CN117784768A - Vehicle obstacle avoidance planning method, device, computer equipment and storage medium - Google Patents
Vehicle obstacle avoidance planning method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a vehicle obstacle avoidance planning method, a vehicle obstacle avoidance planning device, computer equipment and a storage medium. The method comprises the following steps: firstly, acquiring self-vehicle information and dangerous target information, determining collision points based on the self-vehicle information and the dangerous target information, determining a plurality of obstacle avoidance terminals based on the collision points, planning a plurality of initial obstacle avoidance routes based on the self-vehicle information and the obstacle avoidance terminals, finally, determining cost functions of the initial obstacle avoidance routes, and determining a target obstacle avoidance route based on the cost functions. That is, when the obstacle avoidance planning of the own vehicle is carried out, the collision points are determined by combining dangerous target information, a plurality of obstacle avoidance routes capable of avoiding collision are determined based on the collision points, the optimal obstacle avoidance route is determined by combining cost functions, the change condition of dangerous targets is comprehensively considered, and the accuracy and the effectiveness of the obstacle avoidance route planning are improved.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a vehicle obstacle avoidance planning method, a vehicle obstacle avoidance planning device, computer equipment and a storage medium.
Background
The automatic driving technology relies on cooperation of artificial intelligence, visual calculation, radar, monitoring device and global positioning system, so that the computer can automatically and safely control the vehicle to run without any active operation of human beings. The track planning is one of key technologies of the automatic driving technology, and can plan an effective vehicle driving path on the basis of vehicle-road coordination, and generally, a path from a starting point to a terminal point is planned by using a global path planning method according to high-precision map information.
However, when the vehicle is traveling along the planned path, vehicles, pedestrians, road blocks and the like on the road are all dynamically changed, and belong to unknown information, in the related art, specific information of obstacles such as the vehicles, the road blocks and the like appearing in front of the vehicle traveling is usually obtained in real time based on a sensing system, and a route which needs to be traveled for avoiding collision with the obstacles is planned by using a local path planning method. However, since the vehicle and the obstacle may be in the process of dynamic movement, in the existing path planning method, path planning is continuously performed according to the change, so that the accuracy and the effectiveness of the obstacle avoidance route are reduced, and resources are greatly wasted.
Therefore, there is a need in the art for a way to improve the accuracy and effectiveness of obstacle avoidance planning routes for vehicles.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a vehicle obstacle avoidance planning method, apparatus, computer device and computer readable storage medium capable of improving accuracy and effectiveness of a vehicle obstacle avoidance planning route.
In a first aspect, the present application provides a vehicle obstacle avoidance planning method. The method comprises the following steps:
acquiring own vehicle information and dangerous target information;
determining a collision point based on the own vehicle information and the dangerous target information;
determining a plurality of obstacle avoidance endpoints based on the collision points, and planning a plurality of initial obstacle avoidance routes based on the self-vehicle information and the plurality of obstacle avoidance endpoints;
and determining cost functions of the plurality of initial obstacle avoidance routes, and determining a target obstacle avoidance route based on the cost functions.
Optionally, in an embodiment of the present application, the vehicle information includes a vehicle curvature and a vehicle curvature change rate, the dangerous target information includes a dangerous target position and a dangerous target speed, and determining the collision point based on the vehicle information and the dangerous target information includes:
and if the dangerous target speed is zero, determining a collision longitudinal position based on the dangerous target position, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
Optionally, in an embodiment of the present application, the vehicle information includes a vehicle speed and an acceleration, the dangerous target information includes a dangerous target acceleration, and determining the collision point based on the vehicle information and the dangerous target information further includes:
and if the dangerous target speed is not zero, determining a collision longitudinal position based on the dangerous target position, the vehicle speed and acceleration, the dangerous target speed and the dangerous target acceleration, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
Optionally, in an embodiment of the present application, the planning a plurality of initial obstacle avoidance routes based on the self-vehicle information and a plurality of obstacle avoidance endpoints includes:
determining an obstacle avoidance route starting point based on the self-vehicle information;
determining planning parameters based on the starting point information and the end point information, wherein the starting point information comprises a starting point transverse position, a starting point course angle, a starting point curvature and a starting point curvature change rate, and the end point information comprises an end point transverse position, an end point longitudinal position, an end point course angle, an end point curvature and an end point curvature change rate;
and determining a plurality of initial obstacle avoidance routes based on the planning parameters, the start points of the obstacle avoidance routes and the end points of the obstacle avoidance routes.
Optionally, in an embodiment of the present application, the initial obstacle avoidance line includes a trend of change in acceleration of the vehicle, a total distance of the line, and the determining the cost function of the plurality of initial obstacle avoidance lines includes:
and determining a cost function based on the self-vehicle acceleration change trend, the total distance of the route and the route safety coefficient, wherein the route safety coefficient is obtained by judging based on the self-vehicle information and the dangerous target information.
Optionally, in an embodiment of the present application, the method further includes:
and carrying out avoidance side planning risk assessment based on the vehicle information and the environment information.
Optionally, in an embodiment of the present application, the performing the avoidance side planning risk assessment based on the vehicle information and the environmental information includes:
dividing adjacent lanes based on environmental information, and judging whether dangerous vehicles exist in each area of the adjacent lanes;
and if dangerous vehicles exist in each area, carrying out avoidance side planning risk assessment by combining the safe distance in each area.
In a second aspect, the application further provides a vehicle obstacle avoidance planning device. The device comprises:
the information acquisition module is used for acquiring the vehicle information and the dangerous target information;
the collision point determining module is used for determining a collision point based on the vehicle information and the dangerous target information;
The initial obstacle avoidance route planning module is used for determining a plurality of obstacle avoidance endpoints based on the collision points and planning a plurality of initial obstacle avoidance routes based on the vehicle information and the obstacle avoidance endpoints;
and the target obstacle avoidance route determination module is used for determining cost functions of the plurality of initial obstacle avoidance routes and determining a target obstacle avoidance route based on the cost functions.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor executing the steps of the method according to the various embodiments described above.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method described in the above embodiments.
