Vehicle assisted driving method, apparatus and vehicle

Abstract

The application discloses a vehicle auxiliary driving method and device and a vehicle, and relates to the technical field of automobiles. A specific embodiment of the method comprises the following steps: collecting safety event data generated by a vehicle in a historical driving period; fusing the trigger frequency and the severity of a safety event in the safety event data to generate a road complexity index; and constructing a driving style assertion index representing the driving style of a driver according to driving operation data of the driver in a non-assisted driving mode. The comfort control parameter in a driving assistance system of the vehicle is dynamically adjusted according to the road complexity index and the driving style assertion index. The embodiment can improve the adaptability of vehicle auxiliary driving and meet the diversity of vehicle driving scenarios.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and more particularly to a vehicle-assisted driving method, device, and vehicle. Background Technology

[0002] Currently, comfort functions in Advanced Driver Assistance Systems (ADAS), such as Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA), and Lane Centering Control (LCC), typically use factory default values ​​or rely on manual settings by the driver for their control parameters, such as following distance, Jerk limit for acceleration and deceleration impact, and aggressiveness of lane-changing strategies.

[0003] In the process of realizing this invention, the inventors discovered that the prior art has at least the following problems: static parameter settings result in poor adaptability and make it difficult to meet the diversity of vehicle driving scenarios. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a vehicle assisted driving method, device, vehicle, equipment, and computer-readable medium, which can improve the adaptability of vehicle assisted driving and meet the diversity of vehicle driving scenarios.

[0005] A vehicle-assisted driving method, comprising: Collect safety event data generated by the vehicle during its historical driving cycle; By integrating the triggering frequency and severity of safety events in the aforementioned safety event data, a road complexity index is generated; and a driving style assertion index characterizing the driver's operating style is constructed based on the driver's driving operation data in non-assisted driving mode. The comfort control parameters in the vehicle's driver assistance system are dynamically adjusted based on the road complexity index and the driving style assertion index.

[0006] The process of integrating the triggering frequency and severity of safety events from the safety event data to generate a road complexity index includes: The road complexity index is obtained by weighted summation of the trigger frequency and severity of safety events in the safety event data; The triggering frequency of the safety events includes one or more of the following: the triggering frequency of forward collision warning events, the number of interventions of automatic emergency braking events, the triggering frequency of lane departure warning, and the correction frequency of lane keeping assist; The severity level includes: the severity-weighted average of the automatic emergency braking events.

[0007] The construction of a driving style assertion index characterizing the driver's operating style based on the driver's driving operation data in non-assisted driving mode includes: The driving style assertion index is constructed based on the vehicle pedal control parameters and steering wheel control parameters of the driver in non-assisted driving mode. The vehicle pedal control parameters include the impact frequency of vehicle pedal operation, and the steering wheel control parameters include the correction frequency of the driver correcting the steering wheel before the lane keeping assist system intervenes.

[0008] The comfort control parameters include one or more of the following: following distance of adaptive cruise control, upper limit of acceleration and deceleration impact, and lane change aggression parameters; The following distance is positively correlated with the road complexity index, and the following distance is negatively correlated with the driving style assertion index. The upper limit of acceleration / deceleration impact is negatively correlated with the road complexity index, and the upper limit of acceleration / deceleration impact is positively correlated with the driving style assertion index; The lane-changing aggression parameter is positively correlated with the driving style assertion index.

[0009] Before dynamically adjusting the comfort control parameters of the vehicle's driver assistance system, the method further includes: Based on the geographic location information, a preset road complexity index is bound to the geographic region corresponding to the geographic location information; Based on the geographical location information in the security event data, it is determined that the vehicle has entered the geographical area, and the road complexity index corresponding to the geographical area is loaded.

[0010] The method further includes: The assisted driving type is displayed through a human-computer interaction interface. The assisted driving type is determined based on the road complexity index and the driving style assertion index. The assisted driving type includes conservative, moderate, or aggressive.

[0011] The collected safety event data generated by the vehicle during its historical driving cycle includes: Collect raw safety data generated by the vehicle during its historical driving cycle, and perform noise reduction processing on the raw safety data to filter out false triggers and / or low-confidence data. High-severity security events are assigned high weights to form the security event data.

[0012] The dynamic adjustment of the comfort control parameters of the vehicle's driver assistance system includes: Compare each adjusted comfort control parameter value with the corresponding lower and upper safety thresholds; If the adjusted comfort control parameter value exceeds the lower safety threshold or the upper safety threshold, the comfort control parameter value is set to the lower safety threshold or the upper safety threshold to prevent the vehicle control behavior from exceeding the safety boundary.

[0013] According to a second aspect of the present invention, a vehicle driver assistance device is provided, comprising: The data acquisition module is used to collect safety event data generated by the vehicle during its historical driving cycle. A module is established to integrate the triggering frequency and severity of safety events in the safety event data to generate a road complexity index, and to construct a driving style assertion index characterizing the driver's operating style based on the driver's driving operation data in non-assisted driving mode. The adjustment module is used to dynamically adjust the comfort control parameters in the vehicle's driver assistance system based on the road complexity index and the driving style assertion index.

[0014] According to a third aspect of the present invention, a vehicle is provided, including the vehicle driver assistance device as described above.

[0015] According to a fourth aspect of the present invention, a vehicle-assisted driving electronic device is provided, comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors perform the methods described above.

[0016] According to a fifth aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0017] One embodiment of the above invention has the following advantages or beneficial effects: It collects safety event data generated by the vehicle during its historical driving cycle, and then generates a road complexity index characterizing the road environment and a driving style assertion index characterizing the driver's operating style. By adjusting comfort control parameters using the road complexity index and driving style assertion index, the adaptability of vehicle assisted driving can be improved while maintaining road safety, thus meeting the diverse needs of vehicle driving scenarios.

