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WO2019085904A1 - 一种基于蜂窝网络的辅助驾驶方法及交通控制单元 - Google Patents

一种基于蜂窝网络的辅助驾驶方法及交通控制单元 Download PDF

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Publication number
WO2019085904A1
WO2019085904A1 PCT/CN2018/112722 CN2018112722W WO2019085904A1 WO 2019085904 A1 WO2019085904 A1 WO 2019085904A1 CN 2018112722 W CN2018112722 W CN 2018112722W WO 2019085904 A1 WO2019085904 A1 WO 2019085904A1
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WIPO (PCT)
Prior art keywords
trajectory
vehicle
occurrence
travel
travel trajectory
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PCT/CN2018/112722
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English (en)
French (fr)
Inventor
宋永刚
李辉
杨肖
Original Assignee
华为技术有限公司
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Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP18874771.1A priority Critical patent/EP3696790A4/en
Priority to JP2020524330A priority patent/JP7047089B2/ja
Publication of WO2019085904A1 publication Critical patent/WO2019085904A1/zh
Priority to US16/862,593 priority patent/US11107356B2/en

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    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Definitions

  • Embodiments of the present invention relate to the field of intelligent transportation technologies, and in particular, to a method and a traffic control unit based on a cellular network.
  • V2X Vehicle to x
  • V2X vehicle to x
  • V2V vehicle to vehicle
  • V2I vehicle to installation
  • People communication etc.
  • Real-time road conditions, road information, and pedestrian information can be obtained through V2X technology to improve driving safety.
  • V2X assisted driving is implemented through a self-organizing network based on dedicated short range communication (DSRC). Due to the technical limitations and high deployment costs of DSRC-based solutions, it has been difficult to effectively promote applications.
  • DSRC dedicated short range communication
  • This scheme can be used for risk analysis and scheduling based on the roadside traffic control unit (TCU), which is more significant than the DSRC-based scheme. Promote advantages and achieve advantages. After conducting a risk analysis, the TCU issues an early warning based on the analysis results.
  • TCU roadside traffic control unit
  • Embodiments of the present invention provide a cellular network-based assisted driving method and a traffic control unit for providing a new risk estimation and implementing traffic early warning scheme in a process of implementing V2X assisted driving based on a cellular network.
  • a cellular network-based assisted driving method includes at least a traffic control unit TCU, and a vehicle within the TCU jurisdiction can access the cellular network through a communication module.
  • the TCU estimates the pre-occurrence trajectory of the vehicle through the acquired driving state information of the vehicle within its jurisdiction, and determines whether the pre-occurrence trajectory of the target vehicle intersects with the pre-occurring trajectory of the adjacent vehicle through the pre-occurrence trajectory of the target vehicle and the adjacent vehicle. If they intersect, the neighboring vehicle is a risky vehicle and an early warning is issued to the target vehicle. In this way, it can help achieve more accurate and effective risk estimation and provide a more valuable safety warning for the target vehicle.
  • the method is simple and effective, and the warning information provided to the target vehicle is more targeted and more accurate, and can simplify the information processing of the vehicle terminal and reduce the terminal requirements.
  • the target vehicle is recorded as a first vehicle
  • the adjacent vehicle is a second vehicle.
  • the specific method is: determining a first pre-occurrence trajectory of the first vehicle according to the driving state information of the first vehicle, where The first pre-occurrence trajectory is a trajectory that occurs within a preset time period from the current time of the first vehicle, and the second pre-occurrence trajectory is that the second vehicle is in the a driving trajectory occurring within a preset time period, the first pre-occurring trajectory comprising a first main driving trajectory and at least one first driving trajectory, the occurrence probability of the first main driving trajectory being greater than the first driving trajectory a probability of occurrence, a probability of occurrence of the first main travel trajectory and an occurrence probability of the first travel trajectory are determined by driving state information of the first vehicle, and the TCU determines that there is a second within the jurisdiction of the TCU And a second pre-occurrence trajectory of the vehicle intersects the first pre-occurrence trajectory, and then sends an early warning message to the first vehicle, wherein the second pre-occurrence trajectory of
  • the preset duration is determined based on the user's reaction time and vehicle brake time. In this way, the risk can be judged more effectively, and the warning information is more accurate.
  • the driving state information includes at least one of a position, a head pointing, a steering wheel angle, a vehicle speed, an acceleration, an angular velocity, and an angular acceleration.
  • the TCU acquires driving state information of the first vehicle by at least one of the following manners: a manner in which the first vehicle reports itself, a manner in which the vehicle is perceived by other vehicle sensors, and a road side.
  • the TCU acquires driving state information of the second vehicle by at least one of the following manners: a manner in which the second vehicle reports itself, a manner in which the vehicle is perceived by other vehicle sensors, and a road side.
  • the first pre-occurrence trajectory may also be determined in combination with at least one of a driving direction attribute of a first lane in which the first vehicle is currently located and driving intention information of the first vehicle;
  • the second pre-occurrence trajectory may also be determined in combination with at least one of a driving direction attribute of a lane in which the second vehicle is currently located and driving intention information of the second vehicle.
  • the driving intention information includes at least one of information of route planning, information of a turn signal, and intention information reported by a driver.
  • determining that the second pre-occurrence trajectory intersects the first pre-occurrence trajectory possibly including: determining the first main travel trajectory and the second main travel trajectory or The second travel trajectory intersects; and/or, determining that the first travel trajectory intersects the second main travel trajectory or the second travel trajectory.
  • the warning information includes a risk level; the TCU is based on a distance from the intersected location to the first vehicle, a distance from the intersected location to the second vehicle, The risk level is determined by at least one of the probability of occurrence of intersecting travel trajectories.
  • the intersecting position is located at a risk level of a preceding segment of the first travel trajectory, above a risk level of the intersecting position at a later stage of the first travel trajectory; the intersecting position is located at the second main a risk level of a front section of the travel track, a risk level higher than a position of the intersection where the intersecting position is located; the intersection position is located at a risk level of a front section of the second travel track, high a risk level at a position of the intersection of the second travel trajectory; wherein the front section of the first main travel trajectory and the front end of the first travel trajectory are: a trajectory pre-occurring within a first sub-time period from
  • the intersecting position is located at a risk level on the first main travel trajectory, higher than a risk level of the intersecting position on the first travel trajectory; the intersecting position is located at the second main travel
  • the risk level on the trajectory is higher than the risk level at which the intersecting position is on the second travel trajectory.
  • the warning information may further include: at least one of an intersection location, a risk level, attribute information of the risk vehicle, and a recommendation to take measures.
  • the attribute information of the risk vehicle may include a position, a heading, a speed, a model, a color, a vehicle with a special task, and the like.
  • Suggestions for taking action may include recommendations for acceleration, deceleration, lane change, etc. for the target vehicle.
  • the intersection includes an intersection point; when it is determined that the first pre-occurrence trajectory partially overlaps with the second pre-occurrence trajectory, determining that the preset time period is from the current time Whether the second vehicle is located within a relative travel trajectory of the first vehicle and the second vehicle, and if so, issuing an early warning message, wherein the partial overlap means that there are at least two intersections, the relative travel trajectory It refers to a trajectory formed by a change in the relative position of the first vehicle and the second vehicle within the preset duration from the current time.
  • the length of the relative driving track is a product of a relative speed of the first vehicle and the second vehicle and the preset duration. In this way, it can be processed in combination with the normal car-following application scenario, which not only ensures basic driving safety, but also avoids excessive invalid warning.
  • this situation can be regarded as a special intersecting scene, and intersect.
  • the position can be set to overlap the start or midpoint of a partial line segment.
  • a traffic control unit TCU having the functionality to implement the behavior of the sender in any of the possible aspects of the first aspect and the first aspect described above.
  • the functions may be implemented by hardware or by corresponding software implemented by hardware.
  • the hardware or software includes one or more modules corresponding to the functions described above.
  • the TCU may be a chip or an integrated circuit.
  • the TCU when part or all of the function is implemented by software, the TCU includes: a processor, configured to execute a program, when the program is executed, the TCU may implement the first Aspects and methods described in any of the possible designs of the first aspect.
  • a memory is further included, configured to store a program executed by the processor.
  • the above memory may be a physically separate unit or may be integrated with the processor.
  • the TCU when some or all of the functionality is implemented in software, the TCU includes a processor.
  • a memory for storing a program is located outside the TCU, and the processor is connected to the memory through a circuit/wire for reading and executing a program stored in the memory.
  • a computer storage medium stored with a computer program comprising instructions for performing the method of the first aspect or any of the possible designs of the first aspect.
  • an embodiment of the present invention provides a computer program product comprising instructions that, when run on a computer, cause the computer to perform the methods described in the above aspects.
  • FIG. 1 is a schematic structural diagram of an intelligent transportation system according to an embodiment of the present invention.
  • FIG. 2 is a schematic flowchart of a method for assisting driving based on a cellular network according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a network formed by a pre-occurring trajectory of a vehicle according to an embodiment of the present invention
  • FIG. 4 is a schematic diagram of a main travel trajectory and a secondary travel trajectory according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of pre-occurrence trajectory intersection according to an embodiment of the present invention.
  • 6a is a schematic diagram of a pre-occurring trajectory segmentation according to an embodiment of the present invention.
  • 6b is a second schematic diagram of pre-occurring trajectory segmentation according to an embodiment of the present invention.
  • 6c is a third schematic diagram of pre-occurring trajectory segmentation according to an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of dividing a risk level according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of determining a risky vehicle in a car-following scenario according to an embodiment of the present invention.
  • FIG. 9 is a second schematic diagram of determining a risky vehicle in a car-following scenario according to an embodiment of the present invention.
  • FIG. 10 is a schematic structural diagram of a traffic control unit TCU according to an embodiment of the present invention.
  • FIG. 11 is a second schematic structural diagram of a traffic control unit TCU according to an embodiment of the present invention.
  • the present invention can be applied to the field of V2X assisted driving based on a cellular network.
  • LTE technology is applied to a vehicle network system to form an LTE-vehicle (LTE-V) system.
  • LTE-V LTE-vehicle
  • the embodiment of the present invention can be applied to the LTE-V.
  • 5G 5th-generation mobile communication systems
  • the cellular network-based assisted driving method and apparatus provided by the embodiments of the present invention can be applied to an intelligent transportation system.
  • FIG. 1 is a schematic structural diagram of a possible intelligent transportation system framework according to an embodiment of the present invention.
  • the intelligent transportation system includes a TCU 101, an On-Board Unit (OBU) 102, and a base station 103.
