WO2019178907A1 - 一种用于智能网联车的平行遥控驾驶系统 - Google Patents
一种用于智能网联车的平行遥控驾驶系统 Download PDFInfo
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- 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
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- G05D1/0038—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement by providing the operator with simple or augmented images from one or more cameras located onboard the vehicle, e.g. tele-operation
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Definitions
- the present invention relates to the field of intelligent networked driving technology, and in particular to a parallel remote driving system for an intelligent networked vehicle.
- intelligent network driving technology can improve the safety and traffic efficiency of road traffic, and greatly change the way people live, this technology will become one of the most important advanced technologies in the next few years.
- the vehicle driving process needs to configure the human driver in the vehicle to monitor the vehicle state at all times to supervise the vehicle operation. Emergency takeover.
- the current driving control method of the intelligent networked vehicle is not conducive to the improvement of the intelligent network connection technology and the popularity of the intelligent networked vehicle.
- the present invention provides an intelligent network car control device, and the intelligent network car control device includes:
- a first communication module for performing data communication with the parallel driving control device, capable of transmitting driving data of the intelligent networked vehicle to the parallel driving control device, and capable of receiving a driving mode signal transmitted by the parallel driving control device ;
- a mode switching module which is connected to the vehicle control system of the intelligent network vehicle and the first communication module, configured to determine a driving mode for the intelligent networked vehicle according to the driving mode signal, so that the vehicle
- the control system is capable of controlling the driving state of the intelligent networked vehicle based on the respective vehicle control signals.
- the driving mode configured by the mode switching module includes an automatic driving mode and a remote driving mode, wherein the remote driving mode has a higher priority than the automatic driving mode.
- the vehicle control system controls the driving state of the intelligent networked vehicle according to a vehicle control signal generated by itself.
- the vehicle control system controls the driving state of the intelligent networked vehicle according to the vehicle control signal transmitted by the parallel driving control device.
- the mode switching module when receiving the remote driving signal transmitted by the parallel driving control device, the mode switching module will control the mode. Switch to remote control mode.
- the mode switching module is configured to generate a remote driving takeover request signal, and pass the remote driving takeover request signal through the first
- the communication module is transmitted to the parallel driving control device.
- the present invention also provides a parallel driving control device for communicating with an intelligent network car control device and a remote control driving device, configured to receive the intelligent network car control device transmission After the remote control driving takes over the request signal, generating a remote driving request signal and transmitting the remote driving request signal to the remote driving device;
- the parallel driving control device is further configured to receive a driving mode signal generated by the remote driving device in response to the remote driving request signal, and transmit the driving mode signal and the vehicle control signal to the intelligent network car to Determining a driving mode for the intelligent networked vehicle based on the driving mode signal by the intelligent networked vehicle.
- the parallel driving control device comprises:
- a second communication module configured to be connected to the intelligent network car control device, capable of implementing data communication between the parallel driving control device and the intelligent network car control device;
- a virtual system module configured to receive the remote driving takeover request signal transmitted by the second communication module, generate a remote driving request signal according to the remote driving takeover request signal, and generate the remote control
- the driving request signal is transmitted to the remote driving device, and is further configured to transmit a driving mode signal transmitted by the remote driving device to the second communication module.
- the virtual system module is further configured to acquire, by the second communication module, driving data of the intelligent network car transmitted by the intelligent network car control device, and according to the driving data Determining a driving state of the intelligent network car, wherein
- the virtual system module is configured to actively generate a remote driving request signal and transmit the remote driving request signal to the remote driving device.
- the virtual system module comprises:
- a virtual scene unit for constructing a virtual traffic environment and traffic flow
- a virtual interaction unit configured to be connected to the virtual scene unit, configured to construct a virtual vehicle in the virtual traffic environment, and use the virtual vehicle to map the intelligent network connection according to the acquired driving data of the intelligent networked vehicle
- the driving state of the vehicle is monitored by comparing and analyzing the traveling state of the intelligent networked vehicle and the preset reference driving state.
- the virtual system module further includes:
- a virtual simulation unit configured to be connected to the virtual interaction unit, configured to detect a related control algorithm of the intelligent networked vehicle according to the mapped driving state of the intelligent networked vehicle.
- the virtual system module is further configured to generate, according to the driving state of the intelligent networked vehicle, simulated state information for a specified virtual scene in a future specified time period, and generate an optimal according to the simulated state information. Forecasting the planning information, and transmitting the optimal prediction decision planning information to the intelligent network car through the second communication module.
- the parallel driving control device further comprises:
- a cloud platform connected to the second communication module and communicatively coupled to the intelligent network car control device, wherein the second communication module performs data communication with the intelligent network car control device through the cloud platform.
- the virtual interaction unit and/or the virtual simulation unit are integrated in the cloud platform.
- the parallel driving control device further comprises:
- a video module which is connected to the cloud platform, and the cloud platform is configured to receive traveling video information about the smart network car transmitted by the intelligent network car control device, where the video module is used to The cloud platform downloads the travel video information and transmits the travel video information to the remote control driving device.
- the present invention also provides a remote control driving device, the remote control driving device comprising:
- a driving simulator which is communicably connected to the parallel driving control device, configured to generate a remote driving driving signal according to the remote driving driving signal transmitted by the parallel driving control device, and generate a driving mode signal and a vehicle control according to a user operation
- the signal is transmitted to the intelligent network car control device through the parallel driving control device to realize remote control of the intelligent network car.
- the driving simulator includes a display screen and an operation portion for visually displaying related data transmitted by the parallel driving control device, and the operation portion is configured to be based on a user
- the operation generates a corresponding driving mode signal and a vehicle control signal and transmits the vehicle control signal to the parallel driving control device.
- the present invention also provides a parallel remote control driving system for an intelligent networked vehicle, the system comprising:
- the intelligent networked vehicle control device according to any of the preceding claims; the parallel driving control device according to any of the above; and the remotely controlled driving device as described above.
- the parallel remote driving system for the intelligent networked vehicle provided by the invention makes it unnecessary for the intelligent networked vehicle to be equipped with a human driver when the vehicle is on the road, so that the labor cost and technical requirements such as training for the driver can be significantly reduced.
- the safety cost is conducive to the promotion of intelligent networked vehicles.
- the system can realize multi-vehicle coordination, so that one driver in the control center can remotely control multiple intelligent network vehicles, thereby significantly reducing labor costs and improving intelligence.
- the management efficiency of the networked car can realize multi-vehicle coordination, so that one driver in the control center can remotely control multiple intelligent network vehicles, thereby significantly reducing labor costs and improving intelligence.
- the parallel driving control device can simultaneously supervise and guide the operation of the intelligent network car in the real traffic environment.
- the parallel driving control device can actively take over when the vehicle is abnormal when the intelligent network car does not actively request to take over, which can effectively improve traffic safety.
- the parallel driving control device can also combine the big data analysis technology to analyze the data and traffic flow information of the intelligent network connected vehicle accumulated in the current environment, and combine the real-time status information of the real intelligent networked vehicle to predict the real car in the future.
- the actual state guides the operation of the smart car in real time.
- the data obtained by the parallel driving control device can also be used to optimize the relevant algorithms offline, thereby effectively promoting the development of intelligent networked vehicles.
- FIG. 1 is a schematic structural view of a parallel remote control driving system for an intelligent networked vehicle according to an embodiment of the present invention
- FIG. 2 is a schematic diagram showing the data flow of a parallel remote control driving system of an intelligent networked vehicle according to an embodiment of the present invention
- FIG. 3 is a schematic structural diagram of a virtual system module according to an embodiment of the present invention.
- FIG. 4 is a flow chart showing the operation of a parallel remote control driving system in accordance with one embodiment of the present invention.
- the current driving control method has high requirements for human driver and system development.
- the current driving control method requires a human driver to master a wealth of smart car technology points, so that it can monitor the driving situation of the smart car at all times, and also need to be able to detect potential safety hazards in time and take over; current driving control methods It is necessary for human drivers to be familiar with the switching and taking-over modes of different driving modes. However, in some sudden traffic situations, it is difficult to achieve smooth takeover and transition of vehicle control.
- the current driving control methods for intelligently driven vehicles focus on manual intervention after vehicle failure, and can not provide assistance and guidance for intelligent vehicle driving in combination with massive traffic data.
- the present invention proposes a new intelligent networked vehicle control system, that is, a parallel remote control driving system, which can allow a human driver to control the driving of an intelligent networked vehicle in an intelligent networked vehicle, but One or more intelligent networked vehicles can be controlled at a remote location (eg, within a control center) by parallel remote driving.
- FIG. 1 is a schematic structural diagram of a parallel remote driving system for an intelligent networked vehicle provided by the embodiment
- FIG. 2 is a schematic diagram showing a data flow of the parallel remote driving system.
- the parallel remote control driving system for the intelligent networked vehicle includes: an intelligent network connected vehicle control device 101, a parallel driving control device 102, and a remote control driving device 103.
- the intelligent network car control device 101 is preferably disposed on the intelligent network car and communicatively coupled to the vehicle control system 104 of the intelligent network car.
- the intelligent network car control device 101 can acquire real-time information of three dimensions of the vehicle-road-person-personal relationship such as the intelligent networked vehicle, the road, the traffic flow, and the driving data of the intelligent networked vehicle through the vehicle control system 104. And transmitting the information to the parallel driving control device 102 communicatively coupled thereto.
- the intelligent network car control device 101 preferably includes: a first communication module 101a and a mode switching module 101b.
- the first communication module 101a is communicably connected to the parallel driving control device 102, and can transmit the driving data of the intelligent networked vehicle to the parallel driving control device 102, and can also receive the driving mode signal transmitted by the parallel driving control device 102, and the like. data.
