An automatic driving-oriented structured road traffic environment cognition method

Abstract

This invention relates to the field of intelligent vehicle technology and provides a method for understanding the traffic environment of structured roads for autonomous driving. The method includes: uniformly representing the road segment ahead onto a road coordinate system; recognizing the traffic attributes inherent in traffic markings, traffic signs, and road structures and indexing them into geometric regions; fusing multi-source attributes to determine the final type, speed limit, boundary line type, and exit direction of each region; subdividing the geometric region into traffic regions based on the principle of rule consistency, constructing a structured environment model, and then recognizing the connection relationships between each region, dynamically integrating the influence of traffic lights, traffic participants, and obstacles. This invention achieves deep integration of traffic rule semantics and road geometric information, creating a well-defined, dynamically updatable environment model with "traffic regions" as the basic unit, significantly improving the accuracy and reliability of autonomous driving systems in understanding and making decisions about complex traffic environments.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent vehicles, and particularly relates to a traffic environment cognition method for structured roads for automatic driving. BACKGROUND

[0002] With the development of automatic driving vehicles, current assisted driving technologies have been widely applied in mass-produced vehicles, while L4-level automatic driving vehicles are still in the demonstration operation stage.

[0003] The current technical route of L4-level automatic driving vehicles, whether the classic "perception-decision-control" or the automatic driving system based on artificial intelligence, cannot guarantee that the decision result is always legal. The fundamental reason for the illegal behavior is that the current automatic driving system mainly deals with traffic regulations by optimizing traffic regulations as constraints together with other constraints in motion planning, which leads to traffic regulations becoming "soft constraints", resulting in the inability to guarantee that the decision result is completely legal every time. SUMMARY

[0004] The purpose of the embodiment of the application is to provide a traffic environment cognition method for structured roads for automatic driving, which aims to solve the problems raised in the background technology.

[0005] The embodiment of the application is implemented as follows: a traffic environment cognition method for structured roads for automatic driving, comprising the following steps:

[0006] Step 1: driving section cognition. Determine the cognition distance according to the current visibility and sensor range, and based on the travel task, uniformly represent the road sequence (including intersections and cognized as one-way roads with entrances and exits) within the cognition distance in the road coordinate system (Frenet coordinate system).

[0007] Step 2: traffic marking cognition. Analyze traffic markings (such as indication, prohibition, and warning markings), cognize them as constraints on vehicle position, speed, and exit direction (i.e. traffic attributes), and index these attributes to the geometric area where they act.

[0008] Step 3: traffic sign cognition. Analyze traffic signs (such as prohibitions, indications, and warning signs), cognize them as constraints on vehicle position, speed, size, mass, and exit direction, and index them to the corresponding geometric area.

[0009] Step 4: road structure cognition. Cognize road network connection relationships, lane numbers and adjacency relationships, and determine road types (such as expressways, highways, and urban roads).

[0010] Step 5: Traffic attribute fusion. The cognitive results of steps 2-4 are fused to determine the final traffic attributes of each geometric region, including: a) traffic region type (lane, through zone, no-entry zone); b) speed upper and lower limits; c) left and right side boundary line type (solid line, dashed line); d) exit direction.

[0011] Step 6: Traffic region cognition. Based on the principle of complete consistency of traffic attributes, each geometric region is further divided to form multiple "traffic regions" that cover the entire forward driving section, forming a traffic environment model based on traffic regions.

[0012] Step 7: Connection relationship cognition. Determine the entrance, exit connection relationship and left, right side adjacency relationship of each traffic region.

[0013] Step 8: Traffic signal light cognition. Identify the color of the traffic light and change the type of the related traffic region according to the color (e.g. when the light is red / yellow, the type of the region in front of the stop line is changed to through zone).

[0014] Step 9: Traffic participant cognition. Identify the type and state (position, speed, etc.) of traffic participants such as pedestrians and vehicles, and according to their type and position, affect the type of the traffic region they are in (e.g. when there are pedestrians on a pedestrian crossing, the region is changed to a through zone).

[0015] Step 10: Obstacle cognition. Identify static or dynamic obstacles and index them to the traffic region they are in.