  • an intelligent service center (CSU) 104, a signal light/signage 105, and a roadside sensor 106 are also included.
  • the TCU 101 and the OBU 102 interact through the interface 1, and the interface 1 is an application layer interface for communication between the OBU 102 and the TCU 101.
  • the TCU 101 interacts with the base station 103 through the interface 2, and the interface 2 is the interface between the TCU 101 and the cellular network.
  • the TCU 101 needs to utilize the LBO capability of the network and the mobile edge computing (MEC) capability to reduce the communication delay. High-speed anti-collision assisted driving application.
  • MEC mobile edge computing
  • the TCU 101 will connect the network elements of different cellular networks according to the deployment requirements.
  • the interfaces provided by different network elements are different.
  • the TCU 101 needs to adapt these interfaces to ensure the communication delay, reliability and bandwidth between the OBU 102 and the TCU 101.
  • the TCU 101 interacts with the roadside sensor 106 through the interface 3, which is an interface between the TCU 101 and the roadside sensor 106.
  • the transmitted data for transmission may be video stream, radar point cloud data or a structured person. , car, and material data.
  • the TCU 101 interacts with the signal light/signage 105 through an interface 4 for transmitting traffic signal data and traffic sign data.
  • the TCU 101 interacts with the CSU 104 through the interface 5, and the CSU 104 can send alarms and events to the OBU 102 through the TCU 101.
  • the base station 103 and the OBU 102 interact with each other through an interface between the terminal and the cellular network.
  • the base station 103 is a base station in the long term evolution (LTE), and the base station 103 can communicate with the OBU 102 through the LTE-Uu interface.
  • LTE-Uu interface is a physical access layer interface for communication between the OBU 102 and the TCU 101, and is an interface between the terminal and the base station defined by the 3rd generation partnership project (3GPP).
  • the Uu interface can be an interface between the terminal and the 2G, 3G, 4G, and 5G cellular networks.
  • the TCU 101 is a core component of the intelligent transportation system 100.
  • the communication data between the V2Xs is mastered on the cellular network side, and the communication delay is reduced by using the LBO or mobile edge computing (MEC) capabilities of the network.
  • LBO is the abbreviation of local break out.
  • the TCU 101 is configured to collect vehicle status information and alarm data sent by the OBU 102 through the base station 103, and is used to filter risk data and alarm data for the target vehicle, thereby reducing bandwidth requirements required for V2X communication.
  • the TCU 101 is also configured to collect the sensing data of the roadside sensor 106, the signal data, the sign data, the notification of the CSU 104 to the OBU 102, and the alarm data, and filter and send the data to the target vehicle.
  • the OBU 102 may be in the form of a vehicle, or may be a combination of a T-Box and a smart mobile terminal.
  • the OBU 102 is used to acquire vehicle travel state information, and transmits vehicle travel state information to the TCU 101 through the base station 103.
  • the OBU 102 is also used to receive risk data, alarms, events, traffic lights, and signage data, and prompt the driver by voice or video.
  • the OBU 102 can be described as a vehicle.
  • the functions of the two methods in the implementation of the cellular network-based assisted driving method provided by the embodiments of the present invention are the same. It can be considered that the steps performed by the vehicle are specifically the steps performed by the OBU 102.
  • the embodiment of the present invention is described by taking a vehicle as an example, the method is applicable to any traffic participant, for example, a pedestrian, a bicycle, a stationary obstacle, or the like.
  • the CSU 104 can send the warning data to the OBU 102 through the TCU 101.
  • the signal light/signage 105 is used to provide traffic signal data and traffic sign data to the TCU 101, and is forwarded by the TCU 101 to the vehicle of the signal light and the sign control area.
  • the roadside sensor 106 is configured to provide roadside sensing data to the TCU 101, so that the TCU 101 can analyze the risk of the vehicle running in conjunction with the roadside sensing data.
  • the cellular network includes at least a TCU, and the vehicle within the jurisdiction of the TCU can access the cellular through the communication module.
  • the internet The internet.
  • the cellular network-based assisted driving method provided by the embodiment of the present invention may be performed by a TCU, and a vehicle that needs to perform risk estimation and receive early warning information may be referred to as a target vehicle, and may also be referred to as a host vehicle.
  • the following description may also be referred to as a first vehicle.
  • a vehicle that poses a risk to a target vehicle in a risk estimate may be referred to as a risk vehicle, and may also be referred to as a second vehicle in the following description.
  • the specific process of the cellular network-based assisted driving method provided by the embodiment of the present invention may be as follows.
  • Step 201 The TCU determines a first pre-occurrence trajectory of the first vehicle according to the driving state information of the first vehicle.
  • Step 202 The TCU sends an early warning message to the first vehicle if it is determined that the second pre-occurrence trajectory of the second vehicle intersects with the first pre-occurrence trajectory.
  • the pre-occurrence trajectory described in the embodiment of the present invention refers to a trajectory that the vehicle is about to take, and may refer to a trajectory that the vehicle will start within a preset time period from the current time.
  • the first pre-occurrence trajectory is a trajectory that occurs within a preset time period from the current time of the first vehicle
  • the second pre-occurrence trajectory is a trajectory that occurs within a preset time period from the current time of the second vehicle.
  • the preset duration is an empirical value determined by the driver's reaction time and the vehicle's brake time. For example, the preset duration can be set to 5 seconds.
  • the following describes how the TCU determines the pre-occurrence trajectory of the vehicle, including how to determine the first pre-occurrence trajectory of the first vehicle and how to determine the second pre-occurrence trajectory of the second vehicle.
  • the vehicle reports the driving state information in a high-density cycle, and the vehicle can be considered to report the driving state information in real time.
  • the driving state information includes at least one of a position of the vehicle, a heading of the vehicle, a steering wheel angle, a vehicle speed, an acceleration, an angular velocity, and an angular acceleration.
  • the TCU receives the driving state information reported by the vehicle, obtains the current location of the vehicle in the driving state information, and can infer the driving trajectory that the vehicle may occur within the preset time length according to some information in the driving state information.
  • determining the current location of the vehicle determining that the vehicle is traveling in a straight line according to the steering wheel angle, and according to the vehicle speed and acceleration information, the displacement of the vehicle within the preset time length can be obtained according to the existing physical knowledge, and the vehicle can be roughly determined according to the displacement. Pre-occurrence trajectory within the duration.
  • the driving intention information may also be combined to determine the pre-occurrence trajectory.
  • the driving intention information may include, but is not limited to, at least one of path planning information, turn signal information, and driver reported intention information.
  • the TCU can determine the driving intention according to the information of the path planning, the information of the turn signal, the intention information reported by the driver, and the phase information of the traffic light, and the route that the vehicle will travel in the future preset time, whether the route is straight or left. Turning or turning right or turning around can help the TCU to more accurately determine the pre-occurrence trajectory.
  • the intent information reported by the driver may be that the driver reports his or her driving intention through the terminal voice of the OBU or the mobile phone.
  • the TCU can also determine the pre-occurrence trajectory in conjunction with the direction of travel of the vehicle's current lane.
  • the lane has clear driving direction attributes, such as straight lane, left/right turn lane, straight/left/right turn lane, etc.
  • the current vehicle is in a straight lane, and the TCU can consider the vehicle to be preset.
  • a possible driving trajectory that will occur within the duration is a straight line.
  • the TCU can also acquire the vehicle by means of on-board sensor sensing reporting, roadside sensor acquisition reporting, roadside signal device reporting, CSU notification, and neighboring TCU notification. Travel status information and other supplemental information used to determine the pre-occurrence trajectory of the vehicle.
  • the in-vehicle sensor perceptual reporting means that the sensor data can be reported to the TCU if the sensor such as the in-vehicle camera or the radar can sense other surrounding objects.
  • the roadside signal device may be a signal light/signage 105 in the system of FIG.
  • the TCU may also determine the pre-occurrence trajectory of the vehicle in combination or according to other reference factors.
  • the TCU can determine the pre-occurrence trajectory of any vehicle within the jurisdiction, and the pre-occurrence trajectory of each vehicle can form a network or a map.
  • a schematic representation of the network or map is shown in Figure 3.
  • the solid line with the arrow is the pre-occurrence trajectory of the vehicle determined by the TCU.
  • the pre-occurrence trajectories of different vehicles form a network.
  • Fig. 3 there may be intersections between the line segments of the two pre-occurring trajectories, and the intersection point can be understood as the intersection of the trajectories or the intersection of the trajectories, that is, the intersection of the two vehicles at the space-time position here may occur. Collision, based on which a risk assessment can be performed.
  • the first pre-occurrence trajectory is determined according to at least one of driving state information of the first vehicle, a driving direction attribute of a current lane of the first vehicle, and driving intention information of the first vehicle.
  • the second pre-occurrence trajectory is determined according to at least one of driving state information of the second vehicle, a traveling direction attribute of a lane in which the second vehicle is currently located, and driving intention information of the second vehicle.
  • a vehicle traveling in a straight line may have a behavior of changing lanes, then the pre-occurrence trajectory of the vehicle may be straight-line driving, Drive on the left lane and change lanes on the right.
  • the plurality of pre-occurrence trajectories of the vehicle are divided into a main driving trajectory and a secondary driving trajectory, and the occurrence probability of the main driving trajectory is higher than the occurrence probability of the secondary traveling trajectory, and the TCU can determine the main driving according to the driving state information.
  • the probability of occurrence of the trajectory and the probability of occurrence of the secondary travel trajectory are determined by the travel state information of the first vehicle, and the second pre-occurrence trajectory of the second vehicle is determined by the travel state information of the second vehicle.
  • a vehicle may have one or more secondary travel trajectories.
  • the TCU may determine the primary travel trajectory and the secondary travel trajectory according to at least one of driving state information of the vehicle, a traveling direction attribute of the current lane of the vehicle, and driving intention information of the vehicle.
  • driving state information of the vehicle For example, the property of the current lane of the vehicle is a straight lane, but the lane can be changed to the left or right. If the steering angle of the vehicle is close to zero, it is judged that the possibility of the vehicle going straight is greater, and the straight pre-occurrence trajectory is the main trajectory. The pre-occurrence trajectory of the lane change to the left or right is the secondary travel trajectory.
  • the TCU receives the information of the turn signal, and according to the steering wheel angle, it can be determined that the vehicle may have a right turn, then the right-turning pre-occurrence trajectory is the main travel trajectory, and the straight-travel and left-turn pre-occurrence trajectories are the second travel trajectory.
  • a confidence coefficient is given to the main driving trajectory and the secondary driving trajectory, wherein the confidence coefficient is used to represent the occurrence probability of the driving trajectory, and the larger the confidence coefficient is, the greater the probability of occurrence of the driving trajectory.
