Combined driving assistance vehicle running control method, system and equipment and medium

Abstract

The method is mainly applied to the technical field of vehicle engineering. The invention discloses a combined driving assistance vehicle driving control method, system and device and a medium. The method comprises the steps that running state information of a vehicle and traffic state information of the position where the vehicle is located are obtained; generating a driving path for guiding the vehicle to pass through the traffic signal prompt area according to the traffic state information, and determining the speed of the vehicle when the vehicle runs on the driving path; inputting the running state information and the vehicle speed into a preset energy consumption characteristic model, and determining the energy consumption of the vehicle when the vehicle runs on the running path through the energy consumption characteristic model; and generating a control instruction set based on the driving path, the vehicle speed and the energy consumption, and controlling the vehicle to execute driving operation according to the control instruction set. According to the invention, through the combined decision and the driving assistance control operation, the driving intelligence is obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle engineering, in particular to a combined driving assistance vehicle driving control method, system, device and medium. BACKGROUND

[0002] With the continuous progress of automobile technology, driving assistance systems (ADAS) have been widely used. Traditional driving assistance functions such as automatic emergency braking (AEB), lane keeping assistance (LKA), adaptive cruise control (ACC) and the like have been able to provide a certain degree of safety and convenience for drivers. These systems can perceive the surrounding environment through the vehicle's own sensors, such as cameras, millimeter wave radars, etc., so as to realize local control decisions of the vehicle. For example, adaptive cruise control can automatically adjust the vehicle speed according to the speed of the preceding vehicle to maintain a safe distance, and lane keeping assistance can help the vehicle to drive in the center of the lane to avoid the danger of deviating from the lane.

[0003] Although the driving assistance technology of existing intelligent connected vehicles has certain achievements in local perception, energy consumption management and vehicle control, in actual traffic scenarios, the integrated application of these technologies has obvious defects. Due to the lack of control of global traffic flow, the organic combination of energy consumption and driving strategy, and the coordination of vehicle control systems, the intelligent connected functions cannot realize closed-loop optimization between road condition perception, energy consumption optimization and vehicle control, resulting in that the automobile cannot achieve the optimal efficiency and energy consumption, and cannot provide users with a more intelligent, comfortable and efficient driving experience. SUMMARY

[0004] The present application provides a combined driving assistance vehicle driving control method, system, device and medium, which significantly improves the intelligence of driving by combining decision-making and driving assistance control operations.

[0005] The present application provides a combined driving assistance vehicle driving control method, which comprises: obtaining the running state information of the vehicle and the traffic state information of the location where the vehicle is located; generating a driving path for guiding the vehicle to pass through the traffic signal prompt area according to the traffic state information, and determining the vehicle speed of the vehicle when driving on the driving path; inputting the running state information and the vehicle speed into a preset energy consumption characteristic model, and determining the energy consumption of the vehicle when driving on the driving path through the energy consumption characteristic model; based on the driving path, the vehicle speed and the energy consumption, generating a control instruction set, and controlling the vehicle to perform driving operations according to the control instruction set.

[0006] Optionally, the manner of generating, according to the traffic state information, a driving path for guiding the vehicle to pass through a traffic signal prompt area comprises: When it is perceived according to the traffic state information that there is a slow-moving vehicle or an accident in front of the vehicle, a vehicle speed is determined according to a front signal light timing state in the traffic state information, and it is judged whether a lane-changing condition is met; If the lane-changing condition is met, a driving path containing a lane-changing decision instruction is generated.

[0007] Optionally, the traffic state information is a phase timing scheme of a traffic signal light in a position range where the vehicle is located, and the phase timing scheme comprises variation timing of a red light, a green light and a yellow light of the traffic signal light in a future time period; The manner of determining a vehicle speed of the vehicle when the vehicle is driving on the driving path comprises: Based on a traffic light position detected by a road side unit, a key point corresponding to each of the traffic light positions on the driving path is determined; According to a current speed of the vehicle and a distance between each of the key points, a time required for the vehicle to reach each of the key points is calculated; The time required for the vehicle to reach each of the key points is matched with the phase timing scheme to determine a signal light phase state corresponding to each of the key points when the vehicle reaches each of the key points, wherein if a time point at which the vehicle reaches a target key point in the phase timing scheme corresponds to a signal light state of a green light, a red light or a yellow light, it is predicted that the signal light state of the target key point corresponds to the green light, the red light or the yellow light; Based on the signal light state of each of the key points, a set vehicle speed of a road section corresponding to each of the key points on the driving path is determined.