According to the vehicle obstacle avoidance planning method, device, computer equipment and storage medium, firstly, the vehicle information and the dangerous target information are acquired, then, collision points are determined based on the vehicle information and the dangerous target information, then, a plurality of obstacle avoidance terminals are determined based on the collision points, a plurality of initial obstacle avoidance routes are planned based on the vehicle information and the obstacle avoidance terminals, finally, cost functions of the initial obstacle avoidance routes are determined, and a target obstacle avoidance route is determined based on the cost functions. That is, when the obstacle avoidance planning of the own vehicle is carried out, the collision points are determined by combining dangerous target information, a plurality of obstacle avoidance routes capable of avoiding collision are determined based on the collision points, the optimal obstacle avoidance route is determined by combining cost functions, the change condition of dangerous targets is comprehensively considered, and the accuracy and the effectiveness of the obstacle avoidance route planning are improved.
Drawings
FIG. 1 is an application environment diagram of a vehicle obstacle avoidance planning method in one embodiment;
FIG. 2 is a flow chart of a vehicle obstacle avoidance planning method according to one embodiment;
FIG. 3 is a schematic diagram of an initial obstacle avoidance routing step, in one embodiment;
FIG. 4 is a schematic diagram of an initial obstacle avoidance line, in one embodiment;
FIG. 5 is a schematic diagram of a dodge side planning risk assessment step in one embodiment;
FIG. 6 is a practical application scenario of a vehicle obstacle avoidance planning method in one embodiment;
fig. 7 is a practical application scenario of a vehicle obstacle avoidance planning method in one embodiment;
FIG. 8 is a block diagram of a vehicle obstacle avoidance planning device in one embodiment;
fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The term "system" as used herein refers to mechanical and electrical hardware, software, firmware, electronic control components, processing logic, and/or processor devices, which may provide the described functionality alone or in combination. May include, but is not limited to, an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) that executes one or more software or firmware programs, a memory containing software or firmware instructions, a combinational logic circuit, and/or other components.
The vehicle obstacle avoidance planning method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Fig. 1 shows a side view of a vehicle 10, the vehicle 10 being disposed on a travel surface 70 (e.g., a paved road surface) and being capable of traversing travel on the travel surface 70. The vehicle 10 includes a vehicle on-board navigation system 24, a computer readable storage or medium (memory) 23 of a digitized road map 25, a space monitoring system 100, a vehicle controller 50, a Global Positioning System (GPS) sensor 52, a human/machine interface (HMI) device 60, and in one embodiment, an autonomous controller 65 and a telematics controller 75. The vehicle 10 includes, but is not limited to, a commercial vehicle, an industrial vehicle, an agricultural vehicle, a passenger vehicle, an aircraft, a watercraft, a train, an all-terrain vehicle, a personal mobile device, a robot, and similar forms of mobile platforms for the purposes of this application.
In one embodiment, the spatial monitoring system 100 includes: one or more space sensors and systems configured to monitor a viewable area 32 in front of the vehicle 10; and a space monitoring controller 110. The spatial sensors configured to monitor the viewable area 32 in front of the vehicle 10 include, for example, a lidar sensor 34, a radar sensor 36, a digital camera 38, and the like. Each spatial sensor arrangement includes onboard vehicles to monitor all or a portion of the viewable area 32 for detecting proximity to remote objects, such as road features, lane markings, buildings, pedestrians, road signs, traffic control lights and signs, other vehicles, and geographic features proximal to the vehicle 10. The spatial monitoring controller 110 generates a representation number of the viewable area 32 based on data input from the spatial sensor. The space monitoring controller 110 may evaluate the inputs from the space sensors to determine the linear range, relative speed, and trajectory of the vehicle 10 based on each near-remote object. The space sensors may be disposed at various locations on the vehicle 10, including front corners, rear sides, and mid sides. In one embodiment, the spatial sensor may include, but is not limited to, a front radar sensor and a camera. The spatial sensors are arranged in a manner that enables the spatial monitoring controller 110 to monitor traffic flow, including approaching vehicles, intersections, lane markings, and other objects surrounding the vehicle 10. A lane marker detection processor (not shown) may estimate a road based on data generated by the spatial monitoring controller 110. The spatial sensors of the vehicle spatial monitoring system 100 may include object location sensing devices including range sensors, such as FM-CW (frequency modulated continuous wave) radar, pulse and FSK (frequency shift keying) radar, and Lidar (light detection and ranging) devices, as well as ultrasonic devices, that rely on effects such as doppler effect measurements to locate a forward object. The object positioning device may include a Charge Coupled Device (CCD) or a Complementary Metal Oxide Semiconductor (CMOS) video image sensor as well as other camera/video image processors that utilize digital photography methods to 'view' the object in front (including one or more vehicles).
