Vehicle speed determination method and device, equipment and medium

Abstract

The invention discloses a vehicle speed determination method and device, equipment and a medium. The method comprises the following steps: acquiring map data and vehicle state data of a vehicle; determining a basic cruise speed according to the map data and the vehicle state data; determining at least one road scene event, and determining event speed limit information corresponding to each road scene event; and determining a target driving speed of the vehicle based on the event speed limit information and the basic cruise speed. According to the technical scheme, the map data and the real-time vehicle state data are fused, and a plurality of road scene event arbitration mechanisms are combined, so that dynamic, safe and smooth vehicle speed decision is realized, and the problem of longitudinal vehicle speed dynamic planning in a complex scene is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous driving, and particularly to a method, device, equipment and medium for determining the vehicle speed of a vehicle. Background Art

[0002] With the development of intelligence and the maturity of electronic product technology, the driving assistance system has been rapidly popularized due to its comfort and safety.

[0003] The ADAS domain controller (Advanced Driver Assistance System Domain Controller) is the core component in an automobile for implementing the advanced driving assistance system (ADAS). It is responsible for processing key tasks such as perception, decision-making, execution, safety, and communication by integrating various sensors, actuators, and control algorithms. However, existing intelligent vehicle cruise control systems usually rely on fixed speed limits or driver settings to plan the longitudinal vehicle speed, and this method is insufficient in responding to different event scenarios such as obstacle avoidance, lane change decision-making, and curvature speed limits, and it is difficult to meet the dynamic requirements of complex road scenario events. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for determining the vehicle speed of a vehicle. By fusing map data and real-time vehicle state data and combining multiple road scenario event arbitration mechanisms, dynamic, safe and smooth vehicle speed decision-making is achieved, and the problem of dynamic planning of longitudinal vehicle speed in complex scenarios is solved.

[0005] According to one aspect of the present invention, a method for determining the vehicle speed of a vehicle is provided, including:

[0006] Obtaining the map data and vehicle state data of the vehicle;

[0007] Determining the basic cruise vehicle speed according to the map data and the vehicle state data;

[0008] Determining at least one road scenario event and determining the event speed limit information corresponding to each road scenario event;

[0009] Determining the target driving vehicle speed of the vehicle based on the event speed limit information and the basic cruise vehicle speed.

[0010] According to another aspect of the present invention, a device for determining the vehicle speed of a vehicle is provided, including:

[0011] A data acquisition module for acquiring the map data and vehicle state data of the vehicle;

[0012] A basic cruise vehicle speed determination module for determining the basic cruise vehicle speed according to the map data and the vehicle state data;

[0013] An event speed limit information determination module, configured to determine at least one road scene event and determine event speed limit information corresponding to each of the road scene events;

[0014] A target driving speed determination module, configured to determine a target driving speed of the vehicle based on the event speed limit information and the basic cruise speed.

[0015] According to another aspect of the present invention, there is provided an electronic device, including:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the vehicle speed determination method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the vehicle speed determination method according to any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention is as follows: by acquiring map data and vehicle state data of the vehicle; determining a basic cruise speed according to the map data and the vehicle state data; determining at least one road scene event and determining event speed limit information corresponding to each of the road scene events; and determining a target driving speed of the vehicle based on the event speed limit information and the basic cruise speed. This technical solution realizes dynamic, safe and smooth vehicle speed decision-making by fusing map data with real-time vehicle state data and combining multiple road scene event arbitration mechanisms, and solves the problem of longitudinal vehicle speed dynamic planning in complex scenarios.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0023] Figure 1 It is a flowchart of a method for determining the vehicle speed provided in Embodiment 1 of the present invention;

[0024] Figure 2 It is a flowchart of a method for determining the vehicle speed provided in Embodiment 2 of the present invention;

[0025] Figure 3 It is a schematic structural diagram of a device for determining the vehicle speed provided in Embodiment 3 of the present invention;

[0026] Figure 4 It is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed implementation manners

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0028] It should be noted that the terms "first", "second", and "target" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0029] Embodiment 1

[0030] Figure 1 It is a flowchart of a method for determining the vehicle speed provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of determining the corresponding vehicle speed of a vehicle in different road scenarios. This method can be executed by a device for determining the vehicle speed of a vehicle, and this device for determining the vehicle speed of a vehicle can be implemented in the form of hardware and / or software. This device for determining the vehicle speed of a vehicle can be configured in an electronic device with data processing capabilities. As Figure 1 shown, this method includes:

[0031] S110. Obtain the map data and vehicle status data of the vehicle.