[0016] The traffic environment cognition method for structured roads for autonomous driving provided by the embodiment of the present application decouples traffic regulations from other constraints, and before behavior decision or motion planning, the system comprehensively analyzes traffic regulations, recognizes structured roads as traffic environments represented by traffic regions, and according to the traffic attribute information of each traffic region, filters out a legal traffic region candidate set to ensure that the subsequent decision results are completely legal. This method can realize diversified motion planning, i.e. the through zone is also included in the traffic region candidate set, and when deciding, the legal nature can be sacrificed for a short time to exchange the benefits of other values, so that the driver model with aggressive driving style can be realized. If the traffic region candidate set only contains lanes, then the ordinary driver model or the driver model with defensive driving style is realized. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a diagram for traffic region type;

[0018] Figure 2 is a technical roadmap of the traffic environment cognition method for structured roads for autonomous driving provided by the embodiment of the present application;

[0019] Figure 3 a crossroad recognition graph;

[0020] Figure 4 a traffic environment recognition result example graph. DETAILED DESCRIPTION

[0021] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0022] The specific implementation of the present application will be described in detail below in conjunction with specific embodiments.

[0023] First of all, it needs to be pointed out that the traffic area is a two-dimensional closed area with specific traffic meaning on the road surrounded by traffic road markings, wherein the traffic attribute information in each traffic area is completely consistent. The traffic area is divided into three categories as shown in Figure 1

[0024] (1) a lane allowed by traffic regulations for motor vehicles to travel and having an entrance and an exit, such as a motor vehicle lane;

[0025] (2) a passing area with road surface conditions for motor vehicles to travel but not allowed by traffic regulations, such as a non-motor vehicle, a flow guide area, a center circle, etc.;

[0026] (3) a no-go area with road surface conditions not suitable for motor vehicles to travel, such as a separation belt in the center of a highway, a sidewalk on both sides of a road, etc.

[0027] As shown in Figure 2 , a traffic environment recognition method for a structured road for automatic driving provided by an embodiment of the present application includes the following steps:

[0028] Step 1: driving section recognition;

[0029] In order to adapt to bad weather conditions, the recognition distance is determined according to the current line of sight visibility and the effective sensing range of the vehicle-mounted sensor. According to the current travel task, a plurality of single-direction roads connected end to end within the recognition distance are intercepted from the current position, i.e. the front driving section is a sequence of a plurality of single-direction roads.

[0030] Since when a motor vehicle passes through a crossroad, it will only enter the crossroad from a certain road and then exit the crossroad to enter a certain road again, therefore, when recognizing the crossroad, according to the current travel task, the crossroad can be recognized as a single-direction road with an entrance and an exit, as shown in Figure 3 ​As shown, if the travel task of the motor vehicle is to drive from road 1 to road 4, intersection 2 is recognized as a straight road; if the travel task of the motor vehicle is to drive from road 1 to road 5, intersection 2 is recognized as a curved road. That is, under the premise of the current travel task, all structured roads are recognized as a single-entry and single-exit one-way road, so all structured roads can be represented by a road coordinate system. The road coordinate system belongs to the Frenet coordinate system, and the shape of the road, the geometric region in the road, the traffic region, the traffic participants, the obstacles, etc. are represented based on the road coordinate system.

[0031] The attribute data of the road includes the number, length, coordinate system, shape, and traffic region list of the road, as shown in Table 1.

[0032] Table 1 Road entity attribute data

[0033]

[0034] Road number: Each road has a unique number in the traffic environment.

[0035] Road length: The length from the starting point to the ending point of the road along the driving direction of the motor vehicle.

[0036] Road coordinate system: Since the road is one-way and has an entrance and an exit, a coordinate system is established along the driving direction of the motor vehicle with the leftmost boundary of the road as the reference line. The road coordinate system is expressed in the form of a polyline, which is represented by a point sequence in the rectangular coordinate system, and the expression is as follows:

[0037]

[0038] In the formula, is the road coordinate system, represents a point on the reference line of the road coordinate system, and the specific expression is as follows:

[0039]

[0040] In the formula, is the mileage coordinate of the i-th reference point, is the horizontal coordinate of the i-th reference point in the rectangular coordinate system, is the vertical coordinate of the i-th reference point in the rectangular coordinate system, is the heading angle at the i-th reference point.