[0018] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0019] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein: Figure 1 This is a schematic diagram of the main process of a vehicle assisted driving method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the process for collecting security event data according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the process of obtaining the road complexity index corresponding to a geographical region according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the application of the road complexity index according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the process for adjusting comfort control parameters according to an embodiment of the present invention; Figure 6 This is a schematic diagram comparing assisted driving trajectories according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the architecture of a vehicle-assisted driving method according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the main structure of a vehicle driver assistance device according to an embodiment of the present invention; Figure 9 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied; Figure 10 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0020] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0021] Static parameter settings in advanced driver assistance systems (ADAS) have significant shortcomings in practical applications. First, the vehicle's driving environment is dynamically changing. In complex or high-risk areas, such as pedestrian-dense areas or road construction zones, fixed parameters cannot adapt to changes in environmental risk levels, resulting in poor environmental adaptability. In other words, fixed parameters may cause the vehicle's assisted driving style to be too aggressive or conservative, failing to effectively adapt to environmental changes.

[0022] Secondly, there is a mismatch in driving styles: drivers' driving styles, such as an aggressive or conservative preference, differ in their expectations of advanced driver assistance systems (ADAS). Fixed parameters are insufficient to meet individual needs, easily leading to driver distrust of ADAS or frequent takeovers. Furthermore, existing vehicle architectures exhibit significant data silos; that is, risk data generated by active safety systems, such as forward collision warning, automatic emergency braking, and lane departure warning, is not effectively utilized by comfort-oriented driver assistance systems, resulting in data waste.

[0023] To address the problem that existing driver assistance systems cannot adapt to environmental changes due to fixed parameters, the following technical solutions from the embodiments of the present invention can be adopted.

[0024] See Figure 1 , Figure 1 This is a schematic diagram of the main process of a vehicle assisted driving method according to an embodiment of the present invention, which specifically includes the following steps: S101. Collect safety event data generated by the vehicle during the historical driving cycle.

[0025] In embodiments of the present invention, the operation can be performed in the vehicle-mounted control unit or vehicle-mounted control system. Figure 1 The technical solutions described herein include, for example, execution within the domain controller of an Advanced Driver Assistance System (ADAS). The ADAS domain controller, as a dedicated high-performance computing platform for advanced driver assistance functions, undertakes the core computational tasks of perception fusion, planning and decision-making, and control execution. It can be applied to scenarios where multiple drivers share the same vehicle to achieve targeted vehicle-assisted driving.

[0026] Historical driving cycles are time intervals accumulated from multiple completed trips by a vehicle. For example, a historical driving cycle can be a time window measured in days or weeks, long enough to stabilize the statistical frequency of safety events and avoid drastic fluctuations caused by isolated incidents. Based on the effective data accumulated within historical driving cycles, quantitative indicators reflecting road environment characteristics and driver habits are obtained.

[0027] Safety event data is data generated when a vehicle detects a potential collision risk or lane departure risk while driving, triggering intervention control.

[0028] S102. By integrating the triggering frequency and severity of safety events in the safety event data, a road complexity index is generated. Also, a driving style assertion index characterizing the driver's operating style is constructed based on the driver's driving operation data in non-assisted driving mode.

[0029] Safety incident data, as well as driving operation data acquired from the vehicle's CAN bus or in-vehicle Ethernet, are processed and converted into road complexity index and driving style assertion index, respectively.

[0030] The Road Complexity Index (RCI) is used to characterize the level of road environmental risk in a vehicle's operating area. By fusing the trigger frequency and severity of multiple types of safety event data, discrete, multidimensional risk events are transformed into a quantifiable Road Complexity Index (RCI).

[0031] The Driving Assertion Index (DAI) characterizes a driver's driving style in non-assisted driving modes, quantifying the driver's driving habits. A high DAI indicates that the driver prefers a more aggressive driving style, while a low DAI suggests a more conservative driving style.

[0032] The driving style assertion index is constructed based on driver operation data in non-assisted driving mode. Non-assisted driving mode is when the driver manually controls the vehicle, and the comfort functions of the advanced driver assistance systems are not activated or are in standby mode. Driving operation data from non-assisted mode is selected because vehicle control is entirely driver-led, and this data accurately reflects the driver's driving style without interference from advanced driver assistance systems.

[0033] S103. Based on the road complexity index and driving style assertion index, dynamically adjust the comfort control parameters in the vehicle's driving assistance system.

[0034] The road complexity index and driving style assertion index are transformed into specific adjustments to the comfort control parameters of advanced driver assistance systems, so as to achieve a balance between environmental constraints and personalized preferences in driver assistance style.

[0035] exist Figure 1 In this embodiment, safety event data generated by the vehicle during its historical driving cycle is collected to generate a road complexity index characterizing the road environment and a driving style assertion index characterizing the driver's operating style. By adjusting comfort control parameters using the road complexity index and driving style assertion index, the adaptability of vehicle assisted driving can be improved while maintaining road safety, thus meeting the diverse needs of vehicle driving scenarios.

[0036] See Figure 2 , Figure 2 This is a schematic diagram of the process for collecting security incident data according to an embodiment of the present invention. Specifically, it includes the following steps: S201. Collect raw safety data generated by the vehicle during the historical driving cycle, perform noise reduction processing on the raw safety data, and filter out false triggers and / or low confidence data.