[0032] Among them, the vehicle may refer to an intelligent driving vehicle that is currently using the advanced driving assistance function. The vehicle in this embodiment can implement the advanced driving assistance function of the vehicle based on the ADAS domain controller. The map data may refer to high-precision map data. The high-precision map (HD map) in this embodiment is a high-precision map for autonomous driving, which expresses map elements such as roads, lanes, roadside traffic signs, and ground markings. The map data in this embodiment may include data such as the current speed limit of the driving road, the future speed limit of the road, the curvature, and the road scene event information. The vehicle status data can be understood as the driving status data of the currently driving vehicle. The vehicle status data in this embodiment may include data such as the vehicle's real-time driving speed, the driver-set speed, and the vehicle positioning. The ADAS domain controller of the vehicle in this embodiment can receive high-precision map data such as the current road speed limit, the future road speed limit, the curvature, and the road scene event information of the vehicle, and vehicle status data such as the vehicle's real-time speed, the driver-set speed, and the vehicle positioning.

[0033] S120. Determine the basic cruise speed according to the map data and vehicle status data.

[0034] Among them, the basic cruise speed can be understood as the basic driving speed of the vehicle determined based on data such as the current road speed limit and the future road speed limit during the driving process. In this embodiment, when no road scene event is triggered on the road, the vehicle can drive according to the basic cruise speed. In this embodiment, the current road speed limit and the future road speed limit in the map data can be combined with the vehicle's current position in the vehicle status data to determine a dynamic map decision function for the vehicle, so as to determine different speed limit data within different distance ranges. Then, based on the different speed limit data corresponding to different distance ranges in the dynamic map decision function and the speed limit data set by the driver in real time, the basic cruise speed is obtained through the timeliness priority rule.

[0035] In this embodiment, optionally, the map data includes the speed limit data of the current driving section and the speed limit data of the section to be driven; the vehicle status data includes the vehicle position; determining the basic cruise speed according to the map data and vehicle status data includes: determining the map speed limit data based on the speed limit data of the current driving section, the speed limit data of the section to be driven, and the vehicle position; determining the basic cruise speed according to the set speed limit data and the map speed limit data.

[0036] Among them, the speed limit data of the current driving section can be understood as the speed limit data corresponding to the road on which the vehicle is currently driving. The speed limit data of the upcoming driving section can be understood as the speed limit data corresponding to the road that the vehicle is about to drive on. It can be understood that the upcoming driving section in this embodiment can also be referred to as the future road. The map speed limit data can be understood as the speed limit value output in real time by the dynamic map speed limit decision function. The set speed limit data can be understood as the speed limit value set by the driver in real time.

[0037] In this embodiment, the specific method for determining the map speed limit data based on the speed limit data of the current driving section, the speed limit data of the upcoming driving section, and the vehicle position can be to determine the distance between the vehicle's current position and the speed limit switching point, and then compare the distance between the vehicle's current position and the speed limit switching point with the set threshold distance. Combining with the dynamic map decision function determined according to the speed limit data of the current driving section and the speed limit data of the upcoming driving section, the corresponding map speed limit value can then be determined from the map decision function based on the comparison result of the distance between the vehicle's real-time position and the speed limit switching point and the set threshold distance. Among them, the speed limit switching point can be understood as the position where the two speed limits of the speed limit of the current driving section and the speed limit data of the upcoming driving section need to be switched. The set threshold distance can be preset. Exemplarily, the set threshold distance can be 100 meters.

[0038] The function of the map speed limit decision function in this embodiment is to gradually adjust the vehicle speed according to different distances, so as to transition from the current road speed limit to the upcoming road speed limit during the vehicle's driving process. Exemplarily, the speed limit of the current driving section is 60 km / h, and the speed limit within 100 meters of the future driving section is 80 km / h. If it directly changes from 60 km / h to 80 km / h, it will be uneven. Therefore, it is necessary to perform a speed limit transition according to the map speed limit decision function, so as to smoothly switch the speed limit values of different roads.

[0039] Specifically, in this embodiment, the current road speed limit in the high-precision map data can be set as v cur_map , the upcoming road speed limit can be set as v fut_map , and the distance between the vehicle's current position and the speed limit switching point can be set as d map . Then the dynamic map speed limit decision function is:

[0040]

[0041] Among them, α∈(0, 1], which is the map confidence factor and is related to the high-precision map accuracy level; β is the transition curve steepness coefficient; d th_map is the set threshold distance for map speed limit switching.

