfying the Markov property.
  • the process being the traffic in the geographical region 200 and the geographical sub-regions 200a-f. This allows for constructing a stochastic transition matrix "P" describing the transitions between the different states 200a-f of the Markov chain.
  • the stochastic transition or probability matrix describes probabilities of moving from any of the states to each of the other states of the Markov chain.
  • each element e.g. "p i,j "of the transition matrix "P” denotes the probability of transitioning from the state "i” to the state "j".
  • the probability of being in a particular state in "n"-discrete time steps into the future can be calculated.
  • the methods and systems of the present disclosure are adapted to determine a future traffic state on the one or more road segment(s) of the geographical region 200, within a predetermined time period i.e. "n" discrete time steps, ensuing the current traffic state in any of the geographical sub-regions 200a-f.
  • the transition matrix "P” By iteratively applying the transition matrix "P", the Markov chain model can progress to the future state of traffic in any of the geographical sub-regions accounting for the movement of the fleet vehicles 110 among the geographical sub-regions 200a-f i.e. moving from one state to another state of the Markov model and thus affecting the future traffic in that next state.
  • X m i .
  • the model can calculate what a future traffic state would be in any of the states.
  • a future traffic congestion in any of the geographical sub-regions 200a-f can be determined based on the obtained vehicle data and the calculated probabilities of transitions of the one or more fleet vehicles 110 to that geographical sub-region.
  • the parameters in the transition matrix "P" can be estimated with maximum likelihood, thanks to the obtained vehicle data representative of the present velocity and position i.e. paths of the one or more vehicles in the fleet of vehicles 110.
  • the transition matrix "P" can be estimated very accurately in real time.
  • a plurality of alternative future traffic states based on a plurality of predetermined vehicle behavior criteria configured to influence the determined future traffic states on the one or more road segment(s) may be determined.
  • the alternative future traffic states are in fact, predictions of future outcomes of the traffic state in the geographical sub-regions 200a-f based on a specific behavior, as a function of the velocity and position of the fleet vehicles 110, adapted by the one or more vehicles 110 in the subset of vehicles.
  • the future outcomes of the alternative vehicle behaviors and their impact on the future traffic state of the geographical region 200 are calculated by the Markov chain model.
  • the TMS 30 is configured to control the future traffic state by selecting a predetermined vehicle behavior, comprised in the plurality of the predetermined vehicle behavior criteria, resulting in an improved future traffic state among the alternative future traffic states.
  • the Markov model is adapted to calculate which vehicle behavior adopted by the one or more fleet vehicles 110 would result in resolving the traffic congestion in the geographical region 200 and/or in a certain geographical sub-region 200a-f in question. For instance, the effect of a predetermined speed reduction by the one or more fleet vehicles 110 on a certain portion of the one or more road segment(s) on the future state of congestion in the geographical region in question is calculated.
  • the impact of reducing speed by the one or more fleet vehicles for a certain predetermined period of time in the current traffic state in one or more of the geographical regions is calculated.
  • a particular pattern of behavior for reducing speed on a certain portion of the road and/or for a certain period of time can thus be selected as the most suitable intervention resulting in the most promising future traffic state i.e. resolution of the future traffic congestion.
  • the selected predetermined vehicle behavior can then be communicated by the TMS 30 to one or more of the fleet vehicle(s) 110.
  • the one or more or each vehicle 110 in the vehicle fleet 110 may also receive a signal from the TMS 30, the signal being comprised in the communication with the fleet of vehicles 110.
  • the transmitted signal by the TMS 30 comprises instructions, instructing the one or more or each of the fleet vehicles 110 to adopt the selected predetermined vehicle behavior.
  • the TMS 30 may be configured to transmit the instruction signal to a vehicle control system 10 of the one or more fleet vehicle(s) 110 for controlling a driver-assistance or an autonomous driving (ADS) feature of the one or more or each of the vehicles 110, thus influencing and controlling the future traffic state on the one or more road segment(s) of the geographical region 200, based on the current traffic state in the geographical region 200.
  • ADS autonomous driving
  • the method comprises obtaining 301 vehicle data from at least one subset of vehicles 110 among a plurality of vehicles 100 in the current traffic state, the vehicle data comprising velocity data and position data of one or more vehicle(s) comprised in the at least one subset of vehicles.
  • the method further comprises determining 303, based on the obtained vehicle data, the future traffic state on the one or more road segment(s) within a predetermined time period ensuing the current traffic state and determining 305 a plurality of alternative future traffic states based on a plurality of predetermined vehicle behavior criteria configured to influence the determined future traffic state on the one or more road segment(s).
  • the method 300 comprises selecting 307 a predetermined vehicle behavior, comprised in the plurality of the predetermined vehicle behavior criteria, resulting in an augmented future traffic state among the alternative future traffic states and being representative of a most desired future traffic state on the one or more road segment(s).