[0037] After collecting raw safety data generated by the vehicle during its historical driving cycle, the raw safety data received from the active safety controllers is filtered. The active safety controllers include one or more of the following: Forward Collision Warning (FCW), Automatic Emergency Braking (AEB), Lane Departure Warning (LDW), and Lane Keeping Assist (LKA). Specifically, noise reduction processing includes one or more of the following methods: filtering out false triggers and / or low-confidence data; filtering out instantaneous trigger signals with durations shorter than a preset time threshold (e.g., 0.1 seconds); and filtering out data with confidence levels lower than a preset confidence threshold (e.g., 60% confidence threshold, filtering out FCW trigger data with a confidence level lower than 60%). The confidence level can be provided by the forward-looking perception system when outputting the target detection results. When the confidence level of target recognition is too low, the forward collision warning may be triggered by a false target.

[0038] S202. Assign high weights to security incidents with high severity to form security incident data.

[0039] High-severity safety incidents are given higher weights, allowing them to have a greater impact on the calculation of the road complexity index.

[0040] For example, automatic emergency braking is a core function of a vehicle's active safety system. When the system detects that the time between the vehicle and a target ahead is below a safety threshold, and the driver fails to respond effectively to the warning, the automatic emergency braking system intervenes proactively, applying braking force to the vehicle through the electronic braking system to avoid a collision or mitigate its severity. Deceleration refers to the rate at which a vehicle's speed decreases during braking. High deceleration automatic emergency braking intervention indicates that the vehicle has encountered a sudden and highly dangerous collision risk scenario. Therefore, high deceleration automatic emergency braking is considered a high-severity safety event.

[0041] exist Figure 2 In this embodiment, the effectiveness of security event data is improved by denoising and assigning high weights.

[0042] In one embodiment of the present invention, the road complexity index is obtained by weighted summation of the triggering frequency and severity of safety events in the safety event data.

[0043] The triggering frequency of safety events includes one or more of the following: the triggering frequency of forward collision warning events, the number of interventions of automatic emergency braking events, the triggering frequency of lane departure warning, and the correction frequency of lane keeping assist.

[0044] The trigger frequency of forward collision warning events is the number of times the forward collision warning controller is triggered within a unit of time or unit of mileage within a preset geographical area. The intervention frequency of automatic emergency braking events is the cumulative number of times the automatic emergency braking controller system actively applies braking within a historical driving cycle. The trigger frequency of lane departure warning is the statistical value of the number of times the lane departure warning controller triggers a warning. The correction frequency of lane keeping assist is the statistical value of the number of times the lane keeping assist controller system actively applies steering torque to correct lane departure. All the above frequencies are based on unit of time or unit of mileage.

[0045] Severity includes the deceleration value of the automatic emergency braking event. The deceleration value is the peak braking deceleration reached by the vehicle during each automatic emergency braking controller intervention. The deceleration value is the core indicator that distinguishes between normal automatic emergency braking controller intervention and high-severity automatic emergency braking controller intervention; the higher the deceleration value, the higher the risk level of the event.

[0046] After determining the triggering frequency and severity of safety events in the safety event data, the Road Complexity Index (RCI) is calculated according to Formula 1.

[0047] RCI = W1 Freq FCW + W2 Freq LDW + W3 AvgSeverity AEB Formula 1

[0048] Freq FCW It is the trigger frequency of forward collision warning events, Freq LDW It refers to the trigger frequency of lane departure warning and / or the correction frequency of lane keeping assist, AvgSeverity. AEB It is the weighted average of deceleration severity for automatic emergency braking events, AvgSeverity. AEB The average value is obtained by weighting the deceleration values ​​of each automatic emergency braking controller event with severity weighting coefficients. W1, W2, and W3 are preset weighting coefficients.

[0049] A high road complexity index indicates a high number of pedestrians and confusing lane markings, necessitating a more conservative driving assistance style. A quantifiable road environment risk index can be established by integrating forward collision risk, lane departure risk, and emergency braking severity.

[0050] In one embodiment of the present invention, a driving style assertion index characterizing the driver's operating style is constructed based on the driver's driving operation data in non-assisted driving mode.

[0051] Vehicle pedal control parameters include the impact frequency of pedal operations. Impact is the rate of change of acceleration over time, reflecting the abruptness of the driver's operation of the accelerator and / or brake pedals. The impact frequency of pedal operations is the number of times the impact generated by the driver's pedal operations exceeds a preset threshold in non-assisted driving modes within a historical driving cycle. The impact frequency of pedal operations is a statistical value per unit time or unit mileage. A higher impact frequency indicates a more abrupt and decisive driver pedal operation, leaning towards an aggressive driving style.

[0052] Steering wheel control parameters include the frequency with which the driver corrects the steering wheel before lane keeping assist intervenes. Steering wheel control parameters refer to the number of times the driver manually corrects the steering wheel when the vehicle tends to deviate from its lane, but before lane keeping assist actively applies steering torque, within a historical driving cycle in non-assisted driving mode. Steering wheel control parameters are statistical values ​​per unit time or unit mileage. The correction frequency reflects the driver's sensitivity to vehicle deviation and the decisiveness of their intervention. A higher correction frequency indicates a more proactive and decisive driver control, leaning towards an aggressive driving style; conversely, a lower correction frequency indicates a greater reliance on driver assistance systems, leaning towards a conservative driving style.

[0053] Obtain the vehicle pedal control parameters and steering wheel control parameters, and calculate the driving style assertion index (DAI) according to Formula 2.

[0054] DAI = W4 Freq Jerk + W5 Freq LKA_PreCorrection Formula 2

[0055] Freq Jerk Freq is the impact frequency of vehicle pedal operation. LKA_PreCorrection This refers to the frequency at which the driver corrects the steering wheel before the lane keeping assist system intervenes. W4 and W5 are preset weighting coefficients.