The lidar sensor 34 measures the range or distance to the object based on the pulsed and reflected laser beams. The radar sensor 36 determines the range, angle and/or speed of the object based on the radio waves. The camera 38 includes an image sensor, a lens, and a camera controller. An image sensor is an electro-optical device that converts an optical image into an electronic signal using a multi-dimensional array of photosensitive sensing elements. The camera controller is operatively connected to the image sensor to monitor the viewable area 32. The camera controller is arranged to control the image sensor for capturing an image of a field of view (FOV) associated with a field of view 32 projected onto the image sensor via the lens. The optical lens may include a pinhole lens, a fisheye lens, a stereoscopic lens, a telescopic lens, and the like. The camera 38 periodically captures image files associated with the viewable area 32 via the image sensor at a desired rate (e.g., 30 image files per second). Each image file includes 2D or 3D pixelated representations of all or a portion of the viewable area 32 captured at the original resolution of the camera 38. In one embodiment, the image file is in the form of a 24-bit image including spectral values and depth values of RGB (red-green-blue) visible light representing the viewable area 32. Other embodiments of the image file may include a 2D or 3D image at a resolution level depicting a spectrum of black and white or gray-scale visible light of the viewable area 32, an infrared spectrum of the viewable area 32, or other images, as this application is not specifically limited. In one embodiment, images of multiple image files may be evaluated for parameters related to brightness and/or luminance. Alternatively, the image may be evaluated based on RGB color components, brightness, texture, contours, or combinations thereof. The image sensor communicates with an encoder that performs Digital Signal Processing (DSP) for each image file. The image sensor of camera 38 may be configured to capture images at a nominal standard definition resolution (e.g., 640x480 pixels). Alternatively, the image sensor of camera 38 may be configured to capture images at a nominal high definition resolution (e.g., 1440x1024 pixels) or at another suitable resolution. The image sensor of camera 38 may capture still images or alternatively digital video images at a predetermined image capture rate. In one embodiment, the image file is sent to the camera controller as an encoded data file that is stored in a non-transitory digital data storage medium for on-board or off-board analysis.
The camera 38 is disposed and positioned on the vehicle 10 in a position capable of capturing an image of the viewable area 32, wherein the viewable area 32 includes at least in part a portion of the travel surface 70 forward of the vehicle 10 and including a trajectory of the vehicle 10. The viewable area 32 may also include the surrounding environment, including, for example, vehicle traffic, roadside objects, pedestrians and other features, sky, horizon, travel lanes, and vehicles coming in front of the vehicle 10. Other cameras (not shown) may also be included, including, for example, a second camera disposed on a rear or side portion of the vehicle 10 for monitoring the rear of the vehicle 10 and either the right or left side of the vehicle 10.
The autonomous controller 65 is used to implement autonomous driving or Advanced Driver Assistance System (ADAS) vehicle functionality. Such functionality may include a vehicle onboard control system capable of providing a level of driving automation. The terms 'driver' and 'operator' describe the person responsible for directing the operation of the vehicle 10, who may be involved in controlling one or more vehicle functions, or directing an autonomous vehicle. Driving automation may include dynamic driving and vehicle operation. Driving automation may include some level of automatic control or intervention involving individual vehicle functions (e.g., steering, acceleration, and/or braking), wherein the driver may continuously control the vehicle 10 as a whole. Driving automation may include some level of automatic control or intervention involving simultaneous control of multiple vehicle functions (e.g., steering, acceleration, and/or braking), wherein the driver may continuously control the vehicle 10 as a whole. Driving automation may include simultaneous automatic control of vehicle driving functions (including steering, acceleration, and braking), wherein the driver may relinquish control of the vehicle for a period of time during the course. The driving automation may include simultaneous automatic control of vehicle driving functions (including steering, acceleration, and braking), wherein the driver may override control of the vehicle 10 throughout the journey. The driving automation comprises hardware and a controller arranged to monitor the spatial environment in various driving modes for performing various driving tasks during dynamic vehicle operation. Driving automation includes, but is not limited to, cruise control, adaptive cruise control, lane change warning, intervention and control, automatic stopping, acceleration, braking, and the like. Autonomous vehicle functions include, but are not limited to, adaptive Cruise Control (ACC) operations, lane guidance and lane keeping operations, lane changing operations, steering assist operations, object avoidance operations, parking assist operations, vehicle braking operations, vehicle speed and acceleration operations, vehicle lateral movement operations, e.g., as lane guidance, lane keeping and lane changing operations, and the like. Based thereon, the brake command may be generated by the autonomous controller 65 independent of the action by the vehicle operator and in response to the autonomous control function.
Operator controls may be included in the passenger compartment of the vehicle 10 including, but not limited to, steering wheels, accelerator pedals, brake pedals, and operator input devices that are elements of the HMI device 60. The vehicle operator may interact with the running vehicle 10 based on operator controls and direct the operation of the vehicle 10 for providing passenger transport. In some embodiments of the vehicle 10, operator controls may be omitted, including steering wheels, accelerator pedals, brake pedals, gear-change range selectors, and other control devices of the like.
The HMI device 60 provides man-machine interaction for guiding the infotainment system, global Positioning System (GPS) sensor 52, navigation system 24, and similar operational functions, and the HMI device 60 may include a controller. The HMI device 60 monitors operator requests and provides information to the operator including status, service, and maintenance information of the vehicle system. HMI device 60 may communicate with and/or control operation of a plurality of operator interface devices capable of communicating messages associated with operation in an automatic vehicle control system. HMI device 60 may also communicate with one or more devices that monitor biometric data associated with the vehicle operator, including, for example, eye gaze location, pose, and head position tracking, among others. For simplicity of description, the HMI device 60 is depicted as a single device, but in embodiments of the system of the present application may be provided as multiple controllers and associated sensing devices. The operator interface device may include a device capable of transmitting a message prompting an operator action, and may include an electronic visual display module, such as a Liquid Crystal Display (LCD) device, head-up display (HUD), audio feedback device, wearable device, and haptic seat. The operator interface device capable of prompting an operator action may be controlled by the HMI device 60 or by the HMI device 60. In the operator's field of view, the HUD may project information reflected onto the interior side of the vehicle's windshield, including conveying a confidence level associated with operating one of the automatic vehicle control systems. The HUD may also provide augmented reality information, such as lane position, vehicle path, direction and/or navigation information, and so forth.
