Passenger car auxiliary driving self-adaptive cruise control method and system

Abstract

The invention provides a passenger car auxiliary driving self-adaptive cruise control method and system, and belongs to the technical field of auxiliary driving. The method comprises the steps that perception information around a vehicle and vehicle state information are acquired; main target locking is carried out based on sensing information and target screening, and cruising is carried out by adopting different self-adaptive control algorithms according to whether a main target exists or not; if the main target exists, a double-ring-PID acceleration model is adopted for control, a first acceleration is output, the first acceleration is filtered and then matched to different main scene control torques and accelerator opening degrees, and the whole vehicle is controlled to be accelerated or decelerated so as to achieve the state of self-adaptive adjustment of the vehicle following speed; and if the main target does not exist, the sectional PID acceleration model is adopted for controlling and outputting a second acceleration, the control torque and the accelerator opening degree, so that the vehicle speed is controlled to reach the set target speed. According to the invention, through the double-ring-proportion correction variable integral-PID dynamic adjustment advantages, rapid and accurate control of the vehicle speed is realized.

Description

Technical Field

[0001] This invention belongs to the field of driver assistance technology, and particularly relates to a method and system for adaptive cruise control in passenger vehicles. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Adaptive cruise control (ACC) technology, as a core function of driver assistance vehicles, plays a significant role in improving bus driving safety and reducing driver fatigue. Bus adaptive cruise control systems, in particular, are gradually being adopted in highway passenger transport and urban public transport due to their high speed tracking accuracy, flexible following distance adjustment, and strong adaptability to complex road conditions. However, the widespread adoption of this system still faces objective challenges, especially in dynamic road conditions. When encountering acceleration or deceleration from the vehicle ahead, changes in road gradient, or crosswinds, traditional control algorithms are prone to problems such as delayed speed response and large oscillations, which not only affect passenger comfort but may also reduce driving safety.

[0004] In existing technologies, the adaptive cruise control speed control of buses suffers from unstable speed control and inaccurate distance control, resulting in vehicle vibration and affecting the driving experience. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, this invention proposes a method and system for adaptive cruise control in passenger vehicles. It employs a control algorithm based on a dual-loop proportional-corrected variable integral-PID controller. Based on real-vehicle performance tests, the intrinsic parameter relationships between acceleration *a* and requested torque *Tor*, and between accelerator pedal opening and torque under different road conditions and gears are determined. By sensing and fusing road condition information and vehicle CAN information from multiple inputs, multi-scenario road conditions are constructed. Combined with the dynamic adjustment advantages of the dual-loop proportional-corrected variable integral-PID controller, rapid and precise control of vehicle speed is achieved.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: In a first aspect, the present invention discloses a method for adaptive cruise control in a passenger vehicle, comprising: Acquire sensory information about the vehicle's surroundings and vehicle status information; Based on the perceived information and target selection, the main target is locked, and different adaptive control algorithms are used for cruising depending on whether the main target exists; If there is a primary target, the adaptive cruise control algorithm uses a dual-loop-PID acceleration model for control and outputs the first acceleration. After filtering the first acceleration, it is matched to different control torques and throttle openings for different primary scenarios to control the vehicle's acceleration or deceleration in order to achieve adaptive adjustment of following speed. If there is no primary target, the adaptive cruise control algorithm uses a segmented PID acceleration model for control and outputs a second acceleration. After filtering the second acceleration, the torque and throttle opening are controlled to control the vehicle speed to the set target speed.

[0007] Further technical solutions, the different main scenarios include: The first scenario is an acceleration zone, where the distance error is greater than the first set distance. The second scenario is the comfort zone, where the distance error is greater than the second preset distance but does not exceed the first preset distance; The third scenario is a relatively dangerous area where the distance error is less than the second set distance and the longitudinal relative speed is greater than or equal to the preset relative speed. The fourth scenario is a dangerous area where the distance error is less than the second set distance and the longitudinal relative speed is less than the preset relative speed.