- the mode switching module 101b is connected to the vehicle control system of the intelligent network car and the first communication module 101a, and is capable of determining a driving mode for the smart network car according to the driving mode signal transmitted by the first communication module 101a, thereby making the smart
- the vehicle control system of the networked vehicle controls the driving state of the intelligent networked vehicle based on the corresponding vehicle control signal.
- the driving mode configured by the mode switching module preferably includes an automatic driving mode and a remote driving mode.
- the vehicle control system controls the driving state of the intelligent networked vehicle according to the vehicle control signal generated by itself (ie, the vehicle control system is configured according to itself)
- the intelligent driving control algorithm performs autonomous decision-making and control according to its own sensing, decision-making unit, etc., thereby autonomously controlling the driving state of the intelligent networked vehicle.
- the vehicle control system will associate the smart network vehicle according to the vehicle control signal transmitted by the parallel driving control device 102 received by the first communication module 101a.
- the driving state is controlled.
- the driving mode configured by the mode switching module may further include other reasonable driving modes, and the present invention is not limited thereto.
- the driving mode configured by the mode switching module may further include a manual driving mode, wherein the activation of the manual driving mode requires the presence of an intelligent networked vehicle in the human driver, and the manual driving mode is prioritized.
- the level is preferably higher than the remote control driving mode. If the current control mode of the mode switching module 101b is the manual driving mode, the driving state of the intelligent networked vehicle is also controlled by the human driver in the vehicle.
- the priority of the remote driving mode is higher than that of the automatic driving mode. Specifically, if the current control mode of the mode switching module 101b is the automatic driving mode, when the remote driving signal transmitted by the parallel driving control device 102 is received through the first communication module 101a, the mode switching module 101b will automatically control the mode. The driving mode is switched to the remote driving mode.
- functional modules such as perceptual positioning, decision planning, vehicle control, and vehicle body hardware are provided in the intelligent networked vehicle.
- the sensory positioning function module on the intelligent networked vehicle can sense the environmental information through devices such as laser radar, millimeter wave radar and camera, and can also obtain the position information of the vehicle through the combined navigation of the inertial navigation instrument and the GPS device.
- the vehicle control system of the intelligent networked vehicle can perform decision planning according to the environment sensing information and the positioning information acquired by the sensing positioning function module. When the sensor information is normal and the resolved motion planning problem can be resolved, the vehicle control system transmits vehicle control signals (eg, vehicle longitudinal control signals and/or vehicle lateral control signals, etc.) to the mode switching module 101b.
- vehicle control signals eg, vehicle longitudinal control signals and/or vehicle lateral control signals, etc.
- the intelligent network car control device 101 may further include other reasonable functional modules according to actual needs, and the present invention is not limited thereto.
- the intelligent network car control device 101 may further include an emergency stop module, a human-machine interaction module, and/or an in-vehicle video module.
- the emergency stop module is used for electronic emergency braking of the intelligent network connection vehicle, thereby controlling the intelligent network connection vehicle to quickly stop.
- the parallel driving control device 102 detects that the intelligent networked vehicle is in a very dangerous situation (for example, there is an obstacle in front of the vehicle) by monitoring the traveling state of the intelligent networked vehicle, the parallel driving control device 102 will control
- the emergency stop module works directly to achieve emergency braking of the vehicle, thereby avoiding traffic accidents.
- the human-computer interaction module can display the sensing, positioning and driving mode types of the intelligent networked vehicle and the information of various devices, thereby providing auxiliary information and interactive functions for drivers in different driving modes.
- the vehicle video module can record and return the vehicle road environment of the driver's perspective, the vehicle dashboard and the status information video of the HMI device in real time through the vehicle camera.
- the mode switching module 101b is capable of simultaneously receiving vehicle control signals from the vehicle control system 104 of the intelligent networked vehicle and the parallel driving control device 102. The mode switching module 101b then causes the vehicle control system to control the driving state of the intelligent networked vehicle based on the corresponding vehicle control signal according to the received driving mode signal.
- the mode switching module 101b will cause the vehicle control system to drive the intelligent networked vehicle based on the vehicle control signal transmitted by the parallel driving control device 102.
- the state is controlled; and if the driving mode signal received by the mode switching module 101b is a smart driving signal, the mode switching module 101b will cause the vehicle control system to act on the intelligent networked vehicle based on the vehicle control signal generated by the vehicle control system itself.
- the driving state is controlled.
- the mode switching module 101b when there is a first type of abnormality in the intelligent networked vehicle (for example, when the intelligent networked vehicle has limited presence or internal fault), the mode switching module 101b generates a remote control.
- the driver takes over the request signal and transmits the remote driving takeover request signal to the parallel driving control device 102 through the first communication module 101a.
- the parallel driving control device 102 is communicably connected to the intelligent network car control device 101 and the remote control driving device 103, and can generate a remote control when receiving the remote driving takeover request signal transmitted by the intelligent network car control device 101.
- the driving request signal is transmitted to the remote driving device 103.
- the parallel driving control device 102 preferably The travel data of the received intelligent network car is transmitted to the remote control driving device 103 in real time.
- the remote control driving device 103 preferably includes a driving simulator. After receiving the remote driving driving signal sent by the intelligent network vehicle control device 101, the driving simulator generates a corresponding remote control according to the remote driving driving signal. Driving an indication signal to prompt the human driver to respond to the remote control driving request signal. When the human driver responds to the remote driving request signal (for example, agreeing to remote driving), the remote driving device 103 generates a driving mode signal and generates a corresponding vehicle control signal according to the actual operation of the user (ie, the remote driving request signal is performed). Taking over the feedback), the remote control driving device 103 then transmits the above driving mode signal and the vehicle control signal to the intelligent network connection control device 101 through the parallel driving control device 102 to realize remote control of the intelligent networked vehicle.
- the driving simulator After receiving the remote driving driving signal sent by the intelligent network vehicle control device 101, the driving simulator generates a corresponding remote control according to the remote driving driving signal. Driving an indication signal to prompt the human driver to respond to the remote control driving request signal.
- the remote control driving device 103 preferably includes a display screen and an operation portion.
- the display screen can visually display related data transmitted by the parallel driving control device.
- the display screen can display in real time the driving state of the virtual vehicle corresponding to the intelligent network connected vehicle in the real environment, the traveling video of the real vehicle, the road environment video, the vehicle dashboard data recorded in real time by the in-vehicle camera, and The information of the HMI module, etc., so as to provide real-time feedback to the human driver in a multi-dimensional, real-time manner, the location, perception, decision-making plan of the intelligent networked vehicle and the status information of various devices, so that the human driver can accurately understand the intelligent networked vehicle.
- Driving situation the display screen can display in real time the driving state of the virtual vehicle corresponding to the intelligent network connected vehicle in the real environment, the traveling video of the real vehicle, the road environment video, the vehicle dashboard data recorded in real time by the in-vehicle camera, and The information of the HMI module, etc.
- the human driver generates corresponding vehicle control signals by operating the operation portion based on the image displayed on the display screen, and these vehicle control signals can be transmitted to the parallel driving control device 102 through the driving simulator, and then transmitted to the intelligent network connection control device 101.
- these vehicle control signals can be transmitted to the parallel driving control device 102 through the driving simulator, and then transmitted to the intelligent network connection control device 101.
- the intelligent network connection control device 101 In order to achieve remote manual operation of the intelligent networked vehicle.
- the operation portion of the driving simulator preferably includes components such as a steering wheel, a throttle, and a brake pedal, which preferably correspond to an operation portion of the intelligent network car.
- the operating portion of the driving simulator may also include other reasonable components, and the present invention is not limited thereto.
- Parallel driving consists of parallel worlds of three levels, of which the first level is the physical world, the second level is the spiritual world, and the third level is the artificial world.
- the physical world mainly refers to the actual operation of real intelligent networked vehicles.
- the spiritual world mainly identifies the behavior and intention of driving.
- the artificial world consists of two layers.
- the first layer is the virtual driving layer, which is used to simulate the virtual driving behavior of the virtual driver in the artificial environment.
- the second layer is the information layer, which mainly includes social factors, geographical factors and sensor information factors.
- the controller in each virtual car interacts with the rest of the virtual car, the driver's intention of the spiritual world and the real smart car in the physical world, and enhances the virtual system in the artificial world through computational experiments. The modeling accuracy, while guiding the actual operation of the physical smart car.
- the parallel driving control device 102 provided in this embodiment is constructed based on the above-described parallel driving ideology. Specifically, as shown in FIG. 1 , in the embodiment, the parallel driving control device 102 preferably includes: a cloud platform 102a, a second communication module 102b, a virtual system module 102c, and a video module 102d.
- the cloud platform 102a is connected to the second communication module 102b and is also in communication with the intelligent network car control device 101.
- the intelligent network car control device 101 and the second communication module 102b can be implemented by the cloud platform 102a. data communication.
- the intelligent network car control device 101 transmits vehicle travel data (for example, travel video information about the smart network car) to the cloud platform 102a for storage by the cloud platform 102a.
- vehicle travel data for example, travel video information about the smart network car
- the video module 102d of the parallel driving control device 102 is connected to the cloud platform 102a, which is capable of downloading relevant video data from the cloud platform 102a and transmitting it to the remote driving device 103 for visual display by the remote driving device 103 to assist the driver.
- Remotely piloted driving based on the actual traffic environment shown.
- the second communication module 102b can implement data communication between the cloud platform 102a and the virtual system module 102c.
- the virtual system module 102c can receive the remote control driving takeover request signal transmitted by the intelligent networked vehicle control device 101 through the second communication module 102b and the cloud platform 102a, and generate a remote control driving request signal according to the remote control driving takeover request signal. After obtaining the remote driving request signal, the virtual system module 102c transmits the remote driving request signal to the remote driving device 103, so that the driver responds to the remote driving takeover request of the intelligent networked vehicle control device 101 through the remote driving device 103. .