[0042] In this embodiment, the specific method for determining the basic cruising speed according to the set speed limit data and the map speed limit data may be to determine the basic cruising speed according to the time stamps corresponding to the set speed limit data and the map speed limit data and the time limit priority decision function. Among them, the time limit priority decision function may be that the highest priority corresponds to the latest time limit.

[0043] Specifically, in this embodiment, it may be set that the current timestamp of the intelligent driving system is t, and v map (t) is the speed limit value output in real time by the dynamic map speed limit decision function, that is, the map speed limit data, and v set (t) is the speed limit value set by the driver in real time, that is, the set speed limit data. Then, through the following time limit priority decision function, the basic cruising speed is obtained:

[0044]

[0045] Among them, t set is the timestamp when the driver modifies the speed limit, and t map is the timestamp when the dynamic map speed limit decision function changes.

[0046] In this embodiment, through such a setting, by comparing the speed limit set by the driver with the map predicted speed limit in real time, the cruising speed can be dynamically adjusted based on the time limit priority principle, realizing smooth speed adjustment in advance, avoiding the risk of speeding in the speed limit section, and through a progressive vehicle speed transition, it is more in line with the human driving behavior mode.

[0047] S130. Determine at least one road scene event and determine the event speed limit information corresponding to each road scene event.

[0048] Among them, the road scene event can be understood as various events that occur in the road environment and affect traffic operation. The road scene events in this embodiment may refer to scene events such as curve speed limits, lane-changing obstacles, closed merge areas, approaching toll stations, ramps, and tunnels. The event speed limit information can be understood as the speed limit information corresponding to the road scene event. It can be understood that each road scene event in this embodiment has its own corresponding deceleration type and target vehicle speed, so that the target decision speed function corresponding to each road scene event can be determined.

[0049] In this embodiment, at least one road scene event in front of the current road can be determined according to the high-precision map data and the vehicle driving position, and the target decision speed function corresponding to each road scene event can be determined. Furthermore, based on the target decision speed function, the time speed limit information corresponding to each road scene event can be obtained.

[0050] S140. Determine the target driving speed of the vehicle based on the event speed limit information and the basic cruising speed.

[0051] Among them, the target driving speed can be understood as the final driving speed of the vehicle finally output by the intelligent assisted driving system. In this embodiment, the event speed limit information and the basic cruise speed can be arbitrated to output the target driving speed of the vehicle. Specifically, in this embodiment, the event trigger speed limit decision value can be determined for the event speed limit information corresponding to each road scene event first, and then the target driving speed of the vehicle can be determined by combining the basic cruise speed and the preset comfort constraint conditions.

[0052] The technical solution of the embodiment of the present invention includes obtaining the map data and vehicle state data of the vehicle; determining the basic cruise speed according to the map data and vehicle state data; determining at least one road scene event and determining the event speed limit information corresponding to each road scene event; determining the target driving speed of the vehicle based on the event speed limit information and the basic cruise speed. This technical solution realizes dynamic, safe and smooth vehicle speed decision-making by fusing map data and real-time vehicle state data and combining multiple road scene event arbitration mechanisms, and solves the problem of longitudinal vehicle speed dynamic planning in complex scenarios.

[0053] Embodiment 2

[0054] Figure 2 is a flowchart of a vehicle speed determination method provided according to Embodiment 2 of the present invention. This embodiment is optimized based on the above embodiment. The specific optimization is as follows: determining at least one road scene event, including: obtaining the type of the to-be-driven road section in the map data; determining at least one of a curve scene event, a ramp scene event, and a lane closure scene event according to the type of the to-be-driven road section. As Figure 2 shown, the method includes:

[0055] S210. Obtain the map data and vehicle state data of the vehicle.

[0056] S220. Determine the basic cruise speed according to the map data and vehicle state data.

[0057] S230. Obtain the type of the to-be-driven road section in the map data.

[0058] Among them, the type of the to-be-driven road section can refer to the road type corresponding to the road section that the vehicle is about to drive. The type of the to-be-driven road section in this embodiment can include types such as the main road, ramp, fork, closed road, and curve. In this embodiment, the road type of the road section that the current vehicle is about to drive can be obtained from the high-precision map data.

