Braking control method and device of unmanned vehicle and unmanned vehicle

Abstract

The invention provides a braking control method and device of an unmanned vehicle and the unmanned vehicle. The braking control method comprises the steps that internal influence factors and external influence factors of the unmanned vehicle are obtained; according to the internal influence factor and the external influence factor, a target braking strategy for the unmanned vehicle is determined, and the target braking strategy comprises a target braking mode for intervention and / or intervention priorities of multiple braking modes; and under the condition that the unmanned vehicle has the braking requirement, the target braking strategy is used for conducting braking control on the unmanned vehicle.

Description

Technical Field

[0001] The present disclosure relates to the field of unmanned driving technology, and in particular to a braking control method and device for an unmanned vehicle and the unmanned vehicle. Background Art

[0002] The unmanned mining trucks currently used in mining production operations are usually new energy mining vehicles. New energy mining vehicles usually use electric drive systems, which can reduce fuel consumption and carbon emissions, and meet the requirements of green mine construction. However, when the unmanned vehicle needs to slow down and brake, the traditional braking control method usually uses mechanical braking methods such as air braking or hydraulic braking, so that the piston in the brake wheel cylinder pushes the brake pad to contact the brake disc, generating different degrees of friction. This friction will hinder the rotation of the wheel, thereby achieving the purpose of slowing down and braking the vehicle. This method cannot give full play to the characteristics of new energy mining vehicles that their energy consumption is better than that of traditional fuel vehicles, resulting in energy waste. Summary of the invention

[0003] The embodiments of the present disclosure provide a braking control method and device for an unmanned vehicle and an unmanned vehicle, so as to solve the problem that the existing braking control method causes energy waste during the vehicle braking process.

[0004] Based on the above problems, in a first aspect, an embodiment of the present disclosure provides a braking control method for an unmanned vehicle, comprising:

[0005] Obtain the internal and external influencing factors of the unmanned vehicle;

[0006] Determining a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors, wherein the target braking strategy includes a target braking mode for intervention and / or intervention priorities of multiple braking modes;

[0007] When the unmanned vehicle has a braking demand, the target braking strategy is used to perform braking control on the unmanned vehicle.

[0008] In combination with the first aspect, in a possible implementation, the internal influencing factors include: real-time status information of the unmanned vehicle; and / or, the external influencing factors include: terrain information of the road ahead of the unmanned vehicle.

[0009] In combination with the first aspect, in a possible implementation manner, determining a target braking strategy for the unmanned vehicle according to the internal influencing factor and the external influencing factor includes:

[0010] Determining a first target braking mode of the unmanned vehicle according to the internal influencing factors and the external influencing factors;

[0011] When the braking force of the first target braking method is insufficient, a second target braking method of the unmanned vehicle is determined according to the internal influencing factors and the intervention priorities of the multiple braking methods.

[0012] In combination with the first aspect, in a possible implementation, the vehicle-side real-time status information includes: empty or loaded status of the vehicle; the terrain information includes: slope category information of the road ahead of the unmanned vehicle;

[0013] The determining, according to the internal influencing factors and the external influencing factors, a first target braking mode of the unmanned vehicle includes:

[0014] When the slope category information corresponding to the road ahead of the unmanned vehicle is a specified slope type, and the empty and loaded state of the unmanned vehicle meets the preset load state, it is determined that the first target braking mode for the unmanned vehicle to intervene is the sliding braking mode; wherein, the specified slope type includes: an uphill section, a flat slope section or a first category downhill section, and the downhill slope corresponding to the first category downhill section is less than the specified slope threshold.

[0015] In combination with the first aspect, in a possible implementation manner, the vehicle-side real-time status information further includes: battery charge status information and / or speed information of the first motor;

[0016] When the braking force of the first target braking method is insufficient, determining the second target braking method of the unmanned vehicle according to the internal influencing factor and the intervention priority of the multiple braking methods includes:

[0017] When the first target braking mode for intervention of the unmanned vehicle is the coasting braking mode and the braking force provided by the coasting braking mode is insufficient, according to the access priorities of the multiple braking modes, determining that the braking mode with the next priority of the coasting braking mode is the electric retarder braking mode;

[0018] Determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor;

[0019] In the case where the electric retarder braking mode can provide braking force, it is determined that the second target braking mode for intervention of the unmanned vehicle is the electric retarder braking mode.

[0020] In combination with the first aspect, in a possible implementation manner, the vehicle-side real-time status information includes: battery charge status information and / or speed information of the first motor; the terrain information includes: slope category information of the road ahead of the unmanned vehicle;

[0021] The determining, according to the internal influencing factors and the external influencing factors, a first target braking mode of the unmanned vehicle includes:

[0022] In the case where the slope category information corresponding to the road ahead of the unmanned vehicle is a second-category downhill section, determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor; wherein the downhill slope corresponding to the second-category downhill section is greater than or equal to a specified slope threshold;

[0023] In the case where the electric retarder braking mode can provide braking force, it is determined that the first target braking mode for intervention of the unmanned vehicle is the electric retarder braking mode.

[0024] In combination with the first aspect, in a possible implementation manner, the vehicle-side real-time status information further includes: actual braking capacity information; and the method further includes:

[0025] In the case where the electric retarder braking mode cannot provide braking force, according to the access priorities of the multiple braking modes, determining that the braking mode with the next priority of the electric retarder braking mode is the mechanical braking mode;

[0026] According to the actual braking capability information of the unmanned vehicle, determining that the target braking mode for the unmanned vehicle to be involved is a mechanical braking mode;

[0027] or,

[0028] In the case where the braking force provided by the electric retarder braking mode is insufficient, determining, according to the access priorities of the multiple braking modes, a braking mode with a next priority of the electric retarder braking mode as a mechanical braking mode;

[0029] According to the actual braking capability information of the unmanned vehicle, it is determined that the target braking mode for intervention of the unmanned vehicle is a mechanical braking mode.

[0030] In combination with the first aspect, in a possible implementation manner, determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor includes:

[0031] When the battery state of charge information is less than a first threshold and the rotation speed of the first motor is greater than a second threshold, determining that the electric retarder braking mode can provide braking force;

[0032] When at least one of the following conditions is met, it is determined that the electric retarder braking mode cannot provide braking force: the battery state of charge information is greater than or equal to a first threshold, and the rotation speed of the first motor is less than or equal to a second threshold.

[0033] In combination with the first aspect, in a possible implementation manner, whether the braking force provided by the current target braking mode is sufficient is determined in the following manner:

[0034] According to the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed, it is determined whether the braking force provided by the current target braking mode is sufficient.

