Roadside sensing unit optimization deployment method under partial vehicle-road cooperation condition

Abstract

The invention discloses a roadside sensing unit optimization deployment method under partial vehicle-road cooperation conditions, and belongs to the technical field of roadside sensing unit optimization deployment, and the method comprises the steps: building traffic scenes, and obtaining a true value probability occupation grid of each traffic scene through multi-time-step continuous simulation; carrying out sensing parametric modeling on the roadside sensing unit and the intelligent networked automobile, establishing a ray tracing algorithm considering the shielding effect, and obtaining an observable probability occupation grid under a vehicle-road collaborative sensing condition through the ray tracing algorithm considering the shielding effect; a cross entropy of a true value probability occupancy grid and an observable probability occupancy grid is used as a traffic flow monitoring-oriented vehicle-road collaborative perception substitution evaluation index, and optimization deployment is carried out on a roadside perception unit; a specific complete technical framework is provided for introducing POG into the field of RPU optimization layout, and the problem that in the prior art, influences on ICV are insufficiently considered when RPU optimization layout modeling is carried out is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized deployment of roadside perception units, and specifically to a method for optimized deployment of roadside perception units under partial vehicle-road cooperative conditions. Background Art

[0002] Intelligent connected vehicles (ICVs) are an important part of future road traffic flows. Compared with traditional human-driven cars, ICVs can actively perceive traffic scenes and share information with other ICVs or intelligent roadside facilities through wireless communication technology. At present, related research on traffic risk identification, vehicle-level control, etc. in the field of intelligent networking or vehicle-road collaboration is based on the fact that the fine-grained motion data of individual traffic in the entire road domain can be accurately obtained through vehicle-road collaboration technology. However, it takes a long period of development to achieve full sample acquisition of traffic flow trajectories, during which the roadside perception system will play an important role. Specifically, the existing road infrastructure needs to deploy roadside perception units (RPUs) to enhance roadside perception capabilities, thereby making up for the blind spots of ICV vehicle-side perception and effectively improving the accuracy of traffic scene perception.

[0003] Thanks to wireless communication technology, existing studies have found that ICV and RPU can improve the accuracy of traffic data collection through the complementarity of perception field of view (FOV). However, in some vehicle-road collaboration development stages where ICV penetration is low, the number of RPUs and ICVs in traffic scenarios is limited. In this context, the dynamic occlusion problem of moving vehicles will still significantly affect the perception performance of vehicle-road collaboration. Specifically, moving vehicles as obstacles will cause perception blind spots for ICV or RPU, resulting in the perception system missing traffic individuals, which in turn affects the normal operation of the vehicle-road collaboration system (such as risk monitoring).

[0004] The existing technologies for optimizing the deployment of RPUs at a microscopic spatial scale can be divided into two categories: those for static scenes and those based on direct simulation. Technical solutions for static scenes account for the majority of existing technologies. The main problem is that they only consider the perception blind spots caused by static obstacles (such as building facades) on the RPU, without considering any traffic individuals. However, mixed traffic scenes are mostly on straight sections of roads, and the impact of roadside obstacles is relatively small. The dynamic occlusion of moving vehicles is the main reason for the decline in the perception efficiency of vehicle-road collaboration. Therefore, static scene methods cannot effectively cope with the optimized deployment of road sensing units under some vehicle-road collaboration conditions.

[0005] Based on the direct simulation technology solution, the traffic scene is built on the existing micro-simulation platform, and the sensor model built into the software is called to quantitatively evaluate the perception efficiency, and the RPU is optimized on this basis. In the study of the optimal deployment method of intelligent highway roadside perception equipment, Qiu Xingyou et al. established a multi-lane highway traffic scene through SUMO, and called the traffic detector of OMNET++ to simulate the perception of traffic events. However, this study ignored the impact of obstacles and set that traffic individuals can be perceived if they fall within the specific radius of RPU. In order to model the dynamic occlusion between vehicles, Jin et al. (JIN S, GAO Y, HUI F, et al. A Novel Information Theory-Based Metric for Evaluating Roadside LiDAR Placement [J]. IEEE Sensors Journal, 2022, 22 (21): 21009-21023) used Unity3D to create a three-dimensional traffic scene and simulate the RPU perception function. Considering the impact of dynamic occlusion, a roadside perception efficiency evaluation index based on information entropy was constructed through continuous simulation and its effectiveness was verified. However, the study only considered a single RPU and did not involve RPU optimization deployment for improving perception performance. Ma et al. (MA Y, ZHENG Y, WANG S, et al. A Virtual Method for Optimizing Deployment of Roadside Monitoring Lidars at As-Built Intersections [J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24 (11): 11835–11849.) combined the data of the same traffic scene obtained by multiple RPUs, and quantitatively evaluated the perception performance by comparing the differences between traffic individuals under collaborative perception conditions and their original models. However, this study relies on the actual collected traffic trajectory data and cannot flexibly respond to diverse traffic scenarios.