The on-board navigation system 24 provides navigation support and information to the vehicle operator based on the digitized road map 25. The autonomous controller 65 controls autonomous vehicle operation or ADAS vehicle functions based on the digitized road map 25.
The vehicle 10 may include a telematics controller 75, the telematics controller 75 including a wireless telematics communication system capable of off-vehicle communication, including communication with a communication network 90 having wireless and wired communication capabilities. The telematics controller 75 is capable of off-vehicle communications, including short range vehicle-to-vehicle (V2V) communications and/or vehicle-to-outside world (V2 x) communications, which may include communications with infrastructure monitors (e.g., traffic cameras). Alternatively or additionally, the telematics controller 75 has a wireless telematics communication system that is capable of short-range wireless communication with a handheld device (e.g., a cellular telephone, satellite telephone, or another telephone device). In one embodiment, the handheld device includes a software application that includes a wireless protocol for communicating with the telematics controller 75, and the handheld device can perform off-vehicle communications, including communication with the off-board server 95 based on the communication network 90. Alternatively or additionally, the telematics controller 75 directly performs off-vehicle communications based on the communication network 90 communicating with the off-board server 95.
The term "controller" and related terms (e.g., microcontroller, control unit, processor, and the like) refer to one or various combinations of the following: application specific integrated circuit(s) (ASIC), field Programmable Gate Array (FPGA), electronic circuit(s), central processing unit(s), e.g., microprocessor(s) and associated non-transitory memory component(s) (indicated by memory 23) in the form of memory and storage (read-only, programmable read-only, random access, hard drive, etc.). The non-transitory memory component is capable of storing machine-readable instructions in the form of: one or more software or firmware programs or routines, combinational logic circuit(s), input/output circuit(s) and devices, signal conditioning and buffering circuitry, and other components accessible by the one or more processors to implement the corresponding functionality. The input/output circuit(s) and devices include analog/digital converters and related devices that monitor inputs from the sensors, which can be monitored at a preset sampling frequency or in response to a trigger event. Software, firmware, programs, instructions, control routines, code, algorithms, and similar terms refer to a controller-executable instruction set, including calibration and lookup tables. Each controller executes control routine(s) for providing the respective function. The routine may be performed at regular intervals, for example, every 100 microseconds during ongoing operation. Alternatively, the routine may be executed in response to a triggering event. Communication between the controllers, actuators, and/or sensors may be implemented using direct wired point-to-point links, networked communication bus links, wireless links, or other suitable communication links. The communication includes corresponding exchanged data signals, including, for example, conductive medium-based electrical signals, air-based electromagnetic signals, optical waveguide-based optical signals, and the like. The data signals may include discrete, analog or digitized analog signals representing inputs from the sensors, actuator commands, and communications between the controllers. The term "signal" refers to a physically identifiable indicator of conveyed information and may be a corresponding waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic), such as, for example, DC, AC, sine wave, triangular wave, square wave, vibration, and the like, capable of propagating through a medium. A parameter is defined as a measurable quantity that represents a physical property of a device or other element that can be identified using one or more sensors and/or physical models. The parameter may have a discrete value, e.g., "1" or "0", or be infinitely variable in value.
In one embodiment, as shown in fig. 2, a vehicle obstacle avoidance planning method is provided, including the following steps:
s201: and acquiring the vehicle information and the dangerous target information.
In this embodiment of the present application, first, vehicle sensing systems such as a camera, a radar, a vehicle-mounted sensor, etc. are used to obtain vehicle information and dangerous target information in real time, where the vehicle information includes a current speed and an acceleration of a vehicle, a yaw rate, vehicle position information, etc., and the dangerous target refers to an obstacle such as a vehicle, a pedestrian, a roadblock, etc. that may collide with the vehicle in the future in the current running direction of the vehicle, and the dangerous target information includes a target transverse and longitudinal position, a current speed and an acceleration, a heading angle, etc.
S203: and determining collision points based on the vehicle information and the dangerous target information.
In this embodiment of the present application, after the vehicle information and the dangerous target information are acquired, the collision point is determined based on the vehicle information and the dangerous target information, that is, based on the current states of the vehicle and the dangerous target, the position information when the vehicle is not in a changed state and is likely to collide with the dangerous target in the future is predicted, and the vehicle state and the dangerous target state determine whether the collision position will change.
S205: and determining a plurality of obstacle avoidance endpoints based on the collision points, and planning a plurality of initial obstacle avoidance routes based on the vehicle information and the plurality of obstacle avoidance endpoints.
In this embodiment of the present application, after determining a collision point based on the vehicle information and the dangerous target information, determining a plurality of obstacle avoidance endpoints based on the collision point, that is, selecting a plurality of position points around the collision point, where a vehicle capable of avoiding collision with the dangerous target may pass, assuming the position points as endpoints of local travel paths of the vehicle, and planning a plurality of initial obstacle avoidance routes based on the vehicle information and the plurality of obstacle avoidance endpoints, that is, travel routes capable of avoiding collision with the dangerous target. Specifically, the current position of the own vehicle is used as a starting point for planning an obstacle avoidance route, a plurality of obstacle avoidance terminals are combined, and a plurality of selectable driving routes for avoiding collision are planned by combining a kinematic formula or Bezier curve planning and other modes.
S207: and determining cost functions of the plurality of initial obstacle avoidance routes, and determining a target obstacle avoidance route based on the cost functions.
In the embodiment of the application, after a plurality of initial obstacle avoidance routes are planned, the cost function value of each initial obstacle avoidance route is determined by combining the cost function and the information of the plurality of initial obstacle avoidance routes, wherein the cost function refers to the energy value required to be consumed by a self-vehicle to drive according to the current obstacle avoidance route, the smaller the cost function value is, the better the obstacle avoidance planning execution result is, the correlation with the length of the obstacle avoidance route, the stability of the acceleration change rate of the self-vehicle and the like is usually achieved, the initial obstacle avoidance route with the minimum cost function value, namely the optimal initial obstacle avoidance route with the minimum required energy consumption is selected based on the size of the cost function value, the target obstacle avoidance route is determined, and the vehicle is guided to finish obstacle avoidance.