  • the false set letter coefficient is denoted by C.
  • a possible example is shown in Figure 4. The vehicle has three pre-occurrence trajectories.
  • One of the three pre-occurrence trajectories has one main trajectory and two secondary trajectories.
  • the main trajectory is indicated by a solid line with an arrow.
  • the travel track is indicated by a dashed line with an arrow.
  • the TCU can also analyze the driver's difference through big data, determine the driver's driving characteristics, and assist the judgment of the main driving trajectory and the secondary driving trajectory according to the driving characteristics. For example, for a vehicle driven by a driver who suddenly changes lanes, the confidence coefficient of the secondary travel trajectory to the left or right needs to be appropriately adjusted.
  • each possible pre-occurrence trajectory of the target vehicle is subjected to risk estimation, or a pre-occurrence trajectory with a large weight is selected for risk estimation.
  • the confidence coefficient of the main driving track is higher than the set first threshold, only the risk of the main driving track is evaluated; or the difference between the confidence coefficient of the main driving track and the confidence coefficient of the secondary traveling track is greater than the setting.
  • the second threshold value is only used for risk assessment of the main driving trajectory.
  • the difference between the confidence coefficient of the main driving trajectory and the confidence coefficient of the secondary driving trajectory is not greater than the set second threshold value, the possibility that the vehicle has a secondary traveling trajectory is more likely. Higher, it is more valuable to conduct a risk assessment of the secondary trajectory. At this time, not only the risk assessment of the main trajectory but also the risk assessment of the secondary trajectory is required.
  • the first threshold and the second threshold are empirical values, for example, the first threshold is 0.9 and the second threshold is 0.85.
  • the case where the first pre-occurrence trajectory in step 202 intersects the second pre-occurrence trajectory of the second vehicle may include the following:
  • the first main travel trajectory intersects with the second main travel trajectory, or the first main travel trajectory intersects the second travel trajectory, or the first travel trajectory intersects with the second main travel trajectory, or the first travel trajectory and the second travel trajectory
  • the secondary travel trajectories intersect.
  • the TCU can issue an early warning.
  • an early warning is issued when it is determined that the first main travel trajectory intersects with the second main travel trajectory or the first main travel trajectory intersects with the second travel trajectory.
  • the TCU determines the pre-occurrence trajectories of the respective vehicles, and the pre-occurrence trajectories of the respective vehicles form a network.
  • the pre-occurrence trajectory is not shown in Figure 5 for some vehicles.
  • the vehicle framed by the dotted line is the target vehicle, that is, the first vehicle.
  • the target vehicle has three pre-occurrence trajectories, the main trajectory is a forward trajectory, and the secondary trajectory is a leftward and rightward trajectory.
  • the pre-occurrence trajectory is identified by the symbol "x".
  • a position where the pre-occurrence trajectory of the second vehicle intersects with the main traveling trajectory of the first vehicle is shown in FIG. 5, and it can be seen that there are a plurality of second vehicles.
  • Figure 5 also shows the position at which the pre-occurrence trajectory of the second vehicle intersects the left trajectory of the first vehicle.
  • the TCU may include a risk level in the warning information sent. The higher the risk level, the higher the risk value, the greater the probability of collision and the more dangerous.
  • the TCU determines the risk level according to at least one of the distance from the position where the travel trajectory intersects to the distance of the first vehicle, the distance from the position where the travel trajectory intersects, the distance to the second vehicle, and the probability of occurrence of the intersecting travel trajectory.
  • the determination of the risk level may be, but is not limited to, applying the following rules.
  • intersection position is located at a risk level on the first main travel trajectory, and is higher than the risk level of the intersecting position on the first travel trajectory;
  • intersection position is located at a risk level on the second main travel trajectory, and is higher than the risk level of the intersecting position on the second travel trajectory;
  • rules 3 and 4 can also be considered:
  • the intersecting position is located at a risk level of a front section of the first main travel trajectory, and is higher than a risk level of the intersecting position at a later stage of the first main travel trajectory;
  • intersection position is located at the risk level of the front section of the first travel trajectory, and is higher than the risk level of the intersection position of the first travel trajectory;
  • the intersecting position is located at a risk level of a front section of the second main travel trajectory, and is higher than a risk level of the intersecting position at a later stage of the second main travel trajectory;
  • intersection position is located at the risk level of the front section of the second travel trajectory, and is higher than the risk level of the intersection position of the second travel trajectory;
  • the front section of the first main driving trajectory and the front section of the first driving trajectory are: a trajectory pre-occurred in the first sub-time length from the current time of the first vehicle; the rear section of the first main driving trajectory and the first driving The rear section of the trajectory is: a trajectory pre-occurred in the second sub-time length after the first sub-time length; the second main trajectory and the front trajectory of the second travel trajectory are: the second vehicle starts from the current time a pre-occurring trajectory within a sub-time; the second segment of the second main trajectory and the second trajectory of the second trajectory are: a trajectory pre-occurred within the second sub-time length of the second vehicle from the first sub-time duration, preset The duration is the sum of the first sub-time and the second sub-length.
  • the TCU segments the pre-occurrence trajectory into a front segment close to the current position and a rear segment away from the current position, or the front segment may also be Called the A segment, the latter segment can also be called the B segment.
  • the A segment and the B segment may be equal.
  • the midpoint of the entire process of the pre-occurring trajectory is used as the dividing point of the front segment and the rear segment, and the first half segment near the current position is recorded as the A segment, and the second half segment away from the current position is recorded as the B segment.
  • Sections A and B may also be unequal.
  • the TCU can analyze the driver's difference through big data, determine the driver's driving characteristics, and assist in judging the segmentation points of the A and B segments according to the driving characteristics. For example, for a vehicle driven by a driver who frequently overtakes or accelerates, the A-segment of the pre-occurring travel trajectory should be appropriately increased.
  • FIG. 6a Figure 6C, the occurrence of pre-track vehicle straight, respectively, the occurrence of the pre-turn lane changing tracks and the track according to the length of time t 1 of segmenting and t 2 are pre occurs.
  • the risk level is divided into three levels, which are ranked first, second and third according to the emergency and severity of the risk.
  • the first level indicates that there may be an urgently important risk at the intersection, and the probability is high, and immediate response measures are required;
  • the second level indicates that important risks may occur at the intersections and are highly likely to require attention and take countermeasures according to the situation
  • the third level indicates that a slight risk may occur at the intersection position or the time at which the risk is expected to occur is far from the current time, and appropriate attention is required but no countermeasures need to be taken.
  • the TCU determines the risk level based on whether the intersection location is in the A or B segment of the pre-occurring trajectory, and whether it is located in the main or secondary travel trajectory.
  • One possible way to determine is shown in Table 1.
  • the target vehicle in Table 1 is the first vehicle, and the risk vehicle is the second vehicle.
  • the first level is the first level, the second level is the second level, and the third level is the third level.
  • the risk level is set to the first level, if the intersection position is the target vehicle's main travel trajectory B and If the A segment of the main trajectory of the risk vehicle intersects, the risk level is set to the second level. If the intersection position is the intersection of the main trajectory A of the target vehicle and the B segment of the main trajectory of the risk vehicle, the risk level is determined as the first In the second level, if the intersection position is the intersection of the secondary travel trajectory A of the target vehicle and the B segment of the risk vehicle secondary travel trajectory, the risk level is determined to be the third level.
  • the “/” in Table 1 indicates that the risk level is very low and no warning information can be issued.
  • the target vehicles are A, B, C, D, E, and F are risk vehicles, and the solid line with arrows represents the pre-occurrence trajectory, where D has two pre-occurrence trajectories.
  • D1 is the secondary travel trajectory, and D2 is the main travel trajectory.
  • Each pre-occurrence trajectory is divided into two segments.
  • the risk level is determined based on the intersection location.
  • the TCU can further optimize the risk level by combining braking, acceleration, steering, and the like.
  • the warning information may include, but is not limited to, at least one of: a position of intersection, a risk level, attribute information of the risk vehicle, and a suggestion to take measures.
  • the attribute information of the risk vehicle may include a position, a heading, a speed, a model, a color, a vehicle with a special task, and the like.
  • Suggestions for taking action may include recommendations for acceleration, deceleration, lane change, etc. for the target vehicle.
  • the TCU can further issue an early warning based on the information of the traffic lights and whether the vehicle is driving in the correct lane, and give corresponding prompts or suggestions.
  • the target vehicle may have a vehicle located in the same lane as the vehicle.
  • the method of determining whether it is a risky vehicle may employ the following method.
  • the vehicle to be judged may be located in front of or behind the same lane of the target vehicle. If the distance is relatively close, the target vehicle may partially overlap with the pre-occurrence trajectory of the vehicle to be determined, and partial overlap means that there are at least two intersection points, and the present invention implements The case of the above intersection means that there is an intersection point between the two pre-occurrence trajectories, and does not include such partial overlap. Assuming that the target vehicle is still the first vehicle, the vehicle to be judged is still the second vehicle. In this scenario, that is, when it is determined that the first pre-occurrence trajectory of the first vehicle partially overlaps with the second pre-occurrence trajectory of the second vehicle, it is determined whether the second vehicle is located in the first vehicle within a preset time period from the current time.
  • the relative driving trajectory is a relative position change of the first vehicle and the second vehicle within a preset time period from the current time, that is, a change in the relative distance of the first vehicle and the second vehicle occurring within a preset time period, the relative The distance may be lengthened or shortened. If the second vehicle falls within the relative driving trajectory, the landing point may be considered as the intersection position. In this scenario, the risk level and the method for issuing the warning are the same as those in the scene where the trajectory intersects, and the repetition is no longer here. Narration. As shown in FIG.
  • the framed vehicle is the first vehicle
  • the front vehicle is the second vehicle
  • the solid line with the arrow indicates the pre-occurrence trajectory of the first vehicle and the second vehicle, respectively
  • the dotted line with the arrow indicates the preset.
  • the relative travel trajectory between the two vehicles in the duration that is, the distance between the two vehicles within the preset time length shortens the distance indicated by the relative travel trajectory.
  • Figure 8 shows that the second vehicle does not fall within the relative travel trajectory, indicating that the second vehicle is not a risky vehicle and does not need to issue an early warning. In this way, it can help to avoid invalid or false warnings issued in normal carnival scenarios.
  • the situation may be regarded as a special intersecting scene, and the intersecting position may be set.
  • the intersecting position may be set.
