Intelligent driving method, device and equipment based on speed regulation of different road sections

Abstract

The invention discloses an intelligent driving method, device and equipment based on different road section speed regulation, and relates to the technical field of vehicle control, and the method comprises the steps: recognizing the type of a current road section based on navigation data, vehicle end sensing data and road side sensing data; determining a target vehicle speed according to the navigation data, the roadside sensing data and the current road section type; when the environment of the vehicle meets the preset condition, generating a smooth transition vehicle speed curve according to the target vehicle speed; and controlling the vehicle to run according to the smooth transition vehicle speed curve. According to the invention, through fusion and cooperation of multi-source sensing information of the vehicle end and the road side, the defects that the prior art depends on single vehicle-mounted sensing, cannot respond to the blind area risk in time and causes rigid speed regulation according to static speed limitation are overcome, continuous, stable and adaptive control of the vehicle speed under a full-scene road section is realized, and the speed regulation efficiency is improved. The driving safety, the riding comfort and the passing efficiency of the intelligent driving system under complex road conditions are effectively improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to an intelligent driving method, device and equipment based on speed adjustment for different road sections. Background Technology

[0002] With the increasing complexity of urban and intercity transportation networks and the widespread application of intelligent driving vehicles, clear technical requirements have emerged for smooth, safe, and adaptive speed control of vehicles across different types of road sections. This requirement necessitates that intelligent driving systems accurately identify diverse road scenarios and dynamically and continuously adjust vehicle speed based on multi-source real-time information to ensure driving safety, improve traffic efficiency, and optimize passenger comfort.

[0003] Currently, existing technologies mainly rely on onboard sensors for perception and speed adjustment strategies based on fixed speed limits. The perception range of these solutions is limited by the field of view of the onboard sensors and obstruction from vehicles in front. In blind spot scenarios such as intersections, they cannot detect lateral traffic participants in time, leading to collision risks. The speed adjustment strategies are relatively rigid, usually adjusting only based on static speed limits in the map, without fully integrating real-time traffic flow, road conditions, and historical driving data. Especially in scenarios such as highway ramp connections, sudden changes in vehicle speed can easily occur, causing driving discomfort and affecting safety.

[0004] Therefore, how to overcome the limitations of single vehicle-mounted perception and realize an intelligent dynamic speed adjustment method that can integrate multi-source information, adapt to the characteristics of all road segments, and make smooth speed transitions has become a technical problem that needs to be solved.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide an intelligent driving method, device, and equipment based on speed adjustment for different road segments, aiming to solve the technical problem of how to achieve differentiated, safe, and stable speed adjustment for multiple road segments.

[0007] To achieve the above objectives, this application proposes an intelligent driving method based on speed adjustment for different road sections, the method comprising: Acquire vehicle-side perception data, roadside perception data, and navigation data; Based on the navigation data, the vehicle-side perception data, and the roadside perception data, the current road segment type is identified; The target vehicle speed is determined based on the navigation data, the roadside perception data, and the current road segment type; When the vehicle's environment meets the preset conditions, a smooth transition speed curve is generated based on the target vehicle speed; The vehicle's movement is controlled according to the smooth transition speed curve.

[0008] In one embodiment, identifying the current road segment type based on the navigation data, the vehicle-side perception data, and the roadside perception data includes: Based on the navigation data, the vehicle-side perception data, and the roadside perception data, the number of lanes and signal control information are obtained. The radius of curvature is obtained based on the vehicle-side sensing data. The current road segment type is identified based on the number of lanes, the radius of curvature, and the signal control information, wherein the current road segment type includes expressway main roads, ramps, urban roads, rural narrow roads, intersections, and special road segments.

[0009] In one embodiment, determining the target vehicle speed based on the navigation data, the roadside perception data, and the current road segment type includes: Obtain the radius of curvature; The basic speed limit for the current road segment is obtained based on the navigation data; Based on the radius of curvature, the curvature coefficient is obtained; Traffic flow coefficient and road surface coefficient are obtained based on the roadside sensing data; The target vehicle speed is determined based on the current road segment type, the current road segment basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

[0010] In one embodiment, determining the target vehicle speed based on the current road segment type, the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient includes: When the historical vehicle speed data corresponding to the current road segment type stored in the historical database is obtained, the target vehicle speed is obtained based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, the road surface coefficient, and the historical vehicle speed data. When historical vehicle speed data corresponding to the current road segment type is not available in the historical database, the target vehicle speed is obtained based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

[0011] In one embodiment, before generating a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions, the method further includes: Obtain the radius of curvature; Based on the navigation data, the speed limit difference between adjacent road segments is obtained; When the speed limit difference between adjacent road segments is greater than a preset speed limit threshold or the radius of curvature is less than a preset radius of curvature threshold, it is determined that the environment in which the vehicle is located meets the preset conditions.

[0012] In one embodiment, generating a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions includes: When the vehicle's environment meets the preset conditions, the transition distance constraint is obtained based on the current vehicle speed; A smooth transition speed curve is generated based on the transition distance constraint and the target vehicle speed.

[0013] In one embodiment, acquiring vehicle-side perception data and roadside perception data includes: Acquire vehicle-surrounding target and lane line information collected by onboard sensors, as well as roadside point cloud data collected by roadside sensors; Based on the vehicle's surrounding targets and lane line information, target detection and localization are performed to obtain vehicle-side perception data; Blind spot target data is extracted based on the roadside point cloud data to obtain roadside perception data.