[0008] A further technical solution is that the dual-loop-PID acceleration model is specifically as follows: In the first scenario, a distance-proportional correction-variable integral PID is used to follow the vehicle. The distance error is obtained based on the difference between the longitudinal relative distance and the set distance. The weighting coefficient is determined based on the distance error and applied to the integral and proportional PID components to drive the vehicle to accelerate. In the second scenario, a speed-proportional correction-variable integral PID is used to follow the vehicle. The speed error is obtained based on the difference between the speed of the vehicle and the speed of the main target vehicle. The weighting coefficient is determined based on the speed error and applied to the integral and proportional parts of the PID to drive the vehicle to accelerate or decelerate. In the third scenario, after relinquishing control for several seconds, the torque and accelerator pedal opening are reset to zero for several seconds. In the fourth scenario, the acceleration is calculated according to the distance-proportional correction-variable integral PID, the velocity-proportional correction-variable integral PID and the uniformly accelerated displacement formula, and the minimum value is obtained by comparing the three accelerations. The deceleration control is then performed based on the minimum value.

[0009] A further technical solution involves obtaining the distance error based on the difference between the longitudinal relative distance and the set distance, and determining the weighting coefficient based on the distance error, specifically as follows: Based on the longitudinal relative distance D x and the set distance D set The difference is used to calculate the distance error. ; The weighting coefficients are determined based on the distance error, when |err1| < M d The weighting coefficient is f id =f1; when M d ≤|err1|<Nd The weighting coefficient is f id =(N d -|err1|) / (N d -M d When |err1|≥N d The weighting coefficient is f id =f2, err1 is the distance error, f id M is the distance weighting coefficient. d and N d To set the range for distance error, M d <N d f1 and f2 are fixed coefficients set by the controller.

[0010] A further technical solution involves applying the weighting coefficients to the integral and proportional components of the PID controller, specifically as follows: Integral coefficients: K1=K id ×f id In the formula, K1 is the coefficient of the integral element in the first scenario; K id K in the distance PID i Parameter; f id These are the weighting coefficients; Meanwhile, the proportional coefficient is given by the following formula, and the proportional coefficient is continuously modified and iterated with the variable integral coefficient: K2=K pd +|K id -K id ×f id | Where K2 is the proportional element coefficient of the first scenario; K pd The proportional coefficient K is the distance from the PID controller. p .

[0011] A further technical solution is that the perception information includes whether the target exists, the target mass, the lateral relative distance, the longitudinal relative distance, whether they are in the same lane, and the target vehicle speed; the vehicle status information includes the cruise speed setting and the vehicle speed.

[0012] A further technical solution is that the target screening is specifically as follows: when a target exists, the target vehicle has a high detection quality level, the target is in the current lane or not in the same lane, and the longitudinal distance is less than a preset distance, it is determined to be the main target vehicle.

[0013] Secondly, the present invention discloses a passenger vehicle driver assistance adaptive cruise control system, comprising: The data acquisition module is configured to acquire perception information about the vehicle's surroundings and vehicle status information. The adaptive control module is configured to: lock onto the primary target based on the perception information and target selection, and use different adaptive control algorithms for cruising depending on whether the primary target exists; If there is a primary target, the adaptive cruise control algorithm uses a dual-loop-PID acceleration model for control and outputs the first acceleration. After filtering the first acceleration, it is matched to different control torques and throttle openings for different primary scenarios to control the vehicle's acceleration or deceleration in order to achieve adaptive adjustment of following speed. If there is no primary target, the adaptive cruise control algorithm uses a segmented PID acceleration model for control and outputs a second acceleration. After filtering the second acceleration, the torque and throttle opening are controlled to control the vehicle speed to the set target speed.

[0014] Thirdly, the present invention discloses an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when run by the processor, complete the steps of the above-mentioned adaptive cruise control method for assisted driving of a bus.

[0015] Fourthly, the present invention discloses a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the aforementioned adaptive cruise control method for assisted driving of a passenger vehicle.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method for adaptive cruise control in passenger vehicles. It utilizes sensor fusion information and vehicle CAN information as inputs, combining traditional PID algorithms with distance and speed data. A modified proportional-integral (PI) approach is added for dynamic adjustment and optimization. For larger errors, the integral effect is reduced, enhancing the proportional effect; for smaller errors, the integral effect is enhanced, leading to a more stable proportional effect. The output of the PID model is then filtered second-order and distributed to four defined scenarios to achieve precise speed control. This algorithm effectively improves system response speed, reduces speed oscillations under complex road conditions, enhances system robustness and anti-interference capabilities, and ultimately achieves precise and stable speed control during adaptive cruise control in passenger vehicles.

[0017] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0019] Figure 1This is a flowchart of the adaptive cruise control method for assisted driving of a passenger vehicle as described in Embodiment 1 of the present invention. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] Example 1 In one or more embodiments, a method for adaptive cruise control in a passenger vehicle is disclosed, such as... Figure 1 As shown, it includes: Acquire sensory information about the vehicle's surroundings and the vehicle's status.

