# US 10,371,534 B2 · Apparatus and method for sharing and learning driving environment data to improve decision intelligence of autonomous vehicle

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- Number: US 10,371,534 B2
- Document ID: US10371534B2
- Status: Granted
- Kind: B2
- Application number: 15/602,912
- Filed: 2017-05-23
- Published: 2018-04-12
- Granted: 2019-08-06
- Earliest claimed: 2016-10-12
- Assignees: Electronics and Telecommunications Research Institute
- Applicants: Electronics and Telecommunications Research Institute
- Inventors: Kyoung Wook Min; Jeong Dan Choi; Jun Gyu Kang; Sang Heon Park; Kyung Bok Sung; Joo Chan Sohn; Dong Jin Lee; Yong Woo Jo; Seung Jun Han
- Web page: https://patentgrep.com/patent/US10371534B2

## CPC

- G01C 21/3841: {Data obtained from two or more sources, e.g. probe vehicles}
- G01C 21/3848: {Data obtained from both position sensors and additional sensors}
- G01C 21/3893: {Transmission of map data from distributed sources, e.g. from roadside stations}
- G01C 21/3896: {Transmission of map data from central databases}
- G05D 1/0287: {involving a plurality of land vehicles, e.g. fleet or convoy travelling (traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16)}
- H04W 4/023: {using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds}
- H04W 4/44: for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
- H04W 4/46: for vehicle-to-vehicle communication [V2V]
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Provided are an apparatus and method for sharing and learning driving environment data to improve the decision intelligence of an autonomous vehicle. The apparatus for sharing and learning driving environment data to improve the decision intelligence of an autonomous vehicle includes a sensing section which senses surrounding vehicles traveling within a preset distance from the autonomous vehicle, a communicator which transmits and receives data between the autonomous vehicle and another vehicle or a cloud server, a storage which stores precise lane-level map data, and a learning section which generates mapping data centered on the autonomous vehicle by mapping driving environment data of a sensing result of the sensing section to the precise map data, transmits the mapping data to the other vehicle or the cloud server through the communicator, and performs learning for autonomous driving using the mapping data and data received from the other vehicle or the cloud server.

## Claims

### Claim 1

An apparatus for sharing and learning driving environment data to improve decision intelligence of an autonomous vehicle, the apparatus comprising:
at least one sensor configured to sense surrounding vehicles traveling within a preset distance from the autonomous vehicle;
a communicator transceiver configured to transmit and receive data between the autonomous vehicle and the surrounding vehicles or a cloud server;
a storage configured to store lane-level map data;
a learning computer configured to:
generate mapping data by mapping driving environment data of the autonomous vehicle obtained from a sensing result of the at least one sensor and driving environment data of the surrounding vehicles received through the communicator transceiver to the lane-level map data,
determine whether a situational judgment condition of a driving mission is satisfied based on the mapping data,
extract training data to perform the driving mission, and
control driving of the autonomous vehicle with a learning result based on the extracted training data.

### Claim 2 (depends on 1)

The apparatus of claim 1, wherein the driving environment data includes a current location and a speed of the autonomous vehicle, speeds of the at least one vehicle, and distances between the at least one vehicle and the autonomous vehicle.

### Claim 3 (depends on 1)

The apparatus of claim 1, wherein the mapping data includes tracking identifiers (IDs) assigned to the surrounding vehicles, and includes speeds of the surrounding vehicles, distances between the surrounding vehicles and the autonomous vehicle, and traveling lanes of the surrounding vehicles, corresponding to the tracking IDs.

### Claim 4 (depends on 1)

The apparatus of claim 1, wherein the communicator transceiver transmits the mapping data to the surrounding vehicles through vehicle-to-vehicle (V2V) communication or to the cloud server through vehicle-to-cloud server (V2C) communication.

### Claim 5 (depends on 1)

The apparatus of claim 1, wherein the driving mission includes at least one of a lane change, lane keeping, inter-vehicle distance keeping, passing through an intersection, and driving on a curved road.