[0014] In one embodiment, the method further includes: Based on the roadside sensing data, blind spot target data is obtained; Generate blind zone early warning information based on the blind zone target data; The blind spot warning information and the current road segment type are pushed to the vehicle for reminder; The current road segment type, the vehicle-side perception data, the roadside perception data, and the target vehicle speed are used to update the historical database.

[0015] Furthermore, to achieve the above objectives, this application also proposes an intelligent driving device based on speed adjustment for different road sections, wherein the intelligent driving device based on speed adjustment for different road sections includes: The acquisition module is used to acquire vehicle-side perception data, roadside perception data, and navigation data; The identification module is used to identify the current road segment type based on the navigation data, the vehicle-side perception data, and the roadside perception data; The vehicle speed module is used to determine the target vehicle speed based on the navigation data, the roadside perception data, and the current road segment type; The curve module is used to generate a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions. The control module is used to control the vehicle's movement according to the smooth transition speed curve.

[0016] In addition, to achieve the above objectives, this application also proposes an intelligent driving device based on speed adjustment for different road segments. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the intelligent driving method based on speed adjustment for different road segments as described above.

[0017] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the intelligent driving method based on speed adjustment for different road segments as described above.

[0018] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the intelligent driving method based on speed adjustment for different road segments as described above.

[0019] This application acquires vehicle-mounted perception data, roadside perception data, and navigation data; identifies the current road segment type based on the navigation data, vehicle-mounted perception data, and roadside perception data; determines the target vehicle speed according to the navigation data, roadside perception data, and the current road segment type; generates a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions; and controls vehicle movement based on the smooth transition speed curve. This application achieves the fusion and coordination of multi-source perception information from the vehicle and roadside, and combines high-precision navigation data for accurate road segment type identification. Based on this identification result, it dynamically calculates the target vehicle speed to adapt to different road segments, overcoming the shortcomings of existing technologies that rely on single vehicle-mounted perception, cannot respond to blind spot risks in a timely manner, and suffer from rigid speed control due to static speed limits. Furthermore, by generating a speed planning based on a smooth transition curve through the target vehicle speed, it achieves continuous, stable, and adaptive speed control in all road scenarios, effectively improving the driving safety, ride comfort, and traffic efficiency of the intelligent driving system in complex road conditions. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an embodiment of the intelligent driving method based on speed adjustment for different road sections in this application. Figure 2 This is a flowchart illustrating a second embodiment of the intelligent driving method based on speed adjustment for different road sections in this application. Figure 3A simplified flowchart is provided for Embodiment 2 of the intelligent driving method based on speed adjustment on different road sections in this application; Figure 4 This is a schematic diagram of the module structure of an intelligent driving device based on speed adjustment on different road sections, according to an embodiment of this application. Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the intelligent driving method based on speed adjustment on different road sections in the embodiments of this application.

[0023] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] The main solution of this application embodiment is as follows: acquiring vehicle-side perception data, roadside perception data, and navigation data; identifying the current road segment type based on the navigation data, vehicle-side perception data, and roadside perception data; determining the target vehicle speed according to the navigation data, roadside perception data, and the current road segment type; generating a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions; and controlling the vehicle's movement according to the smooth transition speed curve.

[0027] In this embodiment, for ease of description, the following will describe the execution subject of the intelligent driving system based on speed adjustment for different road sections.

[0028] Existing technologies primarily rely on onboard sensors for perception and speed adjustment strategies based on fixed speed limits. The perception range of these solutions is limited by the field of view of the onboard sensors and obstructions from vehicles ahead. In blind spot scenarios such as intersections, they cannot detect lateral traffic participants in a timely manner, leading to collision risks. Furthermore, the speed adjustment strategies are relatively rigid, typically adjusting only based on static speed limits on maps, without fully integrating real-time traffic flow, road conditions, and historical driving data. This can easily cause sudden speed changes, especially in scenarios such as highway ramp connections, resulting in driving discomfort and compromising safety.

[0029] This application provides a solution that acquires vehicle-mounted perception data, roadside perception data, and navigation data; identifies the current road segment type based on the navigation data, vehicle-mounted perception data, and roadside perception data; determines a target vehicle speed based on the navigation data, roadside perception data, and the current road segment type; generates a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions; and controls vehicle movement based on the smooth transition speed curve. This application achieves the fusion and coordination of multi-source perception information from the vehicle and roadside, and combines high-precision navigation data for accurate road segment type identification. Based on this identification result, it dynamically calculates target vehicle speeds suitable for different road segments, overcoming the shortcomings of existing technologies that rely on single vehicle-mounted perception, cannot respond to blind spot risks in a timely manner, and suffer from rigid speed control due to static speed limits. Furthermore, by generating speed planning based on a smooth transition curve using the target vehicle speed, it achieves continuous, stable, and adaptive speed control across all road scenarios, effectively improving the driving safety, ride comfort, and traffic efficiency of intelligent driving systems in complex road conditions.

[0030] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, or an intelligent driving system based on speed adjustment for different road sections. The following description uses an intelligent driving system based on speed adjustment for different road sections as an example to illustrate this embodiment and the subsequent embodiments.

[0031] Based on this, the embodiments of this application provide an intelligent driving method based on speed adjustment for different road segments, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the intelligent driving method based on speed adjustment for different road sections in this application.