- the virtual system module 102c is also capable of transmitting the driving mode signal and the vehicle control signal transmitted by the remote control driving device 103 to the second communication module 102b, and then transmitted by the second communication module 102b to the intelligent network connection control device 101 through the cloud platform 102a. This will enable the driver to control the operation of the intelligent network car at the remote end.
- the virtual system module 102c may be configured to acquire the travel data of the intelligent network car transmitted by the intelligent network car control device 101 through the second communication module 102b and the cloud platform 102a. And determining the driving state of the intelligent networked vehicle according to the driving data, thereby realizing the supervision of the driving state of the intelligent networked vehicle.
- the virtual system module 102c will actively generate a remote driving request signal and transmit the remote driving request signal to the remote driving device 103.
- the second type of abnormality of the intelligent networked vehicle preferably refers to the abnormal behavior of the intelligent networked vehicle, but the intelligent networked vehicle itself does not detect or recognize the abnormality, so the intelligent networked vehicle control device 101 cannot actively generate the remote control driving takeover. Request signal.
- the second type of abnormality is usually caused by a vehicle software failure of the intelligent network car.
- the second type of abnormality of the intelligent networked vehicle may also be that the other intelligent network connection control device 101 cannot actively generate the remote control driving takeover request signal.
- Other reasonable abnormal conditions, the invention is not limited thereto.
- the virtual system module 102c preferably includes: a virtual scene unit, a virtual interaction unit, and a virtual simulation unit.
- the virtual scene unit is used to build a virtual traffic environment and traffic flow.
- the virtual scene unit may provide an optional plurality of natural environments, road types, vehicle types, and vehicle sensing positioning devices to construct a virtual traffic environment and a traffic flow.
- the virtual scene unit can also construct a virtual data scene that is mapped to the real environment according to the real scene image and the location information of the intelligent network vehicle driving venue transmitted by the smart network vehicle control device 101.
- the virtual interaction unit is connected to the virtual scene unit, and is configured to construct a virtual vehicle in the virtual traffic environment built by the virtual scene unit, and use the virtual vehicle to map the driving state of the intelligent network connected vehicle according to the acquired driving data of the intelligent network connected vehicle.
- the traveling state of the mapped intelligent networked vehicle and the preset reference driving state for example, the driving state of the vehicle under ideal conditions
- the virtual simulation unit is connected to the virtual interaction unit, and is capable of detecting the relevant control algorithm of the intelligent network connection vehicle according to the traveling state of the mapped intelligent network connection vehicle.
- the virtual simulation unit supports manually setting up the traffic environment and equipment and algorithms such as vehicle sensing, and detects related algorithms through the operation of the virtual vehicle.
- the virtual system module may be configured to generate simulation state information for the specified virtual scene in a specified period of time according to the driving state of the intelligent networked vehicle, and generate an optimal prediction decision plan according to the simulated state information. And transmitting the optimal prediction decision planning information to the intelligent network car through the second communication module.
- the virtual system module can interact with the remote control driving device and the real physical environment to map the real driving state of the intelligent networked vehicle in real time.
- the virtual system module can also be run in the specified artificial traffic scenario, so that the operating state of the intelligent networked car in the future period of time can be predicted online according to a large amount of manual data, so that the intelligent networked vehicle can be targeted for a specified period of time in the future.
- the simulation state information of the virtual scene is specified, and the operation of the smart network car is guided according to the simulation state information.
- the virtual system module can also use the obtained data to perform an offline optimization algorithm at this time. Compared with a large number of road tests conducted by real intelligent network vehicles, the virtual system module provided by this embodiment can greatly reduce the data acquisition cost.
- the virtual simulation unit may use an artificial intelligence algorithm such as big data analysis to analyze the multi-source raw data of the real vehicle and the virtual vehicle running in different traffic environments, and based on the data, select the corresponding algorithm offline optimization intelligence from the algorithm library.
- the network's perception, decision, planning, and control algorithms are determined, and these optimized algorithms will be used to guide the operation of the real intelligent networked vehicle.
- the virtual interaction unit and/or the virtual simulation unit are preferably integrated in the cloud platform, that is, the data analysis of the analysis of the driving state of the intelligent networked vehicle and/or related big data analysis. The process will be done in the cloud platform.
- the parallel driving control device 102 may not configure the cloud platform 102a and/or the video module 102d according to actual needs, and the present invention is not limited thereto.
- the virtual system module 102c and the video module 102d will perform data communication with the intelligent network car control device 101 through the first communication module 102b, and at the same time, for the intelligent network car
- the data operation process such as analysis of the driving state and/or related big data analysis, will be performed locally at the virtual system module 102c.
- the intelligent network car control device 101 will transmit relevant driving data (including video information and state information of the vehicle, etc.) to the parallel driving control device 102 in real time.
- the intelligent network car control device 101 records the traveling video of the vehicle and the state information of the human-machine interaction module through the vehicle video module, and the intelligent network car control device 101 transmits the above data to the parallel driving control device 102 in real time.
- Cloud platform 102a the intelligent network car control device 101 records the traveling video of the vehicle and the state information of the human-machine interaction module through the vehicle video module, and the intelligent network car control device 101 transmits the above data to the parallel driving control device 102 in real time.
- the video module 102d of the parallel driving control device 102 downloads relevant video data from the cloud platform 102a and transmits the video data to the display module of the remote control driving device 103 in real time for visual display by the display module.
- the virtual system module 102c of the parallel driving control device 102 continuously acquires the vehicle state information of the intelligent networked vehicle from the cloud platform 102a through the second communication module 102b, and monitors the running state of the vehicle according to the vehicle state information. And guidance. At the same time, in order to take over the intelligent network car in a timely manner, in this embodiment, the virtual system module 102c synchronizes the vehicle state information to the remote control driving device 103.
- the intelligent network car control device 101 continuously detects whether there is a first type of abnormality in the smart network car in step S401. Wherein, if it is detected that there is a first type of abnormality in the smart network car, the intelligent network car control device 101 generates a remote control driving takeover request signal in step S402, and transmits the remote control driving takeover request signal to the step S403 to A parallel steering control device 102 in communication with it.
- the parallel driving control device 102 After receiving the remote control takeover request signal, the parallel driving control device 102 generates a remote driving request signal according to the remote driving takeover request signal and transmits the remote driving request signal to the remote driving device 103 communicably connected thereto in step S404.
- the remote driving request signal sent by the parallel driving control device 102 to the remote driving device 103 may also be a remote driving takeover request signal.
- the remote driving device 103 Upon receiving the remote driving request signal transmitted by the parallel driving control device 102, the remote driving device 103 will generate a remote driving driving signal in step S405 to prompt the driver to remotely drive the intelligent networked vehicle at this time.
- the remote control driving device 103 will generate a driving mode signal in step S406, and return the driving mode signal to the parallel driving control device 102 in step S407.
- the parallel driving control device 102 After receiving the driving mode signal transmitted from the remote driving device 103, the parallel driving control device 102 synchronizes the vehicle running data in step S408. For example, the parallel driving control device 102 synchronizes the current vehicle state information of the intelligent networked vehicle to the remotely controlled driving device 103 in step S408. Upon completion of the synchronization, the remotely controlled steering device 103 preferably transmits a formal takeover signal to the parallel steering control device 102. The parallel driving control device 102 then transmits the official takeover signal as a remote control driving signal to the intelligent network connection control device 101 in step S409.
- the smart networked vehicle 101 After receiving the remote driving signal transmitted by the parallel driving control device 102, the smart networked vehicle 101 switches the current control mode of the vehicle from the automatic driving mode to the remote driving mode in step S410.
- the driver will operate the remote control driving device 103 to control the driving of the intelligent network car based on the vehicle video information and the vehicle state information returned by the smart car vehicle control device 101 in real time.
- the remote control driving device 103 generates a corresponding vehicle control signal according to the driver's operation in step S411 and transmits the vehicle control signal to the parallel driving control device 102.
- the parallel driving control device 102 transmits the above vehicle control signal to the intelligent network connection control device 101 in step S412 until the remote driving mode is exited, thus realizing remote control of the intelligent networked vehicle.
- the intelligent network connection vehicle control device 101 continuously detects whether the first type of abnormality disappears in step S413. Wherein, if the first type of abnormality has not disappeared, the intelligent network car will continue to synchronize the vehicle control signals from the remote control driving device 103.
- the intelligent network car control device 101 If the first type of abnormality disappears, the intelligent network car control device 101 generates an automatic driving request signal in step S414, and transmits the automatic driving request signal to the parallel driving control device 102 in step S415.
- the parallel driving control device 102 after receiving the automatic driving request signal, the parallel driving control device 102 generates an automatic driving enable signal according to the automatic driving request signal in step S416, and the automatic driving enable signal is generated in step S417. It is transmitted to the remote control driving device 103, and the automatic driving enable signal is also transmitted to the intelligent network connection control device 101 in step S418.
- the remote driving device 103 after receiving the automatic driving signal transmitted by the parallel driving and controlling device 102, the remote driving device 103 generates a corresponding automatic driving instruction signal in step S419 to prompt the driver to no longer need Remotely driving the intelligent networked car.
- the network car control device 101 After receiving the automatic driving enable signal transmitted from the parallel driving control device 102, the network car control device 101 synchronizes the vehicle running data in step S420 and switches the control mode to the automatic driving mode in step S421. This also achieves the cutting out of the remote driving mode.
- the parallel remote driving system for the intelligent networked vehicle makes it unnecessary for the intelligent networked vehicle to be equipped with a human driver when approaching the road, which can significantly reduce the driver's Labor costs, technical requirements, and safety costs are conducive to the promotion of intelligent networked vehicles.
- the system can realize multi-vehicle coordination, so that one driver in the control center can remotely control multiple intelligent network vehicles, thereby significantly reducing labor costs and improving intelligence.
- the management efficiency of the networked car can realize multi-vehicle coordination, so that one driver in the control center can remotely control multiple intelligent network vehicles, thereby significantly reducing labor costs and improving intelligence.