[0041] ​​​​​​Road shape: the boundary of the road, the road curvature at each reference point, are all represented in the road coordinate system, and the specific expressions are as follows:

[0042] ;

[0043] In the formula, is the left side road boundary in the road coordinate system, is the right side road boundary in the road coordinate system, is the road curvature at each reference point in the road coordinate system, is the lateral coordinate of the first reference point on the left side road boundary, is the lateral coordinate of the first reference point on the right side road boundary, is the road curvature at the first reference point.

[0044] Traffic area list: store the traffic areas belonging to the road, after step 6, index the recognition results in this list.

[0045] Step 2: traffic marking recognition;

[0046] Parse the traffic marking in the traffic regulations, according to the definition of the guide marking, prohibition marking and warning marking in GB 5768.3, recognize the traffic marking as the requirement for the position, speed and exit direction of the motor vehicle, i.e. the traffic attribute, and index these traffic attributes in the geometric area where the traffic marking works. The geometric area is represented based on the road coordinate system it belongs to, and the representation formula is as follows:

[0047] ;

[0048] In the formula, is the starting mileage coordinate of the geometric area, is the ending mileage coordinate of the geometric area.

[0049] The traffic marking describing the lane boundary in the guide marking, prohibition marking and warning marking, such as lane dividing line, road edge line, tidal lane line, etc., is recognized as the traffic attribute restricting the position of the motor vehicle, including: the boundary of the geometric area, whether the motor vehicle is allowed to enter the geometric area. The recognition results of the common traffic marking describing the lane boundary are shown in the following table 2.

[0050] Table 2 Recognition results of traffic marking describing lane boundary

[0051]

[0052] For traffic markings that describe non-lane boundaries, such as pedestrian crossing lines, stop lines, and guide arrows, the cognitive traffic attributes that constrain the position, speed, and exit direction of motor vehicles are described in detail as follows:

[0053] (1) A pedestrian crossing is a geometric area formed by pedestrian crossing lines. Only when a non-motor vehicle such as a pedestrian or an electric motorcycle is located on the pedestrian crossing, the pedestrian crossing is recognized as a geometric area that motor vehicles cannot enter. Otherwise, the pedestrian crossing is recognized as a geometric area that allows motor vehicles to enter;

[0054] (2) A stop line is recognized as a geometric area with an exit speed of 0 when the traffic signal is red or yellow;

[0055] (3) A guide arrow constrains the exit direction of a motor vehicle, representing the connection relationship of the geometric area, i.e., after exiting the current geometric area, the motor vehicle can only enter the entrance of certain geometric areas.

[0056] The cognitive results of common non-lane boundary traffic markings are shown in Table 3.

[0057] Table 3 Cognitive results of non-lane boundary traffic markings

[0058]

[0059] Step 3: Traffic sign cognition;

[0060] Analyzing traffic signs in traffic regulations, according to the prohibition signs, indication signs, and warning signs in GB 5768.2. Traffic signs are recognized as requirements that constrain the position, speed, size, mass, and exit direction of motor vehicles, i.e., traffic attributes, and these traffic attributes are indexed in the geometric area where the traffic sign is effective. The geometric area is represented based on the road coordinate system to which it belongs.

[0061] The constraints of prohibition signs on motor vehicles are mainly divided into seven categories:

[0062] (1) Signs that prohibit specific motor vehicles from entering certain geometric areas, such as no passing, no entry, and no motor vehicle entry signs, are recognized as the type of geometric area where the sign is effective, i.e., a passing area;

[0063] (2) Signs that constrain the speed of motor vehicles reaching the sign, such as stop and yield, slow down and yield, and meet and yield signs, are recognized as the speed requirement at the exit of the geometric area where the sign is effective;

[0064] (3) Restricting the speed range of motor vehicles within a section of road, such as speed limit, speed limit lifting, minimum speed limit, and no parking signs, and recognizing the upper and lower speed limits of the geometric area where the sign is effective;

[0065] (4) Limiting the upper limit of motor vehicle size, such as width limit signs, height limit signs, etc., which are recognized as the requirements for motor vehicle size in the geometric area where the sign is applied;