[0032] In this embodiment, the intelligent driving method based on speed adjustment for different road sections includes steps S10 to S50: Step S10: Acquire vehicle-side perception data, roadside perception data, and navigation data; It should be noted that vehicle-side perception data refers to information collected by the vehicle's own sensing devices to characterize the state of the environment around the vehicle; roadside perception data refers to environmental information collected by sensing devices set up at key locations on the road (such as intersections, narrow roads, etc.) to supplement or cover the vehicle's blind spots; navigation data refers to comprehensive data that includes high-precision map information, real-time positioning information, and route planning information.

[0033] Understandably, single vehicle-mounted perception is easily affected by factors such as obstruction from vehicles in front and limited sensor field of view. Especially in complex scenarios such as intersections and curves, it is difficult to obtain a complete traffic situation. Therefore, step S10 is carried out by integrating data sources from the vehicle, roadside, and navigation systems. This can avoid decision-making errors caused by incomplete or delayed perception information, thereby improving the globality, accuracy, and timeliness of environmental perception and laying a reliable data foundation for subsequent precise speed adjustment decisions.

[0034] In one feasible implementation, step S10 may include: acquiring vehicle-surrounding target and lane line information collected by vehicle-mounted sensors and roadside point cloud data collected by roadside sensors; performing target detection and localization based on the vehicle-surrounding target and lane line information to obtain vehicle-side perception data; and extracting blind spot target data based on the roadside point cloud data to obtain roadside perception data.

[0035] It should be noted that vehicle-mounted sensors refer to devices such as lidar, millimeter-wave radar, and cameras integrated on vehicles; targets around vehicles include, but are not limited to, other vehicles, pedestrians, non-motorized vehicles, and other traffic participants; lane lines refer to road markings used to indicate lane boundaries and driving directions; and roadside sensors refer to devices such as lidar and high-definition cameras deployed along the road.

[0036] For example, when a vehicle approaches an intersection with a blind spot, the onboard camera may be unable to detect vehicles approaching laterally due to obstruction by the vehicle in front. However, the roadside lidar deployed at the intersection can capture the point cloud information of the vehicles in the blind spot and acquire it as part of the roadside perception data, thereby compensating for the lack of vehicle-side perception.

[0037] In this embodiment, by synchronously collecting and fusing the original perception information from the vehicle and the roadside, a more comprehensive and redundant environmental perception layer is constructed. This solves the problems of high risk of lateral collision and delayed speed adjustment response caused by blind spots or missing information in the prior art, and provides accurate and reliable input for subsequent adaptive speed adjustment in all road scenarios.

[0038] The above are merely feasible implementations of step S10 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S10.

[0039] Step S20: Based on the navigation data, the vehicle-side perception data, and the roadside perception data, identify the current road segment type; It should be noted that the current road segment type refers to the comprehensive classification of the road attributes and traffic scene characteristics of the road segment where the vehicle is currently located, which determines the specific rules and parameters applicable to the subsequent speed adjustment strategy.

[0040] Understandably, different types of road sections (such as highways and narrow rural roads) differ significantly in speed limits, traffic density, road geometry, and traffic rules. Adopting a uniform speed control strategy would lead to a mismatch between vehicle speed and road conditions, causing safety risks or reduced traffic efficiency. Therefore, step S20, by accurately identifying the current road section type, avoids problems such as poor strategy adaptability and insufficient safety redundancy caused by a "one-size-fits-all" approach to speed control, thereby improving the scenario-specificity and safety basis of speed control decisions.

[0041] In one feasible implementation, step S20 may include: obtaining the number of lanes and signal control information based on the navigation data, the vehicle-mounted perception data, and the roadside perception data; obtaining the radius of curvature based on the vehicle-mounted perception data; and identifying the current road segment type based on the number of lanes, the radius of curvature, and the signal control information, wherein the current road segment type includes highway main roads, ramps, urban roads, rural narrow roads, intersections, and special road segments.

[0042] It should be noted that the number of lanes refers to the number of physical lanes on the current road in the same direction of travel; signal control information refers to the status information indicating whether there are traffic lights (such as red and green lights) controlling the current road segment; radius of curvature refers to the geometric parameter describing the curvature of the road curve, reflecting the straightness or curvature of the road, which can be directly calculated using the vehicle's own sensor data; the current road segment type is a specific road category divided according to predefined quantification rules (combining parameters such as the number of lanes, radius of curvature, and signal control).

[0043] For example, road segments are divided into 6 categories based on the number of lanes, radius of curvature, and signal control information, as shown in Table 1: Table 1

[0044] In this embodiment, key road feature parameters are extracted by fusing multi-source data, and road segment type is identified according to preset quantitative classification standards. This solves the problem in the prior art that the speed adjustment strategy is out of sync with the road scene and cannot be finely adapted to the characteristics of different road segments, and provides accurate scene input for subsequent dynamic and differentiated vehicle speed calculation.

[0045] The above are merely feasible implementations of step S20 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S20.

[0046] Step S30: Determine the target vehicle speed based on the navigation data, the roadside perception data, and the current road segment type; It should be noted that the target speed refers to an expected speed value calculated by the system for the current scenario based on the characteristics of the current road segment, real-time traffic and road conditions, and historical driving data, and is used to guide vehicle speed control.

[0047] It is understandable that different road types have inherent safety and efficiency requirements for vehicle speed, and real-time road conditions (such as traffic density and road surface slipperiness) have a dynamic impact on safe vehicle speed. If the speed limit is set solely based on a static map or information from a single sensor, the resulting target speed may not match the actual road capacity and safety boundaries. Therefore, step S30, which determines the target speed by comprehensively considering static road attributes and dynamic environmental information, avoids the problem of unreasonable speed settings caused by a single or outdated speed adjustment basis, thereby improving the proactive safety and scenario adaptability of vehicle driving.