[0024] The perceived information includes the presence of the target, the detection quality of the target vehicle, the lateral relative distance, the longitudinal relative distance, whether they are in the same lane, and the target vehicle's speed. The vehicle status information includes the cruise speed setting (as the vehicle's maximum speed limit) and the current instrument speed (i.e., the vehicle's own speed). The perceived information can be obtained through cameras and machine vision algorithms. The target vehicle detection quality characterizes the reliability of the algorithm's detection results.

[0025] The control system hardware includes cameras, radar, and driver assistance controllers, while peripheral hardware includes components such as ECUs and EBS. The driver assistance controllers obtain perception information and vehicle status information from cameras, radar, and the vehicle's CAN bus.

[0026] The main target is locked based on perception information and target selection, and different adaptive control algorithms are used for cruise depending on whether the main target exists; If there is a primary target, the adaptive cruise control algorithm uses a dual-loop-PID acceleration model for control and outputs the first acceleration. After filtering the first acceleration, it is matched to different control torques and throttle openings for different primary scenarios to control the vehicle's acceleration or deceleration in order to achieve adaptive adjustment of following speed. If there is no primary target, the adaptive cruise control algorithm uses a segmented PID acceleration model for control and outputs a second acceleration. After filtering the second acceleration, the torque and throttle opening are controlled to control the vehicle speed to the set target speed.

[0027] The specific process of target selection through data fusion is as follows: By obtaining perception fusion data, including whether a target exists, its quality, lateral relative distance, longitudinal relative distance, and whether it is in the same lane, the vehicle ahead is selected as the primary target vehicle based on this information. In this embodiment, the target selection method is as follows: if the following conditions are met: a target exists, the target vehicle detection quality level is high, the target is in the current lane or not in the same lane, and the longitudinal distance is less than a preset distance, then it is determined to be the primary target vehicle. This determination process can be implemented using filtering, delay algorithm models, etc.

[0028] In this embodiment, real vehicle performance tests are conducted to determine the intrinsic relationship between vehicle acceleration 'a' and demand torque 'Tor' under different road conditions and in different gears, as well as the relationship between the accelerator pedal opening signal 'aPed' and demand torque 'Tor'. Adaptive cruise control with a primary target uses a dual-loop variable integral PID control algorithm with proportional correction. Cruise control without a primary target uses a speed-segmented PID control algorithm.

[0029] Specifically, the real-vehicle performance test includes: first, fixing the accelerator pedal opening virtual signal variable aPed, and keeping the vehicle in the same gear throughout all test phases. Using the vehicle's engine torque MAP, the actual torque is divided into n segments of required torque (Tor1, Tor2, ..., Tor...). n The required torque is sent to the drive engine, allowing the vehicle to accelerate from 0 km / h to its maximum speed limit V. max The system synchronously records the time it takes for the vehicle to reach its maximum speed under various torque ranges. Based on the speed calculation formula, it fits an expression for the relationship between acceleration and required torque at each gear, forming a two-dimensional chart. The expression for acceleration and required torque is as follows: Tor = a * A0 + A1 In the formula, Tor is the required torque; a is the acceleration; and A0 and A1 are the fitting parameters.

[0030] Furthermore, the driver was instructed to drive normally, accelerating the vehicle from 0 km / h to its maximum speed limit, V. max Furthermore, at each gear, the vehicle was driven with a steady throttle for a period of time (where steady throttle means the driver gradually accelerates from zero speed to maximum speed, and during this period, after each gear, the throttle opening is kept stable until a steady state is reached, and the throttle pedal opening value at that gear is obtained). The actual throttle pedal opening signal and required torque data (Tor) during the driving process were collected. The relationship between throttle pedal opening and required torque was analyzed, and a two-dimensional graph was generated. The relationship between throttle pedal opening and required torque is as follows: aPed=Tor*A2+A3 In the formula, aPed is the accelerator pedal opening; Tor is the required torque; and A2 and A3 are fitting parameters.

[0031] In this embodiment, in the main scenario, different road conditions are divided into four scenarios according to the distance error and the longitudinal relative speed. The calculation of the distance error is as follows: D error = D x - D set In the formula, D error is the distance error; D x is the longitudinal relative distance between the vehicle and the target; D set is the set distance.