- the parallel driving control device can simultaneously supervise and guide the operation of the intelligent network car in the real traffic environment.
- the parallel driving control device can actively take over when the vehicle is abnormal when the intelligent network car does not actively request to take over, which can effectively improve traffic safety.
- the parallel driving control device can also combine the big data analysis technology to analyze the data and traffic flow information of the intelligent network connected vehicle accumulated in the current environment, and combine the real-time status information of the real intelligent networked vehicle to predict the real car in the future.
- the actual state guides the operation of the smart car in real time.
- the data obtained by the parallel driving control device can also be used to optimize the relevant algorithms offline, thereby effectively promoting the development of intelligent networked vehicles.
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Abstract
一种用于智能网联车的平行遥控驾驶系统，包括智能网联车控制装置（101）、平型驾驶管控装置（102）和遥控驾驶装置（103）。遥控驾驶装置（103）能够根据平行驾驶管控装置（102）传输来的遥控驾驶请求信号生成遥控驾驶指示信号，并根据用户操作生成驾驶模式信号和车辆控制信号通过平行驾驶管控装置（102）传输至智能网联车控制装置，以实现对智能网联车的远程操控。该平行遥控驾驶系统使得智能网联车在上路时不再必须配置人类驾驶员，这样也就能够显著降低对驾驶员的培训等人力成本、技术要求、安全成本，有利于智能网联车的推广。
Description
相关技术的交叉引用
本申请要求享有2018年03月22日提交的名称为：“一种用于智能网联车的平行遥控驾驶系统”的中国专利申请CN 201810239901.4的优先权，其全部内容通过引用并入本文中。
本发明涉及智能网联驾驶技术领域，具体地说，涉及一种用于智能网联车的平行遥控驾驶系统。
作为未来人工智能的重要分支，智能网联驾驶技术可以提高道路交通的安全性及通行效率，极大地改变人们的生活出行方式，该技术将成为未来数年最重要的先进技术之一。
由于目前智能网联驾驶技术尚处于初级发展阶段，无论智能网联车辆上路测试还是一般行驶，车辆行驶过程种均需要在车内配置人类驾驶员来时刻监测车辆状态，以对车辆运行进行监督和紧急接管。
然而，目前的智能网联车辆的这种驾驶管控方式不利于智能网联技术的提升以及智能网联车辆的普及。
发明内容
为解决上述问题，本发明提供了一种智能网联车控制装置，所述智能网联车控制装置包括：
第一通信模块，其用于与平行驾驶管控装置进行数据通信，能够将智能网联车的行驶数据传输至所述平行驾驶管控装置，还能够接收所述平行驾驶管控装置传输来的驾驶模式信号；
模式切换模块，所述模式切换模块与智能网联车的车辆控制系统和第一通信模块连接，用于根据所述驾驶模式信号确定针对所述智能网联车的驾驶模式，以使得所述车辆控制系统能够基于相应车辆控制信号对所述智能网联车的行驶状态进行控制。
根据本发明的一个实施例，所述模式切换模块所配置的驾驶模式包括自动驾驶模式和遥控驾驶模式，其中，所述遥控驾驶模式的优先级高于所述自动驾驶模式。
根据本发明的一个实施例，如果所述模式切换模块的当前控制模式为自动驾驶模式，所述车辆控制系统则会根据自身产生的车辆控制信号来对所述智能网联车的行驶状态进行控制；
如果所述模式切换模块的当前控制模式为遥控驾驶模式，所述车辆控制系统则会根据所述平行驾驶管控装置传输来的车辆控制信号来对所述智能网联车的行驶状态进行控制。
根据本发明的一个实施例，如果所述模式切换模块的当前控制模式为自动驾驶模式，当接收到所述平行驾驶管控装置传输来的遥控驾驶信号时，所述模式切换模块则会将控制模式切换为遥控驾驶模式。
根据本发明的一个实施例，当所述智能网联车存在第一类异常时，所述模式切换模块配置为生成遥控驾驶接管请求信号，并将所述遥控驾驶接管请求信号通过所述第一通信模块传输至平行驾驶管控装置。
本发明还提供了一种平行驾驶管控装置，所述平行驾驶管控装置用于与智能网联车控制装置和遥控驾驶装置通信连接，其配置为在接收到所述智能网联车控制装置传输来的遥控驾驶接管请求信号后，生成遥控驾驶请求信号并将所述遥控驾驶请求信号传输至所述遥控驾驶装置；
所述平行驾驶管控装置还配置为接收所述遥控驾驶装置响应所述遥控驾驶请求信号而生成的驾驶模式信号，并将所述驾驶模式信号和车辆控制信号传输至所述智能网联车，以由所述智能网联车根据所述驾驶模式信号确定针对所述智能网联车的驾驶模式。
根据本发明的一个实施例，所述平行驾驶管控装置包括：
第二通信模块，其用于与所述智能网联车控制装置连接，能够实现所述平行驾驶管控装置与智能网联车控制装置之间的数据通信；
虚拟系统模块，其与所述第二通信模块连接，用于接收所述第二通信模块传输来的遥控驾驶接管请求信号，根据所述遥控驾驶接管请求信号生成遥控驾驶请求信号并将所述遥 控驾驶请求信号传输至所述遥控驾驶装置，还用于将所述遥控驾驶装置传输来的驾驶模式信号传输至所述第二通信模块。
根据本发明的一个实施例，所述虚拟系统模块还配置为通过所述第二通信模块获取所述智能网联车控制装置所传输来的智能网联车的行驶数据，并根据所述行驶数据确定所述智能网联车的行驶状态，其中，
如果所述智能网联车存在第二类异常时，所述虚拟系统模块配置为主动生成遥控驾驶请求信号并将所述遥控驾驶请求信号传输至所述遥控驾驶装置。
根据本发明的一个实施例，所述虚拟系统模块包括：
虚拟场景单元，其用于搭建虚拟交通环境和交通流；
虚拟交互单元，其与所述虚拟场景单元连接，用于在所述虚拟交通环境中构建虚拟车，并根据所获取到的智能网联车的行驶数据利用所述虚拟车映射所述智能网联车的行驶状态，通过对比分析映射的所述智能网联车的行驶状态与预设参考行驶状态，实现对所述智能网联车行驶状态的监测。
根据本发明的一个实施例，所述虚拟系统模块还包括：
虚拟仿真单元，其与所述虚拟交互单元连接，用于根据映射的所述智能网联车的行驶状态来对所述智能网联车的相关控制算法进行检测。
根据本发明的一个实施例，所述虚拟系统模块还配置为根据所述智能网联车的行驶状态生成未来指定时段内针对指定虚拟场景的模拟状态信息，并根据所述模拟状态信息生成最优预测决策规划信息，将所述最优预测决策规划信息通过所述第二通信模块传输至所述智能网联车。
根据本发明的一个实施例，所述平行驾驶管控装置还包括：
云端平台，其与所述第二通信模块连接并与所述智能网联车控制装置通信连接，所述第二通信模块通过所述云端平台与所述智能网联车控制装置进行数据通信。
根据本发明的一个实施例，所述虚拟交互单元和/或虚拟仿真单元集成在所述云端平台中。
根据本发明的一个实施例，所述平行驾驶管控装置还包括：
视频模块，其与所述云端平台连接，所述云端平台用于接收所述智能网联车控制装置 传输来的关于所述智能网联车的行驶视频信息，所述视频模块用于从所述云端平台下载所述行驶视频信息并将所述行驶视频信息传输至所述遥控驾驶装置。
本发明还提供了一种遥控驾驶装置，所述遥控驾驶装置包括：
驾驶模拟器，其与所述平行驾驶管控装置通信连接，用于根据所述平行驾驶管控装置传输来的遥控驾驶请求信号生成遥控驾驶指示信号，并将根据用户操作生成的驾驶模式信号和车辆控制信号通过所述平行驾驶管控装置传输至智能网联车控制装置，以实现对所述智能网联车的远程操控。
根据本发明的一个实施例，所述驾驶模拟器包括显示屏和操作部，所述显示屏用于对所述平行驾驶管控装置传输来的相关数据进行可视化显示，所述操作部用于基于用户操作生成相应的驾驶模式信号和车辆控制信号并将所述车辆控制信号传输至所述平行驾驶管控装置。
本发明还提供了一种用于智能网联车的平行遥控驾驶系统，所述系统包括：
如上任一项所述的智能网联车控制装置；如上任一项所述的平行驾驶管控装置；以及，如上所述的遥控驾驶装置。
本发明所提供的用于智能网联车的平行遥控驾驶系统使得智能网联车在上路时不再必须配置人类驾驶员，这样也就能够显著降低对驾驶员的培训等人力成本、技术要求、安全成本，有利于智能网联车的推广。
同时，通过基于平行驾驶管控装置的中心化控制，该系统能够实现多车协同，这样使得在管控中心的一个驾驶员可以遥控多辆智能网联车，从而显著降低了人力成本，提高了对智能网联车的管理效率。
此外，平行驾驶管控装置可以同时监管和引导真实交通环境下的智能网联车的运行。尤其是，平行驾驶管控装置在智能网联车未主动请求接管的情况下，能够在监测到车辆异常时进行主动接管，这样可以有效提高交通安全。
另外，平行驾驶管控装置还可以结合大数据分析技术对当前环境累积的智能网联车的数据和交通流信息进行分析，结合真实智能网联车的实时状态信息，在线预测未来一段时间真实车的实际状态，进而实时引导智能车的运行。同时，平行驾驶管控装置获取的数据也可以用于离线优化相关算法，进而高效促进智能网联车辆的研发。
