Patent Description:
Knowledge of driving environment is advantageous to drivers and autonomous driving. Through on-board sensors (e.g., radar, lidar, camera, etc.), knowledge of objects adjacent to a vehicle can be acquired by the vehicle. However, sensing data could be incomplete or inaccurate. For example, a blocked object could be missed by the on-board sensors, or a ghost object could be generated by the on-board sensors. Currently, in order to solve this problem, the sensing data are exchanged between vehicles via an inter-vehicle communication network for sharing knowledge of driving environment such as position of a stationary or moving object, velocity of a moving object, etc. Nevertheless, the shared knowledge of driving environment is abstract and limited. Therefore, there is a need for a method and a device to know driving environment.

<CIT> discloses a system for construction zone object detection. A computing device is configured to receive, from a LIDAR, a 3D point cloud of a road on which a vehicle is travelling. The 3D point cloud comprises points corresponding to light reflected from objects on the road. Also, the computing device is configured to determine sets of points in the 3D point cloud representing an area within a threshold distance from a surface of the road. Further, the computing device is configured to identify construction zone objects in the sets of points. Further, the computing device is configured to determine a likelihood of existence of a construction zone, based on the identification. Based on the likelihood, the computing device is configured to modify a control strategy of the vehicle; and control the vehicle based on the modified control strategy.

<CIT> discloses a system in which a computer in a first vehicle is programmed to receive a first set of data from at least one sensor in the first vehicle and to receive a second set of data from at least one second vehicle. The second set of data is from at least one sensor in the at least one second vehicle. The computer is further programmed to use both the first set of data and the second set of data to identify at least one feature of a road being traversed by the first vehicle.

Embodiments of the present disclosure provide a device for virtualizing a driving environment surrounding a first node, which includes: a data acquisition device, configured to acquire position data of the first node, position data and sensing data of at least one second node, where the at least one second node and the first node are in a first communication network; and a scene construction device, configured to construct a scene virtualizing the driving environment surrounding the first node based on the position data of the fist node and the at least one second node, and on the sensing data of the at least one second node. The sensing data is compressed and comprises a plurality of data sets each comprising two sets of coordinates representing two ends of an edge of an object detected and an index of layer of recursive sub-cube division.

In some embodiments, the device may further include: a decompression device configured to decompress the sensing data of the at least one second node.

In some embodiments, the scene construction device may include: a topology construction device, configured to construct a topology including position coordinates of the at least one second node relative to a position coordinate of the first node, based on the position data of the first node and the at least one second node ; and an image construction device, configured to identify sensing data for objects in the driving environment based on the sensing data of the at least one second node and the topology, and to fuse the identified sensing data to construct the objects and to construct a scene virtualizing the driving environment surrounding the first node based on the constructed objects and the topology.

In some embodiments, the data acquisition device may be further configured to acquire sensing data of the first node that contains information of objects adjacent to the first node, and the image construction device is further configured to identify sensing data for objects in the driving environment based on the sensing data of the first node and the at least one second node, and to fuse the identified sensing data to construct the objects and to construct a scene virtualizing the driving environment surrounding the first node based on the constructed objects and the topology.

In some embodiments, the data acquisition device may be further configured to acquire position data of at least one third node that is not within the first communication network but is within a second communication network together with a part of the at least one second node, and the topology construction device is further configured to construct a topology including position coordinates of the at least one second node and the at least one third node relative to the position coordinate of the first node, where the position data of the at least one third node is obtained from the part of the at least one second node.

In some embodiments, the data acquisition device may be further configured to acquire sensing data of the at least one third node, and the image construction device is further configured to identify sensing data for objects in the driving environment based on the sensing data of the at least one second node and the at least one third node, and to fuse the identified sensing data to construct the objects and to construct a scene virtualizing the driving environment surrounding the first node based on the constructed objects and the topology.

In some embodiments, the sensing data may be compressed and include a node identification and a data set including position data, velocity data, size data or shape data for objects detected.

In some embodiments, each of the multiple data sets may further include intensity data or speed data.

The embodiments of the present disclosure further provide a method for virtualizing a driving environment surrounding a first node, which may includes: acquiring position data of the first node, position data and sensing data of at least one second node, where the at least one second node and the first node are in a first communication network; and constructing a scene virtualizing the driving environment surrounding the first node based on the position data of the fist node and the at least one second node, and on the sensing data of the at least one second node. The sensing data is compressed and comprises a plurality of data sets each comprising two sets of coordinates representing two ends of an edge of an object detected and an index of layer of recursive sub-cube division.