[0032] The four scenarios include: The first scenario is the acceleration area, where the distance error is greater than the first set distance, that is, D error > D 1； The second scenario is the comfortable area, where the distance error is greater than the second set distance and does not exceed the first set distance, that is, D2 < D error <= D 1； The third scenario is the relatively dangerous area, where the distance error is less than the second set distance and the longitudinal relative speed is greater than or equal to the preset relative speed, that is, D error < D2 and ΔV mainx >= V (the preset relative speed threshold V > 0); The fourth scenario is the dangerous area, where the distance error is less than the second set distance and the longitudinal relative speed is less than the preset relative speed, that is, D error < D2 and ΔV mainx <V.

[0033] This embodiment adopts a dual-loop - PID acceleration model to control the adaptive cruise with a main target. The dual-loop - PID acceleration model is a dual-loop - proportional correction variable integral - PID control algorithm. Different control algorithms are matched according to the scenarios, including: In the first scenario (acceleration area), distance - type - proportional correction - variable integral PID is used for following the vehicle. Specifically: Step S101: Calculate the distance error x according to the difference between the longitudinal relative distance D set and the set distance D .

[0034] Step S102: Determine the weight coefficient according to the distance error. When |err1| < M d , the weight coefficient is f id = f1; when M d ≤ |err1| < N d , the weight coefficient is f id = (N d - |err1|) / (N d - Md When |err1|≥N d The weighting coefficient is f id =f2, where M d and N d To set the range for distance error, M d <N d , is a constant threshold set by the controller; f id f1 and f2 are the final distance weighting coefficients; f1 and f2 are fixed coefficients set by the controller. Different weighting coefficients are set according to the distance error err1 under different conditions to improve the PID response speed and gradually introduce integral control.

[0035] Step S103: Apply weighting coefficients to the integral and proportional components of the PID controller to obtain the integral component coefficients: K1=K id ×f id In the formula, K1 is the coefficient of the integral element in the first scenario; K id K in the distance PID i Parameter; f id This is the distance weighting coefficient.

[0036] Meanwhile, the proportional coefficient is as follows, and the proportional coefficient is continuously modified and iterated with the variable integral coefficient; K2=K pd +|K id -K id ×f id | K2 is the proportional coefficient for the first scenario; K pd The proportional coefficient K is the distance from the PID controller. p .

[0037] Step S104: Continuously adjust K in the PID during the following process. pd K id K dd The sum of the three parameter values ​​yields the first acceleration 'a'. d After performing a second-order filter on the acceleration, the relationship expression between torque, acceleration, and throttle pedal opening is input to obtain torque and throttle opening parameters. Torque and throttle opening requests are then sent to drive the vehicle to accelerate. The distance feedback is continuously used to optimize the speed response in order to close the following distance.

[0038] In the second scenario (comfort zone), speed-based proportional correction-variable integral PID is used for following the vehicle. Specifically: Step S201: Calculate the speed error based on the difference between the vehicle's speed and the target vehicle's speed.

[0039] In the formula, V spd V is the vehicle's speed. main The target vehicle speed is 0; err2 is the speed error.

[0040] Step S202: Determine the weighting coefficient based on the speed error. When |err2| < M v The weighting coefficient is f iv =f3; when M v ≤|err2|<N v The weighting coefficient is f iv =(N v -|err2|) / (N v -M v ); when |err2|≥N v The weighting coefficient is f iv =f4;M v and N v All are speed error setting ranges, M v <N v f1 is the constant threshold set by the controller; f3 and f4 are fixed coefficients set by the controller. Different weighting coefficients are set according to the magnitude of the error err2 under different conditions to improve the response speed of the PID and gradually introduce integral control.

[0041] It should be understood that in this embodiment, the subscript d represents distance-related parameters and the subscript v represents speed-related parameters.

[0042] Step S203: Apply weighting coefficients to the integral and proportional components of the PID controller to obtain the integral component coefficients: K3=K id ×f iv In the formula, f iv K1 represents the speed weighting coefficient; K2 represents the integral coefficient for the second scenario.

[0043] Meanwhile, the proportional coefficient is as follows, and the proportional coefficient is continuously modified and iterated with the variable integral coefficient. K4=K pv +|K iv -K iv ×f iv | In the formula, K4 is the proportional element coefficient in the second scenario; K iv K is the coefficient of the reintegral element in the speed PID controller. i ;K pv The coefficient K of the proportional element in the speed PID controller is... p .