[0006] However, high-precision sensor simulation models involve a lot of graphics calculations, so the calculation efficiency is low when using Carla, Unity3D or other software for perception simulation. The optimization of RPU layout may involve multiple times (such as 10 3 To ensure robustness, a single optimization iteration needs to evaluate the average perceived performance in multiple time-step scenarios. Therefore, the optimization deployment of RPU based on direct simulation method is very time-consuming.

[0007] In addition to the above problems, although ICV can make up for the road-side perception blind spots, existing research has not adequately considered the impact of ICV when modeling RPU optimization deployment, resulting in the inability of existing technical solutions to cope with the optimized deployment method of RPU under some vehicle-road cooperative conditions. Summary of the invention

[0008] In order to make up for the shortcomings of the existing technical problems, the purpose of the present invention is to provide a method for optimizing the deployment of roadside perception units under partial vehicle-road cooperative conditions, introduce POG into the field of RPU optimal deployment, provide a specific and complete technical framework, use POG to effectively measure the uncertainty of the scene, and solve the problem that the existing technology does not consider the impact of ICV when carrying out RPU optimal deployment modeling; in addition, by evaluating the vehicle-road cooperative perception effectiveness based on POG cross-entropy, continuous simulation can be avoided, effectively reducing the time cost of RPU optimal deployment.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A method for optimizing the deployment of roadside perception units under partial vehicle-road cooperative conditions, comprising:

[0011] (1) Traffic scenarios are established based on the Vissim-Carla joint simulation method, and the true value probability occupancy grids of various traffic scenarios are obtained through multi-time step continuous simulation;

[0012] (2) Perform parameterized modeling of the perception of roadside sensing units and intelligent connected vehicles. To address the adverse effects of mobile shielding on vehicle-road collaborative perception, a ray tracing algorithm that takes shielding effects into account is established to simulate the collaborative perception process of roadside sensing units and intelligent connected vehicles.

[0013] (3) Through a ray tracing algorithm that takes into account the occlusion effect, the observable probabilistic occupancy grid under the vehicle-road cooperative perception condition is obtained, and the cross entropy between the true value probabilistic occupancy grid and the observable probabilistic occupancy grid is proposed as an alternative evaluation index for vehicle-road cooperative perception for traffic flow monitoring;

[0014] (4) The layout parameters of the roadside perception unit and the cross entropy based on the probability occupancy grid are used as decision variables and objective function values ​​respectively, and the Bayesian optimization method is used to complete the optimal deployment of the roadside perception unit.

[0015] In the present invention, the step of obtaining the true value probability occupancy grid in step (1) includes:

[0016] 1) Establish a global coordinate system of xyz and determine the area of ​​interest for non-hybrid scene perception by constraining the coordinate range of xyz;

[0017] 2) At any simulation time step, create the envelope of the traffic individual, voxelize each envelope, and obtain the true value probability occupancy grid through continuous updating of the simulation step.

[0018] In the present invention, voxelization adopts a step-by-step process from local space to global coordinate frame, as follows:

[0019] Step 1: Grid point coordinate space conversion

[0020] Set x v -y v -z v is the local coordinate system of each traffic entity, where y v Corresponding to the real-time moving direction of the individual, x v Perpendicular to the individual's forward direction, horizontally to the right; in the local coordinate frame of the traffic individual, the envelope is outlined by the length, width and height of the vehicle; within the envelope, δ vx Generate regular grid point data for the interval; set at t i The azimuth of traffic individual j at time Then, the coordinate frame x is transformed into v -y v -z v The grid point data in is converted to the global coordinate system xyz;

[0021]

[0022] in:

[0023]

[0024] in: Yes i The rigid coordinate transformation matrix of traffic individual j at the moment, and are the local coordinates and global coordinates of individual j grid point, Yes i The global coordinate value of the bottom center of traffic individual j at the moment;

[0025] Step 2: Voxelization

[0026] The grid points after coordinate transformation are voxelized, and the voxel size is consistent with the spacing of the grid points; let V s is the road traffic scene after voxelization processing in ROI, V s The data format is a three-dimensional matrix, each matrix element corresponds to a voxel unit and stores the corresponding occupancy probability value; let M v t i The voxel representing the traffic individual in the scene at the moment Vs The position coordinates set within V s In M v The voxel value is set to 1, which is mathematically expressed as V s |t i ,M v =1; the above is only for a single time step. To obtain the POG of a certain traffic scene, continuous simulation and calculation are required; s Medium voxel unit v i For example, the corresponding occupancy probability According to equations (3) to (4), we can obtain:

[0027]

[0028] in:

[0029]

[0030] Where: T is the total number of simulation steps, v i ∈M v and Respectively represent whether the voxel vi is included in the coordinate set of the traffic grid point in Vs at a certain time step. is the occupancy probability of the voxel based on the sample evaluation;

[0031] Assuming that λ is the total number of voxel units that are occupied at least once during the T-step simulation process, then 1≤i≤λ; to simplify the calculation process, it is set under the condition of sufficient simulation steps Each voxel unit is regarded as a 0-1 binary variable, and there is Considering that the occupancy state of a single voxel cannot infer the state of other voxels, it is believed that in V s The voxels within the ROI are mutually independently distributed random variables; under this condition, the joint distribution of multivariate variables within the ROI range is calculated according to formula (5);