In some embodiments, the sensing data of the at least one second node may be compressed, and before constructing the scene virtualizing the driving environment, the method may further include: decompressing the sensing data of the at least one second node.

In some embodiments, constructing a scene virtualizing the driving environment may include: constructing a topology including position coordinates of the at least one second node relative to a position coordinate of the first node, based on the position data of the first node and the at least one second node ; identifying sensing data for objects in the driving environment based on the sensing data of the at least one second node and the topology; fusing the identified sensing data to construct the objects; and constructing a scene virtualizing the driving environment surrounding the first node based on the constructed objects and the topology.

In some embodiments, the method may further include: acquiring sensing data of the first node which contains information of objects adjacent to the first node. In some embodiments, constructing a scene virtualizing the driving environment may include: constructing a topology including position coordinates of the at least one second node relative to a position coordinate of the first node, based on the position data of the first node and the at least one second node; identifying sensing data for objects in the driving environment based on the sensing data of the first node and the at least one second node; fusing the identified sensing data to construct the objects; and constructing a scene virtualizing the driving environment surrounding the first node based on the constructed objects and the topology.

In some embodiments, the method may further include: acquiring position data of at least one third node that is not within the first communication network but is within a second communication network together with a part of the at least one second node, where the position data of the at least one third node is obtained from the part of the at least one second node.

In some embodiments, constructing a scene virtualizing the driving environment may include: construct a topology including position coordinates of the at least one second node and the at least one third node relative to a position coordinate of the first node based on the position data of the at least one second node and at least one third node.

In some embodiments, the method may further include: acquiring sensing data of the at least one third node.

In some embodiments, constructing a scene virtualizing the driving environment may further include: identifying sensing data for objects in the driving environment based on the sensing data of the at least one second node and the at least one third node; fusing the identified sensing data to construct the objects; and constructing a scene virtualizing the driving environment surrounding the first node based on the constructed objects and the topology.

In some embodiments, the method may further include: obtaining sensing data of objects detected by a sensor mounted on the vehicle; compressing the sensing data of objects; and transmitting the compressed sensing data. In some embodiments, the sensor may be a lidar.

By utilizing position data and sensor data of a node, a scene for virtualizing a driving environment can be constructed in real time for a driver, which improves driving safety.

The foregoing and other features of the present disclosure will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only several embodiments in accordance with the disclosure and are, therefore, not to be considered as limitation to its scope, the disclosure will be described with additional specificity and detail through use of the accompanying drawings.

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limitation. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this disclosure.

Typically, there may be multiple nodes in an actual driving environment. The multiple nodes may include a vehicle, a mobile communication device, a stationary communication device, etc., and at least a portion of the multiple nodes includes a GPS and a sensor (e.g., a lidar).

<FIG> schematically illustrates an exemplified actual driving environment <NUM>. The environment <NUM> includes a first car <NUM>, a second car <NUM>, a third car <NUM>, a fourth car <NUM>, two stationary objects <NUM> and <NUM> that each may be a tree, a pedestrian island, a standing person, etc., two motorcycles <NUM> and <NUM>, and two moving persons <NUM> and <NUM>. The first car <NUM>, the second car <NUM>, the third car <NUM> and the fourth car <NUM> each are mounted with a GPS and a lidar.

The first car <NUM> can communicate with the second car <NUM> and the third car <NUM>, thus the first car <NUM>, the second car <NUM> and the third car <NUM> belong to an inter-vehicle communication network <NUM>, such as a Dedicated Short Range Communication (DSRC) network. The fourth car <NUM> can communicate with the second car <NUM> but can not communicate with the first car <NUM>, thus the fourth car <NUM> does not belong to the inter-vehicle communication network <NUM> but belong to an inter-vehicle communication network <NUM> including the second car <NUM> and the fourth car <NUM>. As an example, the first car <NUM> carries a device for virtualizing a driving environment <NUM>.

<FIG> schematically illustrates a structural diagram for the device for virtualizing a driving environment <NUM> as shown in <FIG> according to an embodiment in the present disclosure. The device for virtualizing a driving environment <NUM> at least includes a data acquisition device <NUM>, which includes a position data device <NUM>, a Vehicle to X (V2X) data reception device <NUM>, a sensing data device <NUM>. The device for virtualizing a driving environment <NUM> further includes a scene construction device <NUM>, where the scene construction device <NUM> includes a topology construction device <NUM>, and an image construction device <NUM>.