[0059] S240. Determine at least one of a curve scene event, a ramp scene event, and a lane closure scene event according to the type of the to-be-driven road section, and determine the event speed limit information corresponding to each road scene event.

[0060] Among them, the curved road scenario event can refer to a curved road event. In this embodiment, according to the curvature information in the high-precision map data, the type of the road section to be traveled can be determined as a curve, and then the curved road scenario event can be determined. The ramp scenario event can refer to the scenario where the vehicle drives off the current driving section and enters the ramp. The ramp scenario event in this embodiment can refer to the off-ramp scenario event. The lane closure scenario event can refer to the scenario where there are closed situations such as confluence, divergence, and cone barrel occupying the lane in the self-lane where the current vehicle is traveling.

[0061] In this embodiment, at least one of the curved road scenario event, the ramp scenario event, and the lane closure scenario event can be triggered according to the type of the road section that the vehicle is about to travel determined from the high-precision map data, and the corresponding event speed limit information can be determined based on the determined scenario event.

[0062] In this embodiment, optionally, determining the event speed limit information corresponding to the curved road scenario event includes: obtaining the current lane line curvature and the curvature of the road section to be traveled in the map data; determining the event speed limit information corresponding to the curved road scenario event according to the current lane line curvature, the curvature of the road section to be traveled, and the set lateral acceleration.

[0063] Among them, the current lane line curvature can refer to the lane line curvature data of the current driving lane. The curvature of the road section to be traveled can refer to the lane line curvature of the road section to be traveled. The curvature of the road section to be traveled in this embodiment can also be referred to as the future lane curvature. The set lateral acceleration can be the vehicle's comfortable lateral acceleration. The set lateral acceleration in this embodiment is a pre-set acceleration value and can be dynamically adjusted according to the scenario. Exemplarily, the set lateral acceleration in this embodiment can be 1.5m / s 2 。

[0064] In this embodiment, the target decision speed function corresponding to the curved road event can be determined according to the current lane line curvature, the curvature of the road section to be traveled, and the set lateral acceleration, and then the event speed limit information corresponding to the curved road scenario event can be determined based on the target decision speed function.

[0065] Specifically, in this embodiment, let the current visual lane line curvature be K vis , and the curvature of the lane to be traveled in the high-precision map be K map , then the target decision speed function corresponding to the curved road event determined according to the current lane line curvature, the curvature of the road section to be traveled, and the set lateral acceleration can be:

[0066]

[0067] Among them, the set lateral acceleration a y is the vehicle's comfortable lateral acceleration, which is a calibrated value, and the recommended value is 1.5m / s 2, this value can be dynamically adjusted according to the scenario. For example, when the radius of curvature is less than 300 meters and there is a fork in the road, a is further reduced y threshold to improve the safety of passing through the curve.

[0068] In this embodiment, through such a setting, the target decision speed function in the curve scenario can be determined from the lane line curvature data in the high-precision map data, so that the corresponding event speed limit information can be determined to improve the safety of the vehicle when driving in the curve scenario event.

[0069] In this embodiment, optionally, determining the event speed limit information corresponding to the ramp scenario event includes: obtaining the distance between the current vehicle and the ramp entrance, the triggering deceleration distance, and the target distance in the map data; determining whether the current vehicle meets the deceleration trigger condition; if the current vehicle meets the deceleration trigger condition, determining the event speed limit information corresponding to the ramp scenario event based on the distance between, the triggering deceleration distance, and the target distance.

[0070] Among them, the distance between the current vehicle and the ramp entrance can be understood as the distance between the current vehicle position and the ramp entrance. The triggering deceleration distance can be understood as the starting distance at which the vehicle triggers deceleration. The target distance can be understood as the target distance for the vehicle to complete speed transition. The triggering deceleration distance and the target distance in this embodiment are both distances determined with the ramp entrance as the starting point. The deceleration trigger condition can be a condition for triggering the vehicle to decelerate preset in the ramp scenario event. The deceleration trigger condition in this embodiment can be a deceleration trigger condition of the vehicle formed by comparing the distance between the current vehicle and the ramp entrance with the triggering deceleration distance and combining the comparison result of the speed limit of the current driving section and the speed limit of the section to be traveled.