[0032]

[0033] in

[0034] In the present invention, the perception parameterized modeling of the roadside perception unit and the intelligent connected vehicle includes:

[0035] Let (x R ,y R ,z R ) T is the sensor center of the roadside sensing unit, with (x R ,y R ,z R ) TAs the origin, establish the sensor local coordinate system x of the roadside perception unit s -y s -z s ,make With φ s are the vertical and horizontal field of view angles of the roadside perception unit, d s is the effective sensing range of the roadside sensing unit;

[0036] Taking the center of the sensor on the intelligent connected car as the origin, y vs Corresponding to the real-time moving direction of the individual, x vs Perpendicular to the individual's forward direction and horizontally to the right, establish the local coordinate system x of the on-board sensor of the intelligent networked vehicle vs -y vs -z vs , set the horizontal field of view of the ICV sensor to 360 degrees; let ξ vs is the vertical field of view angle of the ICV sensor, d vs is the effective sensing distance of ICV.

[0037] In the present invention, the observable probability occupancy grid under the vehicle-road cooperative perception condition is obtained as follows:

[0038] Under the cooperative perception conditions of the roadside perception unit and the intelligent connected vehicle, the voxel v i Occupancy probability of a condition observed by all perceptual units:

[0039]

[0040] in: and are the occupancy probabilities of the non-ICV parts of voxel vi under the observation conditions of the mth RPU and the nth ICV sensor, is the occupancy probability of the ICV part corresponding to voxel vi;

[0041] Assuming that the probability distribution of each voxel unit is still independent under the vehicle-road cooperative observation condition, the joint distribution of conditional occupancy probability is

[0042]

[0043] in: μ is the number of voxels with non-zero conditional occupancy probability within the ROI under the vehicle-road cooperative perception condition. In the present invention, based on the independence assumption, the cross entropy can be calculated voxel by voxel, as shown in equations (15)-(16); The smaller it is, the closer the occupancy probability under the vehicle-road cooperative perception condition is to the actual situation;

[0044]

[0045] Where: n c It is the number of voxels occupied by non-motor vehicles among the non-zero voxels. If a voxel has been occupied by both motor vehicles and non-motor vehicles, it is considered to be a voxel occupied by non-motor vehicles.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention establishes a systematic method of "POG construction based on simulation - collaborative perception modeling - perception effectiveness alternative evaluation - RPU optimal deployment", which provides a specific and complete technical framework for introducing POG into the field of RPU optimal deployment, and uses POG to effectively measure the uncertainty of the scene, solving the problem that the existing technology does not adequately consider the impact of ICV when conducting RPU optimal deployment modeling.

[0048] 2. The present invention is aimed at traffic scenes represented by POG. The occupancy probability of voxel units in POG is equivalent to the degree of opacity. Combined with the Bayesian reasoning method, a ray tracing technology that considers the occupancy effect is established. The observable POG of vehicle-road collaborative perception under different intelligent connected vehicle penetration rates and different RPU deployment conditions can be obtained. While considering the uncertainty of traffic scenes, the collaborative perception efficiency of intelligent vehicles and roadside perception units for traffic flow scenes can be quickly and quantitatively evaluated.

[0049] 3. The present invention can incorporate ICV into the optimized layout of RPU, and consider the occlusion effect of the moving vehicle body on the perception of RPU or ICV. By evaluating the vehicle-road cooperative perception performance based on POG cross entropy, continuous simulation can be avoided, effectively reducing the time cost of RPU optimization layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a flow chart of the method.

[0051] Figure 2 It is a schematic diagram of the basic traffic scene of the present invention.

[0052] Figure 3 Obtaining the true value probability occupancy grid

[0053] Figure 4 Schematic diagram of the perception parameterization modeling of roadside perception units and intelligent connected vehicles.

[0054] Figure 5 This is a flow chart of the ray tracing algorithm that takes occlusion effects into consideration in the present invention.

[0055] Figure 6 This is a schematic diagram of the virtual light creation and voxelization processing principle in the present invention.

[0056] Figure 7Schematic diagram of the change of voxel occupancy probability along the virtual ray.

[0057] Figure 8 Schematic diagram of voxel overlap in the present invention.

[0058] Fig. 9 It is a schematic diagram of the collaborative perception process based on POG in the present invention.

[0059] Fig.10 Schematic diagram of the change of voxel occupancy probability along the virtual ray. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Combination Figure 1-10 , the method for optimizing the deployment of roadside perception units under partial vehicle-road cooperative conditions of the present invention is specifically described.