  • the most desired future traffic state in the present context is to be construed as an optimal future traffic state on the one or more road segment(s) 24.
  • the future traffic state on the one or more road segment(s) may comprise a traffic congestion state on the one or more road segment(s) and the most desired future traffic state on the one or more road segment(s) may comprise a resolved traffic congestion state on the one or more road segments 24.
  • the method further comprises communicating 309 the selected predetermined vehicle behavior to one or more vehicle(s) 110 comprised in the at least one subset of vehicles among the plurality of vehicles 100 in the current traffic state.
  • the method may further comprise determining 303 the future traffic state on the one or more road segment(s) within the predetermined time period ensuing the current traffic state by means of generating a Markov chain model 31 based on the velocity and position data of one or more vehicle(s) 110 comprised in the subset of vehicles.
  • the method 300 may comprise determining 305 the plurality of alternative future traffic states based on the plurality of predetermined vehicle behavior criteria for the subset of vehicles by means of the generated Markov chain model 31, the predetermined vehicle behavior criteria being a function of the velocity and position data of the one or more vehicle(s) comprised in the subset of vehicles.
  • the vehicle data may comprise real-time vehicle data in the current traffic state.
  • the TMS 30 may make use of historic vehicle data, historic traffic information in the geographical region, real-time or historic map data such as data from HD-maps, real-time and/or historic weather forecast data, specific traffic restrictions/planned interruptions in certain time points such as a particular time of day, on or during one or more particular day(s) within a month, or one or more particular month(s) during the year, etc. for controlling the traffic on the geographical region.
  • the present model can advantageously be parameterized very quickly. This means that the model framework by construction is capable of accounting for parameters like time of day, time of week, present weather conditions, etc.
  • Such data may be obtained from external communication networks in real time or from data bases stored on external networks such as a cloud network.
  • the predetermined vehicle behavior criteria in various aspects and embodiments may comprise any one of an adjusted velocity of the vehicle 110, an adjusted distance of the vehicle 110 to an external vehicle 100, 110 ahead, and an updated route selection for the vehicle 110.
  • the step of communicating 309 the selected predetermined vehicle behavior may further comprise transmitting 311 a signal to one or more vehicle(s) 110 comprised in the at least one subset of vehicles 110, the signal comprising an instruction to adopt the selected predetermined vehicle behavior by the one or more vehicle(s) comprised in the at least one subset of vehicles.
  • the predetermined time period for determining the future traffic state on the one or more road segment(s) may also be determined based on the velocity and the position data of one or more vehicle(s) in the subset of vehicles.
  • the model 31 is enabled to calculate and predict the whereabouts of the one or more vehicles of the fleet on the road by taking the real-time velocity and position of the one or more vehicle into account. For instance the model could calculate where a particular vehicle 110 would be located within a 10-second, 1-minute, 5-minute, 15-minute, etc. discrete time steps from now, based on the current vehicle data of that particular vehicle.
  • the one or more vehicles 110 comprised in the fleet may be equipped with an automated driving system, ADS, feature.
  • the method 300 may also comprise controlling the one or more vehicles 110 based on the determined suitable vehicle behavior to achieve the most desired future traffic state.
  • the adjusted velocity of the one or more vehicle(s) 110, and/or the adjusted distance of the vehicles 110 to the one or more external vehicles (not shown) ahead, and/or the updated route selection for the one or more vehicle(s) 110, etc. may be used as input to the one or more ADS features of the one or more vehicle(s) 110 of the fleet configured to control one or more of acceleration, steering, braking, route planning, etc. of the vehicles 110.
  • the method 300 and all embodiments of the method 300 are iterative processes, thus obtaining the vehicle data and updating of the Markov chain model 31 are performed continuously.
  • Fig. 4 is a schematic side view of a vehicle 110 comprised in the subset of vehicles travelling in traffic on the geographical region 200.
  • the vehicle 110 comprises velocity measurement and calculation devices to obtain the velocity of the ego vehicle on the road portion 24.
  • the vehicle 110 also comprises a localization system 5 configured to monitor a geographical position and heading of the vehicle, and may be in the form of a Global Navigation Satellite System (GNSS), such as a GPS. However, the localization system may alternatively be realized as a Real Time Kinematics (RTK) GPS in order to improve accuracy.
  • GNSS Global Navigation Satellite System
  • RTK Real Time Kinematics
  • the localization system may further comprise inertial measurement units (IMUs).
  • An IMU may be understood as a device configured to detect linear acceleration using one or more accelerometers and rotational rate using one or more gyroscopes.
  • the localization of the vehicle's heading and orientation may be based on motion sensor data e.g. data from accelerometers and gyroscopes, from the IMU.
  • the vehicle 110 may also have access to a digital map (e.g. a HD-map), either in the form of a locally stored digital map or via a remote data repository accessible via an external communication network 20.
  • the vehicle 110 may further comprise a perception system 6.