本发明的其它特征和优点将在随后的说明书中阐述，并且，部分地从说明书中变得显 而易见，或者通过实施本发明而了解。本发明的目的和其他优点可通过在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要的附图做简单的介绍：
图1是根据本发明一个实施例的用于智能网联车的平行遥控驾驶系统的结构示意图；
图2是根据本发明一个实施例的智能网联车的平行遥控驾驶系统的数据流示意图；
图3是根据本发明一个实施例的虚拟系统模块的结构示意图；
图4是根据本发明一个实施例的平行遥控驾驶系统的工作流程示意图。
以下将结合附图及实施例来详细说明本发明的实施方式，借此对本发明如何应用技术手段来解决技术问题，并达成技术效果的实现过程能充分理解并据以实施。需要说明的是，只要不构成冲突，本发明中的各个实施例以及各实施例中的各个特征可以相互结合，所形成的技术方案均在本发明的保护范围之内。
同时，在以下说明中，出于解释的目的而阐述了许多具体细节，以提供对本发明实施例的彻底理解。然而，对本领域的技术人员来说显而易见的是，本发明可以不用这里的具体细节或者所描述的特定方式来实施。
另外，在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。
目前的驾驶管控方式对人类驾驶员及系统研发具有较高的要求。例如，目前的驾驶管控方式需要人类驾驶员要掌握丰富的智能车技术点，这样才能够时刻监测智能车的驾驶情况，并且还需要能及时发现可能的安全隐患并进行接管；目前的驾驶管控方式需要人类驾驶员要熟悉不同驾驶模式的切换、接管方式，然而在一些突发交通情况下很难实现车辆管控的平滑接管和过渡。此外，目前对智能驾驶车辆的驾驶管控方式都侧重于在车辆故障后进行人工干预，并不能结合海量的交通数据对智能车辆的驾驶提供辅助和引导。
因此，目前的智能网联车辆的驾驶管控方式不利于智能网联技术的提升以及智能网联车辆的普及。针对该问题，本发明提出了一种新的智能网联汽车管控系统，即平行遥控驾驶系统，该系统能够允许人类驾驶员不需要在智能网联车内控制智能网联车的行驶，而是可以在远端(例如管控中心内)通过平行遥控驾驶的方式来管控一台或多台智能网联车。
图1示出了本实施例所提供的用于智能网联车的平行遥控驾驶系统的结构示意图，图2示出了该平行遥控驾驶系统的数据流示意图。
如图1所示，本实施例所提供的用于智能网联车的平行遥控驾驶系统包括：智能网联车控制装置101、平行驾驶管控装置102以及遥控驾驶装置103。其中，智能网联车控制装置101优选地设置在智能网联车上并与智能网联车的车辆控制系统104通信连接。如图2所示，智能网联车控制装置101能够通过车辆控制系统104获取智能网联车、道路、交通流等车-路-人三个维度的实时信息(即智能网联车的行驶数据)，并将这些信息传输至与之通信连接的平行驾驶管控装置102。
具体地，本实施例中，智能网联车控制装置101优选地包括：第一通信模块101a和模式切换模块101b。其中，第一通信模块101a与平行驾驶管控装置102通信连接，其能够将智能网联车的行驶数据传输至平行驾驶管控装置102，还能够接收平行驾驶控制装置102所传输来的驾驶模式信号等数据。
模式切换模块101b与智能网联车的车辆控制系统和第一通信模块101a连接，其能够根据第一通信模块101a所传输来的驾驶模式信号来确定针对智能网联车的驾驶模式，从而使得智能网联车的车辆控制系统来基于相应的车辆控制信号对智能网联车的行驶状态进行控制。
本实施例中，模式切换模块所配置的驾驶模式优选地包括自动驾驶模式和遥控驾驶模式。其中，如果模式切换模块101b的当前控制模式为自动驾驶模式，那么车辆控制系统则会根据自身所产生的车辆控制信号来对智能网联车的行驶状态进行控制(即车辆控制系统基于自身所配置的智能驾驶控制算法来根据自身的感知、决策等单元进行自主决策和控制，从而自主地控制智能网联车的行驶状态)。
而如果模式切换模块101b的当前控制模式为遥控驾驶模式，那么车辆控制系统则会根据通过第一通信模块101a所接收到的平行驾驶管控装置102传输来的车辆控制信号来对智能网联车的行驶状态进行控制。
当然，在本发明的其它实施例中，模式切换模块所配置的驾驶模式还可以包含其它合 理驾驶模式，本发明不限于此。例如，在本发明的一个实施例中，模式切换模块所配置的驾驶模式还可以包括人工驾驶模式，其中，人工驾驶模式的启动需要智能网联车内存在人类驾驶员，同时人工驾驶模式的优先级优选地要高于遥控驾驶模式。如果模式切换模块101b的当前控制模式为人工驾驶模式，那么智能网联车的行驶状态也就是由车内的人类驾驶员进行控制的。
为了保证智能网联车的高效性以及安全运行，对于本实施例所提供的模式切换模块来说，遥控驾驶模式的优先级要高于自动驾驶模式。具体地，如果模式切换模块101b的当前控制模式为自动驾驶模式，当通过第一通信模块101a接收到平行驾驶管控装置102所传输来的遥控驾驶信号时，模式切换模块101b会将控制模式由自动驾驶模式切换为遥控驾驶模式。
本实施例中，智能网联车中设置有感知定位、决策规划、车辆控制以及车身硬件设备等功能模块。智能网联车上的感知定位功能模块可以通过诸如激光雷达、毫米波雷达和相机等设备来感知环境信息，还可以通过惯导仪和GPS设备组合导航的方式获取车辆的位置信息。智能网联车的车辆控制系统则可以根据感知定位功能模块所获取到的环境感知信息和定位信息来进行决策规划。当传感器信息正常并且能够解析所建立的运动规划问题时，车辆控制系统会将车辆控制信号(例如车辆纵向控制信号和/或车辆横向控制信号等)传输至模式切换模块101b。
需要指出的是，在本发明的其它实施例中，根据实际需要，智能网联车控制装置101还可以包含其它合理功能模块，本发明不限于此。例如，在本发明的一个实施例中，智能网联车控制装置101还可以包含急停模块、人机交互模块和/或车载视频模块。
其中，急停模块用于对智能网联车进行电子紧急制动，从而控制智能网联车快速停车。例如，当平行驾驶控制装置102通过对智能网联车的行驶状态进行监测而检测到智能网联车处于非常危险的情况(例如车辆前方存在障碍物)时，那么平行驾驶控制装置102将会控制急停模块直接工作来实现车辆的紧急刹车，从而避免交通事故的发生。
人机交互模块能够显示智能网联车的感知、定位以及驾驶模式类型以及多种设备的信息等，从而为不同驾驶模式下的驾驶员提供辅助信息和交互功能。车载视频模块则能够通过车载摄像头实时录制、回传驾驶员视角的车辆道路环境、车辆仪表盘以及HMI设备的状态信息视频。
模式切换模块101b能够同时接收来自智能网联车的车辆控制系统104以及平行驾驶 管控装置102的车辆控制信号。而模式切换模块101b则会根据所接收到的驾驶模式信号来使得车辆控制系统基于相应的车辆控制信号对智能网联车的行驶状态进行控制。
例如，如果模式切换模块101b所接收到的驾驶模式信号为遥控驾驶信号，那么模式切换模块101b将会使得车辆控制系统基于平行驾驶管控装置102所传输来的车辆控制信号对智能网联车的行驶状态进行控制；而如果模式切换模块101b所接收到的驾驶模式信号为智能驾驶信号，那么模式切换模块101b将会使得车辆控制系统基于车辆控制系统自身所产生的车辆控制信号对智能网联车的行驶状态进行控制。
本实施例中，如图2中虚线所示，优选地，当智能网联车存在第一类异常(例如智能网联车存在感知受限或内部故障时)，模式切换模块101b将会生成遥控驾驶接管请求信号，并将该遥控驾驶接管请求信号通过第一通信模块101a传输至平行驾驶管控装置102。
本实施例中，平行驾驶管控装置102与智能网联车控制装置101和遥控驾驶装置103通信连接，其能够在接收到智能网联车控制装置101传输来的遥控驾驶接管请求信号时，生成遥控驾驶请求信号并将该遥控驾驶请求信号传输至遥控驾驶装置103。
需要指出的是，为了使得遥控驾驶装置103在需要对智能网联车进行遥控驾驶时能够尽快地实现与智能网联车实际运行状态的同步，本实施例中，平行驾驶管控装置102优选地会将接收到的智能网联车的行驶数据实时地传输至遥控驾驶装置103。
本实施例中，遥控驾驶装置103优选地包括驾驶模拟器，驾驶模拟器在接收到智能网联车控制装置101所发送来的遥控驾驶请求信号后，会根据该遥控驾驶请求信号生成相应的遥控驾驶指示信号，以提示人类驾驶员对该遥控驾驶请求信号进行响应。当人类驾驶员对遥控驾驶请求信号进行响应时(例如同意进行遥控驾驶)，遥控驾驶装置103则会生成驾驶模式信号以及根据用户实际操作来生成相应的车辆控制信号(即对遥控驾驶请求信号进行接管反馈)，随后遥控驾驶装置103会将上述驾驶模式信号和车辆控制信号通过平行驾驶管控装置102传输至智能网联车控制装置101，以实现对智能网联车的远程操控。
具体地，如图1所示，本实施例中，遥控驾驶装置103优选地包括显示屏和操作部。其中，显示屏能够对平行驾驶管控装置所传输来的相关数据进行可视化显示。例如，显示屏能够实时显示与真实环境下的智能网联车对应的虚拟车在虚拟环境种的行驶状态、真实车辆的行驶视频以及道路环境视频、车内摄像头实时录制的车辆仪表盘的数据和HMI模块的信息等，从而多维度、实时地向人类驾驶员实时反馈智能网联车的定位、感知、决策规划以及多种设备的状态信息，以使得人类驾驶员能够准确地了解智能网联车的行驶状 况。
人类驾驶员基于显示屏所显示的图像通过操作操作部来生成相应的车辆控制信号，这些车辆控制信号则可以通过驾驶模拟器传输至平行驾驶管控装置102，进而传输至智能网联车控制装置101，从而实现对智能网联车的远程人工操作。
本实施例中，驾驶模拟器的操作部优选地包括方向盘、油门和制动踏板等组件，其优选地与智能网联车的操作部相对应。当然，在本发明的其它实施例中，驾驶模拟器的操作部还可以包含其它合理组件，本发明不限于此。
平行驾驶包含三级并存的平行世界，其中，第一级为物理世界，第二级为精神世界，第三级为人工世界。物理世界主要是指真实智能网联车的实际运行，精神世界主要是对驾驶的行为和意图进行识别。