  • the framed vehicle is the first vehicle
  • the front vehicle is the second vehicle
  • the solid line with the arrow indicates the pre-occurrence trajectory of the first vehicle and the second vehicle, respectively
  • the symbol "X" identifies the intersecting position.
  • the method for judging the risk level and issuing the warning in this scenario may be the same as the scenario in which the trajectory intersects, and may also determine the risk level according to the relative speeds of the first vehicle and the second vehicle, combined with braking, acceleration, steering, and the like, wherein The repetitions are not repeated here.
  • the first vehicle and the second vehicle may have a relative speed, and within the preset time period, a relative displacement occurs between the first vehicle and the second vehicle, if The two vehicles have time to change lanes or turn, and the relative displacement can also be changed to relative distance or relative trajectory difference. Determining whether the second vehicle falls within the trajectory corresponding to the relative displacement within the preset time period, and if yes, determining that the second vehicle is a risky vehicle, and issuing early warning information; otherwise, determining that the second vehicle is not a risky vehicle.
  • the TCU may also determine the risk level by combining the relative speed of the second vehicle with the first vehicle, braking, acceleration, steering, and the like.
  • the embodiment of the present invention may also perform traffic warning by the target vehicle itself, and the TCU sends the determined risk vehicle information and the determined risk estimation result to the target vehicle, and the target vehicle generates the early warning information according to the received information.
  • the embodiment of the present invention determines whether the pre-occurrence trajectory of the target vehicle intersects with the pre-occurrence trajectory of the adjacent vehicle by acquiring the pre-occurrence trajectory of the target vehicle and the adjacent vehicle. If intersected, the neighboring vehicle is a risk vehicle to the target. The vehicle issued an early warning. In this way, it can help achieve more accurate and effective risk estimation and provide a more valuable safety warning for the target vehicle.
  • the method is simple and effective, and the warning information provided to the target vehicle is more targeted and more accurate, and can simplify the information processing of the vehicle terminal and reduce the terminal requirements.
  • an embodiment of the present invention further provides a traffic control unit TCU1000, which is used to perform the traffic control unit TCU1000.
  • the cellular network-based assisted driving method includes the determining unit 1001 and the early warning unit 1002. among them:
  • a determining unit 1001 configured to determine a first pre-occurrence trajectory of the first vehicle according to the driving state information of the first vehicle, where the first pre-occurring trajectory is a driving trajectory that occurs within a preset time period from the current time of the first vehicle,
  • the first pre-occurrence trajectory includes a first main travel trajectory and at least one first travel trajectory, the occurrence probability of the first main travel trajectory is greater than the occurrence probability of the first travel trajectory, the probability of occurrence of the first main travel trajectory and the first time
  • the occurrence probability of the travel track is determined by the travel state information of the first vehicle;
  • the warning unit 1002 is configured to: if it is determined that the second pre-occurrence trajectory of the second vehicle in the jurisdiction intersects with the first pre-occurrence trajectory, send an early warning message to the first vehicle, where the second pre-occurrence trajectory is the second vehicle The current trajectory occurring within the preset time period from the current time, and the second pre-occurrence trajectory of the second vehicle is determined by the traveling state information of the second vehicle.
  • the traffic control unit TCU1100 can be used to perform the traffic control unit TCU1100, as shown in FIG. A cellular-based assisted driving method.
  • the traffic control unit TCU 1100 includes a transceiver 1101 and a processor 1102.
  • a memory 1103 can be further included.
  • the processor 1102 is configured to execute code in the memory 1103. When the code is executed, the execution causes the processor 1102 to execute.
  • the processor 1102 can be a central processing unit (CPU), a network processor (NP), or a combination of a CPU and an NP.
  • CPU central processing unit
  • NP network processor
  • the processor 1102 can also further include a hardware chip.
  • the hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof.
  • the PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a general array logic (GAL), or any combination thereof.