[0035] In combination with the first aspect, in a possible implementation, the internal influencing factors include: real-time status information of the unmanned vehicle; the external influencing factors include: the scene in which the unmanned vehicle is located;

[0036] Determining a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors includes:

[0037] When the unmanned vehicle is in a scenario where emergency braking is required, a target braking strategy using mechanical braking is determined based on the real-time status information of the vehicle.

[0038] In a second aspect, an embodiment of the present disclosure provides a brake control device for an unmanned vehicle, comprising:

[0039] A monitoring module is used to obtain the internal and external influencing factors of the unmanned vehicle;

[0040] a braking strategy module, configured to determine a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors, wherein the target braking strategy includes a target braking mode for intervention and / or intervention priorities of multiple braking modes;

[0041] The braking control module is used to use the target braking strategy to perform braking control on the unmanned vehicle when the unmanned vehicle has a braking demand.

[0042] In a third aspect, an unmanned vehicle provided by an embodiment of the present disclosure is a braking control device for the unmanned vehicle as described in the second aspect.

[0043] The beneficial effects of the embodiments of the present disclosure include:

[0044] The present disclosure provides a braking control method, device and unmanned vehicle for an unmanned vehicle, including: obtaining internal influencing factors and external influencing factors of the unmanned vehicle; determining a target braking strategy for the unmanned vehicle based on the internal influencing factors and the external influencing factors, wherein the target braking strategy includes a target braking method for intervention and / or intervention priorities of multiple braking methods; when the unmanned vehicle has a braking demand, the unmanned vehicle is braked using the target braking strategy. The braking control method for the unmanned vehicle provided by the embodiment of the present disclosure formulates a suitable target braking strategy based on the external influencing factors of the road conditions ahead of the unmanned vehicle monitored in real time, combined with the internal influencing factors of the unmanned vehicle's own operating state. Compared with the prior art, under the premise of ensuring safe driving, the advantages of different braking methods are fully utilized from the perspective of energy consumption optimization to achieve a deceleration braking effect without energy loss and as much energy recovery as possible, thereby achieving the purpose of improving energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a braking control method for an unmanned vehicle provided in an embodiment of the present disclosure;

[0046] Figure 2 A schematic diagram of a braking control method for an unmanned vehicle provided in an embodiment of the present disclosure;

[0047] Figure 3 A structural diagram of a brake control device for an unmanned vehicle provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] The embodiments of the present disclosure provide a brake control method and device for an unmanned vehicle and an unmanned vehicle. The preferred embodiments of the present disclosure are described below in conjunction with the drawings of the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present disclosure and are not used to limit the present disclosure. In addition, the embodiments and features in the embodiments of the present application can be combined with each other if there is no conflict.

[0049] The present disclosure provides a braking control method for an unmanned vehicle. Figure 1 As shown, including:

[0050] S101, obtaining internal and external influencing factors of the unmanned vehicle;

[0051] S102, determining a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors, wherein the target braking strategy includes a target braking mode for intervention and / or intervention priorities of multiple braking modes;

[0052] S103: When the unmanned vehicle has a braking demand, the target braking strategy is used to perform braking control on the unmanned vehicle.

[0053] The disclosed embodiments can be applied to various scenarios of unmanned vehicle operation, especially mining production and operation scenarios. In today's mining production and operation scenarios, the scale of use of unmanned mining trucks is gradually expanding, and most of these unmanned mining trucks are new energy mining vehicles. Relying on advanced electric drive systems, new energy mining vehicles have outstanding performance in reducing fuel consumption and reducing carbon emissions, which is highly consistent with the concept of building green mines and provides strong support for the sustainable development of the mining industry. In order to realize the automatic driving function, unmanned vehicles are integrated with a large number of sensors, such as laser radar, camera, millimeter wave radar, etc. These sensors can be used to detect the environment around the unmanned vehicle. For example, the laser radar constructs a three-dimensional point cloud map of the vehicle's surrounding environment by emitting a laser beam and measuring the time of reflected light, which can accurately identify the position, shape and distance of various obstacles; the camera uses an image recognition algorithm to classify and detect objects within the visual range, whether it is a stationary rock pile or a moving operator; the millimeter wave radar has obvious advantages under severe weather conditions and can monitor the speed and relative position changes of objects around the vehicle in real time. The mutual cooperation of these sensors can provide comprehensive and accurate environmental perception information for unmanned vehicles. Using this information, the unmanned vehicle can plan its driving strategy and accurately identify whether there is a need for braking. However, in the process of vehicle braking, there are urgent problems that need to be solved, which seriously restricts the full play of the advantages of new energy mining vehicles. When the unmanned vehicle has a need to slow down and brake, the traditional braking control method usually relies on mechanical braking methods such as air braking or hydraulic braking. This method uses the piston in the brake wheel cylinder to push the brake pad to contact the brake disc, relying on the friction generated between the two to hinder the rotation of the wheel, thereby achieving the deceleration and braking of the vehicle. However, this process has significant disadvantages. The friction generated by mechanical braking consumes the kinetic energy of the vehicle to achieve braking, but it cannot recycle and reuse this energy, resulting in a waste of energy. This is undoubtedly contrary to the original intention of new energy mining vehicles to save energy and reduce emissions and use energy efficiently, which greatly reduces the advantages of new energy mining vehicles in terms of energy consumption compared to traditional fuel vehicles.

[0054] In the disclosed embodiments, a variety of influencing factors have an impact on the braking strategy of the unmanned vehicle, including internal influencing factors and external influencing factors. During operation, the unmanned vehicle can monitor the internal influencing factors of the vehicle itself. The internal influencing factors affect the braking effect of the unmanned vehicle and are used to judge the operating status of the vehicle. The internal influencing factors at least include the real-time status information of the unmanned vehicle, such as actual braking capacity information, empty and loaded status, tire status information, suspension system status, vehicle speed and acceleration information, vehicle posture information, etc. During operation, the unmanned vehicle can use a variety of sensors to monitor external influencing factors. The external influencing factors may include external environmental factors to which the unmanned vehicle is subjected during operation, and these factors directly affect the operating status of the vehicle and the formulation of the braking strategy.