[0056] A high driving style assertion index indicates that the driver prefers a more aggressive and decisive driving style. Conversely, a low driving style assertion index suggests that the driver prefers a more stable and conservative driving style. A quantifiable driving style assertion index is constructed by integrating vehicle pedal control parameters and steering wheel control parameters.

[0057] In one embodiment of the invention, comfort control parameters in the vehicle's driver assistance system are dynamically adjusted based on a road complexity index and a driving style assertion index. These comfort control parameters may include one or more of the following: following distance for adaptive cruise control, upper limit of acceleration / deceleration impact, and lane-changing aggression parameters.

[0058] The following distance in adaptive cruise control is the distance maintained between the vehicle and the vehicle in front when the adaptive cruise control system is following a target vehicle ahead. The following distance in adaptive cruise control is positively correlated with the road complexity index and negatively correlated with the driving style assertion index.

[0059] D follow = D base (1 + k1 RCI - k2 DAI) Formula 3

[0060] D follow It refers to the following distance in adaptive cruise control, D. base K1 and K2 are the baseline values ​​for following distance, and K1 and K2 are preset positive adjustment coefficients.

[0061] As shown in Formula 3, a higher road complexity index necessitates a longer following distance. This allows drivers more reaction time and braking distance in complex road environments such as dense pedestrian traffic and confusing lane markings, reducing the risk of rear-end collisions. Conversely, a higher driving style assertion index necessitates a shorter following distance to match the driver's preference for decisive and close following, reducing frequent lane-changing caused by excessive following distance.

[0062] The upper limit of acceleration / deceleration impact is the maximum allowable rate of change of acceleration when a driver assistance system performs acceleration or braking control. The upper limit of acceleration / deceleration impact is negatively correlated with the road complexity index and positively correlated with the driving style assertion index.

[0063] J max = J base (1-k3 RCI +k4 DAI) Formula 4

[0064] J max It is the upper limit of acceleration / deceleration impact, J base It is the baseline value for the upper limit of acceleration and deceleration impact, and k3 and k4 are preset positive adjustment coefficients.

[0065] The higher the road complexity index, the stricter the Jerk limit, to avoid discomfort to passengers or the risk of rear-end collisions caused by sudden acceleration and deceleration in complex road environments. The higher the driving style assertion index, the more relaxed the Jerk limit, to match the driver's preference for quick starts and decisive braking, making the acceleration and deceleration control of advanced driver assistance systems more in line with the driver's driving habits.

[0066] Lane change aggression parameters are comprehensive control parameters of the lane centering control system regarding lane change timing, lane change execution speed, and acceptable safety clearance when performing autonomous or assisted lane change functions. Lane change aggression parameters are positively correlated with the driving style assertion index.

[0067] When the driving style assertion index is high, a smaller safety clearance threshold and a faster lane change execution speed can be used to match the driver's decisive driving style; when the driving style assertion index is low, a conservative lane change strategy is adopted, and lane changes are only performed when there is sufficient safety clearance. The adjustment of the lane change aggression parameter reflects the adaptability of the advanced driver assistance system to the driver's personalized preferences.

[0068] See Figure 3 , Figure 3 This is a flowchart illustrating the process of obtaining the road complexity index corresponding to a geographical region according to an embodiment of the present invention. Specifically, it includes the following steps: S301. Based on the geographical location information, bind the preset road complexity index with the geographical area corresponding to the geographical location information.

[0069] To improve the accuracy of the road complexity index, it can be linked to corresponding geographical regions based on GPS data. For example, the road complexity index can be linked to a city or a specific route.

[0070] Based on the GPS coordinate information carried by the safety event data accumulated by vehicles during their historical driving cycles, the road complexity index is divided and bound to geospatial dimensions. Binding methods include: binding based on administrative regions and / or binding based on routes.

[0071] Safety incident data is clustered by city or district, and a corresponding road complexity index is calculated and assigned to each city or district. For example, in densely populated and chaotic city center areas, safety incidents occur more frequently, so a higher road complexity index is assigned to these areas; while in suburban areas with well-regulated roads and orderly traffic, a lower road complexity index is assigned.

[0072] Identify frequently traveled routes and assign independent road complexity indices to segments along those routes. For example, if a route passes near a school or construction area, the road complexity index for that segment is higher; while highway segments on the same route are assigned lower road complexity indices.

[0073] S302. If it is determined that a vehicle has entered a geographical area based on the geographical location information in the security event data, load the road complexity index corresponding to the geographical area.

[0074] During vehicle operation, the vehicle's current GPS coordinates are acquired in real time and compared with the boundary coordinates of the bound geographic area. When the vehicle enters the aforementioned geographic area, the road complexity index corresponding to the geographic area is loaded to adjust the comfort control parameters.

[0075] exist Figure 3 In one embodiment, the correspondence between geographical regions and road complexity indices is utilized to achieve automatic loading of the road complexity index, so as to accurately adjust the comfort control parameters of the driving assistance system.

[0076] See Figure 4 , Figure 4 This is a schematic diagram of the application of the road complexity index according to an embodiment of the present invention.

[0077] S401. Using a forward collision warning controller, automatic emergency braking controller, lane departure warning controller, or lane keeping assist controller, the system collects raw safety event data generated by the vehicle during its historical driving cycle, and obtains the vehicle's geographical coordinates via onboard GPS.

[0078] S402. Preprocess the collected raw event data to obtain security event data.