  • the memory 1103 may include a volatile memory such as a random-access memory (RAM); the memory 1103 may also include a non-volatile memory such as a flash memory (flash) Memory), hard disk drive (HDD) or solid state drive (SSD); the memory 1103 may also include a combination of the above types of memory.
  • RAM random-access memory
  • non-volatile memory such as a flash memory (flash) Memory), hard disk drive (HDD) or solid state drive (SSD); the memory 1103 may also include a combination of the above types of memory.
  • the traffic control unit TCU1100 may be a chip or an integrated circuit when implemented.
  • Embodiments of the present invention provide a computer storage medium storing a computer program including a cellular network-based assisted driving method shown in FIG. 2.
  • Embodiments of the present invention provide a computer program product comprising instructions that, when run on a computer, cause the computer to perform the cellular network based assisted driving method illustrated in FIG. 2.
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

一种基于蜂窝网络的辅助驾驶方法及交通控制单元，用以准确评估风险并为车辆提供有价值的安全预警。该方法为：根据第一车辆的行驶状态信息确定第一车辆的第一预发生轨迹（S201），该第一预发生轨迹为第一车辆从当前时刻起在预设时长内发生的行驶轨迹；确定存在第二车辆的第二预发生轨迹与第一预发生轨迹相交时，向第一车辆发出预警信息（S202），其中，第二预发生轨迹为第二车辆从当前时刻起在预设时长内发生的行驶轨迹。

Description

一种基于蜂窝网络的辅助驾驶方法及交通控制单元 技术领域
本发明实施例涉及智能交通技术领域，尤其涉及一种基于蜂窝网络的辅助驾驶方法及交通控制单元。
背景技术
车辆与其他装置(vehicle to x，V2X)的通信技术用于实现车辆与其他目标物进行通信，例如，车车(vehicle to vehicle，V2V)通信、车路(vehicle to installation，V2I)通信、车人通信等。通过V2X技术能够获得实时路况、道路信息、行人信息，提高驾驶安全性。当前，基于专用短距离通信(dedicated short range communication，DSRC)通过自组织网络实现V2X辅助驾驶。由于基于DSRC的方案存在技术局限及部署成本高等问题，一直难以有效推广应用。
近来，基于蜂窝网络实现V2X辅助驾驶的方案越来越多，这种方案可以基于路侧交通控制单元(traffic control unit，TCU)进行风险分析和调度，相比于基于DSRC的方案更有显著的推广优势和实现优势。TCU在进行风险分析后，根据分析结果发出预警。
但是，现有的基于蜂窝网络实现V2X辅助驾驶的方案中，使用的风险分析方法过于复杂较难实现，不能有效的应用于交通预警。
发明内容
本发明实施例提供一种基于蜂窝网络的辅助驾驶方法及交通控制单元，用以在基于蜂窝网络实现V2X辅助驾驶的过程中，提供一种新的风险预估并实现交通预警的方案。
本发明实施例提供的具体技术方案如下：
第一方面，提供一种基于蜂窝网络的辅助驾驶方法，蜂窝网络至少包括交通控制单元TCU，所述TCU管辖范围内的车辆可以通过通信模块接入所述蜂窝网络。TCU通过获取到的其管辖范围内的车辆的行驶状态信息，推算车辆的预发生轨迹，通过目标车辆以及邻近车辆的预发生轨迹，确定目标车辆的预发生轨迹是否与邻近车辆的预发生轨迹相交，若相交，则邻近车辆为风险车辆，向目标车辆发出预警。这样，能够有助于实现更准确、有效的风险预估，为目标车辆提供更有价值的安全预警。该方法实现过程简单有效，给目标车辆提供的预警信息更加有针对性、更加准确，并可简化车载终端的信息处理，降低终端要求。
在一种可能的设计中，所述目标车辆记为第一车辆，邻近车辆为第二车辆，具体方法为：根据第一车辆的行驶状态信息，确定第一车辆的第一预发生轨迹，其中，所述第一预发生轨迹为所述第一车辆从当前时刻起在预设时长内发生的行驶轨迹，所述 第二预发生轨迹为所述第二车辆从所述当前时刻起在所述预设时长内发生的行驶轨迹，所述第一预发生轨迹包括第一主行驶轨迹和至少一个第一次行驶轨迹，所述第一主行驶轨迹的发生概率大于所述第一次行驶轨迹的发生概率，所述第一主行驶轨迹的发生概率和所述第一次行驶轨迹的发生概率由所述第一车辆的行驶状态信息确定，所述TCU若确定存在所述TCU管辖范围内第二车辆的第二预发生轨迹与所述第一预发生轨迹相交，则向所述第一车辆发出预警信息，其中，所述第二车辆的第二预发生轨迹由所述第二车辆的行驶状态信息确定。
在一个可能的设计中，所述预设时长根据用户的反应时间和车辆刹停时间确定。这样，能够更加有效的判断风险，预警信息更加准确。
在一种可能的设计中，所述行驶状态信息包括：位置、车头指向、方向盘转角、车速、加速度、角速度、角加速度中的至少一种。
在一种可能的设计中，所述TCU通过以下至少一种方式来获取所述第一车辆的行驶状态信息：所述第一车辆自身上报的方式、通过其他车辆传感器感知上报的方式、路侧传感器采集上报的方式、路侧信号设备上报的方式，中心服务单元或相邻TCU通知的方式。
在一种可能的设计中，所述TCU通过以下至少一种方式来获取所述第二车辆的行驶状态信息：所述第二车辆自身上报的方式、通过其他车辆传感器感知上报的方式、路侧传感器采集上报的方式、路侧信号设备上报的方式，中心服务单元或相邻TCU通知的方式。
在一种可能的设计中，所述第一预发生轨迹还可结合所述第一车辆当前所在车道的行驶方向属性和所述第一车辆的驾驶意图信息中的至少一种确定；所述第二预发生轨迹还可结合所述第二车辆当前所在车道的行驶方向属性和所述第二车辆的驾驶意图信息中的至少一种确定。所述驾驶意图信息包括：路径规划的信息、转向灯的信息、驾驶员上报的意图信息中的至少一种。这样，当判断预发生轨迹的因素越多时，获得的预发生轨迹越接近实际，进一步预估风险越准确，发出的预警信息越精确。
在一种可能的设计中，确定存在所述第二预发生轨迹与所述第一预发生轨迹相交，可能的情况包括：确定所述第一主行驶轨迹与所述第二主行驶轨迹或所述第二次行驶轨迹相交；和/或，确定所述第一次行驶轨迹与所述第二主行驶轨迹或所述第二次行驶轨迹相交。这样，能够根据风险最大化原则，防止漏掉可能性小的风险，获取车辆更多可能性的预发生轨迹，所预估的风险更全面，发出的预警信息更全面。
在一种可能的设计中，所述预警信息包括风险等级；所述TCU根据所述相交的位置至所述第一车辆的距离、所述相交的位置至所述第二车辆的距离、所述相交的行驶轨迹的发生概率中的至少一项，确定所述风险等级。
在一种可能的设计中，所述相交的位置越靠近所述第一车辆的当前位置，所述风险等级越高；所述相交的位置越靠近所述第二车辆的当前位置，所述风险等级越高；具体地，所述相交的位置位于所述第一主行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第一主行驶轨迹的后段的风险等级；所述相交的位置位于所述第一次行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第一次行驶轨迹的后段的风险等级；所述相交的位置位于所述第二主行驶轨迹的前段的风险等级，高于所述相 交的位置位于所述第二主行驶轨迹的后段的风险等级；所述相交的位置位于所述第二次行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第二次行驶轨迹的后段的风险等级；其中，所述第一主行驶轨迹的前段和所述第一次行驶轨迹的前段为：所述第一车辆从当前时刻开始在第一子时长内预发生的轨迹；所述第一主行驶轨迹的后段和所述第一次行驶轨迹的后段为：所述第一车辆从所述第一子时长后在第二子时长内预发生的轨迹；所述第二主行驶轨迹和所述第二次行驶轨迹的前段为：所述第二车辆从当前时刻开始在所述第一子时长内预发生的轨迹；所述第二主行驶轨迹的后段和所述第二次行驶轨迹的后段为：所述第二车辆从所述第一子时长后在所述第二子时长内预发生的轨迹，所述预设时长为所述第一子时长与所述第二子时长之和。所述相交的位置位于所述第一主行驶轨迹上的风险等级，高于所述相交的位置位于所述第一次行驶轨迹上的风险等级；所述相交的位置位于所述第二主行驶轨迹上的风险等级，高于所述相交的位置位于所述第二次行驶轨迹上的风险等级。这样，将风险划分不同的等级，有助于驾驶员根据风险等级作出相应的避免风险的措施，根据相交的位置对风险等级进行划分，能够简化车载终端的信息处理，处理方式更加简单有效。
在一种可能的设计中，预警信息中还可以包括：相交的位置、风险等级、风险车辆的属性信息、采取措施的建议中的至少一项。其中，风险车辆的属性信息可以包括位置、车头指向、速度、车型、颜色、是否特殊任务的车辆等。采取措施的建议可以包括对目标车辆给出的加速、减速、换道等建议。
在一种可能的设计中，所述相交包括存在一个交点；在确定所述第一预发生轨迹与所述第二预发生轨迹存在部分重叠时，判断从当前时刻起在所述预设时长内所述第二车辆是否位于所述第一车辆和所述第二车辆的相对行驶轨迹内，若是，则发出预警信息，其中，所述部分重叠是指存在至少两个交点，所述相对行驶轨迹是指从当前时刻起在所述预设时长内所述第一车辆和所述第二车辆的相对位置变化形成的轨迹。可选的，所述相对行驶轨迹的长度为所述第一车辆与所述第二车辆的相对速度和所述预设时长的乘积。这样，能够结合正常跟驰的应用场景进行处理，既保证了基本的行驶安全，又避免了过多无效的预警。
在一种可能的设计中，在同车道行驶的应用场景下，虽然第一车辆与第二车辆的预发生轨迹存在部分重叠，也可以将这种情况视为一种特殊的相交场景，而相交的位置可以设置为重叠部分线段的起点或中点。
第二方面，提供一种交通控制单元TCU，该装置具有实现上述第一方面和第一方面的任一种可能的设计中发送端行为的功能。所述功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。所述硬件或软件包括一个或多个与上述功能相对应的模块。
可选的，所述TCU可以是芯片或者集成电路。
在一个可能的设计中，当所述功能的部分或全部通过软件实现时，所述TCU包括：处理器，用于执行程序，当所述程序被执行时，所述TCU可以实现如上述第一方面和第一方面的任一种可能的设计中所述的方法。可选的，还包括存储器，用于存储所述处理器执行的程序。
可选的，上述存储器可以是物理上独立的单元，也可以与处理器集成在一起。
在一个可能的设计中，当所述功能的部分或全部通过软件实现时，所述TCU包括处理器。用于存储程序的存储器位于所述TCU之外，处理器通过电路/电线与存储器连接，用于读取并执行所述存储器中存储的程序。