[0008] Optionally, the inputting of the running state information and the vehicle speed into a preset energy consumption characteristic model and the determination of energy consumption of the vehicle when the vehicle is driving on the driving path through the energy consumption characteristic model comprise: According to a power mode in the running state information of the vehicle, a kinetic energy recovery strategy matched with the vehicle is determined; Based on the kinetic energy recovery strategy, the vehicle speed and road condition information of the driving path, an energy recovery intensity of the vehicle when the vehicle is driving on the driving path is adjusted to enable the vehicle to recover energy in a process of coasting or braking; The driving path is divided into a plurality of road sections, and a power mode switching time of the vehicle when the vehicle is driving on each of the road sections is planned, wherein based on a road surface type of the road section, a ratio between power output and power recovery or a power output duration and a power recovery duration of the vehicle when the vehicle is driving on the road section is determined to enable switching between a power output mode and a power recovery mode. Through the energy consumption characteristic model, the energy recovery efficiency in the process of coasting or braking is dynamically updated in response to the adjusted energy recovery intensity, and the energy distribution ratio and fuel consumption rate of the power output mode and the power recovery mode are dynamically updated in response to the power mode switching timing.

[0009] Optionally, when the control instruction set is generated, the combined driving assistance vehicle driving control method further comprises: obtaining in-vehicle passenger state information, out-of-vehicle environment state information, and vehicle self-state information; based on the in-vehicle passenger state information, the out-of-vehicle environment state information, and the vehicle self-state information, dynamically adjusting the weights of the efficiency target, the comfort target, and the energy saving target; generating corresponding control strategy parameters according to the adjusted weights and using the control strategy parameters to generate the control instruction set.

[0010] Optionally, when the control instruction set is generated, the combined driving assistance vehicle driving control method further comprises: generating the control strategy parameters according to the scene type, specifically including the following execution steps of at least one scene type: in the scene type of family travel, if a child or an old person is identified in the passengers, the traffic signal light is green and the remaining time is greater than a preset value, and the vehicle energy is greater than a preset threshold, then the weight of the comfort target is increased, and a comfort priority control strategy mainly including gentle acceleration and avoiding sudden braking is generated; in the scene type of business commuting, if the driver is identified to be in a focused state, and the traffic signal light is green but the remaining time is less than the preset value, then the weight of the efficiency target is increased, and an efficiency priority control strategy including suggestions of lane changing or acceleration is generated; in the scene type of meeting break, if the passengers are identified to be in a sleep or video conference state, then the weight of the comfort target is increased and the weight of the efficiency target is decreased, and an extreme comfort control strategy is generated; in the scene type of energy anxiety, if the vehicle energy is identified to be lower than the preset threshold, then the weight of the energy saving target is increased, and an energy saving priority control strategy minimizing energy consumption is generated; in the scene type of mixed complexity, the weights of the comfort target, the efficiency target, and the energy saving target are reset according to a preset priority rule, and a composite control strategy satisfying multi-objective optimization is generated.

[0011] The application also provides a combined driving assistance vehicle driving control system, the system comprising: a vehicle-mounted sensor module for collecting running state information of a vehicle and traffic state information of a location where the vehicle is located; a strategy analysis module configured to generate a driving path for guiding the vehicle to pass through a traffic signal prompt area according to the traffic state information, and determine a vehicle speed of the vehicle when the vehicle drives on the driving path; input the running state information and the vehicle speed into a preset energy consumption characteristic model, and determine an energy consumption of the vehicle when the vehicle drives on the driving path through the energy consumption characteristic model; generate a control instruction set based on the driving path, the vehicle speed and the energy consumption; an instruction execution module configured to control the vehicle to perform a driving operation according to the control instruction set.

[0012] Optionally, the combined driving assistance vehicle driving control system further comprises: an environment sensor module configured to collect traffic state information of a location where the vehicle is located through a road side unit or a cloud server unit and transmit the traffic state information to the strategy analysis module.

[0013] The application further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the combined driving assistance vehicle driving control method according to any one of the above when executing the computer program.

[0014] The application further provides a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the combined driving assistance vehicle driving control method according to any one of the above.

[0015] The application has at least the following beneficial effects: Firstly, the vehicle running state and traffic state information are acquired to provide a data basis for subsequent decision-making. Secondly, the driving path and the vehicle speed are generated based on the traffic state, which not only considers the local road conditions, but also takes into account the global traffic flow, thereby avoiding the low global efficiency caused by local optimization. Then, the running state and the vehicle speed are input into the energy consumption model to accurately calculate the energy consumption, thereby realizing the organic combination of energy consumption and driving strategy. Finally, the control instruction set is generated based on the path, the vehicle speed and the energy consumption to realize vehicle control and ensure the collaborative work of various systems. This closed-loop optimization process effectively solves the problem of independent modules and lack of collaboration in the prior art, improves the efficiency and energy consumption performance of the vehicle, and provides users with a more intelligent, comfortable and efficient and energy-saving driving experience. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the technical solutions of the application, and constitute a part of the specification, and are used to explain the technical solutions of the application together with the embodiments of the application, and do not constitute a limitation on the technical solutions of the application.