[0062] 1. Technical Overview

[0063] The technical framework for optimizing the deployment of RPU under partial vehicle-road cooperative conditions proposed in the present invention mainly includes the following basic steps: Figure 1 As shown. First, a traffic scene is established based on the Vissim-Carla joint simulation method, and then the probabilistic occupancy grid (POG, a spatial representation method for dynamic environments, that is, dividing the scene into countless voxel units, each voxel unit stores the occupancy probability of the corresponding grid space area) of various traffic scenes is obtained through multi-time step continuous simulation. Then the perception modeling of RPU and ICV is carried out, and in view of the adverse effects of mobile shielding effect on vehicle-road collaborative perception, a ray tracing algorithm (occlusion-considered ray-tracing, OCRT) considering the shielding effect is established to simulate the collaborative perception process of roadside perception unit (RPU) and intelligent connected vehicle (ICV). The POG observed under the vehicle-road collaborative perception condition is obtained by the OCRT algorithm, and it is proposed to use the cross entropy of the true value POG and the observable POG as an alternative evaluation indicator of vehicle-road collaborative perception for traffic flow monitoring. Finally, the layout parameters of RPU and the cross entropy based on POG are used as decision variables and objective function values ​​respectively, and the Bayesian optimization method is used to complete the optimal deployment of the road sensing unit. The following will describe in detail each step in the technical solution of the present invention.

[0064] 2. Traffic scene construction

[0065] The present invention focuses on the common two-way multi-lane straight road section in urban road scenes, whose length is L r The number of motor vehicle lanes in each direction is n v , and the outermost lane is a non-motorized vehicle lane. Considering that Vissim can effectively simulate the interaction between motor vehicles and non-motorized vehicles, the present invention uses Vissim as a microscopic traffic flow simulation platform, and sets that non-motorized vehicles can occupy the motor vehicle lane closest to the non-motorized vehicle lane, so that the interaction scene of motor vehicles and non-motorized vehicles is closer to the real situation. To verify the effectiveness of the subsequent alternative evaluation indicators of vehicle-road collaborative perception effectiveness, the quantitative output of perception indicators of motor vehicles and non-motorized vehicles in the mixed traffic scene under vehicle-road collaborative conditions is achieved through the joint simulation of Vissim and Carla.

[0066] When constructing traffic scenarios, considering the possible impact on the perception efficiency of vehicle-road cooperative, the main traffic parameters considered in the present invention include traffic volume Q t 、Traffic composition t , ICV penetration ζ, Figure 2 The basic traffic scene in the present invention is shown. For any traffic scene, based on the sensor simulation function in Carla, the ICV in the scene and the designated roadside perception unit can collaboratively perceive the scene. Considering the advantages of LiDAR in three-dimensional perception and collaborative work, the present invention uses LiDAR as the core sensing device for vehicle-road collaborative perception.

[0067] In order to facilitate the quantitative description of road scenes, Figure 2 The leftmost center of the middle section is the origin O, the horizontal right direction is the x-axis, the vertical upward direction is the y-axis, and the vertical upward direction of the xOy plane is the z-axis to establish a rectangular coordinate system. In the context of the problem concerned by the present invention, xyz is a global coordinate system.

[0068] 3POG Build

[0069] POG is a spatial representation method for dynamic environments, which divides the scene into countless voxel units, and each voxel unit stores the occupancy probability of the corresponding grid space area. The present invention adopts a two-step method to construct the traffic scene POG, such as Figure 3 (a) is shown. First, by constraining the xyz coordinate range, the region of interest (ROI) required for the perception of mixed traffic scenes is determined. At any simulation time step, since the size of the traffic individual is known, its envelope can be created. Each envelope is voxelized and the POG is established through continuous updating of the simulation step. The specific voxelization process is as follows: Figure 3 (b) as shown.

[0070] The voxelization of the present invention adopts a step-by-step process from local space to global coordinate frame. The specific steps are as follows:

[0071] Step 1: Grid point coordinate space conversion.

[0072] Set x v -y v -z v is the local coordinate system of each traffic entity, where y v Corresponding to the real-time moving direction of the individual, x v Perpendicular to the individual's forward direction, horizontally to the right. In the local coordinate frame of the traffic individual, the envelope is outlined by the length, width and height of the vehicle. vx Generate regular grid point data for the interval. Set at t i The azimuth of traffic individual j at time Then, the coordinate frame x is transformed into v -y v -z v The grid point data in is converted to the global coordinate system xyz.

[0073]

[0074] in:

[0075]

[0076] in: Yes i The rigid coordinate transformation matrix of traffic individual j at the moment, the roll angle and pitch angle are not considered in the present invention, and are the local coordinates and global coordinates of individual j grid point, Yes i The global coordinate value of the bottom center of traffic individual j at time instant.

[0077] Step 2: Voxelization.

[0078] The grid points after coordinate transformation are voxelized, and the voxel size is consistent with the spacing of the grid points. s is the road traffic scene after voxelization processing in ROI, V s The data format is a three-dimensional matrix, each matrix element corresponds to a voxel unit and stores the corresponding occupancy probability value. v t i The voxel representing the traffic individual in the scene at the moment V s The position coordinates set within V s In M v The voxel value is set to 1, which is mathematically expressed as V s |t i ,M v= 1. The above is only for the case of a single time step. In order to obtain the POG of a certain traffic scene, continuous simulation and calculation are required.