Referring to <FIG>, the position data device <NUM> is configured to acquire position data of the first car <NUM>. The V2X data reception device <NUM> is configured to acquire position data from cars in a same communication network, namely, from both the second car <NUM> and the third car <NUM>. The sensing data device <NUM> is configured to acquire sensing data from a sensing device mounted on a car. In some embodiments, the sensing device may be a lidar.

In some embodiments, the position data of the first car <NUM>, and the position data from both the second car <NUM> and the third car <NUM> may be acquired based on data of a GPS or a high-precision GPS such as the Real-Time Kinematic (RTK) system. The GPS-RTK can precisely locate an accurate position of a car. In some embodiments, the position data of the first car <NUM> may be the GPS data or the high-precision GPS data.

In some embodiments, the position data of the first car <NUM>, and the position data from both the second car <NUM> and the third car <NUM> may contain identity information of a car carrying the GPS or the high-precision GPS.

In some embodiments, the GPS data may be transmitted within a communication network via a Basic Safety Message (BSM) or a Cooperative Awareness Message (CAM), and besides the GPS data, the BSM or CAM may further include speed data and heading data.

In some embodiments, a car in the communication network can broadcast position data of the car and also broadcast position data of another car communicating with the car, such as the position data from the second car <NUM> may include position data of the second car <NUM>, and may further include position data of the first car <NUM> and the fourth car <NUM>.

The sensing data from both the second car <NUM> and the third car <NUM> may be compressed data. In some embodiments, the sensing data from the second car <NUM> may include sensing data of the second car <NUM> and may further include sensing data received from the fourth car <NUM>.

In some embodiments, the sensing data from both the second car <NUM> and the third car <NUM> may be transmitted to the first car <NUM> in response to a request by the first car <NUM>. In some embodiments, the sensing data from both the second car <NUM> and the third car <NUM> may be respectively broadcasted by the second car <NUM> and the third car <NUM>.

<FIG> schematically illustrates a structural diagram for a data structure of sensing data according to an embodiment in the present disclosure. Referring to <FIG>, the sensing data from a car may include a vehicle identification <NUM> and a data set including position data <NUM>, velocity data <NUM>, size data <NUM> and shape data <NUM> for an object. Specifically, the vehicle identification <NUM> is an identification of a vehicle detecting an object. For example, referring to <FIG>, the third car <NUM> detects the motorcycle <NUM>, thus the vehicle identification <NUM> is "<NUM>" in this case. The position data <NUM> represents a relative position of the object from the vehicle detecting the object. For example, further referring to <FIG>, the motorcycle <NUM> is located at northeast from the third car <NUM>, thus the position data <NUM> represents a position located at northwest from the third car <NUM>. The velocity data <NUM> represents both speed and heading of the object. The size data <NUM> represents length or width of the object. And, the shape data <NUM> represents shape of the object. In some embodiments, a digit "<NUM>" denotes a car, a digit "<NUM>" denotes a motorcycle and a digit "<NUM>" denotes a moving person, thus for the motorcycle <NUM> as shown in <FIG>, the shape data <NUM> is <NUM>.

<FIG> schematically illustrates a structural diagram for a data space for sensing data according to another embodiment in the present disclosure. Referring to <FIG>, a cube <NUM> represents a three-dimensional coordinate system with an origin <NUM>. The cube <NUM> may virtually represent a driving environment in a three-dimensional scale, and multiple nodes in the driving environment share the cube <NUM> and the origin <NUM>. Referring to <FIG>, multiple objects in the driving environment <NUM> are virtually included in the cube <NUM>, and the multiple nodes agree on that position of the stationary object <NUM> is the origin <NUM>. Thus, positions of other objects, such as the stationary object <NUM>, the motorcycle <NUM>, the moving person <NUM>, etc., are relative positions from the origin <NUM> in the cube <NUM>.

Further referring to <FIG>, the cube <NUM> may be recursively divided into eight sub-cubes. Specifically, the cube <NUM> is divided into eight first-layer sub-cubes such as sub-cubes <NUM> and <NUM>, a first-layer sub-cube may be then divided into eight second-layer sub-cubes such as sub-cubes <NUM> and <NUM>, and a second-layer sub-cube may be then divided into eight third-layer sub-cubes such as sub-cubes <NUM> and <NUM>. In some embodiments, for an object, a number of layers may be related to a resolution required for successfully identifying the object. For an example of an object with a large size and a simple shape such as a building, a lower resolution is required to identify the object so that a smaller number of layers is required. For an example of an object with a small size and a complex shape such as a bicycle, a higher resolution is required to identify the object so that a larger number of layers is required. In some embodiments, a number of layers for multiple objects may be related to a highest resolution required among the multiple objects. In some embodiments, the number of layers for an object or multiple objects may be set by a user custom action, where the number of layers may be set to be a specific value.