In the vehicle obstacle avoidance planning method, firstly, the vehicle information and the dangerous target information are acquired, then, collision points are determined based on the vehicle information and the dangerous target information, then, a plurality of obstacle avoidance terminals are determined based on the collision points, a plurality of initial obstacle avoidance routes are planned based on the vehicle information and the obstacle avoidance terminals, finally, cost functions of the initial obstacle avoidance routes are determined, and a target obstacle avoidance route is determined based on the cost functions. That is, when the obstacle avoidance planning of the own vehicle is carried out, the collision points are determined by combining dangerous target information, a plurality of obstacle avoidance routes capable of avoiding collision are determined based on the collision points, the optimal obstacle avoidance route is determined by combining cost functions, the change condition of dangerous targets is comprehensively considered, and the accuracy and the effectiveness of the obstacle avoidance route planning are improved.
In one embodiment of the present application, the vehicle information includes a vehicle curvature, a vehicle curvature change rate, the dangerous target information includes a dangerous target position, a dangerous target speed, and the determining the collision point based on the vehicle information and the target information includes:
and if the dangerous target speed is zero, determining a collision longitudinal position based on the dangerous target position, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
In one embodiment of the present application, when calculating the collision point, the method is divided into two cases of dangerous object rest and dangerous object movement, wherein when the dangerous object is at rest, i.e. the dangerous object speed is zero, the longitudinal position of the dangerous object is the collision longitudinal position where the collision is possible in the future, the lateral collision position is the offset amount which transversely occurs when the vehicle reaches the target longitudinal position, and the lateral collision position is related to the state of the vehicle, such as the curvature of the vehicle and the curvature change rate of the vehicle, and in specific application, the center of the rear axle of the vehicle is taken as the origin, a coordinate system is established, and the dangerous object position information (Px obj ，Py obj ) Then the longitudinal collision position Px reaches the collision point collision I.e. the longitudinal position of the dangerous object from the vehicle, i.e. Px collision ＝Px obj . Thereafter, based on the longitudinal position Px of the dangerous object from the host vehicle obj The collision lateral position Py is calculated from the vehicle curvature C and curvature change rate Cr according to the following formula collision 。
It is to be noted that the longitudinal distance of the determined collision point from the current vehicle position is limited between (10, 50), the transverse distance of the collision point from the current vehicle position is limited between (-10, 10), and the range is obtained through experimental calibration.
In this embodiment, if the speed of the dangerous target is zero, the collision longitudinal position is determined based on the dangerous target position, and the collision transverse position is determined based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate, so that the collision point of the dangerous target when stationary can be accurately calculated, and the accuracy of obstacle avoidance route planning is improved.
In one embodiment of the present application, the vehicle information includes a vehicle speed and an acceleration, the dangerous target information includes a dangerous target acceleration, and determining the collision point based on the vehicle information and the target information further includes:
and if the dangerous target speed is not zero, determining a collision longitudinal position based on the dangerous target position, the vehicle speed and acceleration, the dangerous target speed and the dangerous target acceleration, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
In one embodiment of the present application, as described above, when the dangerous object moves, i.e., the dangerous object speed is not zero, the collision longitudinal position is determined based on the dangerous object position, the self-velocity and the acceleration, the dangerous object speed, and the dangerous object acceleration, specifically, the dangerous object position information (Px) is obtained by the sensing system obj ，Py obj ) Longitudinal position Px of collision at the moment of collision collision Obtained by the following formula.
Px collision ＝Px obj +v obj *TTC
Wherein v is obj For dangerous target speed, TTC is the time from the current moment to the collision of the vehicle and the dangerous target, when the dangerous target acceleration a obj <At the time of 0, the temperature of the liquid,i.e. the dangerous object is decelerating and eventually stops, collision may occur when the dangerous object stops Before, this may also occur after the dangerous object has stopped.
Thereafter, based on the longitudinal position Px of the dangerous object from the host vehicle obj The collision lateral position Py is calculated from the vehicle curvature C and curvature change rate Cr according to the following formula collision I.e. the amount of offset that occurs laterally when the vehicle reaches the target longitudinal position.
It is to be noted that the longitudinal distance of the determined collision point from the current vehicle position is limited between (10, 50), the transverse distance of the collision point from the current vehicle position is limited between (-10, 10), and the range is obtained through experimental calibration.
In this embodiment, if the dangerous target speed is not zero, the collision longitudinal position is determined based on the dangerous target position, the vehicle speed and acceleration, the dangerous target speed and the dangerous target acceleration, and the collision transverse position is determined based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate, so that the collision point when the dangerous target moves can be accurately calculated, and the accuracy of obstacle avoidance route planning is improved.
In one embodiment of the present application, the planning a plurality of initial obstacle avoidance routes based on the self-vehicle information and a plurality of obstacle avoidance endpoints includes:
s301: and determining the starting point of the obstacle avoidance route based on the self-vehicle information.
S303: and determining planning parameters based on the starting point information and the end point information, wherein the starting point information comprises a starting point transverse position, a starting point course angle, a starting point curvature and a starting point curvature change rate, and the end point information comprises an end point transverse position, an end point longitudinal position, an end point course angle, an end point curvature and an end point curvature change rate.
S305: and determining a plurality of initial obstacle avoidance routes based on the planning parameters, the start points of the obstacle avoidance routes and the end points of the obstacle avoidance routes.