[0079] V s Medium voxel unit v i For example, the corresponding occupancy probability It is obtained by evaluating according to equations (3) to (4).

[0080]

[0081] in:

[0082]

[0083] Where: T is the total number of simulation steps, v i ∈M v and Respectively represent whether the voxel vi is included in the coordinate set of the traffic grid point in Vs at a certain time step. is the occupancy probability of the voxel based on the sample evaluation.

[0084] Assuming that λ is the total number of voxel units that are occupied at least once in the T-step simulation process, then 1≤i≤λ. To simplify the calculation process, it is assumed that Each voxel unit is regarded as a 0-1 binary variable, and there is Considering that the occupancy state of a single voxel cannot infer the state of other voxels, it can be considered that s The voxels in the ROI are mutually independently distributed random variables. Under this condition, the joint distribution of multivariate variables within the ROI range can be calculated according to formula (5). In the present invention, Ω r Characterization by Figure 3 The POG obtained by the workflow.

[0085]

[0086] in

[0087] 4. Perception Modeling

[0088] Due to the adverse effects of obstacles and the limitation of sensor FOV, there is a difference between the observable POG under vehicle-road cooperative perception conditions and the actual POG. In order to quantitatively evaluate the POG under RPU and ICV observation conditions, it is necessary to model the perception process. Figure 4 (a) and 4(b) show the relevant parameters of RPU and ICV sensors respectively.

[0089] Let (x R ,y R ,zR ) T is the sensor center of RPU, with (x R ,y R ,z R ) T As the origin, establish the sensor local coordinate system x of RPU s -y s -z s ( Figure 4 (a)). With φ s are the vertical and horizontal field of view angles of the RPU, d s is the effective sensing range of RPU. Unlike RPU, the sensor center of ICV changes with the movement of vehicle position. Figure 3 The vehicle local coordinate system x v -y v -z v The local coordinate system x of the ICV vehicle-mounted sensor is established by vs -y vs -z vs ( Figure 4 (b)). In line with the current mainstream configuration of ICV, in order to ensure that ICV can effectively perceive the surrounding environment, the horizontal field of view of the ICV sensor is set to 360 degrees. Let ξ vs is the vertical field of view angle of the ICV sensor, d vs is the effective sensing distance of ICV. Considering the diversity of ICV in future actual traffic scenarios, in the present invention, both small and medium-sized passenger cars and large trucks can be used as ICVs, and the vehicle type of ICV is randomly assigned. In the present invention, it is set that when the vehicle body length exceeds 8m, it can be regarded as a large vehicle.

[0090] Ray tracing is an important algorithm for image rendering in the field of computational geometry. When the voxelized road traffic scene (i.e., POG) is used as the observation object, ray tracing technology can be applied to the calculation of the three-dimensional view distance of the road. However, in this type of research, the occlusion of the driver by static obstacles is simulated mainly by judging whether the created ray intersects with the voxel unit. In order to effectively consider the impact of uncertain occlusion, the present invention proposes an OCRT algorithm for voxel units. The algorithm flow is as follows: Figure 5 as shown, and includes the following steps.

[0091] Step 1: Virtual light creation.

[0092] The present invention creates a virtual beam through the idea of ​​coordinate transformation. Specifically, χ, ψ and ρ are set as the original information collected by the RPU or ICV visual sensor, namely the horizontal angle, pitch angle and distance parameters, which actually correspond to the three coordinate components of the spherical coordinate system. Figure 6 As shown in Figure 1, a new coordinate system is established with χ, ψ and ρ as the three components of the Cartesian rectangular coordinate system. Through this conversion method, the global coordinate system xyz with the sensor center C s The virtual light ray starting from is mapped to the vertical vector in the χ-ψ-ρ rectangular coordinate system.

[0093] The projection coordinates in the χ-ψ-ρ coordinate frame are (χ k ,ψ k ) T For example, the vector χ k With ψ k In xyz, it is the horizontal angle and pitch angle of the light. In χ-ψ-ρ, sampling can be performed along the vertical vector to obtain a set of discrete points on the virtual light (χ k ,ψ k ,ρ) T , where 0≤ρ≤d s The discrete points in the χ-ψ-ρ coordinate frame are mapped to the xyz coordinate space through equation (7).

[0094]

[0095] Among them: Sphere2Cartesian[.] is the matrix for converting spherical coordinates to Cartesian coordinates, is from the local coordinate system x s -y s -z s The rotation transformation matrix to the xyz coordinates, (α, β , γ) is the pitch, roll and azimuth of the sensor in the xyz coordinate system, and the meanings of other symbols remain unchanged.

[0096] Step 2: Occupancy probability retrieval.