Further referring to <FIG>, for an object in a driving environment, two ends of an edge of the object may be represented by two sets of coordinates in a sub-cube, thus a line segment formed by the two sets of coordinates in the sub-cube represents the edge of the object. If length of an edge is too long to be included in a lower layer sub-cube, a higher layer sub-cube is required to include two sets of coordinates representing two ends of the edge. For example, as shown in <FIG>, a sub-cube with a size and an index of layer same as the sub-cube <NUM> or <NUM> is too small to include a line segment <NUM>, thus a higher layer sub-cube that is the sub-cube <NUM> is used to include the line segment <NUM>. Therefore, besides the two sets of coordinates representing two ends of an edge of an object, the edge may be represented further by an index of layer, thus the two sets of coordinates and the index of layer formed a data set representing the edge. In this case, sensing data exchanged in a network includes multiple data sets. In some embodiments, the data set may further include intensity data or speed data. Therefore, since less 3D coordinate is used to represent an edge of an object, less sensing data is transmitted in a network, which results in overhead reduction for the network.

In some embodiments, referring to <FIG>, the device for virtualizing a driving environment <NUM> may further include a decompression device <NUM> adapted to decompress sensing data acquired from a car.

The scene construction device <NUM> is configured to construct a scene virtualizing the driving environment <NUM> based on the position data of the first car <NUM>, the position data and the sensing data from the second car <NUM> and the third car <NUM>.

Specifically, the topology construction device <NUM> is configured to construct a topology including a position coordinate of the second car <NUM> relative to a position coordinate of the first car <NUM> and a position coordinate of the third car <NUM> relative to the position coordinate of the first car <NUM>, which may be computed based on the position data of the first car <NUM> and the position data from the second car <NUM> and the third car <NUM>. In some embodiments, the topology may further include a position coordinate of the fourth car <NUM> relative to the position coordinate of the first car <NUM>.

<FIG> schematically illustrate a topology <NUM>' corresponding to the multiple cars as shown in <FIG>. Referring to <FIG> in conjunction with <FIG>, a first node <NUM>' corresponding to the position coordinate of the first car <NUM> is connected to a second node <NUM>' corresponding to the position coordinate of the second car <NUM> and a third node <NUM>' corresponding to the position coordinate of the third car <NUM> separately, and moreover, the second node <NUM>' is connected to a fourth node <NUM>' corresponding to the position coordinate of the fourth car <NUM>.

The image construction device <NUM> is configured to analyze the sensing data from the second car <NUM> and the third car <NUM>. Taking the moving person <NUM> as an example, the sensing data from the second car <NUM> and the third car <NUM> both include sensing data of the moving person <NUM>, and the image construction device <NUM> identifies the sensing data of the moving person <NUM> from the sensing data from the second car <NUM> and the third car <NUM> based on analyzing position data, speed data, heading data, size data or shape data individually or in combination. For example, the image construction device <NUM> acquires first data that a first moving person is located at southeast form the second car <NUM>, and further acquires second data that a second moving person is located at north from the third car <NUM>, thus the image construction device <NUM> determines the first moving person and the second moving person are same based on the first data, second data and the position coordinates of the second car <NUM> and the third car <NUM>. For another example, the image construction device <NUM> acquires first shape data for a first object from the second car <NUM>, and further acquires second shape data for a second object from the third car <NUM>, thus the image construction device <NUM> determines the first object and the second object are same based on analyzing the first shape data and the second shape data using a well-known method to the ordinarily skilled person. After analyzing the sensing data from the second car <NUM> and the third car <NUM>, the image construction device <NUM> fuses the identified sensing data to acquire multiple images for multiple virtualized objects to construct the scene based on the topology and the multiple images.

In some embodiments, the scene virtualizing the driving environment <NUM> includes a virtualized first car corresponding to the first car <NUM>, a virtualized second car corresponding to the second car <NUM>, a virtualized third car corresponding to the third car <NUM>, a virtualized fourth car corresponding to the fourth car <NUM>, two virtualized stationary objects corresponding to the two stationary objects <NUM> and <NUM> respectively, two virtualized motorcycles corresponding to the two motorcycles <NUM> and <NUM> respectively, and two virtualized moving persons corresponding to the two moving persons <NUM> and <NUM> respectively. Thus, the multiple virtualized objects correspond to the multiple objects in the driving environment <NUM>.

