Method for capturing static and dynamic information at track level

Abstract

A method for controlling a vehicle, comprising: Receiving, by a processor, vehicle position data from the vehicle; Processing, by the processor, the vehicle position data with vehicle position data from other vehicles to determine a lane topology along a road; Processing, by the processor, the vehicle position data to determine traffic conditions within a lane of the lane topology; Generating, by the processor, a map for controlling the vehicle based on the lane topology and the traffic conditions within the lane of the lane topology, wherein processing the vehicle position data with the vehicle position data from the other vehicles to determine the lane topology comprises: Preprocessing, by the processor, the vehicle position data; Expanding, by the processor, the preprocessed vehicle position data; Aggregating, by the processor, the expanded pre-processed vehicle position data with the pre-processed vehicle position data from the other vehicles; bundling, by the processor, the accumulated vehicle position data to determine lanes; Correcting, by the processor, the road geometry of the determined lanes based on a yaw rate of the vehicle and vehicle position data; and Determining, by the processor, the lane topology based on the corrected road geometry of the determined lanes; further comprising applying constraints to the bundled vehicle position data, and wherein correcting the road geometry is based on the restricted bundled vehicle position data, where the constraints include weights applied to the vehicle position data, where the traffic conditions include temporal-spatial conditions based on a history of traffic conditions.

Description

INTRODUCTION

[0001] The technical field generally relates to methods and systems for mapping lane level information, particularly to methods and systems for mapping lane level information using global positioning system information from one or more vehicles.

[0002] Autonomous and semi-autonomous vehicles use mapping information to control one or more vehicle components. Maps are typically created based on data from survey vehicles that traverse the road network and collect the data. These maps take time to create. Furthermore, these maps quickly become outdated due to road construction and other changes to the road network.

[0003] Accordingly, it is desirable to provide improved methods and systems for mapping lane level information. Furthermore, other desirable features and characteristics of the present invention will become apparent from the following detailed description and the appended claims, taken in conjunction with the accompanying figures and the foregoing technical field and background.

[0004] DE 10 2017 108 774 A1 describes a computer can receive data from a vehicle sensor about a plurality of second vehicles, define two or more vehicle clusters based on position data of second vehicles, each cluster including two or more of the second vehicles determined to be traveling in the same lane, identify two or more lane boundaries according to clusters and use lane boundaries to generate a lane map.

[0005] DE 10 2008 023 970 A1 describes a method and a device designed to carry out the method for recognizing traffic-relevant information in a moving vehicle, wherein image data from a visual sensor and map data from a navigation system are each pre-evaluated for recognition and the results of the pre-evaluation are combined and interpreted.

[0006] DE 10 2016 112 913 A1 describes a system comprising a processor configured to receive image data collected by a vehicle camera and relating to a fixed environmental feature. The processor is also configured to determine a vehicle position relative to the fixed environmental feature and to determine a lane-related vehicle location on a digital map based on the vehicle position relative to the fixed environmental feature. DESCRIPTION

[0007] The object of the invention is to provide improved methods and systems for mapping lane level information. This object is achieved by the subject matter according to claim 1. Further developments can be found in the subclaims.

[0008] Methods and apparatus for controlling a vehicle are provided. In one embodiment, a method includes: receiving, by a processor, vehicle position data from the vehicle; processing, by the processor, vehicle position data with vehicle position data from other vehicles to determine a lane topology; processing, by the processor, the vehicle position data to determine traffic conditions within a lane of the lane topology; generating, by the processor, a map for controlling the vehicle based on the lane topology and traffic conditions.

[0009] In various embodiments, processing the vehicle position data with the vehicle position data of the other vehicles to determine the lane topology includes: preprocessing, by the processor, the vehicle position data; expanding, by the processor, the preprocessed vehicle position data; aggregating, by the processor, the expanded, preprocessed vehicle position data with preprocessed vehicle position data from the other vehicles; aggregating, by the processor, the aggregated vehicle position data to determine lanes; correcting, by the processor, the road geometry of the determined lanes based on the yaw rate of the vehicle and the vehicle position data; and determining, by the processor, the lane topology based on the corrected road geometry of the determined lanes.