[0079] S403. Determine whether the vehicle's geographical coordinates fall within a preset geographical area. If they fall within the preset geographical area, execute S404; otherwise, execute S405.

[0080] S404. When a vehicle is located within a preset geographical area, obtain the road complexity index corresponding to that geographical area.

[0081] S405. When a vehicle is located outside a preset geographical area, the road complexity index is generated by integrating the triggering frequency and severity of safety events from safety event data.

[0082] S406. Based on the road complexity index and driving style assertion index, dynamically adjust the comfort control parameters in the vehicle's driver assistance system.

[0083] The above process enables the determination of comfort control parameters based on safety events.

[0084] See Figure 5 , Figure 5 This is a flowchart illustrating the adjustment of comfort control parameters according to an embodiment of the present invention. Specifically, it includes the following steps: S501. Compare each adjusted comfort control parameter value with the corresponding lower safety threshold and upper safety threshold.

[0085] Each comfort control parameter has a preset lower safety threshold and a preset upper safety threshold. The lower and upper safety thresholds can be preset based on vehicle performance and / or safety requirements. For example, for the following distance parameter of adaptive cruise control, the lower safety threshold can be set to 0.8 seconds and the upper safety threshold to 3.0 seconds.

[0086] S502. If the adjusted comfort control parameter value exceeds the lower safety threshold or the upper safety threshold, the comfort control parameter value is set to the lower safety threshold or the upper safety threshold to prevent the vehicle control behavior from exceeding the safety boundary.

[0087] It is necessary to ensure that the comfort control parameter values ​​always remain within the safe lower and upper threshold ranges. If the adjusted comfort control parameter value is less than the corresponding safe lower threshold, then the comfort control parameter is set to the safe lower threshold. If the adjusted comfort control parameter value is greater than the corresponding safe upper threshold, then the comfort control parameter is set to the safe upper threshold.

[0088] exist Figure 5 In the embodiments, by setting a lower safety threshold and a higher safety threshold, boundary checks are performed on the adjusted comfort control parameter values ​​to ensure that the control behavior of the assisted driving system is always within a safe and controllable range, regardless of changes in the road complexity index and driving style assertion index.

[0089] In one embodiment of the invention, the assisted driving type is displayed through a human-machine interface to inform the driver of the vehicle status. The assisted driving type is determined based on a road complexity index and a driving style assertion index. For example, the assisted driving type may include conservative, moderate, or aggressive.

[0090] Specifically, after determining the road complexity index and driving style assertion index for the current area, the applicable driving assistance type is determined. For example, when the road complexity index is higher than a first preset threshold, the driving assistance type is determined to be conservative, prioritizing driving safety in complex environments. When the road complexity index is lower than a second preset threshold and the driving style assertion index is higher than a third preset threshold, the driving assistance type is determined to be aggressive, adapting to driver preferences and improving traffic efficiency. When both the road complexity index and the driving style assertion index are in the middle range—that is, the road complexity index is lower than the first preset threshold and higher than the second preset threshold, and the driving style assertion index is lower than the third preset threshold—the driving assistance type is determined to be moderate.

[0091] After determining the type of driver assistance, the display is performed through the in-vehicle human-machine interface. The advanced driver assistance system interface on the instrument panel visually prompts the driver with the current assistance style. This visualization includes one or more of the following: text prompts, color-coded indicators, and graphic indicators.

[0092] Through the aforementioned visual feedback, drivers can perceive in real time the vehicle's response to changes in the environment and its own style.

[0093] See Figure 6 , Figure 6 This is a schematic diagram comparing assisted driving trajectories according to an embodiment of the present invention. Figure 6 The study exemplifies the impact of high and low road complexity indices on vehicle assisted driving trajectories and braking strategies under the same driving scenario.

[0094] The vehicle travels on an urban road segment that includes intersections and pedestrian-intensive areas. The starting point is S, point A is the pedestrian area, point C is the spatial intersection of the trajectories of vehicles with high and low road complexity indices, and point E is the ending point. The solid line trajectory SAB1CE represents the vehicle trajectory corresponding to the high road complexity index, and the dashed line trajectory SAB2CE represents the vehicle trajectory corresponding to the low road complexity index.

[0095] When the vehicle's current location is determined to have a high road complexity index based on historical safety event data, the driving environment is deemed to be of high risk, and a more conservative assisted driving strategy is adopted.

[0096] Regarding following distance, the adaptive cruise control sets the target following distance to a longer D_high value. Compared to the base following distance, D_high is numerically larger, allowing for a greater safe distance between the vehicle and the vehicle in front.

[0097] Regarding braking strategy, the braking trigger point is determined significantly earlier. For example... Figure 6 As shown in the diagram, the braking trigger point B1 under the high road complexity index occurs at an earlier moment, farther from the vehicle in front. This allows for more reaction time and braking distance to deal with unexpected situations.

[0098] Due to the high road complexity index, the acceleration and deceleration of vehicles are smoother, and the Jerk value is lower, which effectively avoids the discomfort caused by rapid acceleration or deceleration, and at the same time reduces the risk of loss of control due to aggressive operation in complex environments.

[0099] When the road complexity index of the area where the vehicle is currently located is calculated to be low, the driving environment is considered to be of low risk, and a relatively aggressive assisted driving strategy can be adopted to improve traffic efficiency.

[0100] Regarding following distance, the adaptive cruise control adjusts the target following distance to a shorter D_low value. Compared to D_high under a high road complexity index, D_low is significantly shorter, reducing the distance between the vehicle and the vehicle in front, which helps improve road traffic efficiency.