[0066] (5) Limits the upper limit of the mass of motor vehicles, such as mass limit signs, axle load limit signs, etc., which are recognized as the mass requirements of motor vehicles in the geometric area where the sign is applied;

[0067] (6) Restricting motor vehicles from entering certain specific geometric areas after leaving the current geometric area, such as signs prohibiting left turns, U-turns, and going straight, and recognizing the exit direction requirements of the geometric area where the sign is applied;

[0068] (7) Other specific restrictions, such as the recognition of a no-overtaking sign as changing the left or right boundary line of the geometric area from a dashed line to a solid line, and the recognition of a no-overtaking sign as changing the left or right boundary line of the geometric area from a solid line to a dashed line, etc.

[0069] The recognition results of common prohibition signs are shown in Table 4 below.

[0070] Table 4. Cognitive Results of Prohibition Signs

[0071]

[0072] The restrictions imposed on motor vehicles by directional signs can be mainly divided into four categories:

[0073] (1) It is stipulated that after a motor vehicle leaves the current lane, it can only perform specific behaviors, such as going straight, turning left, driving in a roundabout, or following a U-turn sign. This is recognized as the direction of departure in traffic attributes, meaning that it can only enter a few fixed geometric areas.

[0074] (2) Restrict the speed of motor vehicles when they arrive at signs such as priority for oncoming traffic, crosswalks, and pedestrian signs. The speed is recognized as the exit speed of the current geometric area and only takes effect when it is recognized that there are traffic participants in the specific geometric area ahead.

[0075] (3) The type of motor vehicle traveling in the designated lane, such as motor vehicle lane, small passenger vehicle lane, bus lane, etc., is recognized as the type of the geometric area. If the motor vehicle meets the requirements of the sign, the type of traffic area is lane; if the motor vehicle does not meet the requirements of the sign, the type of traffic area is passage area.

[0076] (4) The speed range for driving within the lane is specified, such as the minimum speed limit sign, and is recognized as the upper and lower limits of speed in that geometric area.

[0077] The cognitive results of common directional signs are shown in Table 5.

[0078] Table 5. Cognitive Results of Indicative Signs

[0079]

[0080] Warning signs primarily constrain motor vehicles by reducing speed at the exit of a geometric area, such as signs for intersections, sharp turns, and consecutive turns. The recognition results of common warning signs are shown in Table 6.

[0081] Table 6. Cognitive Results of Warning Signs

[0082]

[0083] Step 4: Understanding Road Structure;

[0084] This method identifies the road network relationships (connections between roads), the number of lanes traveling in the same direction, and the left-right adjacency relationships of each lane. Based on the speed limits stipulated in the "Regulations for the Implementation of the Road Traffic Safety Law of the People's Republic of China" for different road types, this method categorizes road types into three types: expressways, highways, and urban roads. Specifically, the road types belong to the following set:

[0085] ;

[0086] in, Road type For highways, For highways, For city roads.

[0087] Step 5: Integration of traffic attributes;

[0088] By integrating the different sources of traffic attribute information (such as traffic markings, traffic signs, traffic lights, etc.) identified in steps 2 to 4 above, the final value of each traffic attribute in each geometric area is determined.

[0089] Step 5.1: Determine the type of traffic area. Based on the findings of Steps 2 to 4, clarify the traffic area type for each geometric region. There are three types of traffic areas:

[0090] (1) Define a traffic area where traffic regulations allow the motor vehicle to travel and where there are entrances and exits as a lane, such as a motor vehicle lane;

[0091] (2) Define traffic areas that meet the conditions for driving this motor vehicle but are not permitted to drive this motor vehicle by regulations as traffic zones, such as non-motor vehicle lanes, diversion zones, and central rings;

[0092] (3) Define traffic areas that do not meet the conditions for driving this motor vehicle as restricted areas, such as the median strip in the middle of the highway and the sidewalks on both sides of the road.

[0093] Therefore, the traffic area type belongs to the following set:

[0094] ;

[0095] in, As a traffic area type, For lanes, For passage, This is a restricted area.