[0010] In various embodiments, the method further includes applying constraints to the aggregated vehicle position data, and the road geometry correction is based on the aggregated vehicle position data. In various embodiments, the constraints are compared with data from a camera or lidar of the vehicle. In various embodiments, the constraints include loads applied to the vehicle position data.

[0011] In various embodiments, the vehicle position data includes the vehicle's latitude and longitude from a global positioning system. In various embodiments, the traffic conditions include temporal-spatial conditions based on a history of traffic situations. In various embodiments, the traffic conditions include an indication of a traffic incident. In various embodiments, the traffic conditions also include detected lane changes.

[0012] In another embodiment, a system includes: a global positioning system that generates vehicle position data; and a control module that receives, through a processor, the vehicle position data, processes the vehicle position data with vehicle position data from other vehicles to determine a lane topology, processes the vehicle position data to determine traffic conditions within a lane of the lane topology, and generates a map for controlling the vehicle based on the lane topology and the traffic conditions.

[0013] In various embodiments, the control module processes the vehicle position data with the vehicle position data of the other vehicles to determine the lane topology: preprocessing, by the processor, the vehicle position data; expanding, by the processor, the preprocessed vehicle position data; aggregating, by the processor, the expanded, preprocessed vehicle position data with preprocessed vehicle position data from the other vehicles; aggregating, by the processor, the aggregated vehicle position data to determine lanes; correcting, by the processor, the road geometry of the determined lanes based on a yaw rate of the vehicle and vehicle position data; and determining the lane topology based on the corrected road geometry of the determined lanes.

[0014] In various embodiments, the control module applies constraints to the aggregated vehicle position data and corrects the road geometry based on the aggregated vehicle position data. In various embodiments, the constraints are compared with data from a camera or lidar of the vehicle. In various embodiments, the constraints include loads applied to the vehicle position data. In various embodiments, the vehicle position data includes latitude and longitude of the vehicle from a global positioning system. In various embodiments, the traffic conditions include temporal-spatial conditions based on a history of traffic situations.

[0015] In various embodiments, the traffic conditions include an indication of a traffic delay. In various embodiments, the traffic conditions include detected lane changes.

[0016] In another embodiment, a vehicle includes: a vehicle positioning system that provides vehicle position data; a communication system that receives vehicle position data from other vehicles; and a control module that receives, through a processor, the vehicle position data, processes the vehicle position data with vehicle position data from other vehicles to determine a lane topology, processes the vehicle position data to determine traffic conditions within a lane of the lane topology, and generates a map for controlling the vehicle based on the lane topology and the traffic conditions.

[0017] In various embodiments, the control module processes the vehicle position data with the vehicle position data of the other vehicles to determine the lane topology: preprocessing, by the processor, the vehicle position data; expanding, by the processor, the preprocessed vehicle position data; aggregating, by the processor, the expanded, preprocessed vehicle position data with preprocessed vehicle position data from the other vehicles; aggregating, by the processor, the aggregated vehicle position data to determine lanes; correcting, by the processor, the road geometry of the determined lanes based on a yaw rate of the vehicle; and determining the lane topology based on the corrected road geometry of the determined lanes. BRIEF DESCRIPTION OF THE CHARACTERS

[0018] The exemplary embodiments are described below in conjunction with the following reference numerals, where like reference numerals denote like elements and where: Fig. 1 is an illustration of a vehicle with an imaging system according to various embodiments; Fig. 2 and Fig. 3 are data flow diagrams illustrating the imaging system according to various embodiments; Fig. 4 is an illustration of example data generated by the imaging system according to various embodiments; and Fig. 5 is a flowchart illustrating imaging methods that may be performed by the imaging system according to various embodiments. DETAILED DESCRIPTION

[0019] The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description. As used herein, the term module refers to an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group), and a memory that executes one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functionality.

[0020] Embodiments of the present disclosure may be described herein with reference to functional and / or logical block components and various processing steps. It should be appreciated that such block components may be implemented by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure may utilize various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, lookup tables, or the like, capable of performing a variety of functions under the control of one or more microprocessors or other control devices.Furthermore, those skilled in the art will understand that embodiments of the present disclosure may be practiced in connection with any number of systems and that the systems described herein are merely exemplary embodiments of the present disclosure.