[0055] The external influencing factors may include at least one of the following: the terrain information of the road ahead of the unmanned vehicle, and the scene in which the unmanned vehicle is located. Exemplarily, the terrain information of the road ahead may include at least one of the following: the slope category information of the road ahead of the unmanned vehicle, and the bumpy road section information of the road ahead of the unmanned vehicle. For example, when the slope category information of the road ahead of the unmanned vehicle is a large downhill section or the bumpy road section information of the road ahead of the unmanned vehicle is a severe bumpy section, it is necessary to intervene with a target braking method that can provide greater braking force to reduce speed and brake; the scene in which the unmanned vehicle is located may include at least one of the following: emergency braking scenes and complex road conditions scenes. For example, obstacles may appear in emergency braking scenes, and braking is required to ensure safety. Complex road conditions scenes may include muddy roads, icy and snowy roads and other roads with low friction coefficients, and the target braking method needs to be adjusted to avoid slipping or sliding.

[0056] Optionally, the target braking strategy for the unmanned vehicle includes: a target braking method for intervention and intervention priorities of multiple braking methods. The target braking method may include: a coasting braking method, an electric retarder braking method, and a mechanical braking method. The coasting braking method may refer to a braking method in which the unmanned vehicle relies on inertia to glide and gradually decelerate when power output is not required. When the unmanned vehicle has a braking demand, the motor stops outputting power, and the vehicle relies on inertia to glide without consuming additional energy. It is suitable for use on flat roads or when the speed is slightly reduced. The electric retarder braking method may be a braking method based on the reverse action of the motor. The deceleration is achieved by converting the kinetic energy of the vehicle into electrical energy and storing it in the battery. The electric retarder braking method can reversely charge the battery during braking to achieve the purpose of energy recovery and improve energy utilization efficiency. However, when the unmanned vehicle is driving at high speed or fully loaded, the braking force provided by the electric retarder braking method may be insufficient, and the mechanical braking method needs to be intervened. Mechanical braking methods can be traditional braking methods that use friction to hinder the rotation of wheels, including air braking, hydraulic braking, etc. Mechanical braking methods convert the kinetic energy of the unmanned vehicle into heat energy and dissipate it into the air, which cannot be recycled and causes energy waste. The intervention priorities of multiple braking methods from high to low can include: the first priority of intervening in the sliding braking method, the second priority of intervening in the electric retarder braking method, and the third priority of intervening in the mechanical braking method.

[0057] Optionally, after fully considering the internal and external influencing factors, a target braking strategy for the unmanned vehicle is determined. Figure 2As shown in the figure, when the unmanned vehicle is in operation, when there is a braking demand, the coasting braking method is used first to decelerate. When the unmanned vehicle is actually in a continuous deceleration process and the real-time speed and the expected speed do not deviate much, no other braking methods are required to intervene, so there is no braking energy loss and the purpose of lossless braking is achieved. Slopes are common in the operation scene of mining vehicles. When encountering a small downhill section, the coasting braking method may not be able to provide sufficient braking force. For example, when the external influencing factor is monitored that the unmanned vehicle is driving on a small downhill section, and the coasting braking method cannot decelerate within a certain period of time or the actual speed and the expected speed deviate slightly, the electric retarder braking method intervenes. The electric retarder braking method can recover part of the energy during the braking process, reverse charge the battery, and reduce the loss during the braking process. When the braking force provided by the electric retarder braking method is insufficient, for example, when the real-time speed and the expected speed deviate too much or the planned deceleration rate is too large, the mechanical braking method needs to intervene. When the external influencing factor is a downhill road, in order to avoid excessive overspeeding, the electric retarder braking method is directly involved, which can not only avoid excessive overspeeding, but also make full use of the downhill power component to reverse charge the battery. At the same time, it avoids the problem that the electric retarder braking method intervenes too slowly to achieve the braking effect, and the mechanical braking method needs to be frequently intervened. When the external influencing factor is an emergency braking scenario, such as a flashing obstacle, the mechanical braking method is directly intervened to ensure the safety of vehicle driving. When the internal influencing factor indicates that the electric retarder braking method cannot provide braking force, and the braking force provided by the coasting brake is insufficient, it is necessary to switch directly from the coasting brake method to the mechanical brake method to ensure driving safety.

[0058] In the embodiment of the present application, the target braking strategy for the unmanned vehicle is determined by obtaining the internal influencing factors and the external influencing factors of the unmanned vehicle, based on the internal influencing factors and the external influencing factors. If the internal influencing factors and the external influencing factors are not fully considered, it is impossible to maximize energy recovery on the basis of meeting the braking capacity required for operation. Compared with the prior art, on the basis of ensuring safe driving, energy consumption optimization is taken as the starting point, and the advantages of different braking methods are fully utilized, striving to achieve deceleration braking without energy loss, and recover energy as much as possible, thereby improving energy efficiency.

[0059] In another embodiment of the present disclosure, the internal influencing factors include: the real-time status information of the unmanned vehicle; and / or, the external influencing factors include: the terrain information of the road ahead of the unmanned vehicle.

[0060] In the disclosed embodiment, the unmanned vehicle can monitor the internal influencing factors of the vehicle itself and the external influencing factors using a variety of sensors during operation. The internal influencing factors can be an important basis for judging the vehicle's operating status and intervening braking methods. The internal influencing factors at least include the real-time status information of the unmanned vehicle, such as actual braking capacity information, empty and loaded status, tire status information, suspension system status, vehicle speed and acceleration information, vehicle posture information, etc. Exemplarily, the actual braking capacity information may include at least one of the following: brake pad wear degree, brake fluid pressure, brake disc temperature. The actual braking capacity information is used to evaluate the real-time performance of the braking system to ensure that the braking effect meets the vehicle operation requirements while avoiding safety hazards caused by brake system failure or overheating; the empty and heavy load states may include at least one of: empty, partially loaded and fully loaded. The empty and heavy load states can indicate the load condition of the vehicle, which directly affects the inertia and braking requirements of the vehicle. When fully loaded, the vehicle requires greater braking force to achieve deceleration, while when empty, it is necessary to avoid excessive braking to cause energy waste; the tire status information may include at least one of the following: tire pressure, temperature, and degree of wear. The tire status information directly affects the braking efficiency and safety. For example, insufficient tire pressure will increase rolling resistance , reducing the braking effect; the suspension system state includes at least one of the following: suspension height, damping coefficient and suspension system pressure. The suspension system state affects the stability and road adaptability of the vehicle during braking. Optimizing the suspension state can improve braking stability; the vehicle speed and acceleration information can include at least one of the following: the real-time speed, acceleration, and acceleration change rate of the unmanned vehicle. The vehicle speed and acceleration information can be used to predict braking needs, optimize braking force distribution, and ensure a smooth and efficient braking process; the vehicle posture information can include at least one of the following: the pitch angle, roll angle, and yaw angle of the unmanned vehicle. The vehicle posture information can be used to ensure the stability of the unmanned vehicle during braking. For example, during braking, changes in the vehicle posture information may affect the braking effect. Excessive roll angle of the vehicle may cause the vehicle to lose control during braking.