[0096] The steps to determine the traffic zone type for each geometric region are as follows:

[0097] 1) Set the traffic area type of the geometric area between two adjacent lane lines to lane by default;

[0098] 2) Based on the prohibition signs in the traffic signs, such as no entry, no driving, and no motor vehicle entry signs, change the traffic area type of the geometric area affected by the sign to a passable area;

[0099] 3) Based on the instructions in the traffic signs, such as signs for motor vehicle lanes, small passenger vehicle lanes, and bus lanes, if the motor vehicle does not meet the requirements of the sign for motor vehicle type, the traffic area type of the geometric area where the sign applies will be changed to a traffic zone.

[0100] The results of the common traffic area types are shown in Table 7.

[0101] Table 7. Cognitive Results of Traffic Zone Types

[0102]

[0103] Step 5.2: Determine the upper and lower speed limits;

[0104] According to the "Regulations for the Implementation of the Road Traffic Safety Law of the People's Republic of China":

[0105] 1) Prioritize the speed indicated by traffic signs and markings, such as speed limit signs and speed limit markers, to determine the upper and lower speed limits within the geometric area;

[0106] 2) When there are no speed limit signs or markings in the geometric area, the speed limit is 30 km / h when performing specific actions in the geometric area, such as turning, going down a steep slope, or making a U-turn.

[0107] 3) If there are no traffic signs or markings in the geometric area, and no specific actions are performed, then the upper and lower speed limits are determined based on the cognitive results of step 4 (road type and number of lanes in the same direction). For example, the upper speed limit on a city road with only one lane for motor vehicles in the same direction is 50 km / h.

[0108] Common cognitive results for determining the upper and lower limits of speed are shown in Table 8.

[0109] Table 8. Cognitive Results of Upper and Lower Speed ​​Limits

[0110]

[0111] Step 5.3: Determine the type of the left and right boundary lines;

[0112] According to the national standard GB 5768.3, traffic markings can be divided into two types: dashed lines and solid lines. Therefore, the left and right boundary line types belong to the following set:

[0113] ;

[0114] in, For boundary line type, The line is dashed. The line is solid.

[0115] The types of left and right boundary lines are jointly determined based on the cognitive results of steps 2 and 3. For example, if the left boundary line of the current geometric area is a dashed line, but a no-overtaking sign exists in this geometric area, then the type of the left boundary line changes from dashed to solid. Traffic regulations regarding the left and right positions of motor vehicles are entirely reflected in the types of left and right boundary lines. When the boundary line is dashed, motor vehicles can cross it to enter adjacent geometric areas; when the boundary line is solid, motor vehicles cannot cross it to enter adjacent geometric areas. The cognitive results for common left and right boundary line types are shown in Table 9.

[0116] Table 9. Cognitive Outcomes of Left and Right Boundary Line Types

[0117]

[0118] Step 5.4: Determine the exit direction;

[0119] Based on the cognitive results from steps 2 to 4, the exit directions for each geometric region are determined. The exit direction refers to the connection between a vehicle exiting the current geometric region and entering the entrance of another geometric region, including lane keeping, going straight at an intersection, turning left at an intersection, turning right at an intersection, and making a U-turn. Therefore, the exit directions belong to the following set:

[0120] ;

[0121] in, To drive out of the direction, For lane keeping, To turn left at the intersection To turn right at the intersection For going straight at the intersection, To turn around.

[0122] 1) Based on the road network relationship, determine the exit direction of each geometric area, and then eliminate those that do not comply with traffic regulations;

[0123] 2) Based on the understanding of traffic markings, eliminate non-compliant exit directions. For example, if the directional arrow indicates a left turn, eliminate other exit directions and retain only the left turn at the intersection.

[0124] 3) Eliminate non-compliant exit directions based on the understanding of traffic signs. For example, if there is a sign prohibiting left turns, then eliminate left turns at intersections.

[0125] The common exit directions are shown in Table 10.

