CN115257813A

CN115257813A - Intelligent driving control method through construction obstacle and vehicle

Info

Publication number: CN115257813A
Application number: CN202210999747.7A
Authority: CN
Inventors: 刘婷; 褚永强
Original assignee: Zhiji Automobile Technology Co Ltd
Current assignee: Zhiji Automobile Technology Co Ltd
Priority date: 2022-08-19
Filing date: 2022-08-19
Publication date: 2022-11-01
Anticipated expiration: 2042-08-19

Abstract

The invention discloses an intelligent driving control method and a vehicle through a construction barrier, wherein the method comprises the steps of judging whether a current driving path enters a construction road section or not, and if so, acquiring the position coordinate of the construction barrier closest to the vehicle according to vehicle-mounted sensing information; extracting a gauge control track line in an intelligent driving system, wherein the gauge control track line is generated by the intelligent driving system according to a driving environment; calculating the position relation between the gauge control track line and the nearest construction barrier from the self vehicle; and determining a confidence score based on the position relation, and reminding the user to pay attention to the road condition when the confidence score is lower than a reminding threshold value. According to the method and the system, the driver is informed of the system capability of the support system for processing the construction barrier scene in time according to the traffic confidence of the self-vehicle running and the construction barrier, the user is reminded of taking over the driving, and the problem that the user cannot take over the driving in time when encountering the construction scene in the process of using intelligent driving is solved.

Description

Intelligent driving control method through construction obstacle and vehicle

Technical Field

The invention relates to the field of intelligent driving, in particular to an intelligent driving control method through a construction obstacle and a vehicle.

Background

The current intelligent driving technology is limited, when a construction protection road section such as a traffic cone is met, the situation of mistaken identification or missed identification of obstacles exists, so that an intelligent driving system cannot accurately change lanes or stop before the construction of the obstacles, a driver is panic, and serious people cause traffic accidents; in addition, the problem that the driver cannot be effectively warned, so that the driver cannot take over the vehicle in time manually is caused.

The prior art therefore remains to be developed further.

Disclosure of Invention

In order to solve the technical problems, the invention provides an intelligent driving control method through a construction obstacle and a vehicle.

In a first aspect of the present invention, there is provided a method for controlling smart driving through a construction obstacle, comprising:

judging whether the current driving path enters a construction road section, if so, acquiring the position coordinates of a construction barrier closest to the self vehicle according to the vehicle-mounted sensing information;

extracting a gauge control track line in an intelligent driving system, wherein the gauge control track line is generated by the intelligent driving system according to a driving environment;

calculating the position relation between the gauge control track line and the nearest construction barrier from the self vehicle;

and determining a confidence score based on the position relation, and reminding the user to pay attention to the road condition when the confidence score is lower than a reminding threshold value.

In a second aspect of the present invention, there is provided a method for controlling smart driving through a construction obstacle, comprising:

judging whether the current driving path enters a construction road section, and if so, acquiring position coordinates away from a construction barrier according to vehicle-mounted sensing information;

calculating the waiting time of the self-vehicle self-lane-changing driving, and determining a first confidence score based on the waiting time;

calculating the collision time of the self vehicle and a construction barrier, and determining a second confidence score based on the collision time;

and comparing the first confidence score with the second confidence score, taking the minimum confidence score as the confidence score of the intelligent driving control, and reminding the user to pay attention to the road condition when the confidence score of the intelligent driving control is lower than a reminding threshold value.

In a third aspect of the invention, a vehicle is provided, comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, the computer program, when executed by the processor, implementing the method according to the first or second aspect of an embodiment of the invention.

In a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a computer, performs the method according to the first or second aspect of an embodiment of the present invention.

According to the method and the system, the driver is informed of the system capacity of the support system for processing the construction barrier scene in time according to the traffic confidence of the driving of the self-vehicle and the construction barrier, the user is reminded of taking over the driving, and the problem that the user cannot take over the construction scene in the intelligent driving process is solved.