[0071] Specifically, in this embodiment, d can be set ramp as the distance between the current vehicle and the ramp entrance, d dec as the starting distance for triggering deceleration, d tar as the target distance for completing speed transition. The deceleration trigger condition formed in this embodiment by comparing the distance between the current vehicle and the ramp entrance with the triggering deceleration distance and combining the comparison result of the speed limit of the current driving section and the speed limit of the section to be traveled can be:

[0072]

[0073] It can be understood that when I decel = 1, it can be determined that the vehicle meets the deceleration trigger condition, otherwise it does not meet the deceleration trigger condition.

[0074] In this embodiment, if the current vehicle meets the deceleration trigger condition, the target decision speed function corresponding to the ramp-down scenario event is determined based on the distance between, the triggering deceleration distance, and the target distance as follows:

[0075]

[0076]

[0077] Among them, Φ is a transition function designed using a fifth-degree polynomial: Φ(x) = 10x 3 - 15x 4 + 6x 5 .

[0078] In this embodiment, based on the determined target decision speed function corresponding to the off-ramp scenario event, different speed limit information corresponding to different distance ranges can be further obtained. Through such a setting in this embodiment, the speed limit of the vehicle can be flexibly changed according to the distance between the current vehicle position and the ramp entrance, so as to achieve a progressive vehicle speed transition and avoid the situation of sudden vehicle deceleration.

[0079] In this embodiment, optionally, determining the event speed limit information corresponding to the lane closure scenario event includes: obtaining the current driving speed, the target deceleration speed in the map data, and the distance data related to the lane closure point; determining the event speed limit information corresponding to the lane closure scenario event based on the current driving speed, the target deceleration speed, and the distance data related to the lane closure point.

[0080] Among them, the current driving speed can be understood as the current speed at which the current vehicle is driving. The target deceleration speed can be understood as the deceleration speed corresponding to the lane closure scenario time. The distance data related to the vehicle closure point may include the distance between the current vehicle and the lane closure point, the distance from the deceleration starting point to the lane closure point, and the distance from the deceleration ending point to the lane closure point.

[0081] In this embodiment, the distance data required for the current vehicle to decelerate to the target deceleration speed can be determined according to the current vehicle driving speed, the target deceleration speed, the distance between the current vehicle and the lane closure point, and the distance from the deceleration ending point to the lane closure point. Then, based on the distance data required for the vehicle to decelerate to the target speed, the target deceleration speed, and the speed limit of the current driving section, the speed decision function corresponding to the lane closure scenario event is determined, so as to determine the event speed limit information corresponding to different distances according to the speed decision function.

[0082] Specifically, in this embodiment, it can be assumed that the current driving speed of the host vehicle is v cur , the target deceleration speed is v tar , the distance from the host vehicle to the lane closure point is d close , the distance from the deceleration starting point to the closure point is d start , and the distance from the deceleration ending point to the closure point is d end, if the delay of the control system is delay, the speed decision function corresponding to the self-lane closed scenario can be:

[0083]

[0084] Among them, the distance data required for the current vehicle to decelerate to the target deceleration speed is:

[0085] In this embodiment, based on the speed decision function corresponding to the determined lane closed scenario event, different speed limit information corresponding to different distance ranges can be further obtained. Through such a setting in this embodiment, when there are closed situations such as confluence, divergence, and cone barrels occupying the lane in the self-lane, the vehicle speed needs to be decelerated to the target deceleration speed when approaching the closed point, which can leave enough time for the vehicle to complete lane change, thereby preventing the vehicle from rushing out of the lane.

[0086] S250. Determine the target driving speed of the vehicle based on the event speed limit information and the basic cruise speed.

[0087] In this embodiment, optionally, determining the target driving speed of the vehicle based on the event speed limit information and the basic cruise speed includes: determining the decision speed limit data based on the event speed limit information and the basic cruise speed; determining the target driving speed of the vehicle according to the decision speed limit data and the set constraint conditions.

[0088] Among them, the decision speed limit data can be the speed limit value determined by comparing the event speed limit information actually corresponding to the road scenario and the basic cruise speed. The set constraint conditions can be the comfort constraint conditions set for the vehicle driving speed in advance. The set constraint conditions in this embodiment can be determined based on the decision speed limit data, the current vehicle driving speed, and the set maximum comfortable longitudinal acceleration.