[0021] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning models, radar, lidar, image analysis, and other functional aspects of the systems (and the individual operating components of the systems) may not be described in detail herein. Furthermore, the connecting lines depicted in the various figures are intended to represent exemplary functional relationships and / or physical couplings between the various elements. It should be noted that many alternative or additional functional relationships or physical connections may be present in an embodiment of the present disclosure.

[0022] With reference to Fig. 1, a mapping system, generally depicted as 100, is associated with a vehicle 10 according to various embodiments. Generally, the mapping system (or simply "system") 100 generates map data including static and dynamic lane level information for use in controlling the vehicle 10. In various embodiments, the mapping system 100 generates the map data based on information obtained from a tracking system of the vehicle 10 and / or tracking information received from other vehicles (not depicted).

[0023] As in Fig. 1, the vehicle 10 generally includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially encloses components of the vehicle 10. The body 14 and the chassis 12 may together form a frame. The wheels 16-18 are each rotatably coupled to the chassis 12 near a corresponding corner of the body 14.

[0024] In various embodiments, vehicle 10 is an autonomous vehicle or a semi-autonomous vehicle. As can be appreciated, imaging system 100 may be implemented in other non-autonomous systems and is not limited to the present embodiments. Vehicle 10 is depicted as a passenger car in the illustrated embodiment, but it should be understood that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), naval vessels, aircraft, etc., may be used.

[0025] As illustrated, the vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and the communication system 36. The propulsion system 20, in various embodiments, may include an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell propulsion system. The transmission system 22 is configured to transfer power from the propulsion system 20 to the vehicle wheels 16 and 18 according to selectable gear ratios. According to various embodiments, the transmission system 22 may include a continuously variable automatic transmission, a continuously variable transmission, or other suitable transmission.

[0026] The braking system 26 is configured to provide braking torque to the vehicle wheels 16 and 18. In various embodiments, the braking system 26 may include friction brakes, wire brakes, a regenerative braking system such as an electric machine, and / or other suitable braking systems.

[0027] The steering system 24 influences a position of the vehicle wheels 16 and / or 18. Although depicted as a steering wheel for illustrative purposes, the steering system 24 may not include a steering wheel in some embodiments contemplated by the present disclosure.

[0028] The sensor system 28 includes one or more sensor devices 31a-31n that sense observable conditions of the external environment and / or the internal environment of the vehicle 10 (such as the condition of one or more occupants). In various embodiments, the sensor devices 31a-31n include, among others, radars (e.g., long-range, medium-range, and short-range radar), lidars, global positioning systems, optical cameras (e.g., forward, 360-degree, rear, side, stereo, etc.), thermal (e.g., infrared) cameras, ultrasonic sensors, odometry sensors (e.g., encoders), and / or other sensors that may be used in connection with systems and methods according to the present subject matter. The sensor system 28 provides information for determining a position of the vehicle 10.

[0029] The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as the drive system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the autonomous vehicle 10 may also include interior and / or exterior vehicle features that are Fig. 1, such as various doors, a trunk, and cabin features such as air, music, lighting, touchscreen display components (such as those used in conjunction with navigation systems), and the like.

[0030] Data storage device 32 stores data for use in the automatic control of autonomous vehicle 10. In various embodiments, data storage device 32 stores defined maps of the navigable environment. In various embodiments, the defined maps are generated by imaging system 100 and include static and dynamic lane level information, as explained in more detail below.

[0031] The communication system 36 is configured to wirelessly transmit information to and from other units 48, such as, but not limited to, other vehicles (“V2V” communication), infrastructure (“V2I” communication), networks (“V2N” communication), pedestrians (“V2P” communication), remote transportation systems, and / or user devices (described in more detail with respect to Fig. 2). In an exemplary embodiment, the communication system 36 is a wireless communication system configured to communicate over a wireless local area network (WLAN) using the IEEE 802.11 standards or using cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communication channel (DSRC), are also contemplated within the scope of this disclosure. DSRC channels refer to short- to medium-range, one-way or two-way wireless communication channels specifically designed for automotive use, and to a corresponding set of protocols and standards.