Drawings

FIG. 1 is a schematic diagram of a network structure of a vehicle intelligent driving system according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of the structure of the vehicle control function in the embodiment of the present invention;

FIG. 3 is a flow chart illustrating a method for controlling intelligent driving through a construction obstacle according to an embodiment of the present invention;

FIG. 4 is a schematic view of the vehicle having a regulatory trajectory greater than one lane width according to an embodiment of the present invention;

FIG. 5 is a schematic view of the vehicle having a regulatory trajectory less than one lane width and greater than half a lane width in accordance with an embodiment of the present invention;

FIG. 6 is a schematic view of the control trajectory of the host vehicle being less than one lane width in accordance with an embodiment of the present invention;

fig. 7 is a flowchart illustrating another method for controlling intelligent driving through a construction obstacle according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

The smart driving system may make driving decisions using smart sensing calculations in response to an obstacle. At present, the mainstream intelligent driving system only has two gears of opening and closing for controlling the system capacity state per se, and reminds a driver to take over through emergency alarm only when the intelligent driving system exits under dangerous working conditions so as to carry out manual driving. Aiming at a complex scene of a construction environment, a driver is not warned in advance according to a specific environment, and traffic accidents caused by untimely taking over are easily caused. For example, if the intelligent driving system senses that the obstacle is a cone, but the current road driving environment is complex, the vehicle frequently changes lanes, the driving difficulty is increased, and the intelligent driving system cannot complete braking or lane change in time and does not give enough reminding to the driver.

Fig. 1 is a block diagram of the components of a smart driving system in accordance with an embodiment of the present invention. Referring to fig. 1, a smart driving system includes a host vehicle 101 that may be coupled to servers 103, 104 through a network 102. The network 102 may be, for example, a Local Area Network (LAN), a Wide Area Network (WAN) (such as the Internet, a cellular network, a satellite network, or a combination thereof). The servers 103, 104 may be any kind of server or cluster of servers, such as Web or cloud servers, application servers, back-end servers, or a combination thereof. The servers 103, 104 may be data analysis servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, among others.

The own vehicle 101 refers to a vehicle configured to be in an autonomous driving mode in which the vehicle is navigation-driven with little or no intervention of a driver. The host vehicle 101 includes a sensing system having one or more sensors configured to detect information about the environment in which the vehicle is driven. The host vehicle and its associated controller(s) use the detected information to navigate the drive. The self vehicle 101 may complete autonomous driving or assisted driving in a manual mode, a fully autonomous mode, or a partially autonomous mode.

In one embodiment, the host vehicle 101 includes, but is not limited to, a sensing and planning system 110, a vehicle control system 111, a wireless communication system 112, a user interface system 113, and a sensor system 114. The host vehicle 101 also includes certain general components of the vehicle, such as an engine, a braking system, a chassis, a transmission, a power battery, etc., which may be used by the vehicle control system 111 and/or the sensory and planning system 110 to control vehicle travel, such as signaling to accelerate, decelerate, steer, change lanes, etc., using various communication signals and/or commands.

The components, e.g., 110-115, of the host vehicle 101 may be coupled to each other via a signal interconnect, a CAN bus, a network, a local area network, or a combination thereof. For example, 110-115 may be communicatively coupled to each other over a CAN bus via a controller. Among them, the CAN bus is a vehicle bus standard designed to allow a microcontroller and a device to communicate with each other in an application without a host, which is a message-based protocol.

Referring to fig. 2, sensor system 114 includes, but is not limited to, one or more cameras 211, a Global Positioning System (GPS) unit 212, an Inertial Measurement Unit (IMU) 213, a radar unit 214, and a lidar unit 215. The GPS system 212 may include a transceiver operable to provide information regarding the location of the host vehicle. The IMU unit 213 may sense a change in position and orientation of the host vehicle based on inertial acceleration.