[0044] Step S204: Continuously adjust K during the following process pv Kiv K dv The sum of the three parameter values ​​yields the second acceleration 'a'. v After performing a second-order filter on the acceleration, the relationship expression between torque, acceleration, and throttle pedal opening is input to obtain torque and throttle opening parameters. Torque and throttle opening requests are then issued to drive the vehicle to decelerate or accelerate. The speed response is continuously optimized through speed feedback to achieve the following speed and maintain following distance.

[0045] In the third scenario (relatively dangerous area), control is abandoned for t1 seconds, and the torque and accelerator pedal opening are reset to zero for t2 seconds. t1 and t2 are threshold values ​​set for the controller. For example, if control is abandoned and active control is initiated for 5 seconds, the torque and accelerator pedal opening are slowly reset to zero within 1 second, thereby achieving a smooth transition.

[0046] In the fourth scenario (dangerous area), deceleration control will be implemented. The second acceleration 'a' will be calculated using a velocity-proportional correction-variable integral PID controller. v Distance-proportional correction-variable integral PID calculation of the first acceleration a d , and the formula for calculating acceleration a using uniformly accelerated displacement. s The three accelerations are compared to find the minimum value. This minimum value is then processed by second-order filtering and sent to the EBS (Electronic Braking System) to control the vehicle's braking and achieve deceleration. The formula for uniformly accelerated displacement is: relative velocity squared = 2 * acceleration * distance.

[0047] Preferably, when the system deviation of distance error err1 or speed error err2 is large (i.e., exceeds the preset range, different coefficients can be used to control the introduction of the integral element), the integral action is reduced and the proportional action is enhanced, while when the deviation is small, the integral effect is enhanced and the proportional effect tends to be stable.

[0048] In this embodiment, in a targetless scenario, a segmented PID acceleration model is adopted. Based on the acceleration performance obtained from vehicle testing, segmented PID control will be used during targetless cruise control according to the acceleration performance. When the vehicle speed is ≤V0, low-speed PID control is used. When the vehicle speed is >V0, high-speed PID control is used, where V0 is a preset segment threshold.

[0049] This embodiment uses perception information fusion and target selection to lock onto the primary target. When a primary target is detected, adaptive cruise control is initiated, employing a dual-loop-PID acceleration model for control. The output acceleration is filtered and matched to the specific scenario, controlling torque and throttle opening to ultimately accelerate or decelerate the vehicle to achieve adaptive speed adjustment while following the target vehicle. When there is no primary target, a segmented PID acceleration model is used, filtering the output acceleration and substituting the acceleration parameters into the torque and throttle opening relationship expression to ultimately control the vehicle speed to the set target speed. The adaptive cruise control function includes two modes: following a target vehicle and cruise control without a target.

[0050] Example: After the driver activates the ACC function, assuming the cruise speed Vset is set to 65 km / h, this speed will be recorded by the ADAS controller and used as the maximum speed limit for the ACC function. If a target exists in the current lane, the driver assistance controller fuses data from the camera and radar to determine the longitudinal relative distance D. x (CAN message data acquisition) If the target distance is less than 200m, the target quality is relatively high, and the target is in a semi-fusion or semi-fusion state, the target will be locked and treated as the primary target. If the vehicle is not actively taken over by external forces, it will automatically enter the adaptive cruise control phase. The vehicle speed V displayed on the instrument panel will be acquired through the vehicle's CAN network. spd (Unit: km / h) According to the industry standard GA / T 1773.3-2021 "Safe and Civilized Operation Specifications for Motor Vehicle Drivers Part 3: Driving of Large and Medium-sized Passenger and Freight Vehicles", the longitudinal safety distance between a bus and the vehicle in front should be greater than the distance the vehicle travels in 4 seconds. The set distance (unit: m) at different speeds can be calculated, i.e., D. set =(V spd / 3.6)×4. Assuming the current speedometer reading is 85 km / h, then the target distance D set =95m); based on the longitudinal relative distance D between the two vehicles x Distance D set Deviation D error =D x -D set Four adaptive cruise control following scenarios are defined. Scenario 1 (acceleration zone): D error >D1 (D1>0 is the value); Second scenario (comfort zone): D2<D error <=D1 (D2≥0 and D2<D1); Third scenario (relatively dangerous area): D error ≤D2 and longitudinal relative velocity ΔV mainx >=V (V>0). Fourth Scene (Danger Zone): D error ≤D2 and longitudinal relative velocity ΔV mainx<V. In all four scenarios, the following vehicle speed remains below the set speed of 65 km / h, and the following vehicle can be freely switched according to road conditions. In any state, the vehicle will automatically disengage when the driver takes over; all overtaking and lane changing situations meet the following four scenarios.