第三方面，提供了一种计算机存储介质，存储有计算机程序，该计算机程序包括用于执行第一方面或第一方面的任一可能的设计中的方法的指令。
第四方面，本发明实施例提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行上述各方面所述的方法。
附图说明
图1为本发明实施例中智能交通系统结构示意图；
图2为本发明实施例中基于蜂窝网络的辅助驾驶方法流程示意图；
图3为本发明实施例中车辆的预发生轨迹形成的网络示意图；
图4为本发明实施例中主行驶轨迹和次行驶轨迹示意图；
图5为本发明实施例中预发生轨迹相交的示意图；
图6a为本发明实施例中预发生轨迹分段示意图之一；
图6b为本发明实施例中预发生轨迹分段示意图之二；
图6c为本发明实施例中预发生轨迹分段示意图之三；
图7为本发明实施例中风险等级的划分示意图；
图8为本发明实施例中跟驰场景下判断风险车辆的示意图之一；
图9为本发明实施例中跟驰场景下判断风险车辆的示意图之二；
图10为本发明实施例中交通控制单元TCU结构示意图之一；
图11为本发明实施例中交通控制单元TCU结构示意图之二。
具体实施方式
下面将结合附图，对本发明实施例进行详细描述。
本发明可应用于基于蜂窝网络实现V2X辅助驾驶领域，目前将LTE技术应用于车辆网系统中以组成车联网LTE(LTE-vehicle，LTE-V)系统，本发明实施例可以应用于LTE-V系统中，当然，还可以应用于未来第五代移动通信系统(5th-Generation，5G)或者更多其他可能的应用系统中。本发明实施例提供的基于蜂窝网络的辅助驾驶方法及装置可应用于智能交通系统中，图1是本发明实施例中一种可能的智能交通系统框架结构示意图。智能交通系统包括TCU101、车载单元(On-Board Unit，OBU)102、基站103。可选的，还包括智能交通中心服务单元(central service unit，CSU)104、信号灯/标志牌105、路侧传感器106。TCU101与OBU102之间通过接口1进行交互，接口1是OBU102与TCU101之间通信的应用层接口。TCU101与基站103之间通过接口2进行交互，接口2是TCU101与蜂窝网络之间的接口，TCU101需要利用网络的LBO能力、移动边缘计算(mobile edge computing，MEC)能力降低通信时延，以实现实时性高的防碰撞类辅助驾驶应用。TCU101会根据部署需要，连接不同的蜂窝网络的网元，不同的网元提供的接口是不同的，TCU101需要适配这些接口， 保障OBU102与TCU101之间的通信时延、可靠性和带宽。TCU101与路侧传感器106之间通过接口3进行交互，接口3是TCU101与路侧传感器106之间的接口，用于传递的感知数据可以是视频流、雷达的点云数据或者是结构化的人、车、物的数据。TCU101与信号灯/标志牌105之间通过接口4进行交互，接口4用于传递交通信号灯数据、交通标志牌数据。TCU101与CSU104之间通过接口5进行交互，CSU104可以通过TCU101发送告警、事件给OBU102。基站103和OBU102之间通过终端与蜂窝网络之间的接口进行交互，例如，基站103为长期演进(long term evolution，LTE)中的基站，则基站103可以与OBU102之间通过LTE-Uu接口进行交互。LTE-Uu接口是OBU102与TCU101之间通信的物理接入层接口，是第三代合作伙伴计划(3rd generation partnership project，3GPP)定义的终端与基站之间的接口，以LTE网络举例，LTE-Uu接口可以是终端与2G、3G、4G、5G蜂窝网络之间的接口。
其中，TCU101是智能交通系统100的核心部件，掌握V2X之间的通信数据，是部署在蜂窝网络侧的服务器，利用网络的LBO或者移动边缘计算(mobile edge computing，MEC)能力降低通信时延，其中，LBO为local break out的缩写。TCU101用于收集OBU102通过基站103发送的车辆状态信息、告警数据，并用于为目标车辆筛选风险数据、告警数据，减少V2X通信所需要的带宽需求。TCU101还用于收集路侧传感器106的感知数据、信号灯数据、标志牌数据、CSU104给OBU102的通知、告警数据，并筛选后下发给目标车辆。
OBU102可以是车机形态，也可以是T-Box与智能移动终端结合的形态。OBU102用于获取车辆行驶状态信息，并把车辆行驶状态信息通过基站103发送给TCU101。OBU102还用于接收风险数据，告警、事件、信号灯、标志牌数据，通过语音、视频提示驾驶员。本发明实施例中可以将OBU102描述为车辆，两者在本发明实施例提供的基于蜂窝网络的辅助驾驶方法实现中所起的作用相同，可以认为，车辆执行的步骤具体为OBU102执行的步骤。需要说明的是，本发明实施例虽然以车辆为例进行介绍，但所述方法适用于任何交通参与者，例如，行人、自行车、静止障碍物等。
CSU104，可以通过TCU101发送预警数据给OBU102。
信号灯/标志牌105，用于提供交通信号灯数据、交通标志牌数据给TCU101，由TCU101转发给信号灯、标志牌控制区域的车辆。
路侧传感器106，用于提供路侧感知数据给TCU101，以使得TCU101可以结合路侧感知数据分析车辆行驶中的风险。
基于图1所示的智能交通系统，下面将详细介绍一下本发明实施例提供的基于蜂窝网络的辅助驾驶方法，该蜂窝网络至少包括TCU，TCU管辖范围内的车辆可以通过通信模块接入该蜂窝网络。
需要说明的是，本发明实施例提供的基于蜂窝网络的辅助驾驶方法可以由TCU来执行，将需要进行风险预估并接收预警信息的车辆可以称之为目标车辆，也可以称为主车，以下叙述中也可以称为第一车辆。在风险预估中对目标车辆产生风险的车辆可以称之为风险车辆，以下叙述中也可以称为第二车辆。实际应用中，还可能存在两个或以上的风险车辆。
如图2所示，本发明实施例提供的基于蜂窝网络的辅助驾驶方法的具体流程可以 如下所述。
步骤201、TCU根据第一车辆的行驶状态信息，确定第一车辆的第一预发生轨迹。
步骤202、TCU若确定存在第二车辆的第二预发生轨迹与第一预发生轨迹相交，则向第一车辆发出预警信息。
具体地，本发明实施例中所述的预发生轨迹是指车辆将要发生的轨迹，可以是指车辆从当前时刻开始在预设时长内将要发生的行驶轨迹。例如，第一预发生轨迹为第一车辆从当前时刻起在预设时长内发生的行驶轨迹，第二预发生轨迹为第二车辆从当前时刻起在预设时长内发生的行驶轨迹。预设时长是一个经验值，根据驾驶员的反应时间和车辆的刹停时间来确定，例如，预设时长可以设置为5秒。
下面介绍一下TCU如何确定车辆的预发生轨迹，包括如何确定第一车辆的第一预发生轨迹，以及如何确定第二车辆的第二预发生轨迹。
实际应用中，车辆高密度的周期上报行驶状态信息，可以认为车辆实时上报行驶状态信息，行驶状态信息中包括车辆的位置、车头指向、方向盘转角、车速、加速度、角速度、角加速度中的至少一种。TCU接收到车辆上报的行驶状态信息，获取行驶状态信息中车辆当前所在的位置，根据行驶状态信息中的一些信息能够推断车辆在预设时长内可能发生的行驶轨迹。例如，确定车辆当前所在的位置，根据方向盘转角确定车辆在直线行驶，根据车速、加速度信息，根据现有的物理知识能够获得车辆在预设时长内的位移，根据位移能够大致确定车辆在预设时长内的预发生轨迹。
可选的，若TCU能够获得驾驶员的驾驶意图信息，还可以结合驾驶意图信息来确定预发生轨迹。其中，驾驶意图信息可以包括但不限于路径规划的信息、转向灯的信息、驾驶员上报的意图信息中的至少一种。具体的，TCU可以根据路径规划的信息、转向灯的信息、驾驶员上报的意图信息、红绿灯的相位信息等确定驾驶意图，在未来预设时长内车辆将要行驶的路线，该路线为直行还是左转或者右转或者掉头，这样可以有助于TCU更准确的确定预发生轨迹。其中，驾驶员上报的意图信息可以是驾驶员通过OBU或者手机等终端语音上报自身的驾驶意图。
另外，TCU还可以结合车辆当前所在车道的行驶方向属性来确定预发生轨迹。在路口处，车道具有明确的行驶方向属性，如直行车道、左/右转车道、直行+左/右转车道等，例如，当前车辆所在的车道为直行车道，TCU可以认为车辆在预设时长内将要发生的一种可能的行驶轨迹为直线。
由于车辆上报能力不同，除了车辆自身上报行驶状态信息之外，TCU还可以通过车载传感器感知上报、路侧传感器采集上报、路侧信号设备上报、CSU通知、相邻TCU通知等方式结合获取车辆的行驶状态信息及用于确定车辆预发生轨迹的其它补充信息。其中，车载传感器感知上报是指，若车载摄像头、雷达等传感器能够感知其它周围的目标物，可以将感知数据上报给TCU。、路侧信号设备可以是图1所示系统中的信号灯/标志牌105。
以上几种确定车辆的预发生轨迹的参考因素仅仅为举例，实际应用中，TCU还可以结合或者依据其它的参考因素来确定车辆的预发生轨迹。
于此，TCU可以确定管辖范围内的任意车辆的预发生轨迹，各个车辆的预发生轨迹可以形成一个网络或者一个图谱。该网络或者图谱的一种示意如图3所示，带箭头 的实线为TCU确定的车辆的预发生轨迹，可以认为不同车辆的预发生轨迹组成一个网络。从图3可以看出，两个预发生轨迹的线段可能存在交点，交点处即可以理解为轨迹相交处或轨迹交叉处，也就是说，两个车辆在此处的时空位置相交，可能会发生碰撞，据此可以进行风险评估。
对于第一车辆和第二车辆来说，第一预发生轨迹是根据第一车辆的行驶状态信息、第一车辆当前所在车道的行驶方向属性和第一车辆的驾驶意图信息中的至少一种确定，第二预发生轨迹根据第二车辆的行驶状态信息、第二车辆当前所在车道的行驶方向属性和第二车辆的驾驶意图信息中的至少一种确定。
从图3也可以看出，对于一个目标车辆，其可能的预发生轨迹可能不止一条，例如在直线行驶的车辆，可能发生换道的行为，那么该车辆的预发生轨迹可能为直线行驶、向左换道行驶、向右换道行驶几种。
具体的，按照发生概率的高低将车辆的多个预发生轨迹分为主行驶轨迹和次行驶轨迹，主行驶轨迹的发生概率高于次行驶轨迹的发生概率，TCU可以根据行驶状态信息确定主行驶轨迹的发生概率和次行驶轨迹的发生概率。例如，第一主行驶轨迹的发生概率和第一次行驶轨迹的发生概率由第一车辆的行驶状态信息确定，第二车辆的第二预发生轨迹由第二车辆的行驶状态信息确定。当然，一个车辆可能有一个或多个次行驶轨迹。TCU可以根据车辆的行驶状态信息、车辆当前所在车道的行驶方向属性和车辆的驾驶意图信息中的至少一种来确定主行驶轨迹和次行驶轨迹。例如，车辆当前所在车道的属性为直行车道，但是可以向左或向右换道，车辆的方向盘转角接近于零，则判断车辆直行的可能性更大，则直行的预发生轨迹为主行驶轨迹，向左或向右换道行驶的预发生轨迹为次行驶轨迹。又例如，TCU接收打转向灯的信息，且根据方向盘转角可以确定车辆可能要发生右转，则右转的预发生轨迹为主行驶轨迹，直行和左转的预发生轨迹为次行驶轨迹。可选的，对主行驶轨迹和次行驶轨迹赋予置信系数，该置信系数用于表征行驶轨迹的发生概率，置信系数越大表示行驶轨迹的发生概率越大。假设置信系数用C表示。一种可能的示例如图4所示，车辆有三条预发生轨迹，这三条预发生轨迹中有一条主行驶轨迹和两条次行驶轨迹，其中，主行驶轨迹用带箭头的实线表示，次行驶轨迹用带箭头的虚线表示。主行驶轨迹的置信系数C m＝0.82，向左的次行驶轨迹的置信系数C s2＝0.08，向右的次行驶轨迹的置信系数C s1＝0.10。实际应用中，TCU还可以通过大数据来分析驾驶员的差异性，确定驾驶员的驾驶特点，根据驾驶特点来辅助判断主行驶轨迹和次行驶轨迹。例如，对于经常突然变道的驾驶员所驾驶的车辆来说，向左或向右换道的次行驶轨迹的置信系数需要适当调高。