In some embodiments, the virtualized scene may be refreshed frequently. In some embodiments, the scene may be refreshed at least every <NUM>.

In some embodiments, the scene construction device <NUM> constructs a scene virtualizing the driving environment <NUM> based on the sensing data of the second car <NUM> and the third car <NUM>. In some embodiments, the scene construction device <NUM> constructs a scene virtualizing the driving environment <NUM> based on the sensing data of the first car <NUM>, the second car <NUM> and the third car <NUM>. In some embodiments, the scene construction device <NUM> constructs a scene virtualizing the driving environment <NUM> based on the sensing data of the second car <NUM>, the third car <NUM> and the fourth car <NUM>.

The embodiments of the present disclosure further provide a method for virtualizing a driving environment. <FIG> schematically illustrates a flow diagram for a method for virtualizing a driving environment <NUM> according to an embodiment in the present disclosure.

In S601, the second car <NUM> transmits position data to the first car <NUM>. In some embodiments, the position data may be acquired based on the high-precision GPS data such as the GPS-RTK data. In some embodiments, the position data may include position data of the second car <NUM>. In some embodiments, the position data may include position data of the second car <NUM> and the fourth car <NUM>.

In some embodiments, the GPS data may be transmitted within a communication network via the BSM or the CAM, and besides the GPS data, the BSM or CAM may further include speed data and heading data.

In S602, the second car <NUM> compresses sensing data of the second car <NUM>. In some embodiments, the sensing data of the second car <NUM> may be acquired through a lidar mounted on the second car <NUM>.

In S603, the second car <NUM> transmits the compressed sensing data of the second car <NUM> to the first car <NUM>. In some embodiments, the second car <NUM> may transmit the compressed sensing data of the second car <NUM> to the first car <NUM> in response to a request by the first car <NUM>. In some embodiments, the second car <NUM> may broadcast the compressed sensing data of the second car <NUM>.

In some embodiments, referring to <FIG>, sensing data transmitted by the second car <NUM> may include the vehicle identification <NUM> and the data set including the position data <NUM>, the velocity data <NUM>, the size data <NUM> and the shape data <NUM> for an object.

In some embodiments, for an object in a driving environment, two ends of an edge of the object may be represented by two sets of coordinates in a sub-cube, thus a line segment formed by the two sets of coordinates in the sub-cube represents the edge of the object. Besides the two sets of coordinates representing two ends of an edge of an object, the edge may be represented further by an index of layer, thus the two sets of coordinates and the index of layer formed a data set representing the edge. In this case, the sensing data transmitted by the second car <NUM> includes multiple data sets. In some embodiments, the data set may further include intensity data or speed data.

In S604, the device for virtualizing a driving environment <NUM> mounted on the first car <NUM> acquires position data of the first car <NUM>, the position data and the sensing data transmitted by the second car <NUM>. In some embodiments, the position data of the first car <NUM> may be acquired based on the GPS data or the high-precision GPS data. In some embodiments, the position data of the first car <NUM> may be the GPS data or the high-precision GPS data.

In some embodiments, the method <NUM> may further include S606 and, in S606, the device for virtualizing a driving environment <NUM> decompresses the sensing data transmitted by the second car <NUM>.

In S605, the device for virtualizing a driving environment <NUM> constructs a scene virtualizing the driving environment <NUM> based on the position data of the first car <NUM>, the position data and the sensing data transmitted by the second car <NUM>. Wherein, the scene includes multiple virtualized objects corresponding to the multiple objects in the driving environment <NUM>. In some embodiments, the device for virtualizing a driving environment <NUM> constructs a scene virtualizing the driving environment <NUM> further based on sensing data of the first car <NUM>.

In some embodiments, the scene may be refreshed frequently. In some embodiments, the scene may be refreshed at least every <NUM>.

In some embodiments, S605 may include S6051 that the device for virtualizing a driving environment <NUM> constructs a topology at least including the position coordinate of the second car <NUM> relative to the position coordinate of the first car <NUM>. In some embodiments, the topology may further include the position coordinate of the fourth car <NUM> relative to the position coordinate of the first car <NUM>.

In some embodiments, after S6051, S605 may further include S6052. In S6052, the device for virtualizing a driving environment <NUM> analyzes the sensing data transmitted by the second car <NUM> together with sensing data transmitted by other cars based on the topology to identify sensing data for objects in the driving environment <NUM>. In some embodiments, the other car may include the first car <NUM>.