[0048] Step S40: When the environment in which the vehicle is located meets the preset conditions, a smooth transition speed curve is generated based on the target vehicle speed. It should be noted that the vehicle's environment refers to the dynamic scene comprised of the geometric features of the current road segment, the features of the next road segment it is about to enter, and the connection between the two. Preset conditions refer to the specific combination of environmental states that trigger a smooth speed transition. A smooth transition speed curve is a function trajectory describing how speed changes continuously and smoothly with time or distance to a target value, aiming to ensure that the speed change process conforms to human comfort and vehicle dynamics requirements.

[0049] Understandably, when vehicles switch between different speed-limited sections (such as entering a ramp from a highway) or navigate sharp curves, abrupt speed adjustments can cause significant acceleration shocks, affecting ride comfort and potentially leading to vehicle instability or even rear-end collisions due to sudden braking or acceleration. Therefore, step S40, which generates a smooth transition speed curve when a scenario requiring stable speed adjustment is detected, avoids the shocks and safety hazards caused by abrupt speed changes, thereby improving the continuity of speed control, ride comfort, and driving stability.

[0050] In one feasible implementation, before step S40, the following steps may be included: obtaining the radius of curvature; obtaining the speed limit difference between adjacent road segments based on the navigation data; and determining that the environment in which the vehicle is located meets preset conditions when the speed limit difference between adjacent road segments is greater than a preset speed limit threshold or the radius of curvature is less than a preset radius of curvature threshold.

[0051] It should be noted that the speed limit difference between adjacent road segments refers to the difference between the base speed limit of the current road segment and the next road segment that the vehicle is about to enter, reflecting the extent to which the speed needs to be adjusted. The preset speed limit threshold is a pre-set speed difference threshold value used to determine whether a smooth speed transition is required. The preset radius of curvature threshold is a pre-set curve geometry threshold value used to determine whether the curve is sharp enough to require a smooth reduction in speed to pass through.

[0052] For example, when a vehicle is about to enter a ramp with a speed limit of 60 km / h from a highway with a speed limit of 120 km / h, the speed limit difference between adjacent road segments is 60 km / h. If the preset speed limit threshold is 20 km / h, since 60 km / h > 20 km / h, the preset condition is determined to be met. Alternatively, when the vehicle is about to pass through a curve with a radius of curvature of 150 meters, if the preset radius of curvature threshold is 200 meters, since 150 meters < 200 meters, the preset condition is also determined to be met.

[0053] In this embodiment, by quantitatively assessing the speed change requirements and road curvature at the junction of road segments, the scenario requiring smooth speed control is accurately identified. This solves the problem in the prior art where speed regulation strategies are insensitive to transitional scenarios, leading to sudden speed changes, and provides an accurate triggering basis for generating smooth speed change trajectories.

[0054] In one feasible implementation, step S40 may include: when the environment in which the vehicle is located meets preset conditions, obtaining a transition distance constraint condition based on the current vehicle speed; and generating a smooth transition speed curve based on the transition distance constraint condition and the target vehicle speed.

[0055] It should be noted that the current vehicle speed refers to the instantaneous speed of the vehicle at the time the triggering condition is met. The transition distance constraint refers to the minimum or recommended distance that the vehicle needs to travel to achieve a smooth transition from the current vehicle speed to the target vehicle speed.

[0056] For example, when transitioning from a current vehicle speed of 120 km / h to a target speed of 60 km / h, the system calculates a transition distance constraint based on the current speed (e.g., the transition distance is not less than twice the current speed in meters). Subsequently, under this distance constraint, the system plans a speed change curve described by a specific mathematical function (such as a cosine function). This curve shows a smooth speed change within the transition distance, with acceleration limited to a comfortable range.

[0057] In this embodiment, by introducing a transition distance as a constraint and generating a continuous and smooth speed planning curve based on it, the problems of discontinuous speed change process and large acceleration fluctuations leading to passenger discomfort and increased vehicle control difficulty are solved, thus achieving a smooth and comfortable speed transition.

[0058] The above are merely feasible implementations of step S40 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S40.

[0059] Step S50: Control the vehicle's movement according to the smooth transition speed curve.

[0060] Understandably, since the final speed plan needs to be precisely executed by the vehicle's powertrain and braking systems to be translated into the actual driving state, if there is a delay or deviation between the control command and the vehicle's dynamic response, the actual vehicle speed will not accurately follow the planned curve, weakening or even negating the design effect of a smooth transition. Therefore, step S50 converts the generated smooth transition speed curve into control commands that can be recognized by the vehicle's execution units (such as motor controllers and brake controllers), and performs real-time closed-loop control to track the curve. This avoids the problems of planning and execution being disconnected and actual speed fluctuations deviating from expectations, thereby improving the accuracy of speed control, the real-time performance of system response, and overall driving smoothness.

[0061] In one feasible implementation, step S50 may include: obtaining blind spot target data based on the roadside perception data; generating blind spot warning information based on the blind spot target data; pushing the blind spot warning information and the current road segment type to the vehicle for reminder; and updating the historical database based on the current road segment type, the vehicle-side perception data, the roadside perception data, and the target vehicle speed.