[0061] External influencing factors may refer to external environmental factors to which the unmanned vehicle is subjected during operation, which directly affect the vehicle's operating state and the formulation of braking strategies. External influencing factors may include terrain information of the road ahead of the unmanned vehicle. Exemplarily, the terrain information of the road ahead may include at least one of the following: slope category information of the road ahead of the unmanned vehicle, bumpy road section information of the road ahead of the unmanned vehicle, for example, when the bumpy road section information of the road ahead of the unmanned vehicle is a light bumpy road section, the sliding braking method is preferentially intervened to decelerate. In the case where the actual vehicle speed still exceeds the expected vehicle speed when the sliding braking method is adopted, the electric retarder braking method is intervened to decelerate. In the case where the actual vehicle speed still exceeds the expected vehicle speed when the electric retarder braking method is intervened, the mechanical braking method is intervened to decelerate. In the case where the electric retarder braking method cannot provide braking force, the sliding braking method is switched to the mechanical braking method for deceleration braking. When the bumpy road section information of the road ahead of the unmanned vehicle is a heavy bumpy road section, the electric retarder braking method is directly intervened for braking. When the electric retarder braking method cannot provide braking force, the mechanical braking method is used for deceleration braking. With full consideration of internal and external influencing factors, a more appropriate target braking strategy can be formulated.

[0062] In another embodiment of the present disclosure, in the above step S102, determining a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors includes the following steps:

[0063] Step 1: Determine the first target braking mode of the unmanned vehicle according to the internal influencing factors and the external influencing factors;

[0064] Step 2: When the braking force of the first target braking method is insufficient, determine the second target braking method of the unmanned vehicle according to internal influencing factors and intervention priorities of multiple braking methods.

[0065] In the disclosed embodiment, in the process of formulating the target braking strategy, dynamically determining the target braking method based on internal and external influencing factors is the key to achieving safe and efficient braking. For the above step 1, combine internal influencing factors, such as battery state of charge information, vehicle posture information, and external influencing factors, such as terrain information of the road ahead, the scene in which the unmanned vehicle is located, etc., and select the first target braking method that best suits the current working conditions. Exemplarily, the unmanned vehicle's current battery state of charge is 30%, and the terrain information of the road ahead is a severely bumpy section, which is suitable for energy recovery using an electric retarder braking method, and the electric retarder braking method is used as the first target braking method for braking.

[0066] For the above step 2, when the braking force of the first target braking method is insufficient, the second target braking method of the unmanned vehicle is determined according to the internal influencing factors and the intervention priority of multiple braking methods. For example, the actual vehicle speed deviates greatly from the expected vehicle speed. In this case, although the electric retarder braking is still an important means of energy recovery, the pitch angle of the vehicle in the vehicle posture information changes greatly, which may pose a safety risk. Therefore, according to the intervention priority of multiple braking methods, the next priority braking method of the electric retarder braking method is the mechanical braking method, and the mechanical braking method is intervened as the second target braking method to brake quickly and ensure driving safety. By rationally utilizing the first target braking method and the second target braking method, the unmanned vehicle can maximize energy consumption and recover energy while ensuring safety, thereby improving overall energy efficiency performance.

[0067] In another embodiment of the present disclosure, the vehicle-side real-time status information includes: the empty or loaded status of the vehicle; the terrain information includes: the slope category information of the road ahead of the unmanned vehicle;

[0068] In the above step 1, the first target braking mode of the unmanned vehicle is determined according to the internal influencing factors and the external influencing factors, including:

[0069] When the slope category information corresponding to the road ahead of the unmanned vehicle is a specified slope type, and the empty and loaded state of the unmanned vehicle meets the preset load state, it is determined that the first target braking mode for the unmanned vehicle to intervene is the sliding braking mode; wherein, the specified slope types include: uphill sections, flat slope sections or first category downhill sections, and the downhill slope corresponding to the first category downhill sections is less than the specified slope threshold.

[0070] In the disclosed embodiment, when the unmanned vehicle detects that the slope category information corresponding to the road ahead is a specified slope type, and the unmanned vehicle's empty and heavy load state meets the preset load state, the coasting braking mode is used as the first target braking mode. The vehicle-side real-time status information includes: the empty and heavy load state of the vehicle, which can indicate the load condition of the vehicle and directly affect the inertia and braking demand of the vehicle. The terrain information includes: the slope category information of the road ahead of the unmanned vehicle. The slope category information includes: uphill section, flat slope section, first category downhill section, and second category downhill section. The downhill slope corresponding to the first category downhill section is less than the specified slope threshold. The downhill slope corresponding to the second category downhill section is greater than or equal to the specified slope threshold. For example, the slope threshold is 5 degrees, the downhill slope corresponding to the first category downhill section is less than 5 degrees, and the slope is relatively gentle, and the downhill slope corresponding to the second category downhill section is greater than or equal to 5 degrees, and the slope is relatively large.

[0071] When the slope category information corresponding to the road ahead of the unmanned vehicle is a specified slope type, the specified slope types include: uphill section, flat slope section or first category downhill section, and the unmanned vehicle's empty and heavy load state meets the preset load state, for example, when the unmanned vehicle's empty and heavy load state is less than 50% of the rated load, the coasting braking method is used as the first target braking method. In this case, the slope is gentle, the vehicle load is light, and the inertia is small, and the coasting braking method is sufficient to meet the deceleration requirements. The coasting braking method with no energy loss is preferred to reduce the use of mechanical braking methods and electric retarder braking methods, thereby reducing energy consumption and component wear.

[0072] In addition, when the slope category information corresponding to the road ahead of the unmanned vehicle is a second-category downhill section, or when the unmanned vehicle's empty-load state is greater than or equal to the preset load state, determine whether the electric retarder braking method can provide braking force. If the electric retarder braking method can provide braking force, the first target braking method for the unmanned vehicle to intervene can be the electric retarder braking method. If the electric retarder braking method cannot provide braking force, the first target braking method for the unmanned vehicle to intervene can be the mechanical braking method. The priority use of the coasting braking method significantly improves the economy and reliability of the unmanned vehicle under low-risk conditions, and also reserves safety redundancy for complex scenarios.