[0126] Table 10 Cognitive Results of the Direction of Exit

[0127]

[0128] Step 6: Traffic Area Recognition;

[0129] Based on the merged traffic attribute information from step 5, and adhering to the principle of complete consistency in traffic rules within each traffic area, the geometric regions of the road ahead are further divided into multiple traffic areas. Since the effects (traffic attributes) and scope (i.e., geometric regions) of traffic regulations are understood, the intersection of the geometric regions of each traffic attribute forms a traffic area. Multiple traffic areas will then "patch together" to cover the entire road ahead, resulting in a traffic environment model based on traffic area representation. Figure 4As shown in the image, the upper part is a top view of a structured road, with vehicles traveling from left to right. The lower part of the image shows the corresponding traffic environment model. The traffic areas enclosed by solid gray lines are lanes, such as traffic areas 1-2, 1-3, 2-4, and 3-2; the closed areas enclosed by dashed black lines are passage zones, such as traffic areas 1-5, 2-5, and 3-5; and the closed areas enclosed by dashed gray lines are no-entry zones, such as traffic areas 1-1, 1-5, and 2-1.

[0130] Step 7: Understanding Connections;

[0131] Based on the road network relationships identified in Step 4 and the traffic areas identified in Step 6, determine the entrance and exit connections of each traffic area; based on the left and right adjacency relationships of each lane identified in Step 4 and the traffic areas identified in Step 6, determine the left and right adjacency relationships of each traffic area. For example... Figure 4 As shown, traffic area 2-2 is adjacent to traffic area 2-1 on the left and traffic area 2-3 on the left. The entrance connects to traffic area 1-2 and the exit connects to traffic area 3-2.

[0132] Step 8: Traffic light recognition;

[0133] According to GB 14886, motor vehicle traffic lights are divided into three types: red, yellow, and green, therefore they belong to the following set:

[0134] ;

[0135] in, For the color of motor vehicle signal lights, It is red. It is yellow. It is green.

[0136] The system can recognize the current traffic light color for motor vehicles and change the type of the relevant traffic area according to the current traffic light color, as shown in Table 11.

[0137] Table 11 Cognitive Results of Traffic Lights

[0138]

[0139] Step 9: Traffic participant perception;

[0140] We recognize the types of traffic participants, including pedestrians, motor vehicles, and trucks. Traffic participant types are categorized into two types: motor vehicles and non-motor vehicles. Therefore, traffic participant types belong to the following set:

[0141] ;

[0142] in, For traffic participant types, For motor vehicles, It is a non-motorized vehicle.

[0143] The attribute data of traffic participants include: ID, type, bounding box size, position, attitude, speed, angular velocity, acceleration, and angular acceleration. All of these attributes are represented in the road coordinate system. Specific examples are shown in Table 12.

[0144] Table 12 Attribute data of traffic participants

[0145]

[0146] Traffic participants are indexed according to their type within the traffic area of ​​their location. The type of the relevant traffic area is changed according to the type of traffic participants within the traffic area. For example, if there are non-motorized vehicle types among the traffic participants in the traffic area of ​​a pedestrian crossing, the traffic area type is changed from lane to passage zone; if there are no non-motorized vehicle types among the traffic participants in the traffic area of ​​a pedestrian crossing, the traffic area type remains lane. Common examples are shown in Table 13.

[0147] Table 13 Results of the role of traffic participants

[0148]

[0149] Step 10: Obstacle Recognition;

[0150] The system identifies obstacles that impede the vehicle's operation, i.e., objects that do not have the ability to move independently, and indexes each obstacle into the traffic area where it is located.

[0151] The obstacle's attribute data includes: number, bounding box size, position, attitude, velocity, angular velocity, acceleration, and angular acceleration. All of these attributes are represented in the road coordinate system, as shown in Table 14.

[0152] Table 14 Attribute Data of Obstacles

[0153]