[0051] For the first scenario: The distance in the first scenario is relatively far, falling within the safe following zone. At this time, if the main target vehicle's speed is V... main For a speed of 50 km / h (less than the set speed of 65 km / h), a distance-proportional-correction variable integral PID controller is used for following the vehicle. Specifically, the distance error is calculated as follows: when |err1| < Md, the weighting coefficient is f. id =1; when M d ≤|err1|<N d The weighting coefficient is f id =(N d -|err1|) / (N d -M d When |err1|≥N d The weighting coefficient is f id =0; Apply these weighting coefficients to the integral stage, and the final coefficients of the integral stage are: K id ×f id Meanwhile, the proportional element coefficient is K. pd +|K id -K id ×f id The proportional coefficient is continuously adjusted and iterated based on the variable integral coefficient. During the vehicle-following process, K in the PID controller is continuously adjusted. pd K id K dd The acceleration *a* is obtained by summing the three parameter values. d After applying a second-order filter to the acceleration, the expression relating torque, acceleration, and throttle opening is input to send torque and throttle opening requests, driving the vehicle to accelerate. Continuous distance feedback and rolling optimization are performed to improve the following distance. If the vehicle's speed reaches 50 km / h and it remains in the first scenario, the following speed can be continuously increased to a maximum of 65 km / h. If the vehicle's speed reaches 65 km / h and it remains in the first scenario, the speed is maintained at 65 km / h for cruising and following until the first scenario is no longer met. The maximum following speed is 65 km / h. If the primary target V... main The following speed is 72 km / h (greater than the set speed of 65 km / h). Distance-proportional correction variable integral PID is used for following the vehicle, with the following speed up to 65 km / h. The PID closed-loop adjustment continues until the first scenario is no longer met.

[0052] For the second scenario: The distance in the second scenario is relatively moderate, falling within the comfort zone of following the vehicle. If the speed of the main target vehicle is V... mainFor a speed of 50 km / h (less than the set speed of 65 km / h), a speed-proportional-correction variable integral PID controller is used for following the vehicle. Specifically, the speed error is calculated as follows: when |err2| < M... v The weighting coefficient is f iv =1; when M v ≤|err2|<N v The weighting coefficient is f iv =(N v -|err2|) / (N v -M v When |err2|≥N, the weighting coefficient is f. iv =0; Apply these weighting coefficients to the integral stage, and the final coefficients of the integral stage are: K id ×f iv Meanwhile, the proportional element coefficient is K. pv +|K iv -K iv ×f iv The proportional coefficient is continuously modified and iterated with the variable integral coefficient, and K is continuously adjusted during the following process. pv K iv K dv The acceleration *a* is obtained by summing the three parameter values. v After applying a second-order filter to the acceleration, the expression relating torque, acceleration, and throttle opening is input to issue torque and throttle opening requests, driving the vehicle to decelerate or accelerate. Continuous speed feedback and rolling optimization are performed to achieve the following speed and maintain a following distance, ensuring the vehicle remains in the second scenario. If the primary target V... main The speed is 72 km / h (greater than the set speed of 65 km / h). At this time, the set speed of 65 km / h is used as the main target speed for following the vehicle. The speed-proportional correction variable integral PID is used for following the vehicle. The following speed is up to 65 km / h until the second scenario is no longer met.

[0053] For the third scenario: The distance in the third scenario is relatively short, but the speed of the vehicle in front is faster than my vehicle, which is a relatively dangerous zone for following. In this case, I relinquish control of my vehicle for t1 seconds, do not accelerate, and the torque and throttle opening decrease to zero at t2 seconds (t2 < t1). If the scenario is still satisfied in the next cycle, the strategy for this scenario continues. If it is not satisfied, other scenario strategies are executed. For example, after relinquishing control, if the vehicle in front continues to be faster than my vehicle, the distance will be increased to proceed to the second scenario; if the vehicle in front suddenly decelerates, satisfying ΔV... mainx If V is less than V, then the game proceeds to the fourth scene, where V... main Main target velocity, ΔV mainx This represents the longitudinal relative velocity.

[0054] For the fourth scenario: In the fourth scenario, the distance is relatively short and the vehicle in front is moving slower than my vehicle, which is a dangerous following zone. At this time, the acceleration av is calculated using the speed-proportional-corrected variable integral PID, the acceleration ad is calculated using the distance-proportional-corrected variable integral PID, and the acceleration as = 1 / 2 × ΔV is calculated using the uniformly accelerated displacement formula for relative motion scenarios. 2 `mainx(Dx-10)` compares the three accelerations to find the minimum value, performs second-order filtering, and then sends it to the EBS to control vehicle braking and achieve deceleration. Based on the real-time status of the vehicle ahead, the vehicle executes other scenarios or decelerates to a stop-and-go state. If the conditions are met again, the vehicle resumes following. For example, in this scenario, if the target vehicle V... main Continuously lower than our vehicle V spd The vehicle, Vspd, will continue to decelerate until it comes to a complete stop. During this process, the torque and pedal opening are reset to zero. In this scenario, if the target vehicle V... main Gradually increase, continuously satisfying V main ≤V spd The vehicle decelerates until the distance meets the requirements of the third and second scenarios; if the target vehicle's speed continues to meet V... main >V spd And ΔV mainx If the distance is greater than or equal to V (V > 0), the vehicle executes the third scenario strategy until the distance increases to satisfy the second scenario.

[0055] The dual-loop proportional-corrected variable integral PID controller reduces the integral action and enhances the proportional action when the system deviation is large, while gradually increasing the integral effect when the deviation is small, and the proportional action tends to stabilize. This achieves the goal of fast response and dynamic adjustment.

[0056] After the driver activates the ACC function, assuming the set cruise speed is 65 km / h, this speed will be considered the maximum speed limit for the ACC function. If there is no target in the current lane or a primary target exists but the information about the primary target is poor, and the driver does not take over the vehicle, the vehicle will enter the cruise control phase. Based on vehicle performance tests to obtain acceleration performance in each gear, the vehicle speed is controlled in segments. For example, if 0 < V... spd ≤V1, execute low-speed PID control, set parameter K pl K il K dl If V1 <V spd ≤V2, execute high-speed PID control, the high-speed PID control parameter is set to K. ph K ih K dhThe system continuously adjusts the three PID parameters at each stage, sums them to obtain the acceleration 'a', performs a second-order filter on the acceleration, and inputs the expression relating torque, acceleration, and throttle opening to issue torque and throttle opening requests to drive the vehicle to decelerate or accelerate. It continuously performs speed feedback and rolling optimization to quickly respond and reach the set vehicle speed.

[0057] Example 2 In one or more embodiments, a bus driver assistance adaptive cruise control system is disclosed, comprising: The data acquisition module is configured to acquire perception information about the vehicle's surroundings and vehicle status information. The adaptive control module is configured to: lock onto the primary target based on the perception information and target selection, and use different adaptive control algorithms for cruising depending on whether the primary target exists; If there is a primary target, the adaptive cruise control algorithm uses a dual-loop-PID acceleration model for control and outputs the first acceleration. After filtering the first acceleration, it is matched to different control torques and throttle openings for different primary scenarios to control the vehicle's acceleration or deceleration in order to achieve adaptive adjustment of following speed. If there is no primary target, the adaptive cruise control algorithm uses a segmented PID acceleration model for control and outputs a second acceleration. After filtering the second acceleration, the torque and throttle opening are controlled to control the vehicle speed to the set target speed.

[0058] Example 3 This embodiment provides an electronic device, including a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, they complete the steps of the above-described adaptive cruise control method for assisted driving of a passenger vehicle.

[0059] Example 4 This embodiment provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, complete the steps of the above-described adaptive cruise control method for assisted driving of a passenger vehicle.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0064] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for adaptive cruise control in passenger vehicle driver assistance systems, characterized in that, include: Acquire sensory information about the vehicle's surroundings and vehicle status information; Based on the perceived information and target selection, the main target is locked, and different adaptive control algorithms are used for cruising depending on whether the main target exists; If there is a primary target, the adaptive cruise control algorithm uses a dual-loop-PID acceleration model for control and outputs the first acceleration. After filtering the first acceleration, it is matched to different control torques and throttle openings for different primary scenarios to control the vehicle's acceleration or deceleration in order to achieve adaptive adjustment of following speed. If there is no primary target, the adaptive cruise control algorithm uses a segmented PID acceleration model for control and outputs a second acceleration. After filtering the second acceleration, the torque and throttle opening are controlled to control the vehicle speed to the set target speed.