[0017] Figure 1is a step flow chart of a combined driving assistance vehicle driving control method; Figure 2 is a step flow chart of step S103 in a combined driving assistance vehicle driving control method; Figure 3 is a step flow chart of generating a control instruction set in a combined driving assistance vehicle driving control method; Figure 4 is a structural schematic diagram of a combined driving assistance vehicle driving control system; Figure 5 is an algorithm architecture diagram of a combined driving assistance vehicle driving control system; Figure 6 is another algorithm architecture diagram of a combined driving assistance vehicle driving control system; Figure 7 is a structural schematic diagram of an electronic device. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0019] Please refer to Figure 1 , Figure 1 is a step flow chart of a combined driving assistance vehicle driving control method.

[0020] The present embodiment provides a combined driving assistance vehicle driving control method comprising: S101, obtaining running state information of the vehicle and traffic state information of the location where the vehicle is located.

[0021] S102, generating a driving path for guiding the vehicle to pass through a traffic signal prompt area according to the traffic state information, and determining a vehicle speed when the vehicle drives on the driving path.

[0022] S103, inputting the running state information and the vehicle speed into a preset energy consumption characteristic model, and determining the energy consumption of the vehicle when driving on the driving path through the energy consumption characteristic model.

[0023] S104, generating a control instruction set based on the driving path, the vehicle speed and the energy consumption, and controlling the vehicle to perform a driving operation according to the control instruction set.

[0024] It can be understood that in the present embodiment, firstly, the vehicle operating state and traffic state information are acquired to provide a data basis for subsequent decision-making. Secondly, the driving path and vehicle speed are generated based on the traffic state, which not only considers the local road conditions, but also takes into account the global traffic flow, avoiding the low global efficiency caused by local optimization. Then, the operating state and vehicle speed are input into the energy consumption model to accurately calculate the energy consumption, realizing the organic combination of energy consumption and driving strategy. Finally, the control instruction set is generated based on the path, vehicle speed and energy consumption to realize vehicle control and ensure the collaborative work of various systems. This closed-loop optimization process effectively solves the problem of independent modules and lack of collaboration in the prior art, improves the efficiency and energy consumption performance of the automobile, and provides users with a more intelligent, comfortable and efficient and energy-saving driving experience.

[0025] In some embodiments, the way of generating a driving path for guiding the vehicle to pass through the traffic signal prompt area according to the traffic state information comprises: When it is perceived according to the traffic state information that there is a slow-moving vehicle or an accident in front of the vehicle, the vehicle speed is determined according to the timing state of the front signal light in the traffic state information, and it is judged whether the current driving environment meets the lane changing condition; if the lane changing condition is met, a driving path containing lane changing decision instructions is generated.

[0026] It can be understood that when it is perceived that there is a slow-moving vehicle or an accident in front, the system will not only adjust the speed according to the timing of the signal light, but also judge whether the lane changing condition is met, and generate a driving path containing lane changing decision. This makes the vehicle more flexible to deal with complex road conditions, reduces congestion and energy consumption increase caused by obstacles in front, further optimizes the driving efficiency and energy consumption performance, enhances the comfort and safety of driving, and provides users with a more intelligent driving assistance experience.

[0027] In some embodiments, the traffic state information is a phase timing scheme of traffic signal lights in the location range of the vehicle, and the phase timing scheme includes the change timing of red light, green light and yellow light of the traffic signal lights in the future time period.

[0028] In some embodiments, the way of determining the vehicle speed when the vehicle is driving on the driving path comprises: Based on the traffic light positions detected by the roadside unit, key points corresponding to each traffic light position on the driving path are determined; the time required for the vehicle to reach each key point is calculated according to the current speed of the vehicle and the distance between each key point; the time required for the vehicle to reach each key point is matched with the phase timing scheme to determine the corresponding signal light phase state of the vehicle when reaching each key point, wherein if the signal light state corresponding to the time point when the vehicle reaches the target key point in the phase timing scheme is green, red or yellow, it is predicted that the signal light state of the target key point corresponds to green, red or yellow; based on the signal light state of each key point, the set speed of the road section corresponding to each key point on the driving path is determined respectively.