[0089] Specifically, the arbitration module in the intelligent driving system in this embodiment can be responsible for arbitrating the results of the basic cruise speed and various event speed limit data, and outputting the final decision speed of the vehicle. The event trigger speed limit arbitration value determined based on each road scenario event in this embodiment can be:

[0090] v event = min(1.2 * v cur , v event1 , v event2 , v event3 );

[0091] It can be understood that the event-triggered speed limit arbitration value in this embodiment is the arbitration of different road scenario events triggered, and the final event speed limit value determined by the speed limit data of each different event. If the corresponding road scenario event is not triggered, there will be no corresponding event speed limit information. In this embodiment, by triggering different road scenario events, the corresponding decision event function will be generated, and then the corresponding event speed limit information will be determined.

[0092] In this embodiment, the decision speed limit value determined based on the event speed limit information and the basic cruise speed can be: v decision = min(v event , v base );

[0093] In this embodiment, to prevent the difference between the decision speed limit value and the current vehicle speed from being too large, resulting in jerks in vehicle control, a comfort constraint is imposed on the decision speed limit value, and the constraint conditions are set as follows:

[0094]

[0095] where a max is the maximum value of the comfortable longitudinal acceleration, which is a calibrated value, and the recommended value is 2 m / s 2 . T is the algorithm scheduling period. v tar is the finally output decision vehicle speed, that is, the target driving speed.

[0096] With such a setting in this embodiment, different road scenario events can be determined according to different road types, and various road scenario events for vehicle driving are defined (such as curve speed limit, lane change obstacle, closed confluence area, approaching toll station, etc.). A deceleration type and a target vehicle speed are assigned to each event, and the speed limit logic is flexibly adjusted to ensure a smooth transition to the target speed limit; and by adopting the "low speed priority" arbitration strategy, the lowest target vehicle speed is selected from all triggered road scenario events as the final decision value, ensuring the safety of vehicle driving.

[0097] The technical solution of the embodiment of the present invention is to obtain the map data and vehicle state data of the vehicle; determine the basic cruise speed according to the map data and vehicle state data; obtain the type of the to-be-driven road section in the map data; determine at least one of the curve scenario event, ramp scenario event, and lane closure scenario event according to the type of the to-be-driven road section, and determine the event speed limit information corresponding to each road scenario event; determine the target driving speed of the vehicle based on the event speed limit information and the basic cruise speed. This technical solution realizes dynamic, safe and smooth vehicle speed decision-making by integrating map data and real-time vehicle state data and combining multiple road scenario event arbitration mechanisms, solves the problem of longitudinal vehicle speed dynamic planning in complex scenarios, and ensures the safety of vehicle driving.

[0098] Embodiment III

[0099] Figure 3 It is a schematic structural diagram of a vehicle speed determination device provided according to Embodiment 3 of the present invention. As Figure 3 shown, the device includes:

[0100] A data acquisition module 310, configured to acquire map data and vehicle state data of the vehicle;

[0101] A basic cruise vehicle speed determination module 320, configured to determine a basic cruise vehicle speed according to the map data and the vehicle state data;

[0102] An event speed limit information determination module 330, configured to determine at least one road scene event and determine event speed limit information corresponding to each road scene event;

[0103] A target driving vehicle speed determination module 340, configured to determine the target driving vehicle speed of the vehicle based on the event speed limit information and the basic cruise vehicle speed.

[0104] Optionally, the map data includes speed limit data of the current driving section and speed limit data of the to-be-driven section; the vehicle state data includes the vehicle position;

[0105] The basic cruise vehicle speed determination module 320 is specifically configured to:

[0106] Determine map speed limit data based on the speed limit data of the current driving section, the speed limit data of the to-be-driven section, and the vehicle position;

[0107] Determine the basic cruise vehicle speed according to the set speed limit data and the map speed limit data.

[0108] Optionally, the event speed limit information determination module 330 includes:

[0109] A section type acquisition unit, configured to acquire the type of the to-be-driven section in the map data;

[0110] A scene event determination unit, configured to determine at least one of a curve scene event, a ramp scene event, and a lane closure scene event according to the type of the to-be-driven section.

[0111] Optionally, the event speed limit information determination module 330 is specifically configured to:

[0112] Acquire the curvature of the current lane line and the curvature of the to-be-driven section in the map data;

[0113] Determine the event speed limit information corresponding to the curve scene event according to the curvature of the current lane line, the curvature of the to-be-driven section, and the set lateral acceleration.