[0032] The controller 34 includes at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 may be any custom-built or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC) (e.g., a custom ASIC implementing a neural network), a field-programmable gate array (FPGA), an auxiliary processor among a plurality of processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or generally any device for executing instructions. The computer-readable storage device or medium 46 may include, for example, volatile and non-volatile read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM).KAM is persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is powered off. The computer-readable storage device or storage medium 46 can be implemented using any number of known storage devices, such as PROMs (programmable read-only memory), EPROMs (electrically PROMs), EEPROMs (electrically erasable PROMs), flash memory, or other electrical, magnetic, optical, or combination storage devices capable of storing data, some of which represent executable instructions used by the controller 34 to control the autonomous vehicle 10. In various embodiments, the controller 34 is configured to implement instructions of the imaging system 100, as explained in detail below.

[0033] In various embodiments, the instructions, when executed by the processor, receive and process position information of the vehicle 10 and / or other vehicles to determine static and dynamic lane information. The instructions create and store a map for use in controlling the vehicle 10 and / or other vehicles based on the static and dynamic lane information.

[0034] With reference to the Fig. 2-3 and with further reference to the Fig. 1 are the Fig. 2-3 Data flow diagrams that further illustrate aspects of the imaging system 100. Fig. is an illustration of the results from the data flow according to various embodiments. As can be seen, the Fig. 2-3 may be combined and / or further subdivided to also perform the functions described herein. Inputs to modules and submodules may be received from the sensor system 28, from other control modules (not shown) associated with the vehicle 10, from the communication system 36, and / or by other submodules (not shown) within the controller 34 of Fig. 1. The modules and submodules shown generally fulfill the functions of determining static and dynamic lane information and creating a map for use in controlling the vehicle 10 based thereon. Thus, the mapping system 100 includes, as shown in Fig. 2, a static data determination module 50, a dynamic data determination module 52 and a map generation module 54.

[0035] The static data determination module 50 receives the position data 56 from the vehicle 10 and / or the position data 60 from other vehicles. The position data 56, 60 may include time series data from, for example, a GPS system. In such examples, the position data 56, 60 are processed to create a GPS track (latitude, longitude) (x t , y t ), a GPS error margin trace δ t and a probability distribution function of the GPS error f σ (σ). In various embodiments, the position data 56, 60 further include information about the camera area, including the track position l t and the lateral distance offset Δdt. Based on the received position data 56, 60, the static data determination module 50 determines static lane data including a topology of lanes and generates topology data 64 based thereon.

[0036] The dynamic data determination module 52 receives the topology data 64 and the preprocessed data 62. The preprocessed data 64 includes vehicle position data 56 that has been processed, as explained in more detail below. Based on the received data 62, 64, the dynamic data determination module 52 determines dynamic lane data 66 for each lane identified in the topology data 64.

[0037] In various embodiments, the dynamic lane data 66 may include a current estimate of the traffic situation. In various embodiments, the dynamic data determination module 52 determines the current conditions based on historical data and temporal-spatial conditions.

[0038] For example, if the topology data 64 indicates that lanes A, B, and C are present, a historical average may be determined for each lane based on an aggregation of preprocessed data 62 associated with the dynamic lane data 66 previously determined to be associated with the lane. For example, historical data f hist (x, t) as the mean of the points S in the trace minus any outlier points S outlier at time t can be estimated as: fhist(x,t)=avg(S(x,t)−Soutlier(x,t)).

[0039] Historical data f hist (x, t) are used to calculate f(x, t).

[0040] The current state of the lane can then be estimated as follows: f⌢(x,t)=αf(x,t)+β⌢f(x,t−1). Where α and β represent parameters calculated from calibrations for the current estimation.

[0041] In various embodiments, the dynamic lane data 66 may take lane changes into account. A lane change may be detected, for example, based on whether the road is straight or curved. In various embodiments, if the road is straight, the lane change may be detected when a steering pattern is identified (wavy, not straight) and a certain lateral shift distance is greater than a threshold. In various embodiments, the lateral shift distance L between points A and B may be calculated as: L=∑AB(v(t)sin θ(t)Δt). where v is the speed and θ is the angle between the straight road center line and the current direction at time t.