[0029] It can be understood that, based on the traffic light positions detected by the roadside unit, the key points are determined and the time for the vehicle to reach each key point is calculated, the signal light phase timing scheme is matched, and the signal light state is predicted. Accordingly, the system sets the optimal speed for each road section, ensuring that the vehicle passes smoothly under the green light and avoiding stopping or decelerating due to red or yellow light. This not only improves the traffic efficiency and reduces unnecessary stopping and acceleration, but also further reduces energy consumption and improves the comfort and smoothness of the driving experience, making the vehicle driving more intelligent and energy-saving.

[0030] Please refer to Figure 2 , Figure 2 A step flowchart of step S103 in a combined driving assistance vehicle driving control method.

[0031] In some embodiments, step S103 comprises: S201, determining a kinetic energy recovery strategy matched with the vehicle according to the power mode in the vehicle operating state information.

[0032] S202, adjusting the energy recovery intensity of the vehicle when driving on the driving path based on the kinetic energy recovery strategy, the vehicle speed and the road condition information of the driving path, so that the vehicle recovers energy in the process of coasting or braking.

[0033] S203, dividing the driving path into multiple road sections and planning the power mode switching time of the vehicle when driving on each road section.

[0034] Specifically, based on the road surface type of the road section, the ratio or duration of power output and power recovery of the vehicle when driving on the road section is determined to realize the switching between the power output mode and the power recovery mode.

[0035] S204, dynamically updating the energy recovery efficiency in the process of coasting or braking in response to the adjusted energy recovery intensity and dynamically updating the energy distribution ratio and fuel consumption rate of the power output mode and the power recovery mode in response to the power mode switching time through the energy consumption characteristic model.

[0036] It can be understood that the kinetic energy recovery strategy is determined according to the power mode of the vehicle, and the energy recovery intensity is adjusted in combination with the vehicle speed and road condition information, to ensure efficient energy recovery during coasting or braking. At the same time, the driving path is divided into multiple road sections, the power mode switching time is accurately planned, and the proportion and time length of power output and recovery are dynamically adjusted according to the road surface type. The energy consumption characteristic model updates the energy recovery efficiency and energy distribution proportion in real time, further optimizing the fuel consumption rate. These measures not only improve the energy utilization efficiency of the vehicle, but also enhance the smoothness and comfort of driving, providing users with a more energy-saving and intelligent driving experience.

[0037] Please refer to Figure 3 , Figure 3 A flowchart of a step of generating a control instruction set in a combined driving assistance vehicle driving control method.

[0038] In some embodiments, when generating the control instruction set, the combined driving assistance vehicle driving control method further comprises: S301, obtaining in-vehicle passenger state information, out-of-vehicle environment state information, and vehicle self-state information.

[0039] S302, dynamically adjusting the weights of the efficiency target, the comfort target, and the energy saving target based on the in-vehicle passenger state information, the out-of-vehicle environment state information, and the vehicle self-state information.

[0040] S303, generating corresponding control strategy parameters according to the adjusted weights and using them to generate the control instruction set.

[0041] It can be understood that when generating the control instruction set, the efficiency, comfort, and energy saving targets are dynamically balanced according to these state information to generate control strategy parameters that better meet actual needs. This enables the vehicle to flexibly adjust the driving strategy in different scenarios, such as prioritizing comfort when passengers are tired, or focusing more on energy saving in congested road sections. This adaptive control method not only improves user experience, but also further optimizes the overall performance of the vehicle, achieving a harmonious combination of intelligence, comfort, and energy saving, and providing users with a more personalized and efficient driving assistance experience.

[0042] In some embodiments, when generating the control instruction set, the combined driving assistance vehicle driving control method further comprises: Generating control strategy parameters according to the scene type, specifically including the following execution steps of at least one scene type: In the scene type of family travel, if it is identified that there are children or old people among the passengers, and the traffic signal light is green and the remaining time is greater than a preset value, and the vehicle energy is greater than a preset threshold, then the weight of the comfort target is increased, and a comfort priority control strategy is generated, which mainly avoids sudden braking and gently accelerates.

[0043] In the business commuting scenario type, if the driver is identified to be in a focused state, and the traffic signal light is green but the remaining time is less than a preset value, the weight of the efficiency target is increased, and an efficiency priority control strategy including a suggestion to change lanes or accelerate is generated.

[0044] In the meeting break scenario type, if the passenger is identified to be in a sleeping or video conference state, the weight of the comfort target is increased and the weight of the efficiency target is reduced, and an extreme comfort control strategy is generated.

[0045] In the energy anxiety scenario type, if the vehicle energy is identified to be lower than a preset threshold, the weight of the energy saving target is increased, and an energy saving priority control strategy that minimizes energy consumption is generated.