In one embodiment of the present application, first, a start point of an obstacle avoidance route is determined based on vehicle information, that is, a current position of a vehicle is taken as a start position of obstacle avoidance route planning, and an end point of obstacle avoidance, that is, a route planning end position, is n×n different end points selected around based on a determined lateral and longitudinal position of a collision point, in a specific application, as shown in fig. 3, taking n=5, taking a total of 25 points as an example, where for the given 25 points, the lateral position of the end point from the collision point is 1m, then the lateral distance of each end point is increased by 0.5m, and the longitudinal distance is increased by 1 m.
Then, determining planning parameters based on the starting point information and the end point information, wherein the obstacle avoidance route is calculated by a seven-degree polynomial with the form of y=a+bt+ct 2 +dt 3 +et 4 +ft 5 +gt 6 +ht 7 The planning parameters, namely the coefficients (a, b, c, d, e, f, g and h) thereof, can be calculated by eight equations consisting of the start point information and the end point information. Eight equations are shown below.
a＝Y 0
b＝H 0
2c＝C 0
6d＝Cr 0
a+b·x+c·x 2 +d·x 3 +e·x 4 +f·x 5 +g·x 6 +h·x 7 ＝Y 1
b+2c·x+3d·x 2 +4e·x 3 +5f·x 4 +6g·x 5 +7h·x 6 ＝H 1
2c+6d·x+12e·x 2 +20f·x 3 +30g·x 4 +42h·x 5 ＝C 1
6d+21e·x+60f·x 2 +120g·x 3 +210h·x 4 ＝Cr 1
Wherein Y is 0 Is the transverse position of the starting point, H 0 C as the starting point course angle 0 As the curvature of the starting point, cr 0 For the rate of change of the curvature of the starting point, Y 1 For the end transverse position H 1 For the end heading angle, C 1 For the end point curvature, cr 1 The rate of change of curvature at the end point, x, is the longitudinal position of the end point.
For example, the starting point (0, 0), the ending point (25,1,0,0,0) and the resulting seven-degree polynomial coefficient is 10 -4 *(0,0,0,0,0.896,-0.086,0.0029,0)。
Finally, determining a plurality of initial obstacle avoidance routes based on the planning parameters, the start points of the obstacle avoidance routes and the end points of the obstacle avoidance routes, as shown in fig. 4, and planning a result of the obstacle avoidance route. It should be noted that, according to the seven-degree polynomial of the obstacle avoidance line, the first derivative of the planned speed v, i.e., v=b+2ct+3dt, can be obtained 2 +4et 3 +5ft 4 +6gt 5 +7ht 6 The planned acceleration a is the second derivative of y, i.e. a=2c+6dt+12et 2 +20ft 3 +30gt 4 +42ht 5 The third derivative of the acceleration change rate j is planned to be y, i.e., j=6d+14et+60 ft 2 +120gt 3 +210ht 4 。
In this embodiment, by determining the start point of the obstacle avoidance route based on the own vehicle information, determining the planning parameter based on the start point information and the end point information, and determining a plurality of initial obstacle avoidance routes based on the planning parameter, the start point of the obstacle avoidance route, and the end point of the obstacle avoidance route, the practicality of the obstacle avoidance route planning can be improved, and the number of times of repeatedly planning can be reduced.
In one embodiment of the present application, the initial obstacle avoidance line includes a trend of change in acceleration of the vehicle, a total distance of the line, and the determining the cost function of the plurality of initial obstacle avoidance lines includes:
and determining a cost function based on the self-vehicle acceleration change trend, the total distance of the route and the route safety coefficient, wherein the route safety coefficient is obtained by judging based on the self-vehicle information and the dangerous target information.
In one embodiment of the present application, the cost function represents the energy required to be consumed by the vehicle to travel along the initial obstacle avoidance route, and the initial obstacle avoidance route with the least consumed energy is optimal and is most suitable for the vehicle to avoid the obstacle, and is constrained by the acceleration change trend of the vehicle, the total distance of the route and the route safety coefficient together, and can be represented by the following formula.
Wherein, the alpha, beta and gamma are corresponding coefficients, jerk i The vehicle acceleration change trend at the ith sampling point is represented, dis represents the distance from the starting point to the end point of the current obstacle avoidance route, isSafe represents the route safety coefficient, and the route safety coefficient is generally obtained by judging according to vehicle information and dangerous target information.
In this embodiment, the effectiveness of obstacle avoidance planning routes can be improved by determining the cost function based on the vehicle acceleration change trend, the total distance of the routes and the route safety coefficient.
In one embodiment of the present application, the method further comprises:
and carrying out avoidance side planning risk assessment based on the vehicle information and the environment information.
In one embodiment of the present application, when performing obstacle avoidance routing, it is further required to determine whether the adjacent left/right lanes of the own vehicle are safe, and whether the own vehicle is supported for lane change so as to avoid collision with an obstacle, so when performing obstacle avoidance routing, the avoidance side risk assessment is performed based on the own vehicle information and the environmental information, and specifically, whether the adjacent lanes are safe or not may be determined based on whether the adjacent lanes have vehicles, the state of the adjacent lanes, and the like.
In this embodiment, the safety of obstacle avoidance route planning can be improved by performing the avoidance risk assessment based on the own vehicle information and the environmental information.
In one embodiment of the present application, the performing the avoidance side planning risk assessment based on the vehicle information and the environmental information includes:
s401: and dividing adjacent lanes based on the environmental information, and judging whether dangerous vehicles exist in each area of the adjacent lanes.
S403: and if dangerous vehicles exist in each area, carrying out avoidance side planning risk assessment by combining the safe distance in each area.