[0097] The discrete points representing the light in the xyz coordinates are voxelized, that is, (x, y, z) T Transformed to a voxel position in Vs. V s Each voxel unit in stores the occupancy probability value obtained through the previous T-step simulation. Based on the voxel position corresponding to the discrete point of the virtual ray, the original occupancy probability curve of the voxel unit along the ray can be obtained (see Figure 7 ).like Figure 6As shown, all rays within the sensor field of view are calculated to obtain a matrix of occupancy probabilities. The row number of the matrix represents the index of the virtual ray, and the column number represents the order of the voxel unit along the direction of the ray emission.

[0098] Step 3: Conditional occupancy probability calculation.

[0099] Since visual sensors cannot penetrate opaque objects, the observability of downstream voxel units along the emission direction of the virtual light will be affected by the upstream voxel units, that is, as the distance along the light direction increases, the observability of the object will decrease. There are two extreme scenarios for observing the state of voxel units: 1) There are voxel units with an occupancy probability of 1 upstream, that is, fixed obstacles, then all downstream voxel units cannot be observed; 2) The occupancy probability of all upstream voxel units is 0, that is, there is no occlusion, then the state of the downstream voxel units can be directly observed without being affected. Based on this

[0100] On this basis, in order to consider the occlusion effect, the present invention obtains the conditional occupancy probability of the voxel unit under the sensor observation condition It is calculated by formula (7).

[0101]

[0102] in: is the observation condition v under the kth perceptual unit i The probability of occupancy.

[0103] In the calculation process of step 3, the operation of converting the light into voxel units after discretization will cause the voxel units corresponding to different light rays to overlap, such as Figure 8 As shown. Under this condition, the same voxel is calculated on different virtual rays. To solve this problem, when a voxel is on the virtual rays of multiple sensing units, the present invention takes The maximum value is taken as the representative value.

[0104] 5. Evaluation of vehicle-road collaborative perception effectiveness

[0105] The steps of evaluating the vehicle-road cooperative perception effectiveness of the present invention are as follows:

[0106] Step 1: Construct conditional occupancy probabilities under multi-sensory unit conditions.

[0107] Both RPU and ICV can perceive traffic scenes. Under this condition, the state of the same voxel can be observed by multiple perception units. Since the observations of the same voxel by different perception nodes are independent of each other, according to the Bayesian formula and the total probability formula, we have:

[0108]

[0109] Where: i∩S k Represents voxel v i Observed by the kth sensor unit (v i When it is not within the field of view of the kth perception unit, ), i∩S represents v i The condition observed by all sensor units, n s is the total number of sensor units.

[0110] In equations (8) and (9), represents the probability of the perception unit appearing. Since the roadside perception unit always exists after being deployed, In contrast, ICV as a sensing node has uncertainty in its appearance, i.e. When building POG in Section 1.3, Fig. 9 As shown in (a), the trajectory point P of the ICV visual sensor vs Voxelization is also performed to obtain the POG corresponding to the ICV sensor, denoted as Ω s Ω s Each voxel unit in stores the spatial occupancy probability of the ICV sensor. Therefore, in the present invention, Ω s The spatial position corresponding to the center of the voxel unit is taken as the possible sensing node of ICV, and Ω s The probability value stored in the voxel unit is used as the ICV perception unit On this basis, formula (9) can be transformed into formula (10).

[0111]

[0112] in: is the voxel v under the observation condition of the mth RPU i Occupancy probability, 1≤m≤n RPU , where n RPU is the number of RPUs, is the voxel v under the observation condition of the nth ICV sensor i Occupancy probability, 1≤n≤n icv , where n icv is Ω s The number of non-zero voxel units in is the probability of the nth ICV sensor appearing, based on Ω s get.

[0113] At the same time, as one of the components of the mixed traffic flow, ICV can actively share information such as the location and size of the vehicle, that is, the part of the POG occupied by the ICV does not need to be sensed. r It can be further divided into Ωicv With Ω nicv , and there is formula (11), under the condition of vehicle-road cooperative perception, the probability of ICV and RPU jointly observing the grid Ω r|S It can be calculated according to formula (12). Specifically, only the non-ICV part of the traffic flow needs to calculate the conditional occupancy probability based on the OCRT algorithm in Section 1.4.

[0114]

[0115]

[0116] in: is the voxel-by-voxel addition, Ω nicv|S It is the POG corresponding to non-ICV traffic individuals under the vehicle-road cooperative perception condition.

[0117] For Ω r|S The conditional probability occupancy value of any voxel unit in , considering that a single voxel may be occupied by both ICV and non-ICV traffic individuals during traffic operation, the occupancy probability of a single voxel unit can also be divided into two parts: ICV part and non-ICV part. Combined with formula (10), we can get Ω under the condition of vehicle-road cooperative perception: r|S The conditional occupancy probability of the voxel unit in is:

[0118]

[0119] in: and are the occupancy probabilities of the non-ICV parts of voxel vi under the observation conditions of the mth RPU and the nth ICV sensor, is the occupancy probability of the ICV part corresponding to voxel vi, and the meanings of other symbols remain unchanged.