[0101] Regarding braking strategy, the braking trigger point is determined relatively late. For example... Figure 6 As shown, under the low road complexity index, the braking trigger point B2 is closer to the vehicle in front. This makes fuller use of the distance between the vehicle and the vehicle in front, delaying braking time while ensuring safety.

[0102] With a low road complexity index, acceleration and braking responses are more rapid, resulting in a more sporty assisted driving style.

[0103] A comparison of the entire trajectory from starting point S to ending point E reveals that the solid line trajectory generally exhibits earlier deceleration and a smoother speed change curve, reflecting a conservative and safe driving style. The dashed line trajectory, on the other hand, generally exhibits later braking and more decisive acceleration and lane-changing maneuvers, reflecting an efficient and aggressive driving style.

[0104] See Figure 7 , Figure 7 This is a schematic diagram of the architecture of a vehicle-assisted driving method according to an embodiment of the present invention. It comprises: a safety event data acquisition module 100, a driving style / environment analysis module 200, an advanced driver assistance system parameter dynamic mapping module 300, an advanced driver assistance system comfort control module 400, a vehicle actuator 500, and a human-machine interaction feedback module 600.

[0105] The vehicle bus and sensors, through CAN bus, radar, cameras and GPS and other sensors and communication interfaces, acquire real-time information on the vehicle's own status, surrounding environment perception and geographical location.

[0106] The safety event data acquisition module 100 collects one or more of the following types of safety events: forward collision warning trigger events, automatic emergency braking intervention events, and lane departure warning trigger events. The safety event data acquisition module 100 outputs raw event data.

[0107] The Driving Style / Environment Analysis Module 200 calculates the Road Complexity Index and Driving Style Assertion Index based on raw event data.

[0108] The Advanced Driver Assistance System Parameter Dynamic Mapping Module 300 calculates the comfort control parameters of the vehicle's driver assistance system based on the road complexity index and driving style assertion index.

[0109] The advanced driver assistance system comfort control module 400 generates vehicle control commands based on comfort control parameters and outputs them to the vehicle actuator 500 via the vehicle bus.

[0110] The vehicle actuator 500 performs corresponding physical actions based on the received vehicle control commands to achieve assisted driving of the vehicle.

[0111] The human-machine interaction feedback module 600 outputs and displays the type of assisted driving based on the road complexity index and driving style assertion index.

[0112] See Figure 8 , Figure 8 This is a schematic diagram of the main structure of a vehicle driver assistance device according to an embodiment of the present invention. The vehicle driver assistance device 800 can implement a vehicle driver assistance method. The vehicle driver assistance device specifically includes: The data acquisition module 801 is used to collect safety event data generated by the vehicle during its historical driving cycle. Module 802 is used to integrate the triggering frequency and severity of safety events in the safety event data to generate a road complexity index, and to construct a driving style assertion index that characterizes the driver's operating style based on the driver's driving operation data in non-assisted driving mode. The adjustment module 803 is used to dynamically adjust the comfort control parameters in the vehicle's driving assistance system based on the road complexity index and the driving style assertion index.

[0113] In one embodiment of the present invention, a module 802 is used to perform a weighted summation of the triggering frequency and severity of safety events in the safety event data to obtain the road complexity index; The triggering frequency of the safety events includes one or more of the following: the triggering frequency of forward collision warning events, the number of interventions of automatic emergency braking events, the triggering frequency of lane departure warning, and the correction frequency of lane keeping assist; The severity level includes: the severity-weighted average of the automatic emergency braking events.

[0114] In one embodiment of the present invention, a module 802 is configured to construct the driving style assertion index based on the vehicle pedal control parameters and steering wheel control parameters of the driver in non-assisted driving mode. The vehicle pedal control parameters include the impact frequency of vehicle pedal operation, and the steering wheel control parameters include the correction frequency of the driver correcting the steering wheel before the lane keeping assist system intervenes.

[0115] In one embodiment of the present invention, the comfort control parameters may include one or more of the following: following distance of adaptive cruise control, upper limit of acceleration / deceleration impact, and lane change aggression parameters; The following distance is positively correlated with the road complexity index, and the following distance is negatively correlated with the driving style assertion index. The upper limit of acceleration / deceleration impact is negatively correlated with the road complexity index, and the upper limit of acceleration / deceleration impact is positively correlated with the driving style assertion index; The lane-changing aggression parameter is positively correlated with the driving style assertion index.

[0116] In one embodiment of the present invention, the acquisition module 801 is used to bind a preset road complexity index to a geographical region corresponding to the geographical location information according to the geographical location information; Based on the geographical location information in the security event data, it is determined that the vehicle has entered the geographical area, and the road complexity index corresponding to the geographical area is loaded.

[0117] In one embodiment of the present invention, the adjustment module 803 is used to display the assisted driving type through a human-computer interaction interface. The assisted driving type is determined based on the road complexity index and the driving style assertion index. The assisted driving type includes conservative, moderate or aggressive.

[0118] In one embodiment of the present invention, the acquisition module 801 is used to acquire raw safety data generated by the vehicle during the historical driving cycle, and to perform noise reduction processing on the raw safety data to filter out false triggers and / or low confidence data. High-severity security events are assigned high weights to form the security event data.

[0119] In one embodiment of the present invention, the adjustment module 803 is used to compare each adjusted comfort control parameter value with the corresponding lower safety threshold and upper safety threshold. If the adjusted comfort control parameter value exceeds the lower safety threshold or the upper safety threshold, the comfort control parameter value is set to the lower safety threshold or the upper safety threshold to prevent the vehicle control behavior from exceeding the safety boundary.

[0120] The vehicle driver assistance device in this embodiment of the invention can be applied to vehicles.