[0042] In various embodiments, if the road is curved, the lane change can be detected based on the curvature of the lane divider caused by the crowd compared to an expected yaw rate without a lane change. The yaw rate can be calculated, for example, as: ω¯=vR=v1Ns∑i∈sviωi

[0043] The lane change can be detected using a shift distance L compared to a threshold value. The shift distance can be calculated as: L(I)=∑i=0IvΔt(I)⋅sin(∑j=0i(ω−ω¯)Δt(j)). In various embodiments, the dynamic lane data 66 may include an indication of traffic congestion. The traffic congestion may be identified based on a comparison between the probability distribution functions of speed during normal traffic conditions and abnormal traffic conditions. A severity of the traffic congestion may be indicated using the Jensen-Shannon deviation and the Kullback-Leibler distance. For example, a lane-level congestion severity indicator may be used to determine which lane is less congested, and a vehicle may be recommended to move to that less congested, desirable lane during navigation instruction.

[0044] The map generation module 54 receives the topology data 64 and the dynamic lane data 66. The map generation module 54 generates map data 68 comprising a map that combines the lane topology with the dynamic lane information to obtain a current representation of the navigable environment. The map can then be used by the controller 34 of the vehicle 10 to control one or more features of the vehicle 10.

[0045] With reference now to Fig. 3 illustrates the static data determination module in more detail according to various embodiments. In various embodiments, the static data determination module 50 includes a position data preprocessing module 72, a dilation module 74, an accumulation module 76, a cluster module 78, and a road geometry correction module 80.

[0046] The position data preprocessing module 72 processes the vehicle position data 56, 60 received from the vehicle 10 and / or other vehicles. The preprocessed data includes the GPS track (x t , y t ), the GPS error margin trace δ t and a probability distribution function of the GPS error f σ (σ).

[0047] The stretching module 74 receives the preprocessed data 84. The stretching module 74 stretches the position traces from the vehicle 10 and generates expanded data 84 based thereon. For example, the stretching module generates for each sample point (x t , y t ) N (e.g., 100 or another integer) pseudopoints. The pseudopoints can be generated based on the following relationships: xti=xt+Δxi yti=yt+Δyi Where(Δxi,Δyi)∼fσ(σ).

[0048] The aggregation module 76 receives the expanded data 84 from vehicle 10 and the expanded data 84 from other vehicles. The aggregation module 76 aggregates the expanded traces 84, 86 from the various vehicles into a two-dimensional scatter plot and generates aggregated data 88 based thereon.

[0049] The cluster module 78 receives the accumulated data 88 and other data 90, e.g., from the camera. The cluster module 78 clusters the accumulated data 88 and generates cluster data 92 based on it. For example, the cluster module 78 defines initial cluster sets C corresponding to the number k of tracks as {C1, C2, C3, ....... C k}. For every extended point S i of the accumulated data 88, the bundle module 78 assigns the point S i a nearest cluster C j based on a distance to. For each cluster C jthe cluster module 78 updates a center by calculating the mean of all elements in cluster C j The bundle module 78 executes these steps until the k-mean algorithm converges.

[0050] In various embodiments, the bundling module 78 bundles the accumulated data 88 based on a comparison of defined constraints with the information determined from the camera data 90 or similar perception sensor sources such as lidar or radar. For example, various constraints may be defined for a series of lanes. Example constraints may include, but are not limited to: 1. Cars A and B are on the same track. 2. Cars A and C are on different tracks. 3. More loads for vehicle A in lane X. 4. Lower loads for vehicle A with turn signal on and driving straight. 5. More loads for vehicle A when the turn signal is on and driving on an edge lane.

[0051] In such embodiments, the bundling module 78 checks whether the assignment to a cluster C j violates the constraints listed in constraints 1 or 2. If the assignment violates one of constraints 1 or 2, the extended point S i assigned to the nearest cluster Cj+1. This check can be performed by the bundle module 78 until the assignment to a cluster does not violate the constraints.

[0052] In various embodiments, the bundle module 78 has the points S i in each cluster C jWeights are assigned based on constraints 3, 4, and 5. The weights are then evaluated by the clustering module 78 in the k-mean algorithm. As can be seen, other constraints may be applied to clustering in various embodiments.