[0046] In the mixed complex scenario type, the weights of the comfort target, the efficiency target, and the energy saving target are reset according to preset priority rules, and a composite control strategy that satisfies multi-objective optimization is generated.

[0047] In some embodiments, the number, position, approximate age group (elderly, adult, child), heart rate, and respiration rate of the passengers, and other passenger biological characteristics, are collected by in-cabin cameras, seat pressure sensors, and physiological sensors; the facial expression (open eyes / closed eyes), head posture (nodding, leaning back), hand gesture, voice activity (whether in conversation / meeting), and other passenger behavior state information are collected by in-cabin cameras, microphone arrays, and seat sensors; the traffic light state and remaining time, lane lines, surrounding vehicles / pedestrians / non-motorized vehicles, road speed limit, real-time traffic flow density electronic map data, and other external environment state information are collected by front-view cameras, millimeter wave radars, laser radars, or V2X vehicle networking terminals; and the current vehicle speed, acceleration, energy type (EV / PHEV / HEV), remaining energy (SOC) / remaining fuel, average energy consumption / fuel consumption, and other vehicle state information are collected by the vehicle control unit (VCU), battery management system (BMS), and fuel tank level sensor.

[0048] In a family travel scenario, during vehicle travel, the system identifies that there are children or elderly people in the back row through in-vehicle cameras and sensors. At this time, the traffic signal light in front of the vehicle is green, and the remaining time is greater than 20 seconds, and the energy state of the vehicle is good (for example, SoC > 50%). The system increases the weight of the comfort target according to these conditions, and generates a comfort priority control strategy. The control instruction set includes gentle acceleration and avoidance of sudden braking driving operations, generates a gentle acceleration curve, accelerates to the target speed in advance and slowly, avoids a pushback feeling, and ensures the comfort of children and elderly people. At the same time, the system suggests waiting for the next green light period rather than accelerating to rush, to ensure the smoothness and safety of the driving process.

[0049] In a business commute scenario, the system identifies that the driver is in a focused state through the driver state monitoring device while the vehicle is in motion. At this moment, the traffic light in front of the vehicle is green, but the remaining time is less than 10 seconds. According to these conditions, the system increases the weight of the efficiency target, and generates an efficiency priority control strategy. The control instruction set includes calculating the time window that can be realized for passing within the safety speed limit, and generating a moderately aggressive acceleration curve. At the same time, the system suggests or executes lane changing to a more open lane to complete the passing, ensuring fast passing through the traffic light area in a limited time, and improving the commuting efficiency.

[0050] In a meeting break scenario, the system identifies that there is a passenger in a sleep or video conference state through the in-vehicle camera and sensor while the vehicle is in motion. Regardless of the traffic light remaining time and vehicle energy state, the system increases the weight of the comfort target to the highest, and reduces the weight of the efficiency target to the lowest, and generates an extreme comfort control strategy. The control instruction set includes reducing the acceleration threshold to the lowest, and all acceleration / deceleration operations are very gentle. The system suggests turning off unnecessary functions such as air conditioning that may affect the passenger's rest or meeting, to ensure that the passenger maintains a comfortable and quiet environment during driving.

[0051] In an energy anxiety scenario, the system detects that the vehicle energy is below a preset threshold (e.g. SoC < 20% or low fuel level) through the vehicle energy management system while the vehicle is in motion. Regardless of the traffic light remaining time and passenger state, the system increases the weight of the energy saving target to the highest, and generates an energy saving priority control strategy. The control instruction set includes adopting a "pulse and coasting" strategy to calculate the optimal speed curve to pass through the green light with the minimum energy consumption, avoiding high power output. The system suggests "coasting through" instead of "accelerating through", and actively suggests turning off unnecessary functions such as air conditioning to save energy, to ensure that the vehicle can travel as far as possible with limited energy.

[0052] In a mixed complex scenario, the system identifies that there is an old person in the vehicle and the vehicle is low on power (e.g. SoC < 15%) while the vehicle is in motion. At this moment, the traffic light is green but the remaining time is tight. According to the preset priority rules (comfort > efficiency > energy saving), the system resets the weights of the comfort target, the efficiency target, and the energy saving target, and generates a composite control strategy that satisfies the multi-objective optimization. The control instruction set includes selecting a "gentle acceleration to economic speed and coasting through" compromise strategy, which takes into account the comfort of the old person, and also considers the energy saving demand under low power, while ensuring safe passing during the green light period, achieving a balanced optimization of comfort, efficiency and energy saving.

[0053] Please refer to Figure 4 , Figure 4 is a structural schematic diagram of a combined driving assistance vehicle driving control system.

[0054] The embodiment also provides a combined driving assistance vehicle driving control system, which comprises: A vehicle-mounted sensor module 401 is configured to collect running state information of the vehicle and traffic state information of a location where the vehicle is located.