In some other embodiments, the sensor system 114 may further include other sensors, such as temperature sensors, wheel speed sensors, cam position sensors, crank position sensors, pressure sensors, sonar sensors, infrared sensors, steering sensors, throttle sensors, brake sensors, and sound sensors (e.g., microphones), to obtain information from the vehicle conditions, etc. The steering sensor may be configured to sense a steering angle of a steering wheel, vehicle wheels, or a combination thereof. The throttle sensor and the brake sensor sense a throttle position and a brake position of the vehicle, respectively. In some cases, the throttle sensor and the brake sensor may be integrated into an integrated throttle/brake sensor, and the controller may control acceleration and deceleration of the vehicle using the detected information. In some embodiments, any combination of sensors (e.g., cameras, lidar, etc.) of the sensing system may be collected for detecting obstacles.

In one embodiment, the vehicle control system 111 includes, but is not limited to, a drive unit 201, a throttle unit 202 (also referred to as an acceleration unit), and a brake unit 203. The driving unit 201 is used to drive the vehicle forward direction. The throttle unit 202 is used to control the power output of the motor or engine, which in turn may control the speed and acceleration of the vehicle. The brake unit 203 is used to decelerate the vehicle by providing braking of the wheels. It should be understood that the implementing functions of the components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

The wireless communication system 112 in fig. 1 is used to allow communication between the own vehicle 101 and an external system (such as a server device, a smart key, another vehicle, or the like). For example, the wireless communication system 112 may be in wireless communication with one or more handset terminals, either directly or via a communication network, such as servers 103, 104 through network 102. The wireless communication system 112 may communicate with another component or system using any cellular communication network or Wireless Local Area Network (WLAN), for example, using WiFi. The wireless communication system 112 commonly employs a Telematics-BOX, referred to as a vehicular T-BOX for short. The wireless communication system 112 can be linked with the user interface system 113 to realize various functional interactions of the user; interaction with the host vehicle or server may be accomplished, such as within the host vehicle 101, using, for example, a keypad, touch screen display, microphone, and speaker.

Some or all of the functions of the host vehicle 101 may be controlled by the sensing and planning system 110. The sensing and planning system 110 includes the necessary hardware (e.g., processors, memory, storage devices) and software (e.g., operating systems, scheduling programs) to receive information from the sensor systems 114, control systems 111, wireless communication systems 112, and/or user interface systems 113, process the received information, plan driving routes, emergency avoidance, overtaking, lane changing, etc., and then travel based on the planned and controlled information. Alternatively, the sensing and planning system 110 may be integrated with the vehicle control system 111 to form a central computing platform.

For example, the driver sets the starting location and destination of the trip based on the user interface system 113. The sensing and planning system 110 may obtain location and navigation route information from the server 104. The server provides map services and POIs.

The sensing and planning system 110 may also obtain real-time traffic information from the navigation service as the host vehicle 101 travels along the planned route. Based on the real-time traffic information, MPOI information, and location information acquired, sensed by the sensor system 114, and real-time local environmental data (e.g., obstacles, people, nearby vehicles), the perception and planning system 110 may plan an optimal route, such as driving the vehicle 101 via the control system 111 according to the planned route, to safely and efficiently reach a designated destination.

Server 103 may be a cluster of services that perform functions of data analysis services, data storage, data analysis, etc. for various clients. In one embodiment, data analysis system 103 includes a data collector 121 and a machine learning engine 122. The data collector 121 collects driving statistics 123 from individual vehicles (own vehicles or regular vehicles driven by human drivers). The driving statistics 123 include information indicative of driving commands issued (e.g., driving habits, commute laws, etc.) and responses of the vehicle captured by sensors of the vehicle at different points in time. The driving statistics 123 may also include information describing the driving environment at different points in time, such as, for example, travel routes, road conditions, weather conditions, and the like.

Based on the driving statistics 123, the machine learning engine 122 can satisfy various intelligent requirements, and complete training and application of an artificial intelligence model that is beneficial to intelligent driving. The algorithmic model 124 may include functionality to calculate the current position of an obstacle, identify the type of obstacle, the predicted trajectory for the obstacle, and the trajectory.