[0073] In another embodiment of the present disclosure, the vehicle-side real-time status information further includes: battery charge status information and / or speed information of the first motor;

[0074] In the above step 2, when the braking force of the first target braking method is insufficient, determining the second target braking method of the unmanned vehicle according to the internal influencing factors and the intervention priorities of the multiple braking methods includes the following steps:

[0075] Step 1: when the first target braking mode for intervention of the unmanned vehicle is the coasting braking mode, and the braking force provided by the coasting braking mode is insufficient, according to the access priorities of multiple braking modes, determining that the braking mode with the next priority of the coasting braking mode is the electric retarder braking mode;

[0076] Step 2: determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor;

[0077] Step 3: When the electric retarder braking mode can provide braking force, determine that the second target braking mode for the unmanned vehicle to intervene is the electric retarder braking mode.

[0078] In the disclosed embodiment, the vehicle-side real-time status information also includes: battery state of charge information, speed information of the first motor. By real-time monitoring of the battery state of charge information and the speed information of the first motor, the availability and braking force of the electric retarder braking are dynamically determined in combination with a preset threshold. The battery state of charge (SOC) can be an important parameter for measuring the remaining battery power, usually expressed as a percentage (%), reflecting the current battery power level relative to its full charge state. For example, an SOC of 80% means that the current battery power is 80% of its full charge state. When the SOC of the vehicle battery is too high, the electric retarder braking method cannot provide braking capability. The first motor can be used as a drive source in the unmanned vehicle drive system, used to undertake the vehicle's driving task, and as a power generation unit during energy recovery. The power generation efficiency of the first motor is positively correlated with the speed. At low speeds, the induced electromotive force is insufficient, and the braking force provided by the electric retarder braking method drops sharply. For the above step 1, when the first target braking mode of the unmanned vehicle is the coasting braking mode, and when the braking force provided by the coasting braking mode is insufficient, for example, when the real-time vehicle speed exceeds the expected vehicle speed when the coasting braking mode is used, according to the access priority of multiple braking modes, it is determined that the braking mode with the next priority of the coasting braking mode is the electric retarder braking mode. For the above step 2, according to the battery state of charge information of the unmanned vehicle and the speed information of the first motor, it is determined whether the electric retarder braking mode can provide braking force. For example, if the battery state of charge information of the unmanned vehicle is less than 90% and the speed information of the first motor is greater than 500rpm, the electric retarder braking mode can provide braking force. For the above step 3, when the electric retarder braking mode can provide braking force, it is determined that the second target braking mode of the unmanned vehicle is the electric retarder braking mode. The first target braking mode is the coasting braking mode and the second target braking mode is the hierarchical formulation of the electric retarder braking mode, and the electric retarder braking mode is verified in combination with the real-time status information of the vehicle end, so as to maximize the energy recovery efficiency under the premise of ensuring safety.

[0079] In another embodiment of the present disclosure, the vehicle-side real-time status information includes: battery charge status information and / or speed information of the first motor; the terrain information includes: slope category information of the road ahead of the unmanned vehicle;

[0080] In the above step 1, determining the first target braking mode of the unmanned vehicle according to the internal influencing factors and the external influencing factors includes the following steps:

[0081] Step (1), when the slope category information corresponding to the road ahead of the unmanned vehicle is a second-category downhill section, determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor; wherein the downhill slope corresponding to the second-category downhill section is greater than or equal to a specified slope threshold;

[0082] Step (2): when the electric retarder braking mode can provide braking force, determine that the first target braking mode for intervention of the unmanned vehicle is the electric retarder braking mode.

[0083] In the disclosed embodiment, when the slope category information corresponding to the road ahead of the unmanned vehicle is a second-category downhill section, it is determined whether the electric retarder braking method can be directly intervened. For the above step (1), when the slope category information corresponding to the road ahead of the unmanned vehicle is a second-category downhill section, for example, the downhill slope corresponding to the second-category downhill section is greater than or equal to 5 degrees, the slope is large, and the sliding braking method cannot meet the braking demand. In this case, according to the battery charge state information of the unmanned vehicle and the speed information of the first motor, it is determined whether the electric retarder braking method can provide braking force; for the above step (2), when the electric retarder braking method can provide braking force, for example, the battery charge state information of the unmanned vehicle is less than 90% and the speed information of the first motor is greater than 500rpm, and the electric retarder braking method can provide braking force, the electric retarder braking method is determined as the first target braking method for the unmanned vehicle to intervene. In the case where the electric retarder braking mode cannot provide braking force, for example, the battery charge state information of the unmanned vehicle is greater than or equal to 90% or the speed information of the first motor is less than or equal to 500rpm, the electric retarder braking mode cannot provide braking force, and the mechanical braking mode is determined as the first target braking mode for the unmanned vehicle to intervene. Through the above steps, the safe driving of the unmanned vehicle can be ensured, and the maximum energy recovery can be achieved.

[0084] In another embodiment of the present disclosure, the vehicle-side real-time status information further includes: actual braking capacity information; and the method further includes the following steps:

[0085] Step (1), when the electric retarder braking mode cannot provide braking force, according to the access priorities of multiple braking modes, determining that the braking mode with the next priority of the electric retarder braking mode is the mechanical braking mode;

[0086] Step (ii), according to the actual braking capability information of the unmanned vehicle, determining that the target braking mode for the unmanned vehicle to intervene is a mechanical braking mode;

[0087] or,

[0088] Step (iii), when the braking force provided by the electric retarder braking mode is insufficient, according to the access priorities of the multiple braking modes, determining the braking mode with the next priority of the electric retarder braking mode to be the mechanical braking mode;

[0089] Step (iv) determines that the target braking mode for the unmanned vehicle to intervene is a mechanical braking mode based on the actual braking capability information of the unmanned vehicle.

[0090] In the disclosed embodiment, when the electric retarder braking method cannot provide braking force or the braking force provided is insufficient, the mechanical braking method is used to perform deceleration braking. The vehicle-side real-time status information also includes: actual braking capacity information. The actual braking capacity information is used to evaluate the real-time performance of the braking system, ensure that the braking effect meets the vehicle operation requirements, and avoid safety hazards caused by braking system failure or overheating. For example, the actual braking capacity information may include at least one of the following: brake pad wear degree, brake fluid pressure, and brake disc temperature.