[0120] In formula (13), and All are calculated by the OCRT algorithm (see Figure 5 ). Specifically, the perception field of view is determined with the position of the RPU in the voxelized space or the voxel position corresponding to the ICV sensor as the center. Then, a virtual ray is created within the field of view, and the occupancy probability is retrieved based on Ωicv and Ωnicv. Finally, the conditional occupancy probability of any voxel unit within the perception range is calculated. Since m and n are generally greater than 1, it is necessary to calculate the conditional probability at each possible sensor position in turn, and use formula (14) to complete the addition of the probabilities. Note in is the occupancy probability of voxel vi obtained by continuous simulation in Section 3. Then That is, the most ideal state under the vehicle-road cooperative perception condition is that the original occupancy probability of the voxel unit is fully observed.

[0121] In order to simplify the calculation process, it is assumed that the probability distribution of each voxel unit is still independent of each other under the vehicle-road cooperative observation condition. On this basis, the joint distribution of conditional occupancy probability can be calculated in a similar way to formula (5).

[0122]

[0123] in: μ is the number of voxels with non-zero conditional occupancy probability within the ROI under the vehicle-road cooperative perception condition. Since some voxels cannot be observed, μ≤λ.

[0124] Step 2: Quantitative evaluation of vehicle-road collaborative perception effectiveness.

[0125] In order to measure the perception effectiveness of vehicle-road cooperative, an intuitive way is to evaluate and The smaller the difference, the higher the perception efficiency. is the true distribution of voxel occupancy probability, and the present invention uses cross entropy to measure and Considering the small size of non-motor vehicles, the number of voxels they occupy in the voxelized space is relatively small. In order to increase the perception effectiveness of the vehicle-road cooperative system for vulnerable road users, the present invention uses weighted cross entropy to measure and Based on the independence assumption, the cross entropy can be calculated voxel by voxel, as shown in equations (15)-(16). The smaller it is, the closer the occupancy probability under the vehicle-road cooperative perception conditions is to the actual situation.

[0126]

[0127] Where: n c It is the number of voxels occupied by non-motor vehicles among the non-zero voxels. If a voxel has been occupied by both motor vehicles and non-motor vehicles, it is considered to be a voxel occupied by non-motor vehicles.

[0128] It should be noted that the present invention mainly focuses on the voxel unit as a binary variable of whether it is occupied or not, and does not consider the category of traffic individuals. Therefore, formula (15) is not a traditional weighted cross entropy calculation formula. The core idea of ​​formula (15) is to give a larger weight to the voxels occupied by non-motor vehicles. Since μ≤λ, there are conditional occupancy probabilities of some voxel units. is zero (i.e., not perceived by any perception unit), which makes the calculation of formula (15) invalid. Therefore, when calculating the cross entropy, the present invention uses T -1 / 10 to replace the zero value to ensure the numerical stability of the calculation.

[0129] 6. RPU Optimization Deployment

[0130] The complete process of the method for RPU optimization deployment in the present invention is as follows: Fig.10 As shown in the figure. First, the traffic scene is created by joint simulation of Vissim and Carla, and different traffic scenes are simulated by adjusting the traffic flow parameters (traffic volume, traffic ratio) and ICV penetration parameters. Then, Ω is obtained through the voxelization process. r ,Ω icv With Ω s The RPU deployment parameters are set as decision variables, and the OCRT algorithm is combined with Ω icv Calculate the occupancy probability under the vehicle-road cooperative perception condition (Equation (14)). Compare the conditional occupancy probability Ω r|S Compared with Ω obtained through continuous simulation r And calculate To reduce To optimize the target, the Bayesian optimizer is used to adjust the RPU layout parameters. During the optimization process, relevant optimization constraints can be added according to needs, such as the distance between different RPUs needs to be greater than a certain threshold. When the optimization process is completed, the program ends.

[0131] What is described above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the deployment of roadside perception units under partial vehicle-road cooperative conditions, characterized in that: include: (1) Establish traffic scenarios based on the Vissim-Carla joint simulation method, and obtain the true value probability occupancy grids of various traffic scenarios through multi-time step continuous simulation; (2) Perform parameterized modeling of the perception of roadside sensing units and intelligent connected vehicles. To address the adverse effects of mobile shielding on vehicle-road collaborative perception, a ray tracing algorithm that takes shielding effects into account is established to simulate the collaborative perception process of roadside sensing units and intelligent connected vehicles. (3) Through a ray tracing algorithm that takes into account the occlusion effect, the observable probabilistic occupancy grid under the vehicle-road cooperative perception condition is obtained, and the cross entropy between the true value probabilistic occupancy grid and the observable probabilistic occupancy grid is proposed as an alternative evaluation index for vehicle-road cooperative perception for traffic flow monitoring; (4) The layout parameters of the roadside perception unit and the cross entropy based on the probability occupancy grid are used as decision variables and objective function values ​​respectively, and the Bayesian optimization method is used to complete the optimal deployment of the roadside perception unit.