In one embodiment of the present application, as shown in fig. 5, an avoidance side is taken as an adjacent left lane of a vehicle, and the adjacent left lane is divided based on environmental information, that is, the adjacent left lane is divided into A, B, C areas, the right left side of the vehicle is an area a, the left lower side of the vehicle is an area B, the left upper side of the vehicle is an area C, specifically, the area a is in the range of [ - α ] VLgt,8, the area B is in the range of [ -80, - α ] VLgt, and the area C is in the range of [8,80], where α=0.4 is a standard amount, VLgt is a vehicle speed (unit: m), and after the adjacent lanes are divided by area, whether dangerous vehicles exist in each area is determined. And if dangerous vehicles exist in each area, carrying out avoidance side planning risk assessment by combining the safe distance in each area. Specifically, if dangerous vehicles exist in the area A, the left adjacent lane is regarded as unsafe; if the dangerous vehicle exists in the area B, determining whether the left adjacent lane is safe or not by judging the sum of the distance S1 required by the dangerous vehicle to be decelerated to be the same as the speed of the vehicle, the driving distance S2 within the reaction time of the driver of the vehicle and the safety distance S3 and the limit distance of a brake anti-lock braking system (antilock brake system, ABS), namely, when ABS (PosnLgtObj) > s1+s2+s3, the left adjacent lane is safe; if a dangerous vehicle exists in the area C, whether the left adjacent lane is safe or not is determined by judging the sum of the distance S1 required by the vehicle to decelerate to be the same as the speed of the dangerous vehicle, the driving distance S2 within the reaction time of the driver of the vehicle and the safety distance S3 and the limit distance of the ABS, namely, when ABS (PosnLgtObj) > s1+s2+s3, the left adjacent lane is safe. Likewise, when the avoidance side is the adjacent right lane of the own vehicle, the avoidance side planning risk assessment can also be performed by adopting the method.
In the embodiment, by dividing the adjacent lanes based on the environmental information, judging whether dangerous vehicles exist in each area of the adjacent lanes, if dangerous vehicles exist in each area, carrying out avoidance side planning risk assessment by combining the safe distance in the areas, and improving the safety of obstacle avoidance route planning.
The following describes, in a specific embodiment, specific implementation steps of the vehicle obstacle avoidance planning method of the present application. Firstly, S501, acquiring vehicle information and dangerous target information, then, determining a collision point based on the vehicle information and the dangerous target information, specifically, dividing the two cases into two cases, S505, if the dangerous target speed is zero, determining a collision longitudinal position based on the dangerous target position, determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate, S507, if the dangerous target speed is not zero, determining a collision longitudinal position based on the dangerous target position, the vehicle speed and acceleration, the dangerous target speed and the dangerous target acceleration, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
And then, S509, carrying out avoidance side planning risk assessment based on the vehicle information and the environment information, specifically, S511-S513, dividing adjacent lanes based on the environment information, judging whether dangerous vehicles exist in each area of the adjacent lanes, and carrying out avoidance side planning risk assessment by combining the safe distances in the areas if dangerous vehicles exist in each area.
Then, S515, determining a plurality of obstacle avoidance endpoints based on the collision points, and planning a plurality of initial obstacle avoidance routes based on the vehicle information and the plurality of obstacle avoidance endpoints, specifically, S517-S521, determining an obstacle avoidance route starting point based on the vehicle information; determining planning parameters based on the starting point information and the end point information, wherein the starting point information comprises a starting point transverse position, a starting point course angle, a starting point curvature and a starting point curvature change rate, and the end point information comprises an end point transverse position, an end point longitudinal position, an end point course angle, an end point curvature and an end point curvature change rate; and determining a plurality of initial obstacle avoidance routes based on the planning parameters, the start points of the obstacle avoidance routes and the end points of the obstacle avoidance routes.
Finally, S523, a cost function of the plurality of initial obstacle avoidance routes is determined, and a target obstacle avoidance route is determined based on the cost function. Specifically, S525 determines a cost function based on the vehicle acceleration variation trend, the total distance of the route, and the route safety coefficient, where the route safety coefficient is determined based on the vehicle information and the dangerous target information.
In one embodiment of the present application, as shown in fig. 6 and 7, in a specific application, the dangerous target may be a vehicle, a pedestrian, a non-motor vehicle, an unknown obstacle or a suddenly appearing crossing vehicle, a pedestrian, a non-motor vehicle, etc. in a specific application, for example, in various application cases shown in the drawings, the lane obstacle avoidance planning method may be applied in the situation that a lane line exists and a lane line does not exist, and specifically includes that a collision risk exists between a self vehicle and a front vehicle, a collision risk exists between a self vehicle and a pedestrian in front and a non-motor vehicle, a collision risk exists between a self vehicle and a unknown obstacle in front, a collision risk exists when a front vehicle suddenly cuts out, a front longitudinal target and a collision risk exists when the crossing target exists simultaneously.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a vehicle obstacle avoidance planning device for realizing the vehicle obstacle avoidance planning method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the vehicle obstacle avoidance planning device provided below may be referred to the limitation of the vehicle obstacle avoidance planning method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 8, a vehicle obstacle avoidance planning device 800 is provided, comprising: an information acquisition module 801, a collision point determination module 803, an initial obstacle avoidance route planning module 805, and a target obstacle avoidance route determination module 807, wherein:
the information acquisition module 801 is configured to acquire vehicle information and dangerous target information.
A collision point determination module 803 for determining a collision point based on the own vehicle information and the dangerous target information.
An initial obstacle avoidance path planning module 805 configured to determine a plurality of obstacle avoidance endpoints based on the collision points, and plan a plurality of initial obstacle avoidance paths based on the vehicle information and the plurality of obstacle avoidance endpoints.
A target obstacle avoidance path determination module 807 configured to determine a cost function for the plurality of initial obstacle avoidance paths, and determine a target obstacle avoidance path based on the cost function.