[0091] For the above step (i), when the electric retarder braking mode cannot provide braking force, for example, when the first target braking mode is the electric retarder braking mode, as the vehicle brakes, the electric retarder braking mode cannot provide braking force, for example, when the battery state of charge information of the unmanned vehicle is greater than or equal to 90% or the speed information of the first motor is less than or equal to 500rpm, the electric retarder braking mode cannot provide braking force, and according to the access priorities of multiple braking modes, the braking mode with the next priority of the electric retarder braking mode is determined to be the mechanical braking mode. For another example, when the first target braking mode is the sliding braking mode, and when the braking force provided by the sliding braking mode is insufficient, according to the access priorities of multiple braking modes, the braking mode with the next priority of the sliding braking mode is determined to be the electric retarder braking mode, and at this time, when the electric retarder braking mode cannot provide braking force, for example, when the battery state of charge information of the unmanned vehicle is greater than or equal to 90% or the speed information of the first motor is less than or equal to 500rpm, the electric retarder braking mode cannot provide braking force, and according to the access priorities of multiple braking modes, the braking mode with the next priority of the electric retarder braking mode is determined to be the mechanical braking mode.

[0092] For the above step (ii), based on the actual braking capacity information of the unmanned vehicle, for example, the actual braking capacity information of the unmanned vehicle shows that the degree of wear of the brake pads is low and lower than the preset value, the brake fluid pressure is sufficient and meets the preset value, and the brake disc temperature is low and lower than the preset value. The mechanical braking method can provide braking force, and it is determined that the target braking method for the unmanned vehicle intervention is the mechanical braking method.

[0093] With respect to the above step (iii), when the braking force provided by the electric retarder braking mode is insufficient, for example, when the first target braking mode is the electric retarder braking mode, and after the electric retarder intervenes in braking, it is found that the braking force provided by the electric retarder braking mode is insufficient, for example, the deviation between the real-time vehicle speed and the expected vehicle speed is large, according to the access priorities of multiple braking modes, it is determined that the braking mode with the next priority under the electric retarder braking mode is the mechanical braking mode. For another example, when the first target braking mode is the coasting braking mode, and after the coasting braking mode is engaged in braking, it is found that the braking force provided by the coasting braking mode is insufficient, according to the access priorities of multiple braking modes, it is determined that the braking mode with the next priority under the coasting braking mode is the electric retarder braking mode, and the electric retarder braking is determined as the second target braking mode, and after the electric retarder intervenes in braking, it is found that the braking force provided by the electric retarder braking mode is insufficient, for example, the deviation between the real-time vehicle speed and the expected vehicle speed is large, according to the access priorities of multiple braking modes, it is determined that the braking mode with the next priority under the electric retarder braking mode is the mechanical braking mode.

[0094] For the above step (iv), according to the actual braking capacity information of the unmanned vehicle, for example, the brake pad wear degree is low, lower than the preset value, the brake fluid pressure is sufficient, meets the preset value, and the brake disc temperature is low, lower than the preset value, it is determined that the target braking mode for the unmanned vehicle to intervene is the mechanical braking mode. By verifying the braking capacity provided by the electric retarder braking mode, the safe driving of the vehicle can be prioritized.

[0095] In another embodiment of the present disclosure, in the above step 2, determining whether the electric retarder braking mode can provide braking force according to the battery charge state information of the unmanned vehicle and / or the speed information of the first motor includes:

[0096] Step (1), when the battery state of charge information is less than a first threshold and the speed of the first motor is greater than a second threshold, determining that the electric retarder braking mode can provide braking force;

[0097] Step (ii), determining that the electric retarder braking mode cannot provide braking force when at least one of the following conditions is met: the battery charge state information is greater than or equal to a first threshold, and the rotation speed of the first motor is less than or equal to a second threshold.

[0098] In the disclosed embodiment, the battery state of charge information and the speed information of the first motor of the new energy unmanned vehicle determine whether the electric retarder braking method can provide braking force. By real-time monitoring of the battery state of charge information and the speed information of the first motor, the availability and braking force of the electric retarder braking are dynamically judged in combination with the preset threshold. The battery state of charge (SOC) can be an important parameter for measuring the remaining battery power, usually expressed as a percentage (%), reflecting the current battery power level relative to its full power state. For example, an SOC of 80% means that the current battery power is 80% of its full power state. When the SOC of the vehicle battery is too high, the electric retarder braking method cannot provide braking ability. The first motor can be used as a motor as a drive source in the unmanned vehicle drive system, used to undertake the driving task of the vehicle, and as a power generation unit during energy recovery. The power generation efficiency of the first motor is positively correlated with the speed. At low speeds, the induced electromotive force is insufficient, and the braking force provided by the electric retarder braking method drops sharply.

[0099] For the above step (a), when the battery state of charge information is less than the first threshold and the speed of the first motor is greater than the second threshold, it is determined that the electric retarder braking method can provide braking force. For example, the first threshold is 90%, and the second threshold is 500rpm. When the battery state of charge information of the unmanned vehicle is less than 90% and the speed information of the first motor is greater than 500rpm, the electric retarder braking method can provide braking force. For the above step (b), when the battery state of charge information is greater than or equal to the first threshold or the speed of the first motor is less than or equal to the second threshold, the electric retarder braking method cannot provide braking force. For example, the first threshold is 90%, and the second threshold is 500rpm. When the battery state of charge information of the unmanned vehicle is greater than or equal to 90% or the speed information of the first motor is less than or equal to 500rpm, the electric retarder braking method cannot provide braking force.

[0100] In another embodiment of the present disclosure, whether the braking force provided by the current target braking mode is sufficient is determined in the following manner:

[0101] According to the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed, it is determined whether the braking force provided by the current target braking mode is sufficient.

[0102] In the disclosed embodiment, the braking effect is checked by comparing the deviation between the real-time vehicle speed and the corresponding expected vehicle speed. The unmanned vehicle will plan the driving trajectory and the expected vehicle speed during operation. In the case of a braking demand, the expected vehicle speed will be replanned. For example, when the unmanned vehicle is detected to be driving on a downhill section or a bumpy section, in order to ensure driving safety, the planned expected vehicle speed is lower than the speed limit threshold. At this time, after adopting the target braking method, for example, the sliding braking method is adopted, when the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed exceeds the third threshold and the duration exceeds the fourth threshold, it is determined that the braking force provided by the sliding braking method is insufficient. According to the intervention priority of multiple braking methods, when the electric retarder braking method can provide braking force, the electric retarder braking method is intervened, and the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed is continuously checked. When the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed exceeds the third threshold and the duration exceeds the fourth threshold, the mechanical braking method is intervened. By checking the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed, it can be determined whether the braking force provided by the current target braking method is sufficient.