[0121] Figure 9 An exemplary system architecture 900 is shown for a vehicle-assisted driving method or vehicle-assisted driving device to which embodiments of the present invention can be applied.

[0122] like Figure 9 As shown, the vehicle system architecture 900 may include various systems, such as a driving control system 901, a power system 902, a sensor system 903, a control system 904, a lane change assistance system 905, one or more peripheral devices 906, a power supply 907, a computer system 908, and a user interface 909. The vehicle-assisted driving method provided in this embodiment can be implemented through interaction with the aforementioned systems. Optionally, the vehicle system architecture 900 may include more or fewer systems, and each system may include multiple components. Furthermore, each system and component of the vehicle system architecture 900 can be interconnected via wired or wireless means.

[0123] The vehicle system architecture 900 includes a driving control system 901, which can be in a fully or partially automated driving mode. For example, the driving control system 901 can automatically control the vehicle's movement based on control signals or control commands without human interaction, interaction with external devices, or interaction with a robot driving the vehicle.

[0124] The powertrain 902 may include components that provide power to the vehicle. For example, the powertrain 902 may include an engine, an energy source, a transmission, wheels, tires, etc. The engine may be an internal combustion engine, an electric motor, an air-compressed engine, or other combinations of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air-compressed engine. The engine converts the energy source into mechanical energy to supply the transmission. Examples of energy sources may include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other electrical sources. The energy source may also provide energy to other systems in the vehicle. Furthermore, the transmission may include a gearbox, a differential, a drive shaft, and a clutch, etc.

[0125] The sensor system 903 may include sensors for sensing the vehicle's surrounding environment (such as sensors for detecting the presence of obstacles) and pressure sensors for sensing the presence of passengers in the seats. Examples include a positioning system (which may be a Global Positioning System (GPS), BeiDou Navigation Satellite System, or other positioning systems), radar, a laser rangefinder, an inertial measurement unit (IMU), and cameras. The positioning system can be used to determine the vehicle's geographical location. The IMU is used to sense changes in the vehicle's position and orientation based on inertial acceleration. In one embodiment, the IMU may be a combination of an accelerometer and a gyroscope. The radar can use radio signals to sense objects in the vehicle's surrounding environment. In some embodiments, in addition to sensing objects, the radar can also be used to sense the speed and / or direction of travel of objects.

[0126] To detect environmental information and objects outside the vehicle, cameras can be configured at appropriate locations on the vehicle's exterior. For example, to acquire environmental images of the vehicle's sides, a camera can be mounted on the side mirror. The camera can be a still or video camera.

[0127] The control system 904 may include software systems for implementing vehicle driving control, such as systems for analyzing the vehicle's surrounding environment, pretensioning seat belts, route planning, obstacle avoidance, and image analysis. The control system 904 may also include hardware systems such as an accelerator, steering wheel system, seat belt system, airbag system, and peripheral devices (such as projection equipment and displays). Furthermore, the control system 904 may add or replace components other than those shown and described. Alternatively, some of the components shown above may be reduced.

[0128] In addition, the control system 904 can also interact with external sensors, other autonomous driving devices, other computer systems, or users via peripheral devices 906. Peripheral devices 906 may include wireless communication systems, on-board computers, microphones and / or speakers, cameras, and projectors, etc.

[0129] In some embodiments, peripheral device 906 provides a means for user interaction with the control system 904 via a user interface. For example, an onboard computer may provide information to a user of the vehicle. The user interface may also operate the onboard computer to receive user input. The onboard computer may be operated via a touchscreen. In other cases, peripheral device may provide a means for communicating with other devices located within the vehicle. For example, a microphone may receive audio (e.g., voice commands or other audio input) from a user of the control system. Similarly, a speaker may output audio to a user of the control system.

[0130] Wireless communication systems can communicate wirelessly with one or more devices, either directly or via a communication network. For example, wireless communication systems can use networks such as cellular networks, WiFi, and wireless local area networks (WLANs), or they can use infrared links, Bluetooth, or ZigBee to communicate directly with devices. Other wireless protocols include those used in various autonomous driving communication systems.

[0131] The power source 907 can provide power to various components of the vehicle. The power source 907 can be a rechargeable lithium-ion battery or a lead-acid battery.

[0132] Computer system 908 may include at least one processor that executes instructions stored in a non-transitory computer-readable medium such as memory. Computer system 908 provides the aforementioned control system with execution code for implementing vehicle-assisted driving.

[0133] The processor can be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor can be a special-purpose device such as an application-specific integrated circuit (ASIC) or other hardware-based processor. Those skilled in the art will understand that the processor, computer, or memory can actually include multiple processors, computers, or memories that may or may not be stored in the same physical housing. For example, memory can be a hard disk drive or other storage media located in a housing different from that of a computer. Therefore, references to processors or computers will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Unlike using a single processor to perform the steps described herein, some components, such as steering and deceleration components, may each have their own processor that performs only determinations related to the component's specific function.

[0134] User interface 909 is used to provide information to or receive information from users of the vehicle. Optionally, user interface 909 may include one or more input / output devices within a set of peripheral devices 906, such as wireless communication systems, on-board computers, microphones, and speakers.

[0135] It should be understood that the components described above are merely an example. In actual applications, components in the various modules or systems mentioned above may be added or removed as needed. Figure 9 This should not be construed as a limitation on the embodiments of this application.

[0136] The following is for reference. Figure 10 It shows a schematic diagram of the structure of a computer system 1000 suitable for implementing a terminal device of the present invention. Figure 10 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0137] like Figure 10 As shown, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1002 or programs loaded from storage section 1008 into random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for the operation of the system 1000. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0138] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 1010 as needed so that computer programs read from it can be installed into storage section 1008 as needed.