[0154] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for recognizing a traffic environment of a structured road for autonomous driving, characterized by, The method comprises the following steps: Step 1: driving route cognition, determining a cognition distance according to a current environment, and uniformly representing a road sequence including intersections within the cognition distance as a plurality of one-way roads in a road coordinate system based on a travel task; Step 2: traffic marking cognition, analyzing traffic markings, and cognizing the traffic markings as traffic attributes of a position, a speed or an exit direction of a motor vehicle, and indexing the traffic attributes to geometric regions in which the traffic attributes act; Step 3: traffic sign cognition, analyzing traffic signs, and cognizing the traffic signs as traffic attributes of a position, a speed, a size, a mass or an exit direction of a motor vehicle, and indexing the traffic attributes to geometric regions in which the traffic attributes act; Step 4: road structure cognition, acquiring a road network connection relationship, a number of lanes, a lane adjacency relationship and a road type of a driving route in front; Step 5: traffic attribute fusion, fusing cognition results of steps 2 to 4, and determining final traffic attributes in each geometric region, wherein the final traffic attributes at least include a traffic region type, an upper speed limit, a lower speed limit, a left boundary line type, a right boundary line type and an exit direction; Step 6: traffic region cognition, cutting each geometric region into a plurality of traffic regions based on a principle of complete consistency of traffic attributes, and splicing the plurality of traffic regions to form a traffic environment model of the driving route in front; Step 7: connection relationship cognition, determining an entrance connection, an exit connection, a left adjacency and a right adjacency relationship of each traffic region based on the road network connection relationship and the lane adjacency relationship; Step 8: traffic signal cognition, identifying a traffic signal color, and dynamically adjusting a traffic region type of a related traffic region according to the color; Step 9: traffic participant cognition, identifying a type and a state of a traffic participant, indexing the traffic participant to a traffic region in which the traffic participant is located, and dynamically adjusting a traffic region type of the traffic region in which the traffic participant is located according to the type of the traffic participant; Step 10: obstacle cognition, identifying an obstacle and indexing the obstacle to a traffic region in which the obstacle is located; In the step 5, a step of determining a traffic region type of a geometric region is as follows: a traffic region type of a geometric region between two adjacent lane lines is set as a lane; a type of a corresponding geometric region is changed to a passing zone according to a prohibition sign in a traffic sign; if a motor vehicle does not conform to a motor vehicle type required by a sign, a traffic region type of a geometric region on which the sign acts is changed to a passing zone according to an indication sign in the traffic sign; a region type without a motor vehicle driving condition is defined as a no-entry zone; traffic regulations are decoupled from other constraints, traffic regulations are analyzed before behavior decision or motion planning, a structured road is cognized as a traffic environment represented by a traffic region as a base element, a legal traffic region candidate set is screened out according to traffic attribute information of each traffic region; a passing zone is also included in the traffic region candidate set, a driving style of an aggressive driver model is realized by sacrificing legality for a short time and obtaining a benefit of other values when a decision is made; if there is only a lane in the traffic region candidate set, an ordinary driver model or a defensive driving style of a driver model is realized.

2. The method of claim 1, wherein the method is a method of recognizing a traffic environment of a structured road for an autonomous vehicle. In the step 1, the intersection is recognized as a one-way road with a single entrance and a single exit, which are determined by the trip mission. 3.The autonomous vehicle-oriented structured road traffic environment cognition method according to claim 1, wherein, In the step 5, the upper and lower speed limits follow the following priority order: The speed value marked by traffic signs or traffic markings is given priority; If there is no clear speed sign or marking, the upper speed limit is set to a preset value in the geometric area where the turning, U-turn or steep slope behavior is performed; If there is neither a speed sign nor a turning, steep slope and U-turn behavior, the legal default upper and lower speed limits are determined according to the road type and the number of same-direction lanes. 4.The autonomous vehicle-oriented structured road traffic environment cognition method according to claim 1, wherein, In the step 5, the specific steps for determining the exit direction are as follows: Based on the road network relationship, all possible exit directions are obtained; According to the directional arrow traffic marking, the exit directions consistent with the arrow direction are retained, and the other directions are excluded; According to the prohibition type traffic sign, the prohibited directions are excluded from the set of all possible directions. 5.The autonomous vehicle-oriented structured road traffic environment cognition method of claim 1, wherein, In the step 8, when the traffic signal is red or yellow, the type of the traffic area in front of the associated stop line is changed from lane to crossing area; when the traffic signal is green, the type is restored to lane. 6.The autonomous vehicle-oriented structured road traffic environment cognition method of claim 1, wherein, In the step 9, when a non-motor vehicle type of traffic participant is identified to be located in the pedestrian crossing traffic area, the type of the pedestrian crossing traffic area is changed from lane to crossing area.

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