[0062] It should be noted that blind spot target data refers to information about traffic participants or obstacles (such as pedestrians crossing laterally or vehicles obscured by the vehicle in front) detected by roadside sensors and located outside the vehicle's own field of vision. Blind spot warning information is a warning signal generated based on the aforementioned blind spot target data to alert the driver or autonomous driving system to potential risks. The historical database refers to an experience-based knowledge base stored in the cloud or on-vehicle, used to record actual safe driving speeds and corresponding perception data for different road types and specific environmental conditions (such as specific traffic flows and road surface conditions).

[0063] For example, when a vehicle is following another vehicle at an intersection, roadside sensors detect a pedestrian entering the intersection from a lateral crosswalk, but the pedestrian is in the blind spot of the vehicle's camera. Based on this blind spot target data, the system generates a "lateral pedestrian crossing" warning, along with the current intersection's road segment type identifier, and pushes it to the vehicle's human-machine interface for audio or visual alerts. Simultaneously, the system stores this experience record in the historical database, combining the traffic flow, road conditions, and the target speed and corresponding perception data used for safe passage at the intersection, for future decision optimization in similar scenarios.

[0064] In this embodiment, by continuously using roadside data to supplement early warning information and accumulating actual driving data after control is executed, the problems of insufficient early warning of sudden blind spot risks during driving and lack of continuous learning and optimization data sources for decision-making models are solved, thus realizing a closed loop of safety early warning and continuous iterative improvement of the system's intelligence level.

[0065] The above are merely feasible implementations of step S50 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S50.

[0066] This embodiment provides an intelligent driving method based on speed adjustment for different road segments. It acquires vehicle-side perception data, roadside perception data, and navigation data; identifies the current road segment type based on the navigation data, vehicle-side perception data, and roadside perception data; determines the target vehicle speed based on the navigation data, roadside perception data, and the current road segment type; generates a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions; and controls vehicle movement based on the smooth transition speed curve. This application achieves the fusion and coordination of multi-source perception information from the vehicle and roadside, and combines high-precision navigation data for accurate road segment type identification. Based on this identification result, it dynamically calculates the target vehicle speed adapted to different road segments, overcoming the shortcomings of existing technologies that rely on single vehicle-side perception, cannot respond to blind spot risks in a timely manner, and suffer from rigid speed adjustment due to static speed limits. Furthermore, by generating a speed planning based on a smooth transition curve using the target vehicle speed, it achieves continuous, stable, and adaptive speed control across all road scenarios, effectively improving the driving safety, ride comfort, and traffic efficiency of the intelligent driving system in complex road conditions.

[0067] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 The intelligent driving method based on speed adjustment for different road sections, step S30, further includes steps S31 to S35: Step S31, obtain the radius of curvature; Understandably, since the geometric curvature of a road directly affects the safe speed limit and driving stability of a vehicle, adopting a straight-line speed limit without considering the curve characteristics could lead to excessively high cornering speeds and the risk of skidding. Therefore, step S31 avoids setting unsafe cornering speeds due to ignoring road curvature characteristics, thereby improving active safety and vehicle handling stability when cornering.

[0068] Step S32: Obtain the basic speed limit for the current road segment based on the navigation data; It should be noted that the current basic speed limit is a legal or design speed limit value pre-stored in the high-precision map navigation, which is specific to the current road segment type.

[0069] Understandably, since the basic speed limit provides a legal safety benchmark for a road segment, if the speed adjustment decision deviates completely from this benchmark, it may lead to vehicles speeding illegally or falling far below the road segment's capacity. Therefore, performing step S32 can avoid the problem of the speed adjustment decision losing a legal speed reference benchmark, thereby ensuring that the target vehicle speed setting complies with regulatory requirements and provides a reasonable initial anchor point for subsequent dynamic corrections.

[0070] Step S33: Obtain the curvature coefficient based on the radius of curvature; It should be noted that the curvature coefficient is a correction factor used to dynamically reduce the basic speed limit based on the curvature of the road. Its value is usually between 0 and 1. The smaller the radius of curvature, the smaller the value of this coefficient.

[0071] Understandably, the sharper the curve, the greater the speed reduction required for a vehicle to safely navigate it. Using only a basic speed limit or a simple fixed reduction cannot accurately match the safety requirements of different curves. Therefore, performing step S33 avoids applying the same speed reduction strategy to all curves, which could lead to high risk on sharp curves or low efficiency on gentle curves, thereby improving the precision and safety of curve speed decisions.

[0072] Step S34: Obtain the traffic flow coefficient and road surface coefficient based on the roadside sensing data; It should be noted that the traffic flow coefficient is a correction factor used to adaptively adjust speed based on real-time traffic density (such as vehicle spacing). The traffic flow coefficient is obtained by analyzing roadside perception data and extracting vehicle density and spacing. The road surface coefficient is a correction factor used to reduce speed for safety based on road surface adhesion conditions (such as dryness, water accumulation, ice and snow). The road surface coefficient is obtained by analyzing roadside perception data and identifying road surface texture and reflective characteristics.

[0073] Understandably, real-time traffic conditions and road surface conditions are key dynamic factors affecting driving safety. Ignoring these factors could lead to a significant discrepancy between the target speed and the actual road capacity. Therefore, step S34 avoids the risk of following too closely or having insufficient braking distance when using ideal speed settings in heavy traffic or on slippery roads, thereby improving the adaptability of speed decisions to dynamic environmental changes and real-time safety.