而人工世界又包含两层，第一层是虚拟驾驶层，用于模拟虚拟驾驶员在人工环境中的虚拟驾驶行为。第二层是信息层，主要包含社会因素、地理因素和传感器信息因素。在人工世界中，每一辆虚拟车中的控制器会与其余的虚拟车、精神世界的驾驶员意图和物理世界的真实智能车进行多方交互，通过计算实验的方式来提升人工世界中虚拟系统的建模精度，同时指导物理智能车的实际运行。
本实施例所提供的平行驾驶管控装置102正是基于上述平行驾驶的思想体系来构建得到的。具体地，如图1所示，本实施例中，平行驾驶管控装置102优选地包括：云端平台102a、第二通信模块102b、虚拟系统模块102c以及视频模块102d。
其中，云端平台102a与第二通信模块102b连接并且还与智能网联车控制装置101通信连接，智能网联车控制装置101与第二通信模块102b能够通过云端平台102a来实现二者之间的数据通信。
具体地，本实施例中，智能网联车控制装置101会将车辆行驶数据(例如关于智能网联车的行驶视频信息)传输至云端平台102a，以由云端平台102a进行存储。平行驾驶管控装置102的视频模块102d与云端平台102a连接，其能够从云端平台102a下载相关视频数据向遥控驾驶装置103发送，以由遥控驾驶装置103向驾驶人员进行可视化显示，从而辅助驾驶人员来根据所显示的实际交通环境来进行远程遥控驾驶。
本实施例中，第二通信模块102b能够实现云端平台102a与虚拟系统模块102c之间的数据通信。虚拟系统模块102c能够通过第二通信模块102b和云端平台102a接收智能 网联车控制装置101所传输来的遥控驾驶接管请求信号，并根据该遥控驾驶接管请求信号生成遥控驾驶请求信号。在得到遥控驾驶请求信号后，虚拟系统模块102c会将该遥控驾驶请求信号传输至遥控驾驶装置103，以使得驾驶人员通过遥控驾驶装置103对智能网联车控制装置101的遥控驾驶接管请求进行响应。虚拟系统模块102c还能够将遥控驾驶装置103所传输来的驾驶模式信号和车辆控制信号传输至第二通信模块102b，进而由第二通信模块102b通过云端平台102a传输至智能网联车控制装置101，这样也就可以使得驾驶人员可以在远端控制智能网联车的运行。
根据实际需要，可选地，本实施例中，虚拟系统模块102c还可以配置为通过第二通信模块102b和云端平台102a获取智能网联车控制装置101所传输来的智能网联车的行驶数据，并根据行驶数据确定智能网联车的行驶状态，从而实现对智能网联车行驶状态的监管。其中，如图2中点虚线所示，如果智能网联车存在第二类异常时，虚拟系统模块102c将会主动生成遥控驾驶请求信号并将该遥控驾驶请求信号传输至遥控驾驶装置103。
智能网联车存在第二类异常优选地指智能网联车存在异常行为但智能网联车本身并未检测或意识到该异常，因此智能网联车控制装置101也就无法主动生成遥控驾驶接管请求信号。本实施例中，该第二类异常通常是由智能网联车的车载软件故障所引起的。当然，在本发明的其它实施例中，智能网联车的上述第二类异常还可以为其它无法被智能网联车自身识别而导致智能网联车控制装置101无法主动生成遥控驾驶接管请求信号的其它合理异常状况，本发明不限于此。
本实施例中，如图3所示，虚拟系统模块102c优选地包括：虚拟场景单元、虚拟交互单元和虚拟仿真单元。
其中，虚拟场景单元用于搭建虚拟交通环境和交通流。具体地，本实施例中，虚拟场景单元可以提供可选的多种自然环境、道路类型、车辆类型、车辆传感定位设备来搭建虚拟交通环境和交通流。此外，虚拟场景单元还可以根据智能网联车控制装置101所传输来的智能网联车行驶场地的实景图像和位置信息，来搭建与真实环境一一映射的虚拟数据场景。
虚拟交互单元与虚拟场景单元连接，其能够在虚拟场景单元所搭建的虚拟交通环境中构建虚拟车，并根据所获取到的智能网联车的行驶数据利用虚拟车映射智能网联车的行驶状态，通过对比分析映射的智能网联车的行驶状态与预设参考行驶状态(例如理想情况下的车辆行驶状态)，实现对智能网联车行驶状态的监测。
虚拟仿真单元与虚拟交互单元连接，其能够根据映射的智能网联车的行驶状态来对智能网联车的相关控制算法进行检测。本实施例中，虚拟仿真单元支持人工设置交通环境以及车辆传感等设备和算法，通过虚拟车的运行来检测相关算法。
此外，本实施例中，可选地，虚拟系统模块可以配置为根据智能网联车的行驶状态生成未来指定时段内针对指定虚拟场景的模拟状态信息，并根据模拟状态信息生成最优预测决策规划信息，将最优预测决策规划信息通过第二通信模块传输至所述智能网联车。
本实施例中，虚拟系统模块可以与遥控驾驶控制装置和真实物理环境进行交互，从而实时映射智能网联车的真实行驶状态。虚拟系统模块还可以在指定人工交通场景中运行，这样也就可以根据大量的人工数据来在线预测未来一段时间段内智能网联车的运行状态，从而得到智能网联车在未来指定时段内针对指定虚拟场景的模拟状态信息，并根据该模拟状态信息来引导智能网联车的运行。此外，虚拟系统模块此时还可以利用得到的数据进行离线优化算法。与真实智能网联车进行大量道路试验相比，本实施例所提供的虚拟系统模块能够大大缩小数据的获取成本。
具体地，虚拟仿真单元可以采用大数据分析等人工智能算法，分析真实车和虚拟车在不同交通环境中运行的多源原始数据，以这些数据为基础，从算法库中选择相应算法离线优化智能网联车的感知、决策、规划和控制算法，而确定的这些优化算法将用于引导真实的智能网联车的运行。
本实施例中，为了提高数据处理的效率，虚拟交互单元和/或虚拟仿真单元优选地集成在云端平台中，即对于智能网联车的行驶状态的分析和/或相关大数据分析等数据运算过程将在云端平台中进行。
需要指出的是，在本发明的其它实施例中，根据实际需要，平行驾驶管控装置102还可以不配置云端平台102a和/或视频模块102d，本发明不限于此。例如，当平行驾驶管控装置102不配置云端平台102a时，虚拟系统模块102c和视频模块102d将通过第一通信模块102b来与智能网联车控制装置101进行数据通信，同时，对于智能网联车的行驶状态的分析和/或相关大数据分析等数据运算过程将在虚拟系统模块102c本地进行。
为了更加清楚地表明本实施例所提供的用于智能网联车的平行遥控驾驶系统的工作原理以及工作过程，以下结合图4所示的平行遥控驾驶系统的过程流程图来进行阐述。
如图4所示，本实施例中，在一般交通场景下，智能网联车控制装置101将会向平行驾驶管控装置102实时传输相关行驶数据(包括视频信息和车辆的状态信息等)。
具体地，智能网联车控制装置101通过车载视频模块来录制车辆的行驶视频以及人机交互模块的状态信息等，智能网联车控制装置101会将上述数据实时传输至平行驾驶管控装置102的云端平台102a。
平行驾驶管控装置102的视频模块102d会从云端平台102a下载相关视频数据并将这些视频数据实时传输至遥控驾驶装置103的显示模块中，以由显示模块进行可视化显示。
平行驾驶管控装置102的虚拟系统模块102c则会通过第二通信模块102b来从云端平台102a中不断地获取智能网联车的车辆状态信息，并根据这些车辆状态信息来对车辆的行驶状态进行监测和引导。同时，为了操作人员更加及时地接管智能网联车，本实施例中，虚拟系统模块102c会将上述车辆状态信息同步到遥控驾驶装置103中。
如图4所示，在行驶过程中，智能网联车控制装置101会在步骤S401中持续检测智能网联车是否存在第一类异常。其中，如果检测到智能网联车存在第一类异常，那么智能网联车控制装置101将会在步骤S402中生成遥控驾驶接管请求信号，并在步骤S403中将该遥控驾驶接管请求信号传输至与之通信连接的平行驾驶管控装置102。
在接收到遥控驾驶接管请求信号后，平行驾驶管控装置102会在步骤S404种根据遥控驾驶接管请求信号生成遥控驾驶请求信号并将遥控驾驶请求信号传输至与之通信连接的遥控驾驶装置103。
需要指出的是，根据实际情况，平行驾驶管控装置102向遥控驾驶装置103所发送的遥控驾驶请求信号也可以直接是遥控驾驶接管请求信号。
在接收到平行驾驶管控装置102所发送来的遥控驾驶请求信号后，遥控驾驶装置103将会在步骤S405中生成遥控驾驶指示信号，以提示驾驶人员此时需要对智能网联车进行遥控驾驶。
如果驾驶人员积极反馈，在做好操作准备后可以进行相应操作来表征同意接管智能网联车的驾驶工作。那么此时遥控驾驶装置103将会在步骤S406中生成驾驶模式信号，并在步骤S407中将该驾驶模式信号回传至平行驾驶管控装置102。
平行驾驶管控装置102在接收到遥控驾驶装置103所发送来的驾驶模式信号后，会在步骤S408中同步车辆行驶数据。例如，平行驾驶管控装置102会在步骤S408种将智能网联车的当前车辆状态信息同步到遥控驾驶装置103。在同步完成后，遥控驾驶装置103优选地会向平行驾驶管控装置102发送正式接管信号。而平行驾驶管控装置102则会在步 骤S409中将该正式接管信号作为遥控驾驶信号回传至智能网联车控制装置101。
智能网联车101在接收到平行驾驶管控装置102所传输来的遥控驾驶信号后，会在步骤S410中将车辆的当前控制模式由自动驾驶模式切换为遥控驾驶模式。
驾驶人员将会基于智能网联车控制装置101所实时返回的车辆视频信息和车辆状态信息来操作遥控驾驶装置103对智能网联车进行操控驾驶。在此过程中，遥控驾驶装置103会在步骤S411中根据驾驶人员的操作来生成相应的车辆控制信号并将该车辆控制信号传输至平行驾驶管控装置102。而平行驾驶管控装置102则会在步骤S412中将上述车辆控制信号传输至智能网联车控制装置101，直至退出遥控驾驶模式，这样也就实现了对智能网联车的远程操控。
当智能网联车处于遥控驾驶模式时，智能网联车控制装置101会在步骤S413中持续检测上述第一类异常是否消失。其中，如果第一类异常未消失，智能网联车将会继续同步来自遥控驾驶控制装置103的车辆控制信号。
而如果上述第一类异常消失，智能网联车控制装置101则会在步骤S414中生成自动驾驶请求信号，并在步骤S415中将上述自动驾驶请求信号传输至平行驾驶管控装置102。
本实施例中，平行驾驶管控装置102在接收到上述自动驾驶请求信号后，会在步骤S416中根据该自动驾驶请求信号生成自动驾驶使能信号，并在步骤S417中将该自动驾驶使能信号传输至遥控驾驶装置103，同时还会在步骤S418中将该自动驾驶使能信号传输至智能网联车控制装置101。