[0139] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention 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 communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs the functions defined above in the system of this invention.

[0140] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0141] 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 the present invention. 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 a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may 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.

[0142] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor may be described as including a data acquisition module, a data establishment module, and an adjustment module. The names of these modules do not necessarily limit the module itself; for example, a data acquisition module may also be described as "for acquiring safety event data generated by a vehicle during its historical driving cycle."

[0143] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to include: collecting safety event data generated by the vehicle during historical driving cycles; fusing the trigger frequency and severity of safety events in the safety event data to generate a road complexity index; constructing a driving style assertion index characterizing the driver's operating style based on driving operation data of the driver in non-assisted driving mode; and dynamically adjusting comfort control parameters in the vehicle's driver assistance system based on the road complexity index and the driving style assertion index.

[0144] According to the technical solution of this invention, safety event data generated by a vehicle during its historical driving cycle are collected, and then a road complexity index characterizing the road environment and a driving style assertion index characterizing the driver's operating style are generated. By adjusting comfort control parameters using the road complexity index and the driving style assertion index, the adaptability of vehicle assisted driving can be improved while maintaining road safety, thus meeting the diverse needs of vehicle driving scenarios.

[0145] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention. It should be noted that the acquisition, storage, and application of user personal information involved in the technical solutions of this disclosure comply with relevant laws and regulations and do not violate public order and good morals.

Claims

1. A vehicle assisted driving method, characterized in that, include: Collect safety event data generated by the vehicle during its historical driving cycle; By integrating the triggering frequency and severity of safety events in the aforementioned safety event data, a road complexity index is generated; and a driving style assertion index characterizing the driver's operating style is constructed based on the driver's driving operation data in non-assisted driving mode. The comfort control parameters in the vehicle's driver assistance system are dynamically adjusted based on the road complexity index and the driving style assertion index.

2. The vehicle assisted driving method according to claim 1, characterized in that, The process of integrating the triggering frequency and severity of safety events from the safety event data to generate a road complexity index includes: The road complexity index is obtained by weighted summation of the trigger frequency and severity of safety events in the safety event data; The triggering frequency of the safety events includes one or more of the following: the triggering frequency of forward collision warning events, the number of interventions of automatic emergency braking events, the triggering frequency of lane departure warning, and the correction frequency of lane keeping assist. The severity level includes: the severity-weighted average of the intervention of the automatic emergency braking event.

3. The vehicle assisted driving method according to claim 1, characterized in that, The process of constructing a driving style assertion index characterizing the driver's operating style based on the driver's driving operation data in non-assisted driving mode includes: The driving style assertion index is constructed based on the vehicle pedal control parameters and steering wheel control parameters of the driver in non-assisted driving mode; wherein, the vehicle pedal control parameters include the impact frequency of vehicle pedal operation, and the steering wheel control parameters include the correction frequency of the driver correcting the steering wheel before the lane keeping assist system intervenes.

4. The vehicle assisted driving method according to claim 1, characterized in that, The comfort control parameters include one or more of the following: following distance of adaptive cruise control, upper limit of acceleration and deceleration impact, and lane change aggression parameters; The following distance is positively correlated with the road complexity index, and the following distance is negatively correlated with the driving style assertion index. The upper limit of acceleration / deceleration impact is negatively correlated with the road complexity index, and the upper limit of acceleration / deceleration impact is positively correlated with the driving style assertion index; The lane-changing aggression parameter is positively correlated with the driving style assertion index.

5. The vehicle assisted driving method according to claim 1, characterized in that, Before dynamically adjusting the comfort control parameters of the vehicle's driver assistance system, the method further includes: Based on the geographic location information, a preset road complexity index is bound to the geographic region corresponding to the geographic location information; Based on the geographical location information in the security event data, it is determined that the vehicle has entered the geographical area, and the road complexity index corresponding to the geographical area is loaded.

6. The vehicle assisted driving method according to claim 1, characterized in that, The method further includes: The assisted driving type is displayed through a human-computer interaction interface. The assisted driving type is determined based on the road complexity index and the driving style assertion index. The assisted driving type includes conservative, moderate, or aggressive.

7. The vehicle assisted driving method according to claim 1, characterized in that, The collected safety event data generated by the vehicle during its historical driving cycle includes: Collect raw safety data generated by the vehicle during its historical driving cycle, and perform noise reduction processing on the raw safety data to filter out false triggers and / or low-confidence data. High-severity security events are assigned high weights to form the security event data.

8. The vehicle assisted driving method according to claim 1, characterized in that, The dynamic adjustment of the comfort control parameters of the vehicle's driver assistance system includes: Compare each adjusted comfort control parameter value with the corresponding lower and upper safety thresholds; If the adjusted comfort control parameter value exceeds the lower safety threshold or the upper safety threshold, the comfort control parameter value is set to the lower safety threshold or the upper safety threshold to prevent the vehicle control behavior from exceeding the safety boundary.

9. A vehicle driver assistance device, characterized in that, include: The data acquisition module is used to collect safety event data generated by the vehicle during its historical driving cycle. A module is established to integrate the triggering frequency and severity of safety events in the safety event data to generate a road complexity index, and to construct a driving style assertion index characterizing the driver's operating style based on the driver's driving operation data in non-assisted driving mode. The adjustment module is used to dynamically adjust the comfort control parameters in the vehicle's driver assistance system based on the road complexity index and the driving style assertion index.

10. A vehicle, characterized in that, Includes the vehicle driver assistance device as described in claim 9.

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