[0074] Step S35: Determine the target vehicle speed based on the current road segment type, the current road segment basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

[0075] Understandably, since the target speed is a final decision value derived by comprehensively considering static road segment attributes and multi-dimensional dynamic safety factors, the decision value may have safety or efficiency defects if any key influencing factor is not considered in conjunction with the overall decision. Therefore, step S35 can avoid the problem of speed decision being based on a single, one-sided approach, which fails to achieve the optimal balance under multiple constraints such as safety, efficiency, and regulations, thus obtaining a more comprehensive and more realistic optimal target speed that better reflects complex driving scenarios.

[0076] In one feasible implementation, step S35 may include: when historical speed data corresponding to the current road segment type stored in the historical database is obtained, obtaining a target speed based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, the road surface coefficient, and the historical speed data; when historical speed data corresponding to the current road segment type is not obtained from the historical database, obtaining a target speed based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

[0077] Specifically, when historical vehicle speed data corresponding to the current road segment type stored in the historical database is available, the target vehicle speed is obtained based on the following five-factor model formula: Vtarget=Vbase×Kcurve×Ktraffic×Ksurface×Khistory Among them, Vbase is the base speed limit of the current road segment; Kcurve (curvature coefficient) = 0.5-1.0 (the smaller the radius, the smaller the value); Ktraffic (traffic flow coefficient) is dynamically corrected based on the distance between vehicles fed back from the roadside (0.7 for distance < 50m); Ksurface (surface surface coefficient) identifies water / ice / snow conditions through roadside cameras (0.4 for water accumulation); Khistory (history coefficient) retrieves historical vehicle speed data of the current road segment from the historical database (deviation < 10%).

[0078] When historical vehicle speed data corresponding to the current road segment type stored in the historical database is not available, the target vehicle speed is obtained based on the following four-factor model formula: Vtarget=Vbase×Kcurve×Ktraffic×Ksurface Wherein, Vbase is the base speed limit for the current road segment; Kcurve (curvature coefficient) = 0.5-1.0 (the smaller the radius, the smaller the value); Ktraffic (traffic flow coefficient) is dynamically corrected based on the distance between vehicles fed back from the roadside (0.7 for distance < 50m); Ksurface (surface surface coefficient) identifies water / ice / snow conditions through roadside cameras (0.4 for water accumulation).

[0079] In this embodiment, by introducing historical optimal vehicle speed data as a decision reference, the "experience wisdom" of group driving is integrated into real-time calculation, which solves the problem of conservatism or recklessness that may exist when relying solely on models and real-time data for decision-making in the absence of prior knowledge or in complex scenarios. This allows the determined target vehicle speed to more effectively approach the optimal solution for traffic efficiency in the scenario while meeting safety requirements.

[0080] The above are merely feasible implementations of step S35 provided in this embodiment. This embodiment does not specifically limit the specific implementation of step S35.

[0081] This embodiment provides an intelligent driving method based on speed adjustment for different road segments. It obtains the radius of curvature; determines the basic speed limit for the current road segment based on navigation data; obtains the curvature coefficient based on the radius of curvature; obtains the traffic flow coefficient and road surface coefficient based on roadside perception data; and determines the target speed based on the current road segment type, the basic speed limit for the current road segment, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient. This solves the problem of existing technologies using a single, rigid speed adjustment basis, which cannot accurately respond to multi-dimensional dynamic factors such as road geometry, real-time traffic flow, and road surface conditions. It avoids safety and efficiency risks caused by the disconnect between speed settings and complex actual road conditions, thereby significantly improving the comprehensiveness, adaptability, and safety of target speed decision-making.

[0082] For example, to help understand the implementation process of the intelligent driving method based on speed adjustment for different road segments obtained by combining this embodiment with the above embodiment one, please refer to... Figure 3 , Figure 3 A simplified flowchart of an intelligent driving method based on speed adjustment for different road segments is provided, specifically: The system begins with multi-source data acquisition and fusion. It simultaneously acquires vehicle-side target information and its own positioning data from vehicle-mounted sensors, as well as blind-spot targets and real-time traffic flow data detected by roadside units in key road sections (such as intersections and narrow roads). Simultaneously, the cloud attempts to retrieve historically optimal vehicle speed data matching the current scenario. All data (vehicle-mounted and roadside) undergoes spatiotemporal alignment and fusion in the cloud, building a comprehensive and reliable foundation of environmental perception and experiential knowledge for subsequent decision-making. The fused data is used in the crucial step of "identifying the current road segment type." Based on pre-defined classification rules (such as lane number, curvature, and signal control), the system determines whether the vehicle is currently on a highway, ramp, or urban road.

[0083] After identifying the road segment type, the process enters the core stage of dynamic speed adjustment calculation. The system first obtains the basic speed limit for the road segment based on navigation data, and then calculates or obtains key correction coefficients affecting vehicle speed by combining real-time perception data: the curvature coefficient obtained from road geometry, the traffic flow coefficient obtained from real-time traffic conditions, and the road surface coefficient obtained from road surface conditions (such as water accumulation). At this point, the process branches based on whether relevant historical data exists: if it does, the basic speed limit, the three real-time coefficients mentioned above, and historical speed data are all substituted into the decision model (i.e., five-factor coupling); if not, calculations are performed only based on the basic speed limit and the three real-time coefficients (i.e., a four-factor model). Through this process, the target speed is determined, which is an optimized result integrating static rules, dynamic environment, and group experience.