[0055] A strategy analysis module 402 is configured to generate a driving path for guiding the vehicle to pass through a traffic signal prompt area according to the traffic state information, and determine a vehicle speed when the vehicle drives on the driving path; input the running state information and the vehicle speed into a preset energy consumption characteristic model, and determine energy consumption of the vehicle when the vehicle drives on the driving path through the energy consumption characteristic model; and generate a control instruction set based on the driving path, the vehicle speed and the energy consumption.

[0056] An instruction execution module 403 is configured to control the vehicle to perform a driving operation according to the control instruction set.

[0057] In some embodiments, the combined driving assistance vehicle driving control system further comprises: An environmental sensor module 404 is configured to collect traffic state information of a location where the vehicle is located through a road side unit or a cloud server unit and transmit the traffic state information to the strategy analysis module.

[0058] Please refer to Figure 5 , Figure 5 An algorithm architecture diagram of the combined driving assistance vehicle driving control system.

[0059] In the embodiment, the perception layer is configured to collect various key information in the running process of the vehicle. The sensors such as radars and cameras carried by the vehicle are configured to detect environmental information such as driving speed, distance and surrounding obstacle position of a front vehicle in real time; at the same time, the distance to the next intersection, signal lamp state data and road slope information are obtained through data interaction with the traffic infrastructure or by obtaining navigation information.

[0060] The decision layer is configured to perform deep analysis and processing based on the data obtained by the perception layer and in combination with advanced algorithms such as large models.

[0061] Firstly, the green wave speed is calculated through an algorithm, and the best driving speed that enables the vehicle to smoothly pass through the green light is planned; when it is perceived that there is a slow vehicle or an accident in front, it is determined whether the current road condition meets the lane changing condition, and if yes, a lane changing decision instruction is generated to ensure efficient passage of the vehicle.

[0062] In addition, the sensors of the vehicle itself also collect vehicle state data such as battery power (for pure electric vehicles, hybrid vehicles and the like) and engine working conditions (for fuel vehicles), to provide accurate input for decision making.

[0063] In terms of energy saving, based on the energy consumption characteristics of different vehicle power modes (fuel, pure electric, PHEV / HEV, etc.), the system prioritizes flat roads or gentle slope sections and avoids high-energy consumption continuous mountainous, steep slope, and bridge sections. It also formulates driving strategies that match energy recovery, such as adjusting energy recovery intensity and planning power mode switching timing, to achieve comprehensive optimization of vehicle driving strategies.

[0064] The execution layer is used to convert the instructions generated by the decision layer into actual vehicle operations. According to the acceleration, deceleration, and steering instructions issued by the decision layer, the power output of the vehicle is precisely controlled. At the same time, the energy recovery system of the vehicle is coordinated to perform operations such as energy recovery intensity adjustment, so that the vehicle can improve driving efficiency while saving energy to the maximum extent, providing users with a comfortable driving experience.

[0065] Please refer to Figure 6 , Figure 6 Another algorithm architecture diagram of a combined driving assistance vehicle driving control system.

[0066] In an embodiment of the intelligent driving assistance system, the system first collects various data through the perception layer. This includes obtaining passenger age and behavior information through cabin cameras, monitoring heart rate and fatigue through vehicle sensors, and identifying traffic light and lane data through the intelligent driving perception system. At the same time, the battery management system provides SOC (state of charge) and fuel level information. These data are transmitted to the data fusion center for comprehensive processing and analysis.

[0067] In the decision module, the strategy matching engine determines the current driving strategy based on the fused data. The weight calculation unit dynamically adjusts the weights of efficiency, comfort, and energy saving targets according to the scene type and passenger state. The mode output unit generates the final control strategy parameters, which will be used to generate specific control instruction sets.

[0068] After receiving the control instruction set, the power control system adjusts the vehicle's power output according to the instructions, while the body control system adjusts the suspension system and damping according to the comfort parameters. The air conditioning, audio, and lighting systems will also be adjusted according to the passenger's comfort requirements. Through torque distribution, the body control unit ensures that the vehicle smoothly travels according to the established control strategy, providing the best driving experience.

[0069] Throughout the process, the system continuously monitors and adjusts to adapt to changing driving conditions and passenger needs, ensuring that the vehicle can achieve optimal driving performance and passenger comfort in various scenarios. This intelligent driving assistance system not only improves driving safety and efficiency but also greatly enhances passenger comfort.

[0070] Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and techniques described above can be embodied in software, firmware, hardware, and / or any suitable combination thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a micro-processing unit, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on computer readable media, which can comprise computer storage media (or non-transitory media), and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage media includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. As is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media.