  • a perception system 6 is in the present context to be understood as a system responsible for acquiring raw as well as processed sensor data of the on-board sensors 6a, 6b, 6c such as cameras, LIDARs and RADARs, ultrasonic sensors, and converting this data into scene understanding.
  • the vehicle 110 also comprises a vehicle control system 10 configured to obtain vehicle data comprising velocity and position data from the localization system 5 and provide the obtained data to the TMS 30 for use in modeling the traffic on the one or more road segment(s) 24.
  • parts of the described solution may be implemented either in the one or more vehicle(s) 110, in a system located external the vehicles 110, or in a combination of internal and external the vehicle; for instance in an external/remote server or remote control center 400 in communication with the one or more fleet vehicle(s) 110.
  • the solution may in some embodiments be implemented on a cloud platform.
  • vehicle data comprising velocity and position data may be transmitted to the remote control center 400 comprising a control system 401 to perform the method steps according to several embodiments of the method 300.
  • the one or more fleet vehicles 110 may be connected to external network(s) 20 via for instance a wireless link.
  • the vehicle control system 10 of the one or more vehicle(s) 110 may be connected to the remote control system 401 and the TMS 30 of the remote control center 400 via the wireless link.
  • the remote control system 401 may fully or partially comprise the TMS 30.
  • the same or some other wireless link may be used to communicate with other external vehicles 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, vehicle to remote control center 400, etc.
  • Examples of cellular radio technologies are GSM, GPRS, EDGE, LTE, 5G, 5G NR, and so on, also including future cellular solutions.
  • GSM Global System for Mobile communications
  • GPRS Global System for Mobile Communications
  • EDGE Evolved Universal Terrestrial Radio Access
  • LTE Long Term Evolution
  • 5G 5G NR
  • future cellular solutions such as Wireless Local Area (LAN), e.g. IEEE 802.11 based 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 vehicle control system 10 and the remote control system 401 may comprise one or more processors 11, one or more memory module(s) 12, sensor interfaces 13 and communication interfaces 14.
  • the processor(s) 11 may also be referred to as a control circuit 11 or control circuitry 11.
  • the control circuit 11 is configured to execute instructions stored in the memory 12 to perform embodiments of method 300 according to the present disclosure.
  • the memory 12 of the vehicle control system 10 can include one or more (non-transitory) computer-readable storage mediums, for storing computer-executable instructions, which, when executed by one or more computer processors 11, for example, can cause the computer processors 11 to perform the techniques described herein.
  • the memory 12 optionally includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices.
  • high-speed random access memory such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices
  • non-volatile memory such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices.
  • 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 computer program product comprising instructions which, when the program is executed by one or more processors of a processing system, causes the processing system to carry out the method according to any one of the embodiments of the method of the present disclosure.
  • 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.
  • a computer-accessible medium may include any tangible or non-transitory storage media or memory media such as electronic, magnetic, or optical media-e.g., disk or CD/DVD-ROM coupled to computer system via bus.
  • tangible and non-transitory are intended to describe a computer-readable storage medium (or “memory”) excluding propagating electromagnetic signals, but are not intended to otherwise limit the type of physical computer-readable storage device that is encompassed by the phrase computer-readable medium or memory.
  • the terms “non-transitory computer-readable medium” or “tangible memory” are intended to encompass types of storage devices that do not necessarily store information permanently, including for example, random access memory (RAM).
  • Program instructions and data stored on a tangible computer-accessible storage medium in non-transitory form may further be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link.
  • transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link.
  • 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 vehicle control system 10 or the remote control system 401 may have 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.
  • the vehicle 110 further comprises the sensor interface 13 which may also provide the possibility to acquire sensor data directly or via dedicated perception module 6 in the vehicle.
  • the vehicle 110 also comprises a communication/antenna interface 14 which may further provide the possibility to send output to and/or receive input from a remote location (e.g. remote operator or control center 400) by means of an antenna 8.
  • a remote location e.g. remote operator or control center 400
  • some sensors in the vehicle may communicate with the vehicle control device 10 using a local network setup, such as CAN bus, I2C, Ethernet, optical fibres, and so on.
  • the communication interface 14 may be arranged to communicate with other control functions of the vehicle and may thus be seen as control interface also; however, a 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.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Traffic Control Systems (AREA)
EP22169701.4A 2022-04-25 2022-04-25 Contrôle d'un état futur de trafic sur un segment de route Pending EP4270352A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP22169701.4A EP4270352A1 (fr) 2022-04-25 2022-04-25 Contrôle d'un état futur de trafic sur un segment de route