[0053] The road geometry correction module 80 receives the cluster data 92 and the vehicle data 94. The vehicle data 94 may include, but is not limited to, the vehicle's yaw rate, the vehicle position data, or other vehicle information indicative of a physical position of the vehicle 10. The road geometry correction module 80 corrects the cluster data 92, including the center of the cluster, based on the known road geometry and generates the topology data 64 therefrom.

[0054] In various embodiments, the road geometry correction module 80 corrects the cluster data 92 based on a mean square error (MSE). In various embodiments, the known road geometry may be indicated by the vehicle data 94. For example, if the yaw rate indicates that the vehicle 10 is traveling straight, the cluster data is corrected based on a road template function: y = ax and a linear MSE regression. In another example, if the yaw rate indicates that the vehicle 10 is traveling along a winding road, the cluster data 82 is corrected based on a road template function y=∑0Kβi∗xi and corrected for MSE regression.

[0055] Fig. 4 illustrates a progression of the position data 56 into the topology data 64. As shown, the preprocessed data 82 is processed to provide expanded data 84. The expanded data 84 is aggregated with other data to obtain aggregate data 88. The aggregate data 88 is clustered and constrained to provide cluster data 92. The lane data 93 is extracted from the cluster data 92, and the road geometry correction is applied to the lane data 93 to provide the topology data 64, including the lane identification.

[0056] With reference to Fig. 5 is a flowchart illustrating an imaging method performed by the imaging system 100 ( Fig. 1) can be carried out according to various embodiments. As can be seen in view of the disclosure, the order of operation within the method is not limited to sequential execution, as in Fig. 5, but may optionally be performed in one or more different orders and in accordance with the present disclosure. In various embodiments, method 200 may be scheduled to run based on one or more predetermined events and / or to run continuously during operation of vehicle 10.

[0057] The vehicle position data, including GPS data, CAN bus trace data, camera data, lidar data, and / or radar data, is received at 210. The vehicle position data is processed to determine a static topology of the lanes at 220, as described above with respect to the Fig. 3 and Fig. 4. The vehicle position data is processed together with the topology of the lanes to determine the traffic conditions in each lane at 230, as described above with regard to Fig. 2. The map is generated based on the lane topology and traffic conditions at 240, and the vehicle is controlled based on the amplifier at 250. The process can then end at 260.

[0058] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that numerous variations exist. It should also be appreciated that the exemplary embodiment or embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description provides those skilled in the art with a convenient roadmap for implementing the exemplary embodiment or embodiments. It should be understood that various changes in the function and arrangement of elements may be made without departing from the scope of the disclosure as set forth in the appended claims and their legal equivalents.

Claims

[1] A method of controlling a vehicle, comprising: Receiving, by a processor, vehicle position data from the vehicle; Processing, by the processor, the vehicle position data with vehicle position data from other vehicles to determine a lane topology along a road; Processing, by the processor, the vehicle position data to determine traffic conditions within a lane of the lane topology; Generating, by the processor, a map for controlling the vehicle based on the lane topology and the traffic conditions within the lane of the lane topology, wherein processing the vehicle position data with the vehicle position data from the other vehicles to determine the lane topology comprises: Preprocessing, by the processor, the vehicle position data; Expanding, by the processor, the preprocessed vehicle position data; Aggregating, by the processor, the expanded pre-processed vehicle position data with the pre-processed vehicle position data from the other vehicles; bundling, by the processor, the accumulated vehicle position data to determine lanes; Correcting, by the processor, the road geometry of the determined lanes based on a yaw rate of the vehicle and vehicle position data; and Determining, by the processor, the lane topology based on the corrected road geometry of the determined lanes; further comprising applying constraints to the bundled vehicle position data, and wherein correcting the road geometry is based on the restricted bundled vehicle position data, where the constraints include weights applied to the vehicle position data, where the traffic conditions include temporal-spatial conditions based on a history of traffic conditions. [2] The method of claim 1, wherein the constraints are compared with data obtained from a camera and / or lidar of the vehicle. [3] The method of claim 1, wherein the vehicle position data includes latitude and longitude tracks of the vehicle from a global positioning system. [4] The method of claim 1, wherein the traffic conditions include an indication of the traffic disruption. [5] The method of claim 1, wherein the traffic conditions include detected lane changes.

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