[0114] Optionally, the event speed limit information determination module 330 is specifically configured to:

[0115] Obtain the separation distance, deceleration trigger distance, and target distance between the current vehicle and the ramp entrance in the map data;

[0116] Determine whether the current vehicle meets the deceleration trigger condition;

[0117] If the current vehicle meets the deceleration trigger condition, determine the event speed limit information corresponding to the ramp scenario event based on the separation distance, deceleration trigger distance, and target distance.

[0118] Optionally, the event speed limit information determination module 330 is specifically configured to:

[0119] Obtain the current driving speed, target deceleration speed, and distance data related to the lane closure point in the map data;

[0120] Determine the event speed limit information corresponding to the lane closure scenario event based on the current driving speed, target deceleration speed, and distance data related to the lane closure point.

[0121] Optionally, the target driving speed determination module 340 is specifically configured to:

[0122] Determine the decision speed limit data based on the event speed limit information and the basic cruise speed;

[0123] Determine the target driving speed of the vehicle according to the decision speed limit data and the set constraint conditions.

[0124] The vehicle speed determination device provided by the embodiment of the present invention can execute the vehicle speed determination method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0125] Embodiment IV

[0126] Figure 4 It is a schematic structural diagram of an electronic device provided according to Embodiment IV of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0127] Such as Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0128] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0129] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vehicle speed determination method.

[0130] In some embodiments, the vehicle speed determination method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle speed determination method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the vehicle speed determination method by any other appropriate means (e.g., by means of firmware).

[0131] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0132] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0133] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0135] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0136] The computing system can include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0137] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0138] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for determining a vehicle speed, characterized in that: include: Obtaining vehicle map data and vehicle status data; determining a basic cruising speed according to the map data and the vehicle status data; Determine at least one road scene event, and determine event speed limit information corresponding to each of the road scene events; A target driving speed of the vehicle is determined based on the event speed limit information and the basic cruising speed.

2. The method according to claim 1, characterized in that The map data includes speed limit data of the current driving section and speed limit data of the road section to be driven; The vehicle status data includes vehicle position; Determining a basic cruising speed according to the map data and the vehicle state data includes: Determine the map speed limit data based on the current driving section speed limit data, the next driving section speed limit data and the vehicle position; The basic cruising speed is determined according to the set speed limit data and the map speed limit data.

3. The method according to claim 1, characterized in that Determine at least one road scene event, including: Obtaining the type of the road section to be traveled in the map data; At least one of a curve scenario event, a ramp scenario event and a lane closure scenario event is determined according to the type of the road section to be traveled.

4. The method according to claim 3, characterized in that: Determining the event speed limit information corresponding to the curve scene event includes: Obtaining the curvature of the current lane line and the curvature of the road section to be traveled in the map data; The event speed limit information corresponding to the curve scene event is determined according to the curvature of the current lane line, the curvature of the road section to be traveled and the set lateral acceleration.

5. The method according to claim 3, characterized in that: Determining the event speed limit information corresponding to the ramp scenario event includes: Obtaining the distance between the current vehicle and the ramp, the deceleration triggering distance, and the target distance in the map data; Determine whether the current vehicle meets the deceleration triggering conditions; If the current vehicle meets the deceleration trigger condition, the event speed limit information corresponding to the ramp scenario event is determined based on the interval distance, the trigger deceleration distance and the target distance.

6. The method according to claim 3, characterized in that Determining the event speed limit information corresponding to the lane closure scenario event includes: Obtaining the current vehicle speed, the target deceleration speed and the distance data related to the lane closure point in the map data; The event speed limit information corresponding to the lane closure scenario event is determined based on the current vehicle speed, the target deceleration speed, and distance data related to the lane closure point.

7. The method according to claim 1, characterized in that Determining a target driving speed of the vehicle based on the event speed limit information and the basic cruising speed includes: Determine decision speed limit data based on the event speed limit information and the basic cruising speed; The target driving speed of the vehicle is determined according to the decision speed limit data and the set constraint conditions.

8. A vehicle speed determination device, characterized in that: include: A data acquisition module, used to acquire the map data and vehicle status data of the vehicle; A basic cruising speed determination module, used for determining a basic cruising speed according to the map data and the vehicle status data; An event speed limit information determination module, used to determine at least one road scene event, and determine the event speed limit information corresponding to each of the road scene events; The target driving speed determination module is used to determine the target driving speed of the vehicle based on the event speed limit information and the basic cruising speed.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle speed determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle speed determination method according to any one of claims 1 to 7 when executed.

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