[0103] In another embodiment of the present disclosure, the internal influencing factors include: the real-time status information of the unmanned vehicle; the external influencing factors include: the scene in which the unmanned vehicle is located;

[0104] In the above step S102, the target braking strategy for the unmanned vehicle is determined according to the internal influencing factors and the external influencing factors, including:

[0105] When the unmanned vehicle is in a scenario that requires emergency braking, the target braking strategy using mechanical braking is determined based on the real-time status information on the vehicle side.

[0106] In the embodiment of the present disclosure, in the case of an emergency braking scenario, a mechanical braking method is intervened to ensure driving safety. The internal influencing factors include: the real-time status information of the unmanned vehicle, for example, the actual braking capacity information of the unmanned vehicle. The actual braking capacity information may include at least one of the following: the current braking force of the braking system, the degree of wear of the brake pads, the brake fluid pressure, and the brake disc temperature. The actual braking capacity information is used to evaluate the real-time performance of the braking system to ensure that the braking effect meets the vehicle operation requirements and avoids safety hazards caused by brake system failure or overheating. The actual braking capacity information can be used to evaluate whether the mechanical braking method can provide braking force. In the case where the mechanical braking method cannot provide braking force, an alarm is issued in time for maintenance. In the case where the unmanned vehicle is in a scene where emergency braking is required, for example, an obstacle suddenly appears on the road in front of the unmanned vehicle, the mechanical braking method can provide braking force according to the real-time status information of the vehicle. At this time, the target braking strategy using the mechanical braking method is determined to ensure driving safety. In an emergency, the mechanical braking method is used first to ensure driving safety.

[0107] Based on the same disclosed concept, the disclosed embodiments also provide a braking control device for an unmanned vehicle and an unmanned vehicle. Since the principles of the problems solved by these devices and the unmanned vehicle are similar to those of the aforementioned braking control method for the unmanned vehicle, the implementation of the device and the equipment can refer to the implementation of the aforementioned method, and the repeated parts will not be repeated.

[0108] The present disclosure provides a braking control device for an unmanned vehicle, such as Figure 3 As shown, including:

[0109] Monitoring module 301, used to obtain internal and external influencing factors of the unmanned vehicle;

[0110] A braking strategy module 302, configured to determine a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors, wherein the target braking strategy includes a target braking mode for intervention and / or intervention priorities of multiple braking modes;

[0111] The braking control module 303 is used to perform braking control on the unmanned vehicle using the target braking strategy when the unmanned vehicle has a braking demand.

[0112] In another embodiment of the present disclosure, the internal influencing factors include: real-time status information of the unmanned vehicle; and / or, the external influencing factors include: terrain information of the road ahead of the unmanned vehicle.

[0113] In another embodiment of the present disclosure, the braking strategy module 302 is used to determine the first target braking mode of the unmanned vehicle according to the internal influencing factor and the external influencing factor;

[0114] When the braking force of the first target braking method is insufficient, a second target braking method of the unmanned vehicle is determined according to the internal influencing factors and the intervention priorities of the multiple braking methods.

[0115] In another embodiment of the present disclosure, the vehicle-side real-time status information includes: the empty or loaded status of the vehicle; the terrain information includes: the slope category information of the road ahead of the unmanned vehicle;

[0116] The braking strategy module 302 is used to determine that the first target braking mode for the unmanned vehicle to intervene is the sliding braking mode when the slope category information corresponding to the road ahead of the unmanned vehicle is a specified slope type and the empty and loaded state of the unmanned vehicle meets the preset load state; wherein the specified slope type includes: an uphill section, a flat slope section or a first category downhill section, and the downhill slope corresponding to the first category downhill section is less than the specified slope threshold.

[0117] In another embodiment of the present disclosure, the vehicle-side real-time status information further includes: battery charge status information and / or speed information of the first motor;

[0118] The braking strategy module 302 is used to determine, according to the access priorities of the multiple braking modes, that the braking mode with the next priority of the sliding braking mode is the electric retarder braking mode when the first target braking mode for the unmanned vehicle to intervene is the sliding braking mode and the braking force provided by the sliding braking mode is insufficient;

[0119] Determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor;

[0120] In the case where the electric retarder braking mode can provide braking force, it is determined that the second target braking mode for intervention of the unmanned vehicle is the electric retarder braking mode.

[0121] In another embodiment of the present disclosure, the vehicle-side real-time status information includes: battery charge status information and / or speed information of the first motor; the terrain information includes: slope category information of the road ahead of the unmanned vehicle;

[0122] The braking strategy module 302 is used to determine whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor when the slope category information corresponding to the road ahead of the unmanned vehicle is a second-category downhill section; wherein the downhill slope corresponding to the second-category downhill section is greater than or equal to a specified slope threshold;

[0123] In the case where the electric retarder braking mode can provide braking force, it is determined that the first target braking mode for intervention of the unmanned vehicle is the electric retarder braking mode.

[0124] In another embodiment of the present disclosure, the vehicle-side real-time status information further includes: actual braking capability information; the braking strategy module 302 is further used to:

[0125] In the case where the electric retarder braking mode cannot provide braking force, according to the access priorities of the multiple braking modes, determining that the braking mode with the next priority of the electric retarder braking mode is the mechanical braking mode;

[0126] According to the actual braking capability information of the unmanned vehicle, determining that the target braking mode for the unmanned vehicle to be involved is a mechanical braking mode;

[0127] or,

[0128] In the case where the braking force provided by the electric retarder braking mode is insufficient, determining, according to the access priorities of the multiple braking modes, a braking mode with a next priority of the electric retarder braking mode as a mechanical braking mode;

[0129] According to the actual braking capability information of the unmanned vehicle, it is determined that the target braking mode for intervention of the unmanned vehicle is a mechanical braking mode.

[0130] In another embodiment of the present disclosure, the braking strategy module 302 is used to determine that the electric retarder braking mode can provide braking force when the battery state of charge information is less than a first threshold and the speed of the first motor is greater than a second threshold;

[0131] When at least one of the following conditions is met, it is determined that the electric retarder braking mode cannot provide braking force: the battery state of charge information is greater than or equal to a first threshold, and the rotation speed of the first motor is less than or equal to a second threshold.