本实施例中，可选地，遥控驾驶装置103在接收到平行驾驶管控装置102所传输来的自动驾驶信号后，会在步骤S419中生成相应的自动驾驶指示信号，以提示驾驶人员不再需要对智能网联车进行遥控驾驶。
网联车控制装置101在接收到平行驾驶管控装置102所传输来的自动驾驶使能信号后，会在步骤S420中同步车辆行驶数据并在步骤S421中将控制模式切换为自动驾驶模式。这样也就实现了遥控驾驶模式的切出。
从上述描述中可以看出，本发明所提供的用于智能网联车的平行遥控驾驶系统使得智能网联车在上路时不再必须配置人类驾驶员，这样也就能够显著降低对驾驶员的培训等人力成本、技术要求、安全成本，有利于智能网联车的推广。
同时，通过基于平行驾驶管控装置的中心化控制，该系统能够实现多车协同，这样使 得在管控中心的一个驾驶员可以遥控多辆智能网联车，从而显著降低了人力成本，提高了对智能网联车的管理效率。
此外，平行驾驶管控装置可以同时监管和引导真实交通环境下的智能网联车的运行。尤其是，平行驾驶管控装置在智能网联车未主动请求接管的情况下，能够在监测到车辆异常时进行主动接管，这样可以有效提高交通安全。
另外，平行驾驶管控装置还可以结合大数据分析技术对当前环境累积的智能网联车的数据和交通流信息进行分析，结合真实智能网联车的实时状态信息，在线预测未来一段时间真实车的实际状态，进而实时引导智能车的运行。同时，平行驾驶管控装置获取的数据也可以用于离线优化相关算法，进而高效促进智能网联车辆的研发。
应该理解的是，本发明所公开的实施例不限于这里所公开的特定结构或处理步骤，而应当延伸到相关领域的普通技术人员所理解的这些特征的等同替代。还应当理解的是，在此使用的术语仅用于描述特定实施例的目的，而并不意味着限制。
说明书中提到的“一个实施例”或“实施例”意指结合实施例描述的特定特征、结构或特性包括在本发明的至少一个实施例中。因此，说明书通篇各个地方出现的短语“一个实施例”或“实施例”并不一定均指同一个实施例。
虽然上述示例用于说明本发明在一个或多个应用中的原理，但对于本领域的技术人员来说，在不背离本发明的原理和思想的情况下，明显可以在形式上、用法及实施的细节上作各种修改而不用付出创造性劳动。因此，本发明由所附的权利要求书来限定。
Claims (17)
- 一种智能网联车控制装置，其特征在于，所述智能网联车控制装置包括：第一通信模块，其用于与平行驾驶管控装置进行数据通信，能够将智能网联车的行驶数据传输至所述平行驾驶管控装置，还能够接收所述平行驾驶管控装置传输来的驾驶模式信号；模式切换模块，所述模式切换模块与智能网联车的车辆控制系统和第一通信模块连接，用于根据所述驾驶模式信号确定针对所述智能网联车的驾驶模式，以使得所述车辆控制系统能够基于相应车辆控制信号对所述智能网联车的行驶状态进行控制。
- 如权利要求1所述的智能网联车控制装置，其特征在于，所述模式切换模块所配置的驾驶模式包括自动驾驶模式和遥控驾驶模式，其中，所述遥控驾驶模式的优先级高于所述自动驾驶模式。
- 如权利要求2所述的智能网联车控制装置，其特征在于，如果所述模式切换模块的当前控制模式为自动驾驶模式，所述车辆控制系统则会根据自身产生的车辆控制信号来对所述智能网联车的行驶状态进行控制；如果所述模式切换模块的当前控制模式为遥控驾驶模式，所述车辆控制系统则会根据所述平行驾驶管控装置传输来的车辆控制信号来对所述智能网联车的行驶状态进行控制。
- 如权利要求3所述的智能网联车控制装置，其特征在于，如果所述模式切换模块的当前控制模式为自动驾驶模式，当接收到所述平行驾驶管控装置传输来的遥控驾驶信号时，所述模式切换模块则会将控制模式切换为遥控驾驶模式。
- 如权利要求2～4中任一项所述的智能网联车控制装置，其特征在于，当所述智能网联车存在第一类异常时，所述模式切换模块配置为生成遥控驾驶接管请求信号，并将所述遥控驾驶接管请求信号通过所述第一通信模块传输至平行驾驶管控装置。
- 一种平行驾驶管控装置，其特征在于，所述平行驾驶管控装置用于与智能网联车控制装置和遥控驾驶装置通信连接，其配置为在接收到所述智能网联车控制装置传输来的遥控驾驶接管请求信号后，生成遥控驾驶请求信号并将所述遥控驾驶请求信号传输至所述遥控驾驶装置；所述平行驾驶管控装置还配置为接收所述遥控驾驶装置响应所述遥控驾驶请求信号而生成的驾驶模式信号，并将所述驾驶模式信号和车辆控制信号传输至所述智能网联车控制装置，以由所述智能网联车控制装置根据所述驾驶模式信号确定针对所述智能网联车的驾驶模式。
- 如权利要求6所述的平行驾驶管控装置，其特征在于，所述平行驾驶管控装置包 括：第二通信模块，其用于与所述智能网联车控制装置连接，能够实现所述平行驾驶管控装置与智能网联车控制装置之间的数据通信；虚拟系统模块，其与所述第二通信模块连接，用于接收所述第二通信模块传输来的遥控驾驶接管请求信号，根据所述遥控驾驶接管请求信号生成遥控驾驶请求信号并将所述遥控驾驶请求信号传输至所述遥控驾驶装置，还用于将所述遥控驾驶装置传输来的驾驶模式信号传输至所述第二通信模块。
- 如权利要求7所述的平行驾驶管控装置，其特征在于，所述虚拟系统模块还配置为通过所述第二通信模块获取所述智能网联车控制装置所传输来的智能网联车的行驶数据，并根据所述行驶数据确定所述智能网联车的行驶状态，其中，如果所述智能网联车存在第二类异常时，所述虚拟系统模块配置为主动生成遥控驾驶请求信号并将所述遥控驾驶请求信号传输至所述遥控驾驶装置。
- 如权利要求7或8所述的平行驾驶管控装置，其特征在于，所述虚拟系统模块包括：虚拟场景单元，其用于搭建虚拟交通环境和交通流；虚拟交互单元，其与所述虚拟场景单元连接，用于在所述虚拟交通环境中构建虚拟车，并根据所获取到的智能网联车的行驶数据利用所述虚拟车映射所述智能网联车的行驶状态，通过对比分析映射的所述智能网联车的行驶状态与预设参考行驶状态，实现对所述智能网联车行驶状态的监测。
- 如权利要求9所述的平行驾驶管控装置，其特征在于，所述虚拟系统模块还包括：虚拟仿真单元，其与所述虚拟交互单元连接，用于根据映射的所述智能网联车的行驶状态来对所述智能网联车的相关控制算法进行检测。
- 如权利要求10所述的平行驾驶管控装置，其特征在于，所述虚拟系统模块还配置为根据所述智能网联车的行驶状态生成未来指定时段内针对指定虚拟场景的模拟状态信息，并根据所述模拟状态信息生成最优预测决策规划信息，将所述最优预测决策规划信息通过所述第二通信模块传输至所述智能网联车。
- 如权利要求9～11中任一项所述的平行驾驶管控装置，其特征在于，所述平行驾驶管控装置还包括：云端平台，其与所述第二通信模块连接并与所述智能网联车控制装置通信连接，所述第二通信模块通过所述云端平台与所述智能网联车控制装置进行数据通信。
- 如权利要求12所述的平行驾驶管控装置，其特征在于，所述虚拟交互单元和/ 或虚拟仿真单元集成在所述云端平台中。
- 如权利要求12或13所述的平行驾驶管控装置，其特征在于，所述平行驾驶管控装置还包括：视频模块，其与所述云端平台连接，所述云端平台用于接收所述智能网联车控制装置传输来的关于所述智能网联车的行驶视频信息，所述视频模块用于从所述云端平台下载所述行驶视频信息并将所述行驶视频信息传输至所述遥控驾驶装置。
- 一种遥控驾驶装置，其特征在于，所述遥控驾驶装置包括：驾驶模拟器，其与所述平行驾驶管控装置通信连接，用于根据所述平行驾驶管控装置传输来的遥控驾驶请求信号生成遥控驾驶指示信号，并将根据用户操作生成的驾驶模式信号和车辆控制信号通过所述平行驾驶管控装置传输至智能网联车控制装置，以实现对所述智能网联车的远程操控。
- 如权利要求15所述的遥控驾驶装置，其特征在于，所述驾驶模拟器包括显示屏和操作部，所述显示屏用于对所述平行驾驶管控装置传输来的相关数据进行可视化显示，所述操作部用于基于用户操作生成相应的驾驶模式信号和车辆控制信号并将所述车辆控制信号传输至所述平行驾驶管控装置。
- 一种用于智能网联车的平行遥控驾驶系统，其特征在于，所述系统包括：如权利要求1～5中任一项所述的智能网联车控制装置；如权利要求6～14中任一项所述的平行驾驶管控装置；以及，如权利要求15或16所述的遥控驾驶装置。
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| CN112925309A (zh) * | 2021-01-22 | 2021-06-08 | 中国汽车技术研究中心有限公司 | 智能网联汽车数据交互方法及交互系统 |
Families Citing this family (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109739224A (zh) * | 2018-12-17 | 2019-05-10 | 北京百度网讯科技有限公司 | 远程驾驶方法、装置、设备及计算机可读存储介质 |
| CN113377093B (zh) * | 2019-01-15 | 2024-04-30 | 北京百度网讯科技有限公司 | 自动驾驶汽车的远程控制方法和装置 |
| CN109753069B (zh) | 2019-01-16 | 2021-04-13 | 北京百度网讯科技有限公司 | 车辆控制方法和装置 |
| CN109808705B (zh) * | 2019-01-23 | 2021-11-02 | 青岛慧拓智能机器有限公司 | 一种用于远程遥控驾驶控制的系统 |
| US11066025B2 (en) * | 2019-03-27 | 2021-07-20 | Jack Chen | Electric vehicle drive system and operation method of the same |
| CN110083109B (zh) * | 2019-05-20 | 2020-08-07 | 中国汽车工程研究院股份有限公司 | 一种无人车远程控制系统及其方法 |
| CN110182219B (zh) * | 2019-05-31 | 2020-10-02 | 江苏盛海智能科技有限公司 | 一种无人驾驶车辆的资源调度方法及无人驾驶车辆 |
| CN112051840A (zh) * | 2019-06-06 | 2020-12-08 | 长沙智能驾驶研究院有限公司 | 车辆控制方法、装置、设备、系统及存储介质 |