In some embodiments, after S6052, S605 may further include S6053. In S6053, the device for virtualizing a driving environment <NUM> fuses the identified sensing data so as to acquire multiple images for the multiple virtualized objects to construct the scene based on the multiple images and the topology.

<FIG> schematically illustrates a flow diagram for a method for virtualizing a driving environment <NUM>' according to another embodiment in the present disclosure.

In S601', the second car <NUM> transmits position data to the first car <NUM>. Specifically, the position data includes position data of the second car <NUM> and the fourth car <NUM>.

In S607', the fourth car <NUM> transmits compressed sensing data of the fourth car <NUM> to the second car <NUM>.

In S603', the second car <NUM> transmits the compressed sensing data of the second car <NUM> and the fourth car <NUM> to the first car <NUM>.

In S604', the device for virtualizing a driving environment <NUM> mounted on the first car <NUM> acquires position data of the first car <NUM>, the position data and the sensing data transmitted by the second car <NUM>.

In some embodiments, the method <NUM> may further include S606', and in S606', the device for virtualizing a driving environment <NUM> decompresses the sensing data transmitted by the second car <NUM>.

In S605', the device for virtualizing a driving environment <NUM> constructs a scene virtualizing the driving environment <NUM> based on the position data of the first car <NUM>, the position data and the sensing data transmitted by the second car <NUM>.

In some embodiments, S605' may include S6051' that the device for virtualizing a driving environment <NUM> constructs a topology at least including the position coordinate of the second car <NUM> relative to the position coordinate of the first car <NUM> and the position coordinate of the fourth car <NUM> relative to the position coordinate of the first car <NUM>.

In some embodiments, after S6051', S605' may further include S6052'. In S6052', the device for virtualizing a driving environment <NUM> analyzes the sensing data transmitted by the second car <NUM> together with sensing data transmitted by other cars based on the topology to identify sensing data for objects in the driving environment <NUM>.

In some embodiments, after S6052', S605' may further include S6053'. In S6053', the device for virtualizing a driving environment <NUM> fuses the identified sensing data so as to acquire multiple images for the multiple virtualized objects to construct the scene based on the multiple images and the topology.

The embodiments of the present disclosure further provide a vehicle. <FIG> schematically illustrates a structural diagram for a vehicle <NUM> according to an embodiment in the present disclosure. The vehicle <NUM> at least includes a GPS <NUM>, a sensor <NUM> and a device for virtualizing a driving environment <NUM>.

In some embodiments, the sensor <NUM> may be a lidar that detects at least one object adjacent to the vehicle <NUM> for acquiring sensing data for the at least one object.

In some embodiments, the device for virtualizing a driving environment <NUM> constructs a scene virtualizing a driving environment surrounding the vehicle <NUM> based on acquired data. In some embodiments, the device for virtualizing a driving environment <NUM> may be any one of the devices for virtualizing a driving environment described in the previous embodiments.

In some embodiments, the vehicle <NUM> may further include a receiver <NUM>. In some embodiments, the receiver <NUM> may receive position data or sensing data from at least one node communicating with the vehicle <NUM>.

In some embodiments, the vehicle <NUM> may further include a compressor <NUM> and a transmitter <NUM>.

In some embodiments, the compressor <NUM> compresses the acquired sensing data for the at least one object. Thus, a reduced network overhead is realized via applying data compression to the sensing data.

In some embodiments, the transmitter <NUM> transmits the compressed sensing data to the at least one node in response to a request from the at least one node. In some embodiments, the transmitter <NUM> broadcasts the compressed sensing data.

Claim 1:
A device (<NUM>) for virtualizing a driving environment surrounding a first node (<NUM>'), comprising:
a data acquisition device (<NUM>), configured to acquire position data of the first node (<NUM>'), position data and sensing data of at least one second node (<NUM>'), wherein the at least one second node (<NUM>') and the first node (<NUM>') are in a first communication network; and
a scene construction device (<NUM>), configured to construct a scene virtualizing the driving environment surrounding the first node (<NUM>') based on the position data of the first node (<NUM>') and the at least one second node (<NUM>'), and on the sensing data of the at least one second node (<NUM>'),
characterized in that:
the sensing data is compressed and comprises a plurality of data sets each comprising two sets of coordinates representing two ends of an edge of an object detected and an index of layer of recursive sub-cube division.