本发明实施例基于风险最大化原则，将目标车辆的每一种可能的预发生轨迹均进行风险预估，或者，选择权重较大的预发生轨迹进行风险预估。一般来说，若主行驶轨迹的置信系数高于设定的第一阈值，则仅对主行驶轨迹进行风险评估；或者，主行驶轨迹的置信系数与次行驶轨迹的置信系数之差大于设定的第二阈值，则仅对主行驶轨迹进行风险评估，主行驶轨迹的置信系数与次行驶轨迹的置信系数之差不大于设定的第二阈值时，说明车辆发生次行驶轨迹的可能性更高一点，对次行驶轨迹进行风险评估更具有价值，这时不仅对主行驶轨迹进行风险评估，还需要对次行驶轨迹进行风险评估。上述第一阈值和第二阈值为经验值，例如，第一阈值为0.9，第二阈值为0.85。
这样，步骤202中第一预发生轨迹与第二车辆的第二预发生轨迹相交的情况可能包括以下几种：
第一主行驶轨迹与第二主行驶轨迹相交，或第一主行驶轨迹与第二次行驶轨迹相交，或第一次行驶轨迹与第二主行驶轨迹相交，或第一次行驶轨迹与第二次行驶轨迹相交。
无论哪一种情况下相交，TCU均可发出预警。或者，在不对次行驶轨迹进行风险预估的场景下，当判定第一主行驶轨迹与第二主行驶轨迹相交或第一主行驶轨迹与第二次行驶轨迹相交时，发出预警。
例如，如图5所示，TCU确定各个车辆的预发生轨迹，各个车辆的预发生轨迹形成网络。图5中针对部分车辆未示出预发生轨迹。用虚线框框定的车辆为目标车辆，即为第一车辆。目标车辆有三条预发生轨迹，主行驶轨迹为向前直行的行驶轨迹，次行驶轨迹为向左和向右的行驶轨迹。如图5所示，用符号“×”标识预发生轨迹相交。图5中示出了第二车辆的预发生轨迹与第一车辆的主行驶轨迹相交的位置，并且可见，第二车辆有多个。图5还示出了第二车辆的预发生轨迹与第一车辆向左的行驶轨迹相交的位置。
图5中第一车辆的预发生轨迹和第二车辆的预发生轨迹相交的位置有多个，不同的相交位置的风险值不同。具体地，TCU在发出的预警信息中可包含风险等级。风险等级越高，说明风险值越高，发生碰撞的概率越大，越危险。
本发明实施例中，TCU根据行驶轨迹相交的位置至第一车辆的距离、行驶轨迹相交的位置至第二车辆的距离、相交的行驶轨迹的发生概率中的至少一项，确定风险等级。
具体的，对于风险等级的确定可以但不限于应用下述规则。
1、相交的位置位于第一主行驶轨迹上的风险等级，高于相交的位置位于第一次行驶轨迹上的风险等级；
2、相交的位置位于第二主行驶轨迹上的风险等级，高于相交的位置位于第二次行驶轨迹上的风险等级；
3、相交的位置越靠近第一车辆的当前位置，风险等级越高；
4、相交的位置越靠近第二车辆的当前位置，风险等级越高。
其中，规则3和4也可以认为：
相交的位置位于第一主行驶轨迹的前段的风险等级，高于相交的位置位于第一主行驶轨迹的后段的风险等级；
相交的位置位于第一次行驶轨迹的前段的风险等级，高于相交的位置位于第一次行驶轨迹的后段的风险等级；
相交的位置位于第二主行驶轨迹的前段的风险等级，高于相交的位置位于第二主行驶轨迹的后段的风险等级；
相交的位置位于第二次行驶轨迹的前段的风险等级，高于相交的位置位于第二次行驶轨迹的后段的风险等级；
其中，第一主行驶轨迹的前段和第一次行驶轨迹的前段为：第一车辆从当前时刻开始在第一子时长内预发生的轨迹；第一主行驶轨迹的后段和第一次行驶轨迹的后段 为：第一车辆从第一子时长后在第二子时长内预发生的轨迹；第二主行驶轨迹和第二次行驶轨迹的前段为：第二车辆从当前时刻开始在第一子时长内预发生的轨迹；第二主行驶轨迹的后段和第二次行驶轨迹的后段为：第二车辆从第一子时长后在第二子时长内预发生的轨迹，预设时长为第一子时长与第二子时长之和。
详细来说，为了更有助于确定风险等级，一种可能的实现方式中，TCU将预发生轨迹进行分段，分为靠近当前位置的前段和远离当前位置的后段，或者，前段也可称为A段，后段也可称为B段。A段和B段可能相等，例如，将预发生轨迹整个过程的中点作为前段和后段的划分点，将靠近当前位置的前半段记为A段，远离当前位置的后半段记为B段。A段和B段也可能不相等，例如，假设预设时长为t，将从当前时刻起t 1时长内发生的行驶轨迹记为A段，将从A段的终点起t 2时长内发生的行驶轨迹记为B段，t 1+t 2＝t。实际应用中，TCU可以通过大数据来分析驾驶员的差异性，确定驾驶员的驾驶特点，根据驾驶特点来辅助判断A段和B段的分割点。例如，对于经常超车或加速的驾驶员所驾驶的车辆来说，预发生行驶轨迹的A段要适当的加大。
假设预设时长为5秒，t 1为3秒，t 2为2秒。如图6a～图6c所示，分别将车辆直行的预发生轨迹、换道的预发生轨迹和转向的预发生轨迹按照的t 1和t 2的时长进行分段。
下面以上述对预发生轨迹进行A段和B段的分割为基础，对风险等级的确定进行具体说明。假设风险等级分为三个等级，按照风险的紧急和严重程度排序依次为第一等级、第二等级和第三等级。
第一等级，表示在相交的位置可能发生紧急重要的风险且可能性很高，需要立即采取应对措施；
第二等级，表示在相交的位置可能发生重要风险且可能性较高，需要关注并根据情况采取应对措施；
第三等级，表示在相交的位置可能发生轻度风险或者预期发生风险的时间距当前时刻较远，需要适当关注但暂且不必采取应对措施。
TCU根据相交位置位于预发生轨迹的A段还是B段，以及位于主行驶轨迹还是次行驶轨迹，来确定风险等级。一种可能的确定方式如表1所示。表1中的目标车辆即第一车辆，风险车辆即第二车辆。一级为第一等级，二级为第二等级，三级为第三等级。
表1
Figure PCTCN2018112722-appb-000001
Figure PCTCN2018112722-appb-000002
如表1中所示，若相交位置为目标车辆的主行驶轨迹A段与风险车辆的A段相交，则将风险等级定为第一等级，若相交位置为目标车辆的主行驶轨迹B段与风险车辆主行驶轨迹的A段相交，则将风险等级定为第二等级，若相交位置为目标车辆的主行驶轨迹A段与风险车辆主行驶轨迹的B段相交，则将风险等级定为第二等级，若相交位置为目标车辆的次行驶轨迹A段与风险车辆次行驶轨迹的B段相交，则将风险等级定为第三等级。表1中“/”表征风险等级很低，可以不发出预警信息。
下面通过图7来示意上述风险等级的划分，目标车辆为A，B、C、D、E、F均为风险车辆，带箭头的实线表征预发生轨迹，其中D有两条预发生轨迹，D1为次行驶轨迹，D2为主行驶轨迹。每条预发生轨迹分为两段。根据相交位置确定风险等级。
当然，以上风险等级的确定仅是一种举例，实际应用中，TCU还可以结合刹车、加速、转向等时间进一步优化风险等级。
预警信息中可以但不限于包括：相交的位置、风险等级、风险车辆的属性信息、采取措施的建议中的至少一项。其中，风险车辆的属性信息可以包括位置、车头指向、速度、车型、颜色、是否特殊任务的车辆等。采取措施的建议可以包括对目标车辆给出的加速、减速、换道等建议。另外，在路口处，TCU还可以进一步根据红绿灯信息以及车辆是否行驶在正确的车道上的信息，发出预警，给出相应的提示或建议。
另外，除上述情况外，目标车辆可能存在与其位于同车道的车辆，对于这种车辆来说，判断其是否为风险车辆的方法可以可以采用下述方法。
待判断的车辆可能位于目标车辆同车道的前方或者后方，若距离较近，则目标车辆与待判断的车辆的预发生轨迹可能存在部分重叠，部分重叠是指存在至少两个交点，本发明实施例上述相交的情况是指两个预发生轨迹存在一个交点，并不包括这种部分重叠的情况。假设目标车辆仍为第一车辆，待判断的车辆仍为第二车辆。在这种场景下，即确定第一车辆的第一预发生轨迹与第二车辆的第二预发生轨迹存在部分重叠时，判断从当前时刻起在预设时长内第二车辆是否位于第一车辆和第二车辆的相对行驶轨迹内，若是，则确定第二车辆为风险车辆，发出预警信息，否则，确定第二车辆不是风险车辆。其中，相对行驶轨迹为从当前时刻起在预设时长内第一车辆和第二车辆发生的相对位置变化，即第一车辆和第二车辆在预设时长内发生的相对距离的变化，该相对距离可能拉长，也可能缩短。若第二车辆落在了相对行驶轨迹内，则落点可能认为是上述相交位置，这种场景下判断风险等级以及发出预警的方法与上述轨迹相交的场景下相同，重复之处在此不再赘述。如图8所示，虚线框所框车辆为第一车辆，前方车辆为第二车辆，带箭头的实线分别表示第一车辆和第二车辆的预发生轨迹，带箭头的虚线表示在预设时长内两车之间的相对行驶轨迹，即在预设时长内两车的距离缩短了相对行驶轨迹表示的距离。图8可见第二车辆未落入相对行驶轨迹内，表明第二车辆非风险车辆，无需发出预警。这样，能够有助于避免在正常跟驰场景下发出的无效或错误预警。
可选的，在同车道行驶的应用场景下，虽然第一车辆与第二车辆的预发生轨迹存在部分重叠，也可以将这种情况视为一种特殊的相交场景，而相交的位置可以设置为重叠部分线段的起点或中点。如图9所示，虚线框所框车辆为第一车辆，前方车辆为 第二车辆，带箭头的实线分别表示第一车辆和第二车辆的预发生轨迹，符号“×”标识相交的位置。这种场景下判断风险等级以及发出预警的方法可以与上述轨迹相交的场景下相同，也可以根据第一车辆与第二车辆的相对速度、结合刹车、加速、转向等事件来确定风险等级，其中，重复之处在此不再赘述。
具体来说，若第一车辆和第二车辆在同车道行驶，则第一车辆和第二车辆会存在相对速度，在预设时长内，第一车辆和第二车辆会发生一段相对位移，若两车存在换道或转向的时间，相对位移也可以更换为相对路程或者相对轨迹差。判断第二车辆在预设时长内是否落在相对位移对应的轨迹线内，若是，则确定第二车辆为风险车辆，发出预警信息，否则，确定第二车辆不是风险车辆。
进一步的，在进行风险分析时，TCU还可以结合第二车辆与第一车辆的相对速度、刹车、加速、转向等上报信息来确定风险等级。
这样，有助于避免在正常跟驰场景下发出无效的预警信息。
另外，本发明实施例还可以由目标车辆自身来进行交通预警，TCU将确定的风险车辆的信息和确定的风险预估结果发送给目标车辆，由目标车辆根据接收到的信息生成预警信息。
综上，本发明实施例通过对目标车辆以及邻近车辆的预发生轨迹的获取，确定目标车辆的预发生轨迹是否与邻近车辆的预发生轨迹相交，若相交，则邻近车辆为风险车辆，向目标车辆发出预警。这样，能够有助于实现更准确、有效的风险预估，为目标车辆提供更有价值的安全预警。该方法实现过程简单有效，给目标车辆提供的预警信息更加有针对性、更加准确，并可简化车载终端的信息处理，降低终端要求。
基于与图2所示的基于蜂窝网络的辅助驾驶方法的同一发明构思，如图10所示，本发明实施例还提供一种交通控制单元TCU1000，该交通控制单元TCU1000用于执行图2所示的基于蜂窝网络的辅助驾驶方法，该交通控制单元TCU1000包括确定单元1001和预警单元1002。其中：
确定单元1001，用于根据第一车辆的行驶状态信息确定第一车辆的第一预发生轨迹，其中，第一预发生轨迹为第一车辆从当前时刻起在预设时长内发生的行驶轨迹，第一预发生轨迹包括第一主行驶轨迹和至少一个第一次行驶轨迹，第一主行驶轨迹的发生概率大于第一次行驶轨迹的发生概率，第一主行驶轨迹的发生概率和第一次行驶轨迹的发生概率由第一车辆的行驶状态信息确定；
预警单元1002，用于若确定存在管辖范围内第二车辆的第二预发生轨迹与第一预发生轨迹相交，则向第一车辆发出预警信息，其中，第二预发生轨迹为第二车辆从当前时刻起在预设时长内发生的行驶轨迹，第二车辆的第二预发生轨迹由第二车辆的行驶状态信息确定。
该交通控制单元TCU1000所执行方法的各细节与上述方法实施例中相同，重复之处在此不再赘述。
基于与图2所示的基于蜂窝网络的辅助驾驶方法的同一发明构思，如图11所示，本发明实施例还提供一种交通控制单元TCU1100，该交通控制单元TCU1100可用于执行图2所示的基于蜂窝网络的辅助驾驶方法。其中，交通控制单元TCU1100包括收发器1101和处理器1102，可选的，还可以包括存储器1103，处理器1102用于执行存 储器1103中的代码，当代码被执行时，该执行使得处理器1102执行图2所示的基于蜂窝网络的辅助驾驶方法。
处理器1102可以是中央处理器(central processing unit，CPU)，网络处理器(network processor，NP)或者CPU和NP的组合。