[0084] Finally, the system performs smooth transition and closed-loop control. After calculating the target vehicle speed, it determines whether the current environment meets the conditions for a smooth transition, the core of which is checking whether the "speed limit difference" with the next road segment is greater than 20 km / h. If the condition is met, a "smooth transition speed curve" is generated, for example, using a cosine function to plan continuous speed changes within a sufficient transition distance to ensure smooth acceleration; if the condition is not met, the target speed is directly output. Regardless of whether smoothing is performed, the final speed command is sent to the vehicle's actuators to "control vehicle movement" to track the target speed or transition curve. The vehicle's actual driving status and the latest road condition information are uploaded to the cloud as feedback data to update the historical database, forming a "data closed-loop update," enabling the system to continuously learn and optimize.

[0085] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent driving method based on speed adjustment on different road sections. Any simple modifications based on this technical concept are within the protection scope of this application.

[0086] This application also provides an intelligent driving device based on speed adjustment for different road sections; please refer to [reference needed]. Figure 4 The intelligent driving device based on speed adjustment for different road sections includes: The acquisition module 10 is used to acquire vehicle-side perception data, roadside perception data, and navigation data; The identification module 20 is used to identify the current road segment type based on the navigation data, the vehicle-side perception data, and the roadside perception data; The vehicle speed module 30 is used to determine the target vehicle speed based on the navigation data, the roadside perception data, and the current road segment type; Curve module 40 is used to generate a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions. The control module 50 is used to control the vehicle's movement according to the smooth transition speed curve.

[0087] The intelligent driving device based on speed adjustment for different road segments provided in this application, employing the intelligent driving method based on speed adjustment for different road segments described in the above embodiments, can solve the technical problem of how to achieve differentiated, safe, and stable speed adjustment across multiple road segments. Compared with the prior art, the beneficial effects of the intelligent driving device based on speed adjustment for different road segments provided in this application are the same as those of the intelligent driving method based on speed adjustment for different road segments provided in the above embodiments, and other technical features in the intelligent driving device based on speed adjustment for different road segments are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0088] The acquisition module 10 is further configured to acquire vehicle-surrounding target and lane line information collected by vehicle-mounted sensors and roadside point cloud data collected by roadside sensors; perform target detection and positioning based on the vehicle-surrounding target and lane line information to obtain vehicle-side perception data; and extract blind spot target data based on the roadside point cloud data to obtain roadside perception data.

[0089] The identification module 20 is further configured to obtain the number of lanes and signal control information based on the navigation data, the vehicle-side perception data and the roadside perception data; obtain the radius of curvature based on the vehicle-side perception data; and identify the current road segment type based on the number of lanes, the radius of curvature and the signal control information, wherein the current road segment type includes expressway main roads, ramps, urban roads, rural narrow roads, intersections and special road segments.

[0090] The vehicle speed module 30 is also used to obtain the radius of curvature; obtain the basic speed limit of the current road segment based on the navigation data; obtain the curvature coefficient based on the radius of curvature; obtain the traffic flow coefficient and the road surface coefficient based on the roadside perception data; and determine the target vehicle speed based on the current road segment type, the basic speed limit of the current road segment, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

[0091] The vehicle speed module 30 is further configured to, when acquiring historical vehicle speed data corresponding to the current road segment type stored in the historical database, obtain a target vehicle speed based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, the road surface coefficient, and the historical vehicle speed data; and when not acquiring historical vehicle speed data corresponding to the current road segment type stored in the historical database, obtain a target vehicle speed based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

[0092] The curve module 40 is also used to obtain the radius of curvature; based on the navigation data, to obtain the speed limit difference between adjacent road segments; and when the speed limit difference between adjacent road segments is greater than a preset speed limit threshold or the radius of curvature is less than a preset radius of curvature threshold, to determine that the environment in which the vehicle is located meets preset conditions.

[0093] The curve module 40 is also used to obtain transition distance constraints based on the current vehicle speed when the environment in which the vehicle is located meets preset conditions; and to generate a smooth transition speed curve based on the transition distance constraints and the target vehicle speed.

[0094] The control module 50 is further configured to obtain blind spot target data based on the roadside perception data; generate blind spot warning information based on the blind spot target data; push the blind spot warning information and the current road segment type to the vehicle for reminder; and update the historical database based on the current road segment type, the vehicle-side perception data, the roadside perception data, and the target vehicle speed.

[0095] This application provides an intelligent driving device based on speed adjustment for different road segments. The intelligent driving device based on speed adjustment for different road segments includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent driving method based on speed adjustment for different road segments in the above embodiment 1.

[0096] The following is for reference. Figure 5 This document illustrates a structural schematic diagram of an intelligent driving device suitable for implementing the embodiments of this application based on speed regulation of different road segments. The intelligent driving device based on speed regulation of different road segments in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The intelligent driving device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0097] like Figure 5As shown, the intelligent driving device based on speed adjustment for different road sections may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into random access memory (RRAM) 1004. RAM 1004 also stores various programs and data required for the operation of the intelligent driving device based on speed adjustment for different road sections. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the intelligent driving device, which adjusts speed based on different road segments, to exchange data with other devices wirelessly or via wired communication. Although the figure shows an intelligent driving device with various systems that adjust speed based on different road segments, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented or possessed alternatively.

[0098] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0099] The intelligent driving device based on speed adjustment for different road segments provided in this application, employing the intelligent driving method based on speed adjustment for different road segments described in the above embodiments, can solve the technical problem of how to achieve differentiated, safe, and stable speed adjustment across multiple road segments. Compared with the prior art, the beneficial effects of the intelligent driving device based on speed adjustment for different road segments provided in this application are the same as those of the intelligent driving method based on speed adjustment for different road segments provided in the above embodiments, and other technical features of this intelligent driving device based on speed adjustment for different road segments are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0100] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0102] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the intelligent driving method based on speed adjustment for different road segments in the above embodiments.