| JP7099412B2 (ja) * | 2019-07-16 | 2022-07-12 | トヨタ自動車株式会社 | 車両制御装置及び車両制御システム |
| CN110708504A (zh) * | 2019-09-06 | 2020-01-17 | 北京智行者科技有限公司 | 基于平行驾驶的数据处理方法及系统 |
| DE102019214429A1 (de) * | 2019-09-23 | 2021-03-25 | Robert Bosch Gmbh | Verfahren zum Fernsteuern eines Kraftfahrzeugs |
| CN110825081B (zh) * | 2019-11-08 | 2021-10-29 | 禾多科技（北京）有限公司 | 应用于自动驾驶高速场景的避免平行驾驶的智能决策方法 |
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| CN112987699B (zh) * | 2021-04-27 | 2022-08-30 | 北京踏歌智行科技有限公司 | 一种基于叠加式抬头显示的矿用卡车远程应急接管系统 |
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| CN115935573A (zh) * | 2021-08-09 | 2023-04-07 | 北京图森智途科技有限公司 | 用于自动驾驶车辆的仿真方法和控制自动驾驶车辆的方法 |
| CN113879335A (zh) * | 2021-09-30 | 2022-01-04 | 北京百度网讯科技有限公司 | 平行驾驶的车辆控制方法、装置、设备以及存储介质 |
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| CN114228743B (zh) * | 2021-11-30 | 2023-08-29 | 上汽通用五菱汽车股份有限公司 | 无人物流车控制方法、装置、系统和可读存储介质 |
| CN113844465B (zh) * | 2021-12-02 | 2022-07-15 | 上海车右智能科技有限公司 | 一种自动驾驶方法及系统 |
| CN114464006B (zh) * | 2022-04-13 | 2022-06-28 | 新石器慧通（北京）科技有限公司 | 自动驾驶车辆的分配方法和装置 |
| CN115022834A (zh) * | 2022-05-12 | 2022-09-06 | 东风汽车集团股份有限公司 | 一种远程驾驶车端网络筛选切换方法与系统 |
| CN114973736A (zh) * | 2022-05-30 | 2022-08-30 | 东风汽车集团股份有限公司 | 基于虚拟仿真的远程驾驶监控系统 |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010134824A1 (en) * | 2009-05-20 | 2010-11-25 | Modulprodukter As | Driving assistance device and vehicle system |
| CN103065487A (zh) * | 2012-12-19 | 2013-04-24 | 深圳市元征科技股份有限公司 | 车联网智能交通系统及车载终端 |
| CN104751669A (zh) * | 2015-03-20 | 2015-07-01 | 江苏大学 | 一种基于车联网的智能驾驶辅助系统及方法 |
| CN204537459U (zh) * | 2015-03-20 | 2015-08-05 | 江苏大学 | 一种基于车联网的智能驾驶辅助系统 |
| CN106302432A (zh) * | 2016-08-11 | 2017-01-04 | 浙江吉利控股集团有限公司 | 一种基于车联网的通信装置及控制方法 |
| CN206147942U (zh) * | 2016-09-28 | 2017-05-03 | 浙江工业大学义乌科学技术研究院有限公司 | 一种基于车联网的辅助驾驶系统 |
| US20170193819A1 (en) * | 2014-05-26 | 2017-07-06 | Telefonaktiebolaget Lm Ericsson (Publ) | Methods and network nodes for notifying vehicle drivers about their driving |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102013201168A1 (de) * | 2013-01-24 | 2014-07-24 | Ford Global Technologies, Llc | Bedarfsweise aktivierbares Fernsteuerungssystem für Kraftfahrzeuge |
| US10328897B1 (en) * | 2015-09-25 | 2019-06-25 | Apple Inc. | Authorized remote control |
| CN106153352B (zh) * | 2016-07-04 | 2018-08-10 | 江苏大学 | 一种无人驾驶车辆测试验证平台及其测试方法 |
| WO2018029101A1 (en) * | 2016-08-10 | 2018-02-15 | Jaguar Land Rover Limited | Control of an autonomous vehicle |
| CN106394545A (zh) * | 2016-10-09 | 2017-02-15 | 北京汽车集团有限公司 | 驾驶系统、无人驾驶车辆及车辆远程控制端 |
| CN107463171A (zh) * | 2017-06-13 | 2017-12-12 | 纵目科技（上海）股份有限公司 | 无人驾驶车辆的远程控制方法、系统、服务平台、车载终端 |
| US20190064803A1 (en) * | 2017-08-28 | 2019-02-28 | nuTonomy Inc. | Mixed-mode driving of a vehicle having autonomous driving capabilities |
| CN107329482A (zh) * | 2017-09-04 | 2017-11-07 | 苏州驾驶宝智能科技有限公司 | 自动驾驶车人机协同驾驶方法 |
| CN107505944B (zh) * | 2017-09-27 | 2021-04-16 | 驭势科技（北京）有限公司 | 一种用于对车辆进行远程协助的方法与设备 |
-
2018
- 2018-03-22 CN CN201810239901.4A patent/CN108428357B/zh active Active
- 2018-04-19 WO PCT/CN2018/083731 patent/WO2019178907A1/zh active Application Filing
- 2018-04-19 US US16/981,904 patent/US11842638B2/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010134824A1 (en) * | 2009-05-20 | 2010-11-25 | Modulprodukter As | Driving assistance device and vehicle system |
| CN103065487A (zh) * | 2012-12-19 | 2013-04-24 | 深圳市元征科技股份有限公司 | 车联网智能交通系统及车载终端 |
| US20170193819A1 (en) * | 2014-05-26 | 2017-07-06 | Telefonaktiebolaget Lm Ericsson (Publ) | Methods and network nodes for notifying vehicle drivers about their driving |
| CN104751669A (zh) * | 2015-03-20 | 2015-07-01 | 江苏大学 | 一种基于车联网的智能驾驶辅助系统及方法 |
| CN204537459U (zh) * | 2015-03-20 | 2015-08-05 | 江苏大学 | 一种基于车联网的智能驾驶辅助系统 |
| CN106302432A (zh) * | 2016-08-11 | 2017-01-04 | 浙江吉利控股集团有限公司 | 一种基于车联网的通信装置及控制方法 |
| CN206147942U (zh) * | 2016-09-28 | 2017-05-03 | 浙江工业大学义乌科学技术研究院有限公司 | 一种基于车联网的辅助驾驶系统 |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112925309A (zh) * | 2021-01-22 | 2021-06-08 | 中国汽车技术研究中心有限公司 | 智能网联汽车数据交互方法及交互系统 |
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| US20210134154A1 (en) | 2021-05-06 |
| US11842638B2 (en) | 2023-12-12 |
| CN108428357B (zh) | 2020-08-18 |
| CN108428357A (zh) | 2018-08-21 |
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