处理器1102还可以进一步包括硬件芯片。上述硬件芯片可以是专用集成电路(application-specific integrated circuit，ASIC)，可编程逻辑器件(programmable logic device，PLD)或其组合。上述PLD可以是复杂可编程逻辑器件(complex programmable logic device，CPLD)，现场可编程逻辑门阵列(field-programmable gate array，FPGA)，通用阵列逻辑(generic array logic,GAL)或其任意组合。
存储器1103可以包括易失性存储器(volatile memory)，例如随机存取存储器(random-access memory，RAM)；存储器1103也可以包括非易失性存储器(non-volatile memory)，例如快闪存储器(flash memory)，硬盘(hard disk drive，HDD)或固态硬盘(solid-state drive，SSD)；存储器1103还可以包括上述种类的存储器的组合。
可选的，交通控制单元TCU1100在具体实现时可以是芯片或者集成电路。
本发明实施例提供了一种计算机存储介质，存储有计算机程序，该计算机程序包括用于执行图2所示的基于蜂窝网络的辅助驾驶方法。
本发明实施例提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行图2所示的基于蜂窝网络的辅助驾驶方法。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
尽管已描述了本发明的优选实施例，但本领域内的技术人员一旦得知了基本创造 性概念，则可对这些实施例作出另外的变更和修改。所以，所附权利要求意欲解释为包括优选实施例以及落入本发明范围的所有变更和修改。
显然，本领域的技术人员可以对本发明实施例进行各种改动和变型而不脱离本发明实施例的精神和范围。这样，倘若本发明实施例的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。

Claims (16)

  1. 一种基于蜂窝网络的辅助驾驶方法，，其特征在于，包括：
    交通控制单元TCU根据所述TCU管辖范围内的第一车辆的行驶状态信息，确定所述第一车辆的第一预发生轨迹，其中，所述第一预发生轨迹为所述第一车辆从当前时刻起在预设时长内发生的行驶轨迹，所述第一预发生轨迹包括第一主行驶轨迹和至少一个第一次行驶轨迹，所述第一主行驶轨迹的发生概率大于所述第一次行驶轨迹的发生概率，所述第一主行驶轨迹的发生概率和所述第一次行驶轨迹的发生概率由所述第一车辆的行驶状态信息确定；
    所述TCU若确定存在所述TCU管辖范围内第二车辆的第二预发生轨迹与所述第一预发生轨迹相交，则向所述第一车辆发出预警信息，其中，所述第二预发生轨迹为所述第二车辆从所述当前时刻起在所述预设时长内发生的行驶轨迹，所述第二车辆的第二预发生轨迹由所述第二车辆的行驶状态信息确定。
  2. 如权利要求1所述的方法，其特征在于，所述行驶状态信息包括：位置、车头指向、方向盘转角、车速、加速度、角速度、角加速度中的至少一种。
  3. 如权利要求1或2所述的方法，其特征在于，所述第二预发生轨迹包括第二主行驶轨迹和至少一个第二次行驶轨迹，所述第二主行驶轨迹的发生概率大于所述第二次行驶轨迹的发生概率，第二主行驶轨迹的发生概率和第二次行驶轨迹的发生概率由所述第二车辆的行驶状态信息确定；
    确定存在所述第二预发生轨迹与所述第一预发生轨迹相交，包括以下至少一种：
    确定存在所述第二主行驶轨迹与所述第一主行驶轨迹相交，或确定存在所述第二次行驶轨迹与所述第一主行驶轨迹相交；
    确定存在所述第二主行驶轨迹与所述第一次行驶轨迹相交，或确定存在所述第二次行驶轨迹与所述第二主行驶轨迹相交。
  4. 如权利要求1～3任一项所述的方法，其特征在于，所述预警信息包括风险等级；
    所述方法还包括：
    所述TCU根据所述相交的位置至所述第一车辆的距离、所述相交的位置至所述第二车辆的距离、所述相交的行驶轨迹的发生概率中的至少一项，确定所述风险等级。
  5. 如权利要求4所述的方法，其特征在于，
    所述相交的位置位于所述第一主行驶轨迹上的风险等级，高于所述相交的位置位于所述第一次行驶轨迹上的风险等级；
    所述相交的位置位于所述第二主行驶轨迹上的风险等级，高于所述相交的位置位于所述第二次行驶轨迹上的风险等级；
    所述相交的位置位于所述第一主行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第一主行驶轨迹的后段的风险等级；
    所述相交的位置位于所述第一次行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第一次行驶轨迹的后段的风险等级；
    所述相交的位置位于所述第二主行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第二主行驶轨迹的后段的风险等级；
    所述相交的位置位于所述第二次行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第二次行驶轨迹的后段的风险等级；
    其中，所述第一主行驶轨迹的前段和所述第一次行驶轨迹的前段为：所述第一车辆从当前时刻开始在第一子时长内预发生的轨迹；所述第一主行驶轨迹的后段和所述第一次行驶轨迹的后段为：所述第一车辆从所述第一子时长后在第二子时长内预发生的轨迹；所述第二主行驶轨迹和所述第二次行驶轨迹的前段为：所述第二车辆从当前时刻开始在所述第一子时长内预发生的轨迹；所述第二主行驶轨迹的后段和所述第二次行驶轨迹的后段为：所述第二车辆从所述第一子时长后在所述第二子时长内预发生的轨迹，所述预设时长为所述第一子时长与所述第二子时长之和。
  6. 如权利要求1～5任一项所述的方法，其特征在于，所述相交包括存在一个交点；
    所述方法还包括：
    确定所述第一预发生轨迹与所述第二预发生轨迹存在部分重叠时，判断从当前时刻起在所述预设时长内所述第二车辆是否位于所述第一车辆和所述第二车辆的相对行驶轨迹内，若是，则发出预警信息；
    其中，所述部分重叠是指存在至少两个交点，所述相对行驶轨迹是指从当前时刻起在所述预设时长内所述第一车辆和所述第二车辆的相对位置变化形成的轨迹。
  7. 如权利要求1～6任一项所述的方法，其特征在于，所述方法还包括：
    所述TCU通过以下至少一种方式来获取所述第一车辆的行驶状态信息：所述第一车辆自身上报的方式、通过其他车辆传感器感知上报的方式、路侧传感器采集上报的方式、路侧信号设备上报的方式，中心服务单元或相邻TCU通知的方式。
  8. 一种交通控制单元TCU，其特征在于，所述TCU包括：
    确定单元，用于根据管辖范围内的第一车辆的行驶状态信息，确定所述第一车辆的第一预发生轨迹，其中，所述第一预发生轨迹为所述第一车辆从当前时刻起在预设时长内发生的行驶轨迹，所述第一预发生轨迹包括第一主行驶轨迹和至少一个第一次行驶轨迹，所述第一主行驶轨迹的发生概率大于所述第一次行驶轨迹的发生概率，所述第一主行驶轨迹的发生概率和所述第一次行驶轨迹的发生概率由所述第一车辆的行驶状态信息确定；
    预警单元，用于若确定存在管辖范围内第二车辆的第二预发生轨迹与所述第一预发生轨迹相交，则向所述第一车辆发出预警信息，其中，所述第二预发生轨迹为所述第二车辆从所述当前时刻起在所述预设时长内发生的行驶轨迹，所述第二车辆的第二预发生轨迹由所述第二车辆的行驶状态信息确定。
  9. 如权利要求8所述的TCU，其特征在于，所述行驶状态信息包括：位置、车头指向、方向盘转角、车速、加速度、角速度、角加速度中的至少一种。
  10. 如权利要求8或9所述的TCU，其特征在于，所述第二预发生轨迹包括第二主行驶轨迹和至少一个第二次行驶轨迹，所述第二主行驶轨迹的发生概率大于所述第二次行驶轨迹的发生概率，第二主行驶轨迹的发生概率和第二次行驶轨迹的发生概率由所述第二车辆的行驶状态信息确定；
    所述预警单元在确定存在所述第二预发生轨迹与所述第一预发生轨迹相交时，所述预警单元具体用于：
    确定存在所述第二预发生轨迹与所述第一预发生轨迹相交，包括以下至少一种：
    确定存在所述第二主行驶轨迹与所述第一主行驶轨迹相交，或确定存在所述第二次行驶轨迹与所述第一主行驶轨迹相交；
    确定存在所述第二主行驶轨迹与所述第一次行驶轨迹相交，或确定存在所述第二次行驶轨迹与所述第二主行驶轨迹相交。
  11. 如权利要求8～10任一项所述的TCU，其特征在于，所述预警信息包括风险等级；
    所述预警单元还用于：根据所述相交的位置至所述第一车辆的距离、所述相交的位置至所述第二车辆的距离、所述相交的行驶轨迹的发生概率中的至少一项，确定所述风险等级。
  12. 如权利要求11所述的TCU，其特征在于，
    所述相交的位置位于所述第一主行驶轨迹上的风险等级，高于所述相交的位置位于所述第一次行驶轨迹上的风险等级；
    所述相交的位置位于所述第二主行驶轨迹上的风险等级，高于所述相交的位置位于所述第二次行驶轨迹上的风险等级；
    所述相交的位置位于所述第一主行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第一主行驶轨迹的后段的风险等级；
    所述相交的位置位于所述第一次行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第一次行驶轨迹的后段的风险等级；
    所述相交的位置位于所述第二主行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第二主行驶轨迹的后段的风险等级；
    所述相交的位置位于所述第二次行驶轨迹的前段的风险等级，高于所述相交的位置位于所述第二次行驶轨迹的后段的风险等级；
    其中，所述第一主行驶轨迹的前段和所述第一次行驶轨迹的前段为：所述第一车辆从当前时刻开始在第一子时长内预发生的轨迹；所述第一主行驶轨迹的后段和所述第一次行驶轨迹的后段为：所述第一车辆从所述第一子时长后在第二子时长内预发生的轨迹；所述第二主行驶轨迹和所述第二次行驶轨迹的前段为：所述第二车辆从当前时刻开始在所述第一子时长内预发生的轨迹；所述第二主行驶轨迹的后段和所述第二次行驶轨迹的后段为：所述第二车辆从所述第一子时长后在所述第二子时长内预发生的轨迹，所述预设时长为所述第一子时长与所述第二子时长之和。
  13. 如权利要求8～12任一项所述的TCU，其特征在于，所述相交包括存在一个交点；
    所述预警单元还用于：
    确定所述第一预发生轨迹与所述第二预发生轨迹存在部分重叠时，判断从当前时刻起在所述预设时长内所述第二车辆是否位于所述第一车辆和所述第二车辆的相对行驶轨迹内，若是，则发出预警信息；
    其中，所述部分重叠是指存在至少两个交点，所述相对行驶轨迹是指从当前时刻起在所述预设时长内所述第一车辆和所述第二车辆的相对位置变化形成的轨迹。
  14. 如权利要求8～13任一项所述的TCU，其特征在于，所述确定单元还用于：
    通过以下至少一种方式来获取所述第一车辆的行驶状态信息：所述第一车辆自身上报的方式、通过其他车辆传感器感知上报的方式、路侧传感器采集上报的方式、路侧信号设备上报的方式，中心服务单元或相邻TCU通知的方式。
  15. 一种交通控制单元TCU，其特征在于，包括处理器和收发器，所述处理器用于执行一组代码，当所述代码被执行时，使得所述处理器执行如权利要求1～7任一项所述的方法。
  16. 一种计算机存储介质，其特征在于，所述计算机存储介质存储有计算机程序，所述计算机程序包括用于执行如权利要求1～7任一项所述方法的指令。
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