[0132] In another embodiment of the present disclosure, the braking strategy module 302 is used to determine whether the braking force provided by the current target braking method is sufficient in the following manner:

[0133] According to the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed, it is determined whether the braking force provided by the current target braking mode is sufficient.

[0134] In another embodiment of the present disclosure, the internal influencing factors include: the real-time status information of the unmanned vehicle; the external influencing factors include: the scene where the unmanned vehicle is located;

[0135] The braking strategy module 302 is used to determine a target braking strategy using a mechanical braking method according to the real-time status information of the vehicle when the scene in which the unmanned vehicle is located is a scene requiring emergency braking.

[0136] An embodiment of the present disclosure provides an unmanned vehicle, comprising: a braking control device for an unmanned vehicle as described in any of the above embodiments.

[0137] Through the description of the above implementation methods, those skilled in the art can clearly understand that the embodiments of the present disclosure can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0138] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present disclosure.

[0139] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the present embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0140] The serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0141] Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is also intended to include these modifications and variations.

Claims

1. A braking control method for an unmanned vehicle, characterized in that: include: Obtain the internal and external influencing factors of the unmanned vehicle; Determining a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors, wherein the target braking strategy includes a target braking mode for intervention and / or intervention priorities of multiple braking modes; When the unmanned vehicle has a braking demand, the target braking strategy is used to perform braking control on the unmanned vehicle.

2. The method according to claim 1, characterized in that The internal influencing factors include: the real-time status information of the unmanned vehicle; and / or, the external influencing factors include: the terrain information of the road ahead of the unmanned vehicle.

3. The method according to claim 1 or 2, characterized in that Determining a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors includes: Determining a first target braking mode of the unmanned vehicle according to the internal influencing factors and the external influencing factors; When the braking force of the first target braking method is insufficient, a second target braking method of the unmanned vehicle is determined according to the internal influencing factors and the intervention priorities of the multiple braking methods.

4. The method according to claim 3, characterized in that The vehicle-side real-time status information includes: the empty or loaded status of the vehicle; the terrain information includes: the slope category information of the road ahead of the unmanned vehicle; The determining, according to the internal influencing factors and the external influencing factors, a first target braking mode of the unmanned vehicle includes: When the slope category information corresponding to the road ahead of the unmanned vehicle is a specified slope type, and the empty and loaded state of the unmanned vehicle meets the preset load state, it is determined that the first target braking mode for the unmanned vehicle to intervene is the sliding braking mode; wherein, the specified slope type includes: an uphill section, a flat slope section or a first category downhill section, and the downhill slope corresponding to the first category downhill section is less than the specified slope threshold.

5. The method according to claim 3, characterized in that The vehicle-side real-time status information also includes: battery charge status information and / or speed information of the first motor; When the braking force of the first target braking method is insufficient, determining the second target braking method of the unmanned vehicle according to the internal influencing factor and the intervention priority of the multiple braking methods includes: When the first target braking mode for intervention of the unmanned vehicle is the coasting braking mode and the braking force provided by the coasting braking mode is insufficient, according to the access priorities of the multiple braking modes, determining that the braking mode with the next priority of the coasting braking mode is the electric retarder braking mode; Determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor; In the case where the electric retarder braking mode can provide braking force, it is determined that the second target braking mode for intervention of the unmanned vehicle is the electric retarder braking mode.

6. The method according to claim 3, characterized in that The vehicle-side real-time status information includes: battery charge status information and / or first motor speed information; the terrain information includes: slope category information of the road ahead of the unmanned vehicle; The determining, according to the internal influencing factors and the external influencing factors, a first target braking mode of the unmanned vehicle includes: In the case where the slope category information corresponding to the road ahead of the unmanned vehicle is a second-category downhill section, determining whether the electric retarder braking mode can provide braking force according to the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor; wherein the downhill slope corresponding to the second-category downhill section is greater than or equal to a specified slope threshold; In the case where the electric retarder braking mode can provide braking force, it is determined that the first target braking mode for intervention of the unmanned vehicle is the electric retarder braking mode.

7. The method according to claim 5 or 6, characterized in that The vehicle-side real-time status information also includes: actual braking capacity information; the method also includes: In the case where the electric retarder braking mode cannot provide braking force, according to the access priorities of the multiple braking modes, determining that the braking mode with the next priority of the electric retarder braking mode is the mechanical braking mode; According to the actual braking capability information of the unmanned vehicle, determining that the target braking mode for the unmanned vehicle to be involved is a mechanical braking mode; or, In the case where the braking force provided by the electric retarder braking mode is insufficient, determining, according to the access priorities of the multiple braking modes, a braking mode with a next priority of the electric retarder braking mode as a mechanical braking mode; According to the actual braking capability information of the unmanned vehicle, it is determined that the target braking mode for intervention of the unmanned vehicle is a mechanical braking mode.

8. The method according to claim 5 or 6, characterized in that The determining, based on the battery state of charge information of the unmanned vehicle and / or the speed information of the first motor, whether the electric retarder braking mode can provide braking force includes: When the battery state of charge information is less than a first threshold and the rotation speed of the first motor is greater than a second threshold, determining that the electric retarder braking mode can provide braking force; When at least one of the following conditions is met, it is determined that the electric retarder braking mode cannot provide braking force: the battery state of charge information is greater than or equal to a first threshold, and the rotation speed of the first motor is less than or equal to a second threshold.

9. The method according to claim 3 or 5, characterized in that The following method is used to determine whether the braking force provided by the current target braking method is sufficient: According to the deviation between the current real-time vehicle speed and the corresponding expected vehicle speed, it is determined whether the braking force provided by the current target braking mode is sufficient.

10. The method according to claim 1, characterized in that The internal influencing factors include: the real-time status information of the unmanned vehicle; the external influencing factors include: the scene where the unmanned vehicle is located; Determining a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors includes: When the unmanned vehicle is in a scenario where emergency braking is required, a target braking strategy using mechanical braking is determined based on the real-time status information of the vehicle.

11. A brake control device for an unmanned vehicle, characterized in that: include: A monitoring module is used to obtain the internal and external influencing factors of the unmanned vehicle; a braking strategy module, configured to determine a target braking strategy for the unmanned vehicle according to the internal influencing factors and the external influencing factors, wherein the target braking strategy includes a target braking mode for intervention and / or intervention priorities of multiple braking modes; The braking control module is used to use the target braking strategy to perform braking control on the unmanned vehicle when the unmanned vehicle has a braking demand.

12. An unmanned vehicle, characterized in that: include: The brake control device for an unmanned vehicle as claimed in claim 11.

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