[0103] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0104] The aforementioned computer-readable storage medium may be included in an intelligent driving device that adjusts speed based on different road sections; or it may exist independently and not be installed in an intelligent driving device that adjusts speed based on different road sections.

[0105] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an intelligent driving device that adjusts speed based on different road segments, cause the intelligent driving device to: acquire vehicle-side perception data, roadside perception data, and navigation data; identify the current road segment type based on the navigation data, the vehicle-side perception data, and the roadside perception data; determine a target vehicle speed based on the navigation data, the roadside perception data, and the current road segment type; generate a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions; and control the vehicle's movement based on the smooth transition speed curve.

[0106] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0109] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described intelligent driving method based on speed adjustment for different road segments, thereby solving the technical problem of how to achieve differentiated, safe, and stable speed adjustment across multiple road segments. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent driving method based on speed adjustment for different road segments provided in the above embodiments, and will not be repeated here.

[0110] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the intelligent driving method based on speed adjustment for different road segments as described above.

[0111] The computer program product provided in this application can solve the technical problem of how to achieve differentiated, safe, and stable speed regulation across multiple road segments. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent driving method based on speed regulation across different road segments provided in the above embodiments, and will not be repeated here.

[0112] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An intelligent driving method based on speed adjustment for different road sections, characterized in that, The method includes: Acquire vehicle-side perception data, roadside perception data, and navigation data; Based on the navigation data, the vehicle-side perception data, and the roadside perception data, the current road segment type is identified; The target vehicle speed is determined based on the navigation data, the roadside perception data, and the current road segment type; When the vehicle's environment meets the preset conditions, a smooth transition speed curve is generated based on the target vehicle speed; The vehicle's movement is controlled according to the smooth transition speed curve.

2. The method as described in claim 1, characterized in that, The step of identifying the current road segment type based on the navigation data, the vehicle-side perception data, and the roadside perception data includes: Based on the navigation data, the vehicle-side perception data, and the roadside perception data, the number of lanes and signal control information are obtained. The radius of curvature is obtained based on the vehicle-side sensing data. The current road segment type is identified based on the number of lanes, the radius of curvature, and the signal control information, wherein the current road segment type includes expressway main roads, ramps, urban roads, rural narrow roads, intersections, and special road segments.

3. The method as described in claim 1, characterized in that, Determining the target vehicle speed based on the navigation data, the roadside perception data, and the current road segment type includes: Obtain the radius of curvature; The basic speed limit for the current road segment is obtained based on the navigation data; Based on the radius of curvature, the curvature coefficient is obtained; Traffic flow coefficient and road surface coefficient are obtained based on the roadside sensing data; The target vehicle speed is determined based on the current road segment type, the current road segment basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

4. The method as described in claim 3, characterized in that, The process of determining the target vehicle speed based on the current road segment type, the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient includes: When the historical vehicle speed data corresponding to the current road segment type stored in the historical database is obtained, the target vehicle speed is obtained based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, the road surface coefficient, and the historical vehicle speed data. When historical vehicle speed data corresponding to the current road segment type is not available in the historical database, the target vehicle speed is obtained based on the current road segment's basic speed limit, the curvature coefficient, the traffic flow coefficient, and the road surface coefficient.

5. The method as described in claim 1, characterized in that, Before generating a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions, the method further includes: Obtain the radius of curvature; Based on the navigation data, the speed limit difference between adjacent road segments is obtained; When the speed limit difference between adjacent road segments is greater than a preset speed limit threshold or the radius of curvature is less than a preset radius of curvature threshold, it is determined that the environment in which the vehicle is located meets the preset conditions.

6. The method as described in claim 1, characterized in that, When the vehicle's environment meets preset conditions, generating a smooth transition speed curve based on the target vehicle speed includes: When the vehicle's environment meets the preset conditions, the transition distance constraint is obtained based on the current vehicle speed; A smooth transition speed curve is generated based on the transition distance constraint and the target vehicle speed.

7. The method as described in claim 1, characterized in that, Acquire vehicle-side perception data and roadside perception data, including: Acquire vehicle-surrounding target and lane line information collected by onboard sensors, as well as roadside point cloud data collected by roadside sensors; Based on the vehicle's surrounding targets and lane line information, target detection and localization are performed to obtain vehicle-side perception data; Blind spot target data is extracted based on the roadside point cloud data to obtain roadside perception data.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Based on the roadside sensing data, blind spot target data is obtained; Generate blind zone early warning information based on the blind zone target data; The blind spot warning information and the current road segment type are pushed to the vehicle for reminder; The current road segment type, the vehicle-side perception data, the roadside perception data, and the target vehicle speed are used to update the historical database.

9. An intelligent driving device based on speed adjustment for different road sections, characterized in that, The device includes: The acquisition module is used to acquire vehicle-side perception data, roadside perception data, and navigation data; The identification module is used to identify the current road segment type based on the navigation data, the vehicle-side perception data, and the roadside perception data; The vehicle speed module is used to determine the target vehicle speed based on the navigation data, the roadside perception data, and the current road segment type; The curve module is used to generate a smooth transition speed curve based on the target vehicle speed when the vehicle's environment meets preset conditions. The control module is used to control the vehicle's movement according to the smooth transition speed curve.

10. An intelligent driving device based on speed adjustment for different road sections, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the intelligent driving method based on speed adjustment for different road segments as described in any one of claims 1 to 8.

Images