Furthermore, by utilizing the modules, partial or all data in a navigation map, a high-precision map, perception fusion information (such as sensor information of radar, camera and the like), vehicle body data and navigation data can be acquired, and the functions of passing driving and corresponding reminding of traffic cones on the corresponding construction roads can be completed.

Based on the sensor data provided by the sensor system 114, the positioning information obtained by the GPS unit 212, and the driving road condition obtained by the navigation map, the host vehicle can know whether to drive to the construction road section, sense the position of the traffic cone relative to the host vehicle in the construction road section, sense the environment around the road by the sensor system 114, and sense whether there are other obstacles, other vehicles, the driving states of other vehicles, the current lane position of the host vehicle, and the like. In addition to the above description, the sensory information may include traffic lights, relative positions of other vehicles, pedestrians, buildings, crosswalks, or other traffic-related signs (e.g., stop signs, yield signs), and the like.

The sensor system 114 includes a computer vision system or functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects and/or features in the environment of the host vehicle. The objects may include traffic signals, road boundaries, other vehicles, pedestrians, and/or other obstacles, and the like. Computer vision systems may use object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system may map the environment, track the object, and estimate the speed of the object, etc.

A predicted trajectory of an obstacle of a path for moving the obstacle is predicted in an area related to a current travel path. The predicted trajectory may be generated based on a current state of the moving obstacle (e.g., a speed, position, heading, acceleration, or type of the moving obstacle), map data, and traffic rules.

For example, the smart driving system may recognize an obstacle as a vehicle sensed to travel in a driving lane according to the heading direction and position of the obstacle, and perform driving control such as passing, lane changing, idling, and the like.

The intelligent driving system is generally divided into two distinguishing functions, one is an intelligent driving system with an automatic lane changing function, and the other is an intelligent driving system without the automatic lane changing function. The intelligent driving system without the autonomous lane changing function is tracking cruise, front vehicle following and the like when automatic driving is executed. Different confidence degrees are adopted for the two intelligent driving systems to represent the processing capacity of the intelligent driving system when the intelligent driving system encounters a construction obstacle, and a driver is reminded. And calculating the control process of intelligent driving by using the driving and obstacle avoiding possibility as confidence.

Referring to fig. 3, a flow diagram of an intelligent driving control method through a construction obstacle is shown, which is suitable for an intelligent driving system without an autonomous lane changing function. The process comprises the following steps:

step 310: and judging whether the current driving path enters a construction road section, if so, acquiring the position coordinates of the construction barrier closest to the self vehicle according to the vehicle-mounted sensing information. The self vehicle can acquire the traffic condition of the navigation route through the navigation function so as to judge whether the self vehicle runs into the construction road section; alternatively, by the above-mentioned pushing of the server 104, when the self-vehicle locates the adjacent construction section, the server 104 pushes information to the self-vehicle through the wireless communication system 112, and the self-vehicle can determine that the self-vehicle has entered the construction section according to the information.

The camera and the lidar of the sensor system 114 can acquire images of a driving path and obstacle information, identify whether a traffic cone exists on a road ahead by an image identification technology, and position coordinates and examples of the traffic cone can be located by using point cloud data of the lidar.

Step 320: and extracting a gauge control track line in the intelligent driving system, wherein the gauge control track line is generated by the intelligent driving system according to the driving environment. The sensing and planning system 110 and the control system 111 can control the vehicle to run according to the sensing of the navigation and sensor system 114, and the automatic driving can run according to the control trajectory. For example, the intelligent driving system plans an expected control track within 6 seconds in the future according to vision, obstacles, lane lines and body postures, and updates the track in real time. Based on the method, the control trajectory can be extracted from the intelligent driving system, and the confidence score calculation is established on the relationship between the control trajectory and the obstacle.

Step 330: and calculating the position relation between the gauge control track line and the nearest construction barrier from the self vehicle.

Step 340: and determining a confidence score based on the position relationship, and reminding the user to pay attention to the road condition when the confidence score is lower than a reminding threshold value.