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP22169701.4A EP4270352A1 (fr) 2022-04-25 2022-04-25 Contrôle d'un état futur de trafic sur un segment de route

Publications (1)

Publication Number Publication Date
EP4270352A1 true EP4270352A1 (fr) 2023-11-01

Family

ID=81386703

Family Applications (1)

Application Number Title Priority Date Filing Date
EP22169701.4A Pending EP4270352A1 (fr) 2022-04-25 2022-04-25 Contrôle d'un état futur de trafic sur un segment de route

Country Status (1)

Country Link
EP (1) EP4270352A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220297689A1 (en) * 2019-06-03 2022-09-22 Daimler Ag Method for determining a set speed of an assistance system for controlling a longitudinal movement of a vehicle

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170337810A1 (en) * 2016-05-19 2017-11-23 Honda Motor Co., Ltd. Traffic condition estimation apparatus, vehicle control system, route guidance apparatus, traffic condition estimation method, and traffic condition estimation program

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170337810A1 (en) * 2016-05-19 2017-11-23 Honda Motor Co., Ltd. Traffic condition estimation apparatus, vehicle control system, route guidance apparatus, traffic condition estimation method, and traffic condition estimation program

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ANTONIOU CONSTANTINOS ET AL: "Traffic state prediction using Markov chain models", 2007 EUROPEAN CONTROL CONFERENCE (ECC), IEEE, 2 July 2007 (2007-07-02), pages 2428 - 2435, XP032751915, ISBN: 978-3-9524173-8-6, [retrieved on 20150325] *
NECULA EMILIAN: "Dynamic Traffic Flow Prediction Based on GPS Data", 2013 IEEE 25TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, IEEE, 10 November 2014 (2014-11-10), pages 922 - 929, XP032703790, ISSN: 1082-3409, [retrieved on 20141212], DOI: 10.1109/ICTAI.2014.140 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220297689A1 (en) * 2019-06-03 2022-09-22 Daimler Ag Method for determining a set speed of an assistance system for controlling a longitudinal movement of a vehicle
US11951985B2 (en) * 2019-06-03 2024-04-09 Mercedes-Benz Group AG Method for determining a set speed of an assistance system for controlling a longitudinal movement of a vehicle

Similar Documents

Publication Publication Date Title
CN112368662B (zh) 用于自主运载工具操作管理的定向调整动作
US11636760B2 (en) Detection and estimation of variable speed signs
US11545033B2 (en) Evaluation framework for predicted trajectories in autonomous driving vehicle traffic prediction
CN110861650B (zh) 车辆的路径规划方法、装置，车载设备和存储介质
CA3052954C (fr) Gestion operationnelle de vehicule autonome comprenant le fonctionnement d'une instance de modele de processus de decision de markov partiellement observable
US11537127B2 (en) Systems and methods for vehicle motion planning based on uncertainty
US9886857B2 (en) Organized intelligent merging
US11815891B2 (en) End dynamics and constraints relaxation algorithm on optimizing an open space trajectory
US20210005088A1 (en) Computing system implementing traffic modeling using sensor view data from self-driving vehicles
CN111380534A (zh) 用于自动驾驶车辆的基于st图学习的决策
EP3869342A1 (fr) Système et procédé pour générer des définitions de scénario de simulation pour un système de véhicule autonome
US11315421B2 (en) Systems and methods for providing driving recommendations
US20200200553A1 (en) Routing autonomous vehicles using temporal data
US11260875B2 (en) Systems and methods for road surface dependent motion planning
US12061480B2 (en) Causing a mobile robot to move according to a planned trajectory determined from a prediction of agent states of agents in an environment of the mobile robot
EP4270352A1 (fr) Contrôle d'un état futur de trafic sur un segment de route
EP4330106A1 (fr) Guidage de véhicule avec optimisation du type systémique
US11904855B2 (en) Cooperative driving system and method
US20230113532A1 (en) Path planning for vehicle based on accident intensity
US20230211802A1 (en) Motion planning using spatio-temporal convex corridors
US20220198325A1 (en) Method, apparatus, and computer program product for predicting a split lane traffic pattern
US20220198923A1 (en) Method, apparatus, and computer program product for determining a split lane traffic pattern
EP4261738A1 (fr) Suivi de voies multi-cibles
EP4279869B1 (fr) Détermination d'un état d'un véhicule sur une route
US11866041B1 (en) Vehicle control in rescue lane scenarios

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN PUBLISHED

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20240429

RBV Designated contracting states (corrected)

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR