Vehicle lane changing method based on electronic rearview mirror

Abstract

The invention discloses a vehicle lane changing method based on an electronic rearview mirror, and belongs to the technical field of automobile auxiliary driving, and the method comprises the following steps: 1, obtaining basic image information of the electronic rearview mirror; 2, environment information of the vehicle is collected; 3, lane changing environment requirements of the vehicle are judged; 4, calculating the preferential lane changing scene of the vehicle, and assisting the vehicle to carry out preferential lane changing; step 5, optimizing an objective function value; and step 6, particle swarm optimization is carried out. According to the invention, a multi-sensor fusion technology of radar, camera and the like is adopted, comprehensive monitoring of the surrounding environment during lane changing is realized, data acquired by sensors are processed through the algorithm of the patent, information of the surrounding environment of the vehicle is analyzed, and when the system detects potential danger, a driver is reminded through light, sound and the like, so that the driving safety is improved. The environment condition around the vehicle is monitored in real time, and a driver is helped to make a more intelligent driving lane changing decision.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile assisted driving, and in particular to a vehicle lane changing method based on an electronic rearview mirror. Background Art

[0002] The current vehicle electronic rearview mirror, also known as the vehicle camera, is a new technology that replaces the traditional rearview mirror. It captures images through a camera installed at the rear or side of the vehicle and displays the images on the in-vehicle screen in real time. This technology was originally designed to solve the driving safety hazards caused by blind spots and blocked vision of traditional rearview mirrors. Electronic rearview mirrors can provide a wider field of view, reduce blind spots, and improve driving safety at night and in bad weather conditions. In addition, since the camera can be replaced or adjusted in angle, the accuracy of monitoring the vehicle's surrounding environment is improved.

[0003] The electronic rearview mirror system mainly includes a camera, a display, a processor and a data transmission system. The camera is responsible for collecting images, the display displays images, the processor processes images, and the data transmission system is responsible for transmitting images. Electronic rearview mirror technology first appeared in high-end cars. With the maturity of technology and the reduction of costs, it has gradually spread to mid- and low-end models. Many countries and regions have begun to implement or are considering the implementation of relevant regulations on electronic rearview mirrors, requiring newly produced vehicles to be equipped with electronic rearview mirrors.

[0004] Regarding the above-mentioned related technologies, the applicant found that by installing radars, cameras and sensors on the rearview mirrors of the vehicle, the environmental conditions around the vehicle can be monitored in real time. However, how to process and use the various data obtained to help the driver make more informed driving and lane changing decisions has become a difficult problem that needs to be solved urgently. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a vehicle lane changing method based on an electronic rearview mirror. By means of a camera and a sensor installed on the vehicle rearview mirror, the environmental conditions around the vehicle are monitored in real time to help the driver make more informed driving and lane changing decisions.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] A vehicle lane changing method based on an electronic rearview mirror,

[0008] Step 1: Acquire basic image information of the electronic rearview mirror, including vehicles, pedestrians, obstacles, road conditions, and the vehicle's own position and driving trajectory;

[0009] Step 2: Vehicle environmental information collection;

[0010] Step 3: Determine the vehicle's lane-changing environment requirements. The lane-changing environment monitoring factors of the electronic rearview mirror include vehicle speed, camera field of view, and sensor detection distance.

[0011] Step 4: Calculate the optimal lane-changing scenario of the vehicle to assist the vehicle in performing the optimal lane-changing;

[0012] Step 5: Optimize the objective function value;

[0013] Step 6: Particle swarm optimization algorithm is used to find the optimal solution. The vehicle intending to change lanes changes lanes according to the particle swarm optimization algorithm.

[0014] Furthermore, v and a in the obstacle situation formula represent the initial vehicle velocity and acceleration obtained by the CAN bus of the vehicle with lane change intention;

[0015] s 1(2) 、s 02 、v 1(2) 、v 02 Indicates the distance and initial velocity between the target vehicle identified by the electronic camera of the vehicle with lane change intention and the vehicles in front and behind in the adjacent lanes on the left and right sides and the vehicle in front of the same lane;

[0016] l1 and l2 represent the safe distance between the vehicle intending to change lanes and the vehicles behind and in front of the adjacent lanes on the left and right sides;

[0017]

[0018] t' represents the reaction time of the driver of the following vehicle. In this case, the vehicle intending to change lanes chooses to change lanes to achieve a higher driving speed in order to obtain a better driving environment.

[0019] Furthermore, in step 4, the lane-changing intention vehicle detection formula is as follows:

[0020]

[0021]

[0022]

[0023] Furthermore, in the lane-changing intention vehicle detection formula, v and a respectively represent the vehicle initial velocity and lane-changing acceleration obtained by the CAN bus of the lane-changing intention vehicle itself;

[0024] v l1(2) 、a l1(2) 、s l1(2) , l l1(2) They respectively represent the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the left adjacent lane identified by the electronic camera of the vehicle intending to change lanes;

[0025] v r1(2) 、a r1(2) 、s r1(2) , l r1(2) They respectively represent the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the right adjacent lane identified by the electronic camera of the vehicle intending to change lanes;

[0026] v 02 、a 02 They respectively represent the initial velocity and acceleration of the vehicle ahead as recognized by the electronic camera of the vehicle intending to change lanes;

[0027] t r With t l The time required for the vehicle to change lanes to the right and left, respectively;

[0028] λ represents the distance parameter of different situations measured by the vehicle's own CAN bus.

[0029] Furthermore, in step 5, the vehicle lane change situation is the following objective function:

[0030]

[0031] α≥0, β∈R, λ∈[0,λ'].

[0032] Furthermore, v0, v, v0', and v' in the objective function represent the initial and final velocities of the target vehicle and the adjacent lane vehicles obtained by the CAN bus of the lane-changing intention vehicle itself;

[0033] s and t represent the distance between the target vehicle and the adjacent vehicle and the time required for the vehicle to change lanes;

[0034] λ represents the distance parameter of different situations measured by the vehicle's own CAN bus;

[0035] When the vehicle in the adjacent lane is behind the target vehicle, β takes a positive value, otherwise it takes a negative value.

[0036] Furthermore, in step 1, the clarity and stability of the captured image are optimized through an image processing algorithm, and then the image signal captured by the camera is transmitted to a display screen inside the vehicle.

[0037] Furthermore, in step 2, the vehicle's own CAN bus is used to obtain the vehicle's initial velocity and lane change acceleration v, a; the vehicle's electronic rearview mirror camera identifies the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the left adjacent lane, expressed as v l1(2) 、a l1(2) 、s l1(2) , l l1(2) ;

[0038] The initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the right adjacent lane are expressed as v r1(2) 、a r1(2) 、s r1(2) , l r1(2) ;

[0039] And the initial velocity and acceleration of the vehicle in front are expressed as v 02 、a 02 ;

[0040] The time required for the vehicle to change lanes to the right and left is t r ,t l express;

[0041] The distance parameter measured by the vehicle's own CAN bus in different situations is represented by λ.

[0042] Furthermore, in step 6, the optimization steps of the particle swarm algorithm are:

[0043] S1: Optimization variables and constraints. The variables are the initial and final speeds of the vehicle v0, v, v0', v'. The constraints refer to the first, second and third level target constraints.

[0044] S2: Determine the encoding method and use the real number encoding method;

[0045] S3: Determine the individual evaluation method, and the fitness function is the objective function;

[0046] S4: Design initial particles;

[0047] S5: Determine the particle swarm algorithm operation parameters.

[0048] In summary, compared with the prior art, the above technical solution has the following beneficial effects:

[0049] (1) The method of the present invention uses multi-sensor fusion technology such as radar and camera to achieve comprehensive monitoring of the surrounding environment when changing lanes. The data obtained by the sensor is processed through the algorithm of the present invention to analyze the environmental information around the vehicle, including vehicles, pedestrians, traffic signs, etc. The sensor, data processing software and control system are integrated into the rearview mirror system to ensure accurate and timely feedback of information. At the same time, multiple operation interfaces such as touch and voice are designed so that the driver can easily view the monitoring information. When the system detects potential danger, it reminds the driver through lights, sounds, etc., monitors the environmental conditions around the vehicle in real time, and helps the driver make more informed driving and lane changing decisions;

[0050] (2) The method of the present invention is an intelligent assisted driving technology that monitors the environment around the vehicle in real time through cameras and sensors installed on the rearview mirror of the vehicle, helping the driver to change lanes more safely and conveniently. This technology can detect the lane change environment, blind spots, side and rear vehicles, pedestrians, etc., and provide visual and sound prompts to help the driver make more intelligent driving decisions;

[0051] (3) The method of the present invention can enhance driving safety by real-time monitoring of the vehicle's surroundings, especially when changing lanes, to detect blind spots or potential dangers in advance and reduce the occurrence of traffic accidents;

[0052] (4) The method of the present invention can improve driving convenience, making it easier for the driver to observe the situation behind the vehicle, especially in narrow spaces or complex traffic environments, which helps to improve driving efficiency and comfort;

[0053] (5) The method of the present invention can effectively prevent malicious lane cutting. When a vehicle changes lanes, the environmental monitoring system can promptly detect illegal behaviors of other vehicles, such as malicious lane cutting, and help the driver to respond quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of demand determination of a lane-changing environment in an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of analyzing the preferred lane changing situation of a vehicle in an embodiment of the present invention;

[0056] Figure 3 Schematic diagram of the optimization process of the particle swarm algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The principles and features of the present invention are described below in conjunction with all the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0058] The embodiment of the present invention discloses a vehicle lane changing method based on an electronic rearview mirror.

[0059] A vehicle lane changing method based on an electronic rearview mirror, the main steps are as follows:

[0060] Step 1: Obtain basic image information of the electronic rearview mirror. Use camera imaging technology to obtain dynamic image information such as vehicles, pedestrians, obstacles, road conditions, and the vehicle's own position and driving trajectory, etc., and optimize the collected images through image processing algorithms to provide clear and stable visual effects. Then, the image signal captured by the camera is transmitted to the display screen inside the vehicle through signal transmission technology.

[0061] Step 2: Vehicle environmental information collection. The vehicle's own CAN bus is used to obtain the vehicle's initial velocity and lane change acceleration v, a. The camera of the vehicle's electronic rearview mirror identifies the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the left adjacent lane, expressed as v l1(2) 、a l1(2) 、s l1(2) , l l1(2) The initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the right adjacent lane are expressed as v r1(2) 、a r1(2) 、s r1(2) , l r1(2) , and the initial velocity and acceleration of the vehicle in front are expressed as v 02 、a 02 The time required for the vehicle to change lanes to the right and left is t r ,t l Indicates that the distance parameters of different situations measured by the vehicle's own CAN bus are represented by λ (if the parameter range is exceeded, the lane change situation is not considered).

[0062] Step 3: Determine the vehicle's lane-changing environment requirements. The specific formula for monitoring the lane-changing environment of the electronic rearview mirror may involve multiple factors, including vehicle speed, camera field of view, sensor detection distance, etc.

[0063] The simplified formula considering the obstacles of the vehicles in front and behind the adjacent lanes of the vehicle intending to change lanes and the vehicles in front of the same lane is:

[0064]

[0065] The above formula also satisfies:

[0066]

[0067] In the obstacle situation formula, v and a represent the initial velocity and acceleration of the vehicle obtained by the CAN bus of the vehicle intending to change lanes, and s 1(2) 、s 02 、v 1(2) 、v 02 It indicates the distance and initial speed between the target vehicle identified by the electronic camera of the vehicle intending to change lanes and the vehicle behind (in front) in the left and right adjacent lanes and the vehicle in front of the same lane. l1 and l2 indicate the safe distance between the vehicle intending to change lanes and the vehicle behind and in front of the left and right adjacent lanes.

[0068] (t' represents the reaction time of the driver of the following vehicle).

[0069] In this case, the vehicle intending to change lanes chooses to change lanes to achieve a higher driving speed in order to obtain a better driving environment. Figure 1 shown.

[0070] Step 4: Analyze and calculate the optimal lane-changing scenario of the vehicle. The electronic rearview mirror can detect the situation around the vehicle through built-in sensors and cameras, helping the driver to have a more comprehensive understanding of the surrounding traffic conditions, thereby assisting the vehicle in optimal lane-changing;

[0071] The lane-changing intention vehicle detection formula is simplified as follows:

[0072]

[0073]

[0074]

[0075] Where v and a represent the initial vehicle velocity and lane change acceleration obtained by the CAN bus of the vehicle intending to change lanes, respectively. l1(2) 、a l1(2) 、s l1(2) , l l1(2) They represent the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the left adjacent lane identified by the electronic camera of the vehicle with lane-changing intention, v r1(2) 、a r1(2) 、s r1(2) , l r1(2) They represent the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the right adjacent lane identified by the electronic camera of the vehicle with lane change intention, v 02 、a 02 They represent the initial velocity and acceleration of the vehicle ahead as recognized by the electronic camera of the vehicle with lane change intention, t r With t l They represent the time required for the vehicle to change lanes to the right and to the left respectively, and λ represents the distance parameter of different situations measured by the vehicle's own CAN bus (if it exceeds the parameter range, the lane change situation will not be considered).

[0076] The process of electronic rearview mirror detection vehicle preferential lane change is compared for details. Figure 2 shown.

[0077] In the comparison of the above situations, the first-level objective function, i.e., formula (2-1), must be satisfied at the same time, otherwise the original driving state is maintained. In addition, if the second-level objective function, i.e., formula (2-2), is satisfied, the left lane is selected; if the third-level objective function, i.e., formula (2-3), is satisfied, the right lane is selected; if both the second-level and third-level objective functions, i.e., formulas (2-2) and (2-3), are satisfied, further comparison is made. r With t l :If t r <t lThen choose to change lane to the right. If t r >t l Then choose to change lane to the left. If t r =t l You can change lanes left or right as long as you obey traffic regulations.

[0078] Step 5: Numerical optimization of the objective function. The lane changing situation of the vehicle can be simplified to the following objective function:

[0079]

[0080] where α ≥ 0, β ∈ R, λ ∈ [0,λ'].

[0081] The v0, v, v0', and v' of the objective function represent the initial and final speeds of the target vehicle and the adjacent lane vehicle obtained by the CAN bus of the lane-changing vehicle, s and t represent the distance between the target vehicle and the adjacent vehicle and the time required for the vehicle to change lanes, and λ represents the distance parameter of different situations measured by the CAN bus of the vehicle itself (if it exceeds the parameter range, the lane change situation is not considered). When the adjacent lane vehicle is behind the target vehicle, β takes a positive value, otherwise it takes a negative value.

[0082] Step 6: Particle swarm optimization algorithm is used to find the optimal solution. The vehicle intending to change lanes changes lanes according to the optimal solution found by the particle swarm optimization algorithm. The steps are as follows:

[0083] S1: Optimization variables and constraints. The variables are the initial and final speeds of the vehicle v0, v, v0', v'. The constraints refer to the first, second and third level target constraints.

[0084] S2: Determine the encoding method and use the real number encoding method;

[0085] S3: Determine the individual evaluation method, the fitness function is the objective function, that is, formula (3-1);

[0086] S4: Design initial particles;

[0087] S5: Determine the particle swarm algorithm operation parameters.

[0088] Detailed information about the illustrated process Figure 3 shown.

[0089] The implementation principle of a vehicle lane changing method based on an electronic rearview mirror in an embodiment of the present invention is as follows:

[0090] A vehicle safe lane changing method based on an electronic rearview mirror adopts multi-sensor fusion technology such as radar and camera to realize comprehensive monitoring of the surrounding environment when changing lanes. The data obtained by the sensor is processed through the patented algorithm to analyze the environmental information around the vehicle, including vehicles, pedestrians, traffic signs, etc. The sensor, data processing software and control system are integrated into the rearview mirror system to ensure accurate and timely feedback of information. At the same time, multiple operation interfaces such as touch and voice are designed so that the driver can easily view the monitoring information. When the system detects potential danger, the driver is reminded by light, sound and other means.

[0091] The method of the present invention is also an intelligent assisted driving technology. Through the camera and sensors installed on the vehicle's rearview mirror, the environmental conditions around the vehicle are monitored in real time to help the driver change lanes more safely and conveniently. This technology can detect the lane changing environment, blind spots, side and rear vehicles, pedestrians, etc., provide visual and sound prompts, and help the driver make more informed driving decisions.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle lane changing method based on an electronic rearview mirror, characterized in that: Step 1: Acquire basic image information of the electronic rearview mirror, including vehicles, pedestrians, obstacles, road conditions, and the vehicle's own position and driving trajectory; Step 2: Vehicle environmental information collection; Step 3: Determine the vehicle's lane-changing environment requirements. The lane-changing environment monitoring factors of the electronic rearview mirror include vehicle speed, camera field of view, and sensor detection distance. Step 4: Calculate the optimal lane-changing scenario of the vehicle to assist the vehicle in performing the optimal lane-changing; Step 5: Optimize the objective function value; Step 6: Particle swarm optimization algorithm is used to find the optimal solution. The vehicle intending to change lanes changes lanes according to the particle swarm optimization algorithm.

2. The vehicle lane changing method based on an electronic rearview mirror according to claim 1, characterized in that: In step 3, the obstacle situation formula for the front and rear vehicles in the adjacent lanes on the left and right sides of the lane-changing vehicle and the front vehicle in the same lane is: The above formula also satisfies:

3. The vehicle lane changing method based on an electronic rearview mirror according to claim 2, characterized in that: In the obstacle situation formula, v and a represent the initial vehicle velocity and acceleration obtained by the CAN bus of the vehicle intending to change lanes; s 1(2) 、s 02 、v 1(2) 、v 02 Indicates the distance and initial velocity between the target vehicle identified by the electronic camera of the vehicle with lane change intention and the vehicles in front and behind in the adjacent lanes on the left and right sides and the vehicle in front of the same lane; l1 and l2 represent the safe distance between the vehicle intending to change lanes and the vehicles behind and in front of the adjacent lanes on the left and right sides; t' represents the reaction time of the driver of the following vehicle. In this case, the vehicle intending to change lanes chooses to change lanes to achieve a higher driving speed in order to obtain a better driving environment.

4. The vehicle lane changing method based on an electronic rearview mirror according to claim 1, characterized in that: In step 4, the lane-changing intention vehicle detection formula is as follows:

5. The vehicle lane changing method based on an electronic rearview mirror according to claim 4, characterized in that: In the lane-changing intention vehicle detection formula, v and a represent the vehicle initial velocity and lane-changing acceleration obtained by the CAN bus of the lane-changing intention vehicle, respectively; v l1(2) 、a l1(2) 、s l1(2) , l l1(2) They respectively represent the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the left adjacent lane identified by the electronic camera of the vehicle intending to change lanes; v r1(2) 、a r1(2) 、s r1(2) , l r1(2) They respectively represent the initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the right adjacent lane identified by the electronic camera of the vehicle intending to change lanes; v 02 、a 02 They respectively represent the initial velocity and acceleration of the vehicle ahead as recognized by the electronic camera of the vehicle intending to change lanes; t r With t l The time required for the vehicle to change lanes to the right and left respectively; λ represents the distance parameter of different situations measured by the vehicle's own CAN bus.

6. The vehicle lane changing method based on an electronic rearview mirror according to claim 1, characterized in that: In step 5, the vehicle lane change situation is the following objective function: α≥0, β∈R, λ∈[0,λ'].

7. The vehicle lane changing method based on an electronic rearview mirror according to claim 6, characterized in that: v0, v, v0', and v' in the objective function represent the initial and final velocities of the target vehicle and the adjacent lane vehicles obtained by the CAN bus of the lane-changing vehicle. s and t represent the distance between the target vehicle and the adjacent vehicle and the time required for the vehicle to change lanes; λ represents the distance parameter of different situations measured by the vehicle's own CAN bus; When the vehicle in the adjacent lane is behind the target vehicle, β takes a positive value, otherwise it takes a negative value.

8. The vehicle lane changing method based on an electronic rearview mirror according to claim 1, characterized in that: In step 1, the clarity and stability of the captured image are optimized through image processing algorithms, and then the image signal captured by the camera is transmitted to the display screen inside the vehicle.

9. The vehicle lane changing method based on an electronic rearview mirror according to claim 1, characterized in that: In step 2, the vehicle's own CAN bus is used to obtain the vehicle's initial velocity and lane change acceleration v, a; the vehicle's electronic rearview mirror camera identifies the vehicle's initial velocity, acceleration, distance to the target vehicle, and safety distance in the left adjacent lane, expressed as v l1(2) 、a l1(2) 、s l1(2) , l l1(2) ; The initial velocity, acceleration, distance to the target vehicle, and safety distance of the vehicle behind (in front of) the right adjacent lane are expressed as v r1(2) 、a r1(2) 、s r1(2) , l r1(2) ; And the initial velocity and acceleration of the vehicle in front are expressed as v 02 、a 02 ; The time required for the vehicle to change lanes to the right and left is t r ,t l express; The distance parameter measured by the vehicle's own CAN bus in different situations is represented by λ.

10. The vehicle lane changing method based on an electronic rearview mirror according to claim 1, characterized in that: In step 6, the optimization steps of the particle swarm algorithm are: S1: Optimization variables and constraints. The variables are the initial and final speeds of the vehicle v0, v, v0', v'. The constraints refer to the first, second and third level target constraints. S2: Determine the encoding method and use the real number encoding method; S3: Determine the individual evaluation method, and the fitness function is the objective function; S4: Design initial particles; S5: Determine the particle swarm algorithm operation parameters.

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