Since the coordinates P (a, b, c) of the construction obstacle are known, the own vehicle can calculate the distance relationship with the construction obstacle based on the coordinate system. As an embodiment, in the calculation process, when the lateral distance between the regulated trajectory and the nearest construction obstacle to the host vehicle is less than half lane width (the width is known), a reminding event is triggered, the confidence score is set to be 1 grade, and the confidence score is lower than a reminding threshold; the user is reminded to pay attention to the road condition in partial or all modes of characters, sound, visual animation, vibration and the like, so that dangers are avoided. On the contrary, when the transverse distance between the regulation and control track line and the nearest construction obstacle from the self vehicle is not less than half lane width, the confidence coefficient can be set to be 2-level and 3-level, the confidence coefficient score is higher than the reminding threshold value, the driver does not need to be reminded to take over driving, and the confidence coefficient score can also be displayed as display data. Wherein the confidence score can also be expressed in numerical percentage and displayed to the user. When expressed as a numerical percentage, a very low confidence is assigned below 30%, a high confidence is assigned above 70%, and a medium confidence is assigned between 30% and 70%.

Calculating the transverse distance between the gauge control track line and the nearest construction barrier from the host vehicle; the lateral distance is based on the lane width. When the transverse distance between the regulation and control track line and the nearest construction obstacle to the host vehicle is less than half lane width, displaying the confidence score as level 1; when the transverse distance between the gauge control track line and the nearest construction obstacle to the host vehicle is greater than half lane width and less than one lane width, displaying the confidence score as level 2; when the transverse distance between the regulation and control track line and the nearest construction barrier to the host vehicle is larger than the width of a lane, displaying the confidence score as 3 grades; wherein, the confidence score is lower than the reminding threshold when being 1 grade.

Please refer to the confidence scores of the vehicles traveling in different driving environments shown in fig. 4 to 6. In fig. 4, the three trolleys are three positions where the self-vehicle travels in sequence along the arrow direction, the traffic cones are located on the self-vehicle, the distance between the self-vehicle and the traffic cones is greater than one lane width, the confidence score is 3, and the traffic cones closest to the self-vehicle are approximately the same. In fig. 5, the lateral distance from the nearest traffic cone is different when the host vehicle is in different positions, but the lateral distance is between one lane width and half lane width, and the confidence score is 2. In fig. 6, the vehicle is located in the middle lane, and different vehicles are located on different positions of the vehicle, and the lateral distances between the vehicle and the nearest traffic cone are different, and there are other vehicles on the right side, and as the vehicle continuously travels at the position of the vehicle, the distance between the vehicle and the nearest traffic cone is gradually less than half the lane width, so that the confidence score is reduced to level 1, and the user needs to be reminded to pay attention to the road condition under the confidence score.

In the above embodiment, the confidence score or the grade corresponding to the confidence score is defined by using the lateral distance between the gauge trajectory line and the traffic cone, and in other embodiments, the confidence score or the grade corresponding to the confidence score may be defined by using the distance relationship between the front end or the side edge of the vehicle and the traffic cone.

The invention calculates the confidence score by using the distance between the running rule control track line of the vehicle and the nearest barrier (one of a plurality of traffic cones), triggers the reminding when the confidence score is lower, and takes over the driving or pays close attention to the road condition by the driver, thereby improving the efficiency of taking over the driving by the driver and facilitating the driver to master the system processing capacity of the intelligent driving system.

Referring to fig. 7, a schematic flow chart of another intelligent driving control method through a construction obstacle is shown, which is suitable for an intelligent driving system with an autonomous lane changing function, and the flow chart includes the following steps:

step 710: and judging whether the current driving path enters a construction road section, and if so, acquiring the position coordinate away from the construction barrier according to the vehicle-mounted sensing information. The self-vehicle can acquire the traffic condition of the navigation route through the navigation function so as to judge whether the self-vehicle runs into the construction road section; alternatively, by the above-mentioned pushing of the server 104, when the self-vehicle locates the adjacent construction section, the server 104 pushes information to the self-vehicle through the wireless communication system 112, and the self-vehicle can determine that the self-vehicle has entered the construction section according to the information. The camera and the laser radar of the sensor system 114 can acquire images of a driving path and obstacle information, identify whether a traffic cone exists on a road ahead by using an image identification technology, and position coordinates and examples of the traffic cone can be located by using point cloud data of the laser radar.