[0071] It can be understood that the contents of the above method embodiments are applicable to the system embodiments, the system embodiments specifically implement the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0072] The embodiments of the present application further provide an electronic device, which comprises a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the driving assistance vehicle driving control method according to any one of the above combinations is implemented.

[0073] Reference Figure 7 , Figure 7 The hardware structure of the electronic device of another embodiment is illustrated, which comprises: The processor 701 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0074] The memory 702 can be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM), etc. The memory 702 can store operating devices and other application programs, and when the technical solutions provided in the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 702 and are called and executed by the processor 701 to execute the combined driving assistance vehicle driving control method of the embodiments of the present application.

[0075] The input / output interface 703 is configured to realize information input and output.

[0076] The communication interface 704 is configured to realize the communication interaction between the device and other devices, and the communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0077] The bus 705 is configured to transmit information between various components (for example, the processor 701, the memory 702, the input / output interface 703, and the communication interface 704) of the device.

[0078] The processor 701, the memory 702, the input / output interface 703, and the communication interface 704 are connected to each other through the bus 705 to realize the communication connection between the device.

[0079] It can be understood that the contents in the above method embodiments are all applicable to the electronic device embodiments, the functions specifically realized by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0080] The embodiments of the present application also provide a computer readable storage medium, wherein a processor executable program is stored, and the processor executable program is executed by a processor to realize the combined driving assistance vehicle driving control method according to any one of the above embodiments.

[0081] The embodiments of the present application also disclose a computer program product, including a computer program or computer instructions, the computer program or computer instructions are stored in a computer readable storage medium, a processor of a computer device reads the computer program or computer instructions from the computer readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the combined driving assistance vehicle driving control method according to any one of the above embodiments.

[0082] It can be understood that the contents in the above method embodiments are all applicable to the present storage medium embodiments, the present storage medium embodiments specifically implement the functions same as those of the above method embodiments, and achieve the beneficial effects same as those of the above method embodiments.

[0083] The terms "first", "second", "third", "fourth" and the like in the description of the application and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of accomplishing functionalities using either term. Any terms of orientation, such as "vertical", "horizontal", "top", "bottom", "upper", "lower", and the like are used for clarity in only the views presented in the drawings and / or discussion and are not intended to be limiting across all possible embodiments of the application. Moreover, terms such as "front", "back", "rear", "side", and the like are used for clarity in only the views presented in the drawings and / or discussion and are not intended to be limiting across all possible embodiments of the application. The terms "including", "containing", "having", and the like are used broadly and mean the inclusion of at least the recited item or list of items, without limitation to other items not recited. It is to be understood that the terminology only is selected and described for the purpose of enhancing the readability and the clarity of the presently disclosed embodiments of the application, and that the terms should not be construed to be limiting in any way.

[0084] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described system embodiments are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0085] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e. they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the present embodiment scheme according to actual needs.

[0086] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0087] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0088] Although the description of the present application has been quite detailed and particularly described with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any special embodiment, but should be considered to effectively cover the intended scope of the present application by referring to the appended claims, taking into account the broadest possible interpretation of these claims in view of the prior art. In addition, the present application is described above in embodiments that the inventors can foresee, and the purpose is to provide a useful description, and non-essential modifications to the present application that have not yet been foreseen can still represent equivalent modifications to the present application.

Claims

1. A combined driving assistance vehicle travel control method characterized by comprising: The method comprises: acquiring running state information of a vehicle and traffic state information of a location where the vehicle is located; generating a driving path for guiding the vehicle to pass through a traffic signal prompt area according to the traffic state information, and determining a vehicle speed of the vehicle when the vehicle drives on the driving path; inputting the running state information and the vehicle speed into a preset energy consumption characteristic model, and determining energy consumption of the vehicle when the vehicle drives on the driving path through the energy consumption characteristic model; generating a control instruction set based on the driving path, the vehicle speed and the energy consumption, and controlling the vehicle to perform a driving operation according to the control instruction set.

2. The method of claim 1, wherein, The way of generating a driving path for guiding the vehicle to pass through a traffic signal prompt area according to the traffic state information comprises: when it is perceived according to the traffic state information that there is a slow-moving vehicle or an accident in front of the vehicle, determining a changed vehicle speed according to a front signal light timing state in the traffic state information, and judging whether a lane changing condition is met; if the lane changing condition is met, generating a driving path containing a lane changing decision instruction.