2. The adaptive cruise control method for assisted driving of a passenger vehicle as described in claim 1, characterized in that, The different main scenarios include: The first scenario is an acceleration zone, where the distance error is greater than the first set distance. The second scenario is the comfort zone, where the distance error is greater than the second preset distance but does not exceed the first preset distance; The third scenario is a relatively dangerous area where the distance error is less than the second set distance and the longitudinal relative speed is greater than or equal to the preset relative speed. The fourth scenario is a dangerous area where the distance error is less than the second set distance and the longitudinal relative speed is less than the preset relative speed.

3. The adaptive cruise control method for assisted driving of a passenger vehicle as described in claim 1, characterized in that, The dual-loop-PID acceleration model is specifically as follows: In the first scenario, a distance-proportional correction-variable integral PID is used to follow the vehicle. The distance error is obtained based on the difference between the longitudinal relative distance and the set distance. The weighting coefficient is determined based on the distance error and applied to the integral and proportional PID components to drive the vehicle to accelerate. In the second scenario, a speed-proportional correction-variable integral PID is used to follow the vehicle. The speed error is obtained based on the difference between the speed of the vehicle and the speed of the main target vehicle. The weighting coefficient is determined based on the speed error and applied to the integral and proportional parts of the PID to drive the vehicle to accelerate or decelerate. In the third scenario, after relinquishing control for several seconds, the torque and accelerator pedal opening are reset to zero for several seconds. In the fourth scenario, the acceleration is calculated according to the distance-proportional correction-variable integral PID, the velocity-proportional correction-variable integral PID and the uniformly accelerated displacement formula, and the minimum value is obtained by comparing the three accelerations. The deceleration control is then performed based on the minimum value.

4. The adaptive cruise control method for assisted driving of a passenger vehicle as described in claim 3, characterized in that, The distance error is obtained based on the difference between the longitudinal relative distance and the set distance, and the weighting coefficient is determined based on the distance error, specifically as follows: Based on the longitudinal relative distance D x and the set distance D set The difference is used to calculate the distance error. ; The weighting coefficients are determined based on the distance error, when |err1| < M d The weighting coefficient is f id =f1; when M d ≤|err1|<N d The weighting coefficient is f id =(N d -|err1|) / (N d -M d When |err1|≥N d The weighting coefficient is f id =f2, err1 is the distance error, f id M is the distance weighting coefficient. d and N d To set the range for distance error, M d <N d f1 and f2 are fixed coefficients set by the controller.

5. The adaptive cruise control method for assisted driving of a passenger vehicle as described in claim 4, characterized in that, The weighting coefficients are applied to the integral and proportional components of the PID controller, specifically as follows: Integral coefficients: K1=K id ×f id In the formula, K1 is the coefficient of the integral element in the first scenario; K id K in the distance PID i Parameter; f id These are the weighting coefficients; Meanwhile, the proportional coefficient is given by the following formula, and the proportional coefficient is continuously modified and iterated with the variable integral coefficient: K2=K pd +|K id -K id ×f id | Where K2 is the proportional element coefficient of the first scenario; K pd The proportional coefficient K is the distance from the PID controller. p .

6. The adaptive cruise control method for assisted driving of a passenger vehicle as described in claim 1, characterized in that, The perception information includes whether the target exists, the target's mass, the lateral relative distance, the longitudinal relative distance, whether they are in the same lane, and the target vehicle's speed; the vehicle status information includes the cruise speed setting and the vehicle's speed.

7. The adaptive cruise control method for passenger vehicle assisted driving as described in claim 1, characterized in that, The target selection process is as follows: if a target exists, the target vehicle has a high detection quality level, the target is in the current lane or not in the same lane, and the longitudinal distance is less than a preset distance, then the vehicle is identified as the primary target vehicle.

8. A passenger vehicle driver assistance adaptive cruise control system, characterized in that, include: The data acquisition module is configured to acquire perception information about the vehicle's surroundings and vehicle status information. The adaptive control module is configured to: lock onto the primary target based on the perception information and target selection, and use different adaptive control algorithms for cruising depending on whether the primary target exists; If there is a primary target, the adaptive cruise control algorithm uses a dual-loop-PID acceleration model for control and outputs the first acceleration. After filtering the first acceleration, it is matched to different control torques and throttle openings for different primary scenarios to control the vehicle's acceleration or deceleration in order to achieve adaptive adjustment of following speed. If there is no primary target, the adaptive cruise control algorithm uses a segmented PID acceleration model for control and outputs a second acceleration. After filtering the second acceleration, the torque and throttle opening are controlled to control the vehicle speed to the set target speed.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the bus assisted driving adaptive cruise control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the bus assisted driving adaptive cruise control method according to any one of claims 1-7.

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