Step 720: calculating the waiting time of the self-vehicle self-lane-changing driving, and determining a first confidence score based on the waiting time. The automatic track switching control mainly comprises dynamic track switching track planning and track switching track tracking control. The dynamic brain trajectory planning method can acquire real-time information according to a V2V technology to update the lane changing trajectory, so that the vehicle can better adapt to the change of the motion state of the surrounding vehicle. The lane change trajectory tracking control calculates the desired speed and heading angle (or yaw rate) required for the trajectory, primarily by the deviation between the actual position and the desired position of the vehicle. It is common to use lane-changing direction information, surrounding vehicle information, front image information, and a self-vehicle status to complete the planning and execution of lane-changing. Calculating the waiting time T, and defining a first confidence score, for example, if the waiting time T is less than the threshold 1, the confidence score F (T) =3 level, the threshold 1 ≦ waiting time T < threshold 2, and the confidence score F (T) =2 level; latency T > threshold 2, confidence F (T) = level 1.

Step 730: and calculating the collision time of the self vehicle and the construction barrier, and determining a second confidence coefficient score based on the collision time. The time of intersection with the contour edge of the construction obstacle can be calculated according to the velocity vector of the contour edge of the self vehicle and recorded as the collision time. For example, the TTC (time to collision) time of the host vehicle including an AEBS (Advanced Emergency Braking System) and the construction obstacle may be calculated, and the predicted collision time of the host vehicle with the construction obstacle may be calculated. Calculating to obtain collision time T by referring to the first confidence score, wherein T is less than a threshold value 1, and the confidence F (TTC) =3 levels; the threshold value 1 is more than or equal to T and less than the threshold value 2, and the confidence coefficient F (TTC) = grade 2; t > threshold 2, confidence F (TTC) = level 1.

The thresholds 1 and 2 are understood to be 1 minute and 2 minutes; the threshold value is of course variable and is not sufficient to limit the invention.

Step 740: and comparing the first confidence score with the second confidence score, taking the minimum confidence score as the confidence score of the intelligent driving control, and reminding the user to pay attention to the road condition when the confidence score of the intelligent driving control is lower than a reminding threshold value.

Specifically, the first confidence score and the second confidence score both correspond to confidence levels. When one of the first confidence score and the second confidence score is smaller than a preset confidence level, the confidence score is lower than a reminding threshold value; displaying the confidence score of the smaller one of the first confidence score and the second confidence score for the intelligent driving control to the user. Further, when the user takes over the driving system while reminding, the automatic driving mode is exited.

Exemplarily, the first confidence score is level 2, the second confidence score is level 1, the second confidence score is recorded as the confidence score of the intelligent driving control, and the second confidence score is level 1, a reminding function is triggered, and the user is reminded to pay attention to the road condition through partial or all of characters, sound, visual animation, vibration and the like, so that danger is avoided. For another example, if the first confidence score is 2 level and the second confidence score is 3 level, the second confidence score is recorded as the confidence score of the intelligent driving control, and the second confidence score is 2 level, and the reminding function is not triggered, only the confidence score of the intelligent driving control may be displayed, so that the driver can know the control capability of the current intelligent driving system.

According to the intelligent driving system, the confidence score is calculated by utilizing the lane changing time and the barrier collision time of the driving of the vehicle, and the system capacity of the intelligent driving system is parameterized, so that a driver can know the intelligent driving capacity state conveniently; when the confidence score is low, the reminding is triggered, and the driver takes over driving or pays attention to the road condition, so that the driving taking over efficiency of the driver can be improved, and the driver can conveniently master the system processing capacity of the intelligent driving system.