3. The method of claim 1, wherein, The traffic state information is a phase timing scheme of traffic signal lights in a location range where the vehicle is located, and the phase timing scheme comprises change timing of red light, green light and yellow light of the traffic signal lights in a future time period; The way of determining a vehicle speed of the vehicle when the vehicle drives on the driving path comprises: determining a key point corresponding to each traffic light position on the driving path based on traffic light positions detected by a road side unit; calculating time required by the vehicle to respectively reach each key point according to a current speed of the vehicle and a distance between each key point; matching the time required by the vehicle to reach each key point with the phase timing scheme to determine a signal light phase state corresponding to the vehicle when the vehicle reaches each key point, wherein if a time point when the vehicle reaches a target key point in the phase timing scheme corresponds to a signal light state of green light, red light or yellow light, it is predicted that the signal light state of the target key point corresponds to green light, red light or yellow light; determining a set vehicle speed of a road section corresponding to each key point on the driving path based on the signal light state of each key point.

4. The method of claim 1, wherein, The way of inputting the running state information and the vehicle speed into a preset energy consumption characteristic model, and determining energy consumption of the vehicle when the vehicle drives on the driving path through the energy consumption characteristic model comprises: determining a kinetic energy recovery strategy matched with the vehicle according to a power mode in the running state information of the vehicle; adjusting an energy recovery intensity of the vehicle when the vehicle drives on the driving path based on the kinetic energy recovery strategy, the vehicle speed and road condition information of the driving path, so that the vehicle recovers energy in a process of coasting or braking; The driving path is divided into multiple road segments, and a power mode switching timing when the vehicle drives on each road segment is planned, wherein, based on a road surface type of the road segment, a ratio between power output and power recovery or a power output duration, a power recovery duration when the vehicle drives on the road segment is determined to switch between a power output mode and a power recovery mode; Through the energy consumption characteristic model, the energy recovery efficiency in the process of coasting or braking is dynamically updated in response to the adjusted energy recovery intensity, and the energy distribution ratio and fuel consumption rate of the power output mode and the power recovery mode are dynamically updated in response to the power mode switching timing.

5. The method of claim 1, wherein, When generating the control instruction set, the method further comprises: obtaining in-vehicle passenger state information, out-of-vehicle environment state information, and vehicle self-state information; based on the in-vehicle passenger state information, the out-of-vehicle environment state information, and the vehicle self-state information, dynamically adjusting the weights of the efficiency target, the comfort target, and the energy saving target; generating corresponding control strategy parameters according to the adjusted weights and using them to generate the control instruction set.

6. The method of claim 5, wherein, When generating the control instruction set, the method further comprises: generating the control strategy parameters according to the scene types, specifically including the following execution steps of at least one of the scene types: in the scene type of family travel, if a child or an old person is identified among the passengers, and the traffic signal light is green and the remaining time is greater than a preset value, and the vehicle energy is greater than a preset threshold, then the weight of the comfort target is increased, and a comfort priority control strategy mainly including gentle acceleration and avoiding sudden braking is generated; in the scene type of business commuting, if the driver is identified to be in a focused state, and the traffic signal light is green but the remaining time is less than the preset value, then the weight of the efficiency target is increased, and an efficiency priority control strategy including suggestions of lane changing or acceleration is generated; in the scene type of meeting break, if the passengers are identified to be in a sleep or video conference state, then the weight of the comfort target is increased and the weight of the efficiency target is decreased, and an extreme comfort control strategy is generated; in the scene type of energy anxiety, if the vehicle energy is identified to be lower than the preset threshold, then the weight of the energy saving target is increased, and an energy saving priority control strategy minimizing energy consumption is generated; in the scene type of mixed complexity, the weights of the comfort target, the efficiency target, and the energy saving target are reset according to a preset priority rule, and a composite control strategy satisfying multi-objective optimization is generated.

7. A combined driving assistance vehicle travel control system characterized by comprising: The system comprises: a vehicle-mounted sensor module for collecting running state information of a vehicle and traffic state information of a location where the vehicle is located; a strategy analysis module for generating a driving path for guiding the vehicle to pass through a traffic signal prompt area according to the traffic state information, and determining a vehicle speed when the vehicle drives on the driving path; inputting the running state information and the vehicle speed into a preset energy consumption characteristic model, and determining energy consumption of the vehicle when driving on the driving path through the energy consumption characteristic model; and generating a control instruction set based on the driving path, the vehicle speed, and the energy consumption; An instruction execution module is configured to control the vehicle to perform a driving operation according to the control instruction set.

8. The system of claim 7, wherein, The system further comprises: An environment sensor module is configured to collect traffic state information of a location where the vehicle is located by a roadside unit or a cloud server unit and transmit the information to the strategy analysis module.

9. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the combined driving auxiliary vehicle driving control method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the combined driving auxiliary vehicle driving control method of any one of claims 1 to 7.

Images