In one embodiment of the present application, the vehicle information includes a vehicle curvature, a vehicle curvature change rate, the dangerous target information includes a dangerous target position, a dangerous target speed, and the collision point determining module is further configured to:
and if the dangerous target speed is zero, determining a collision longitudinal position based on the dangerous target position, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
In one embodiment of the present application, the vehicle information includes a vehicle speed and an acceleration, the dangerous target information includes a dangerous target acceleration, and the collision point determining module is further configured to:
and if the dangerous target speed is not zero, determining a collision longitudinal position based on the dangerous target position, the vehicle speed and acceleration, the dangerous target speed and the dangerous target acceleration, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
In one embodiment of the present application, the initial obstacle avoidance routing module is further configured to:
determining an obstacle avoidance route starting point based on the self-vehicle information;
determining planning parameters based on the starting point information and the end point information, wherein the starting point information comprises a starting point transverse position, a starting point course angle, a starting point curvature and a starting point curvature change rate, and the end point information comprises an end point transverse position, an end point longitudinal position, an end point course angle, an end point curvature and an end point curvature change rate;
and determining a plurality of initial obstacle avoidance routes based on the planning parameters, the start points of the obstacle avoidance routes and the end points of the obstacle avoidance routes.
In one embodiment of the present application, the initial obstacle avoidance line includes a trend of change in acceleration of the vehicle, a total distance of the line, and the target obstacle avoidance line determination module is further configured to:
And determining a cost function based on the self-vehicle acceleration change trend, the total distance of the route and the route safety coefficient, wherein the route safety coefficient is obtained by judging based on the self-vehicle information and the dangerous target information.
The vehicle obstacle avoidance planning device further comprises a risk assessment module. In one embodiment of the present application, the risk assessment module is configured to:
and carrying out avoidance side planning risk assessment based on the vehicle information and the environment information.
In one embodiment of the present application, the risk assessment module is further configured to:
dividing adjacent lanes based on environmental information, and judging whether dangerous vehicles exist in each area of the adjacent lanes;
and if dangerous vehicles exist in each area, carrying out avoidance side planning risk assessment by combining the safe distance in each area.
The modules in the vehicle obstacle avoidance planning device can be realized in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program when executed by a processor implements a vehicle obstacle avoidance planning method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 9 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A vehicle obstacle avoidance planning method, the method comprising:
acquiring own vehicle information and dangerous target information;
determining a collision point based on the own vehicle information and the dangerous target information;
determining a plurality of obstacle avoidance endpoints based on the collision points, and planning a plurality of initial obstacle avoidance routes based on the self-vehicle information and the plurality of obstacle avoidance endpoints;
and determining cost functions of the plurality of initial obstacle avoidance routes, and determining a target obstacle avoidance route based on the cost functions.
2. The method of claim 1, wherein the vehicle information comprises vehicle curvature, vehicle curvature rate of change, the hazardous target information comprises hazardous target position, hazardous target speed, and determining the collision point based on the vehicle information and hazardous target information comprises:
and if the dangerous target speed is zero, determining a collision longitudinal position based on the dangerous target position, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
3. The method of claim 2, wherein the vehicle information includes vehicle speed and acceleration, the hazardous target information includes hazardous target acceleration, and the determining a collision point based on the vehicle information and hazardous target information further comprises:
and if the dangerous target speed is not zero, determining a collision longitudinal position based on the dangerous target position, the vehicle speed and acceleration, the dangerous target speed and the dangerous target acceleration, and determining a collision transverse position based on the dangerous target position, the vehicle curvature and the vehicle curvature change rate.
4. The method of claim 1, wherein the planning a plurality of initial obstacle avoidance routes based on the self-vehicle information and a plurality of obstacle avoidance endpoints comprises:
Determining an obstacle avoidance route starting point based on the self-vehicle information;
determining planning parameters based on the starting point information and the end point information, wherein the starting point information comprises a starting point transverse position, a starting point course angle, a starting point curvature and a starting point curvature change rate, and the end point information comprises an end point transverse position, an end point longitudinal position, an end point course angle, an end point curvature and an end point curvature change rate;
and determining a plurality of initial obstacle avoidance routes based on the planning parameters, the start points of the obstacle avoidance routes and the end points of the obstacle avoidance routes.
5. The method of claim 1, wherein the initial obstacle avoidance path comprises a trend of change in vehicle acceleration, a path total distance, and wherein determining the cost function for the plurality of initial obstacle avoidance paths comprises:
and determining a cost function based on the self-vehicle acceleration change trend, the total distance of the route and the route safety coefficient, wherein the route safety coefficient is obtained by judging based on the self-vehicle information and the dangerous target information.
6. The method according to claim 1, wherein the method further comprises:
and carrying out avoidance side planning risk assessment based on the vehicle information and the environment information.
7. The method of claim 6, wherein the performing the avoidance-side planning risk assessment based on the vehicle information and the environmental information comprises:
Dividing adjacent lanes based on environmental information, and judging whether dangerous vehicles exist in each area of the adjacent lanes;
and if dangerous vehicles exist in each area, carrying out avoidance side planning risk assessment by combining the safe distance in each area.
8. A vehicle obstacle avoidance planning device, the device comprising:
the information acquisition module is used for acquiring the vehicle information and the dangerous target information;
the collision point determining module is used for determining a collision point based on the vehicle information and the dangerous target information;
the initial obstacle avoidance route planning module is used for determining a plurality of obstacle avoidance endpoints based on the collision points and planning a plurality of initial obstacle avoidance routes based on the vehicle information and the obstacle avoidance endpoints;
and the target obstacle avoidance route determination module is used for determining cost functions of the plurality of initial obstacle avoidance routes and determining a target obstacle avoidance route based on the cost functions.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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