Aiming at the auxiliary driving scene of road construction, the confidence score is introduced to express the system processing capacity of the intelligent driving system for the construction obstacles, so that the driver can fully know the current driving state, and the subjective judgment of the driver is facilitated. Compared with the prior art that the driving control right is handed over by a direct system, the method is easier for the driver to receive, and the man-machine driving experience is improved.

The invention also provides a vehicle comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the intelligent driving control method by a construction obstacle as described above.

The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the intelligent driving control method by a construction obstacle as described above.

It is to be understood that the computer-readable storage medium may include: any entity or device capable of carrying a computer program, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), software distribution medium, and the like. The computer program includes computer program code. The computer program code may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), software distribution medium, and the like.

In some embodiments of the present invention, the apparatus may include a controller, which is a single chip integrated with a processor, a memory, a communication module, and the like. The processor may refer to a processor included in the controller. The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An intelligent driving control method through a construction obstacle is characterized by comprising the following steps:

extracting a regulation and control trajectory line in an intelligent driving system, wherein the regulation and control trajectory line is generated by the intelligent driving system according to a driving environment;

calculating the position relation between the gauge control trajectory and the nearest construction barrier from the vehicle;

2. The intelligent driving control method for a construction obstacle according to claim 1, wherein the calculating of the positional relationship between the gauge control trajectory line and the construction obstacle closest to the host vehicle includes: calculating the transverse distance between the gauge control track line and the nearest construction barrier from the host vehicle; the lateral distance is based on the lane width.

3. The intelligent driving control method through a construction obstacle according to claim 1, wherein the determining a confidence score based on the positional relationship includes:

when the transverse distance between the gauge control track line and the nearest construction barrier to the host vehicle is less than half lane width, the confidence score is lower than a reminding threshold value;

when the transverse distance between the gauge control track line and the nearest construction barrier to the host vehicle is not less than half lane width, the confidence score is higher than a reminding threshold value;

wherein the confidence score is expressed in terms of a reminder level or numerical percentage and presented to the user.

4. The intelligent driving control method through a construction obstacle according to claim 3, wherein the determining a confidence score based on the positional relationship further comprises:

when the transverse distance between the regulation and control track line and the nearest construction barrier to the host vehicle is less than half lane width, displaying the confidence score as level 1;

when the transverse distance between the gauge control track line and the nearest construction obstacle to the host vehicle is greater than half lane width and less than one lane width, displaying the confidence score as level 2;

when the transverse distance between the regulation and control track line and the nearest construction obstacle to the host vehicle is larger than the width of a lane, the confidence score is displayed to be 3 grade; wherein, the confidence score is lower than the reminding threshold when being 1 grade.

5. An intelligent driving control method through a construction obstacle is characterized by comprising the following steps:

judging whether the current driving path enters a construction road section, if so, acquiring position coordinates away from a construction barrier according to vehicle-mounted sensing information;

6. The intelligent driving control method for construction barriers according to claim 5, wherein when the confidence score of the intelligent driving control is lower than a reminding threshold, reminding a user of paying attention to a road condition comprises:

when one of the first confidence score and the second confidence score is smaller than a preset confidence level, the confidence score is lower than a reminding threshold value; displaying the confidence score of the smaller one of the first confidence score and the second confidence score for the intelligent driving control to the user;

wherein the first confidence score and the second confidence score both correspond to confidence levels.

7. The intelligent driving control method through a construction obstacle according to claim 5, wherein the calculating of the collision time of the own vehicle with the construction obstacle includes: and calculating the time of intersection with the contour edge of the construction obstacle according to the speed vector of the contour edge of the self vehicle, and recording as collision time.

8. The intelligent driving control method through a construction obstacle according to claim 5, further comprising: the autonomous driving mode is exited when the user takes over the driving system.

9. A vehicle comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing a method of intelligent driving control over a construction obstacle as claimed in any one of claims 1 to 5 or in any one of claims 6 to 8.

10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when executed by a computer, executes the intelligent driving control method by a construction obstacle according to any one of claims 1 to 5 or any one of claims 6 to 8.