### Claim 6 (depends on 1)

The apparatus of claim 1, wherein the learning computer receives the result of learning performed using driving environment data of a plurality of vehicles from the cloud server through the communicator transceiver, and uses the learning result in learning the driving mission.

### Claim 7 (depends on 1)

The apparatus of claim 1, wherein, when the learning computer determines that the driving mission has been executed in the autonomous vehicle according to an operation of a driver of the autonomous vehicle, the learning computer records the training data acquired during the execution of the driving mission, merges the training data recorded in a plurality of vehicles, and performs the learning of the driving mission.

### Claim 8 (depends on 1)

The apparatus of claim 1, wherein when the driving mission is lane change, the learning computer calculates speed variations of the surrounding vehicles based on the mapping data and compares the speed variations and a preset threshold, and
wherein, when the speed variations are smaller than the preset threshold, the learning computer determines that the situational judgment condition is satisfied and extracts the training data including time-to-collision (TTC) between the autonomous vehicle and the surrounding vehicles and trajectory of the autonomous vehicle.

### Claim 9 (depends on 1)

The apparatus of claim 1, wherein the learning computer adjusts the situational judgment condition based on the training data.

### Claim 10

A method of sharing and learning driving environment data to improve decision intelligence of an autonomous vehicle, the method comprising:
sensing, by at least one sensor, surrounding vehicles traveling within a preset distance from the autonomous vehicle;
generating mapping data by mapping driving environment data obtained from a sensing result and driving environment data of the surrounding vehicles received through a communicator transceiver to pre-stored lane-level map data of a storage;
determining, by a learning computer, whether a situational judgment condition of a driving mission is satisfied based on the mapping data;
extracting training data, by the learning computer;
generating a learning result based on the extracted training data; and
controlling driving of the autonomous vehicle using the learning result.

### Claim 11 (depends on 10)

The method of claim 10, wherein the driving environment data includes a current location and a speed of the autonomous vehicle, speeds of the surrounding vehicles, and distances between the surrounding vehicles and the autonomous vehicle.

### Claim 12 (depends on 10)

The method of claim 10, wherein the mapping data includes tracking identifiers (IDs) assigned to the surrounding vehicles, and includes speeds of the surrounding vehicles, distances between the surrounding vehicles and the autonomous vehicle, and traveling lanes of the surrounding vehicles, corresponding to the tracking IDs.

### Claim 13 (depends on 10)

The method of claim 10, further comprising at least one of:
sharing the mapping data with the surrounding vehicles through wireless communication by transmitting the mapping data through vehicle-to-vehicle (V2V) communication; and
sharing the mapping data with a cloud server through wireless communication by transmitting the mapping data through vehicle-to-cloud server (V2C) communication.

### Claim 14 (depends on 10)

The method of claim 10, wherein the driving mission includes at least one of a lane change, lane keeping, inter-vehicle distance keeping, passing through an intersection, and driving on a curved road.

### Claim 15 (depends on 10)

The method of claim 10, wherein generating the learning result comprises receiving a result of learning performed using driving environment data of a plurality of vehicles from a cloud server through the communicator transceiver and using the learning result in learning the driving mission.

### Claim 16 (depends on 10)

The method of claim 10, wherein generating the learning result comprises, when the learning computer determines that the driving mission has been executed in the autonomous vehicle according to an operation of a driver of the autonomous vehicle, recording training data acquired during the execution of the driving mission, merging training data recorded in a plurality of vehicles, and performing the learning of the driving mission.

### Claim 17 (depends on 10)

The method of claim 10, wherein the driving mission is lane change, and wherein generating the learning result comprises:
calculating, by the learning computer, speed variations of the surrounding vehicles based on the mapping data;
comparing, by the learning computer, the speed variations of the surrounding vehicles and a preset threshold;
determining, by the learning computer, that the situational judgment condition is satisfied, when the speed variations are smaller than the preset threshold; and
extracting, by the learning computer, the training data including time-to-collision (TTC) between the autonomous vehicle and the surrounding vehicles and trajectory of the autonomous vehicle.