2. The method for optimizing the deployment of roadside perception units under partial vehicle-road cooperation conditions according to claim 1, characterized in that: The steps for obtaining the true value probability occupancy grid in step (1) include: 1) Establish a global coordinate system of xyz and determine the area of ​​interest for non-hybrid scene perception by constraining the coordinate range of xyz; 2) At any simulation time step, create the envelope of the traffic individual, voxelize each envelope, and obtain the true value probability occupancy grid through continuous updating of the simulation step.

3. The method for optimizing the deployment of roadside perception units under partial vehicle-road cooperation conditions according to claim 2 is characterized in that: Voxelization uses a step-by-step process from local space to a global coordinate frame as follows: Step 1: Grid point coordinate space conversion Set x v -y v -z v is the local coordinate system of each traffic entity, where y v Corresponding to the real-time moving direction of the individual, x v Perpendicular to the individual's forward direction, horizontally to the right; in the local coordinate frame of the traffic individual, the envelope is outlined by the length, width and height of the vehicle; within the envelope, δ vx Generate regular grid point data for the interval; set at t i The azimuth of traffic individual j at time Then, the coordinate frame x is transformed into v -y v -z v The grid point data in is converted to the global coordinate system xyz; in: in: Yes i The rigid coordinate transformation matrix of traffic individual j at the moment, and are the local coordinates and global coordinates of individual j grid point, Yes i The global coordinate value of the bottom center of traffic individual j at the moment; Step 2: Voxelization The grid points after coordinate transformation are voxelized, and the voxel size is consistent with the spacing of the grid points; let V s is the road traffic scene after voxelization processing in ROI, V s The data format is a three-dimensional matrix, each matrix element corresponds to a voxel unit and stores the corresponding occupancy probability value; let M v t i The voxel representing the traffic individual in the scene at each moment is in V s The position coordinates set within V s In M v The voxel value is set to 1, which is mathematically expressed as V s |t i ,M v =1; the above is only for a single time step. To obtain the POG of a certain traffic scene, continuous simulation and calculation are required; s Medium voxel unit v i For example, the corresponding occupancy probability According to equations (3) to (4), we can obtain: in: Where: T is the total number of simulation steps, v i ∈M v and Respectively represent whether the voxel vi is included in the coordinate set of the traffic grid point in Vs at a certain time step. is the occupancy probability of the voxel based on the sample evaluation; Assuming that λ is the total number of voxel units that are occupied at least once during the T-step simulation process, then 1≤i≤λ; to simplify the calculation process, it is set under the condition of sufficient simulation steps Each voxel unit is regarded as a 0-1 binary variable, and there is Considering that the occupancy state of a single voxel cannot infer the state of other voxels, it is believed that s The voxels within the ROI are mutually independently distributed random variables; under this condition, the joint distribution of multivariate variables within the ROI range is calculated according to formula (5); in 4. The method for optimizing the deployment of roadside perception units under partial vehicle-road cooperation conditions according to claim 1, characterized in that: The perception parameterized modeling of the roadside perception unit and the intelligent connected vehicle includes: Let (x R ,y R ,z R ) T is the sensor center of the roadside sensing unit, with (x R ,y R ,z R ) T As the origin, establish the sensor local coordinate system x of the roadside perception unit s -y s -z s ,make With φ s are the vertical and horizontal field of view angles of the roadside perception unit, d s is the effective sensing range of the roadside sensing unit; Taking the center of the sensor on the intelligent connected car as the origin, y vs Corresponding to the real-time moving direction of the individual, x vs Perpendicular to the individual's forward direction and horizontally to the right, establish the local coordinate system x of the on-board sensor of the intelligent networked vehicle vs -y vs -z vs , set the horizontal field of view of the ICV sensor to 360 degrees; let ξ vs is the vertical field of view angle of the ICV sensor, d vs is the effective sensing distance of ICV.

5. The method for optimizing the deployment of roadside perception units under partial vehicle-road cooperation conditions according to claim 1, characterized in that: Obtain the observable probability occupancy grid under the vehicle-road cooperative perception condition as follows: Under the cooperative perception conditions of the roadside perception unit and the intelligent connected vehicle, the voxel v i Occupancy probability of a condition observed by all perceptual units: in: and are the occupancy probabilities of the non-ICV parts of voxel vi under the observation conditions of the mth RPU and the nth ICV sensor, is the occupancy probability of the ICV part corresponding to voxel vi; Assuming that the probability distribution of each voxel unit is still independent under the vehicle-road cooperative observation condition, the joint distribution of conditional occupancy probability is in: μ is the number of voxels with non-zero conditional occupancy probability within the ROI under vehicle-road cooperative perception conditions.

6. The method for optimizing the deployment of roadside perception units under partial vehicle-road cooperation conditions according to claim 1, characterized in that: Based on the independence assumption, the cross entropy can be calculated voxel by voxel, as shown in equations (15)-(16); The smaller it is, the closer the occupancy probability under the vehicle-road cooperative perception condition is to the actual situation; Where: n c It is the number of voxels occupied by non-motor vehicles among the non-zero voxels. If a voxel has been occupied by both motor vehicles and non-motor vehicles, it is considered to be a voxel occupied by non-motor vehicles.

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