Method for Learning Travel Characteristics and Travel Assistance Device

Patent metadata

Field	Value
Publication number	US-2021163015-A1
Application number	US-201716647349-A
Country	US
Kind code	A1
Filing date	Sep 20, 2017
Priority date	Sep 20, 2017
Publication date	Jun 3, 2021
Grant date	—

Abstract

According to a method for learning travel characteristics and a travel assistance device, in a vehicle capable of switching manual driving by a driver and autonomous-driving, continuity of driving characteristics is determined based on travel data during manual driving by a driver, and a start time and an end time of learning target data, being a learning target of the driving characteristics of the travel data, are set by using a determination result of the continuity.

First claim

1 . A method for learning travel characteristics of a travel assistance device that learns driving characteristics of a driver from travel data during manual driving by a driver and applies a learning result to travel control of autonomous-driving, in a vehicle capable of switching manual driving by a driver and autonomous-driving, the method comprising:
determining continuity of the driving characteristics based on the travel data and setting a start time and an end time of learning target data, being a learning target of the driving characteristics of the travel data, by using a determination result of the continuity.

2 . The method for learning travel characteristics according to claim 1 , further comprising, while designating a startup timing of the travel assistance device as a first timing, setting the first timing as a start time of the learning target data.

3 . The method for learning travel characteristics according to claim 1 , further comprising, while designating a stop timing of the travel assistance device as a second timing, setting the second timing as an end time of the learning target data.

4 . The method for learning travel characteristics according to claim 1 , further comprising:
while designating a timing of setting, change, or cancellation of a navigation destination of the travel assistance device as a third timing, setting the third timing as an end time of learning target data indicating the driving characteristics before the third timing; and setting the third timing as a start time of learning target data indicating the driving characteristics after the third timing.

5 . The method for learning travel characteristics according to claim 1 , further comprising:
when a length of a stoppage time of the vehicle is equal to or longer than a first threshold, setting a start timing of the stoppage time as an end time of learning target data indicating the driving characteristics before the stoppage time; and setting an end timing of the stoppage time as a start time of learning target data indicating the driving characteristics after the stoppage time.

6 . The method for learning travel characteristics according to claim 1 , further comprising:
while designating a stop timing of the travel assistance device as a fourth timing and designating a startup timing of the travel assistance device after the fourth timing as a fifth timing, and when a length of time from the fourth timing to the fifth timing is equal to or longer than a second threshold, setting the fourth timing as an end time of learning target data indicating the driving characteristics before the fourth timing; and setting the fifth timing as a start time of learning target data indicating the driving characteristics after the fifth timing.

7 . The method for learning travel characteristics according to claim 1 , further comprising:
while designating a stop timing of the travel assistance device as a sixth timing and designating a startup timing of the travel assistance device after the sixth timing as a seventh timing, and when a navigation destination set at the sixth timing is different from a navigation destination set at the seventh timing, setting the sixth timing as an end time of learning target data indicating the driving characteristics before the sixth timing; and setting the seventh timing as a start time of learning target data indicating the driving characteristics after the seventh timing.

8 . The method for learning travel characteristics according to claim 1 , further comprising:
when number of passengers changes before and after a stoppage time of the vehicle, setting a start timing of the stoppage time as an end time of learning target data indicating the driving characteristics before the stoppage time; and setting an end timing of the stoppage time as a start time of learning target data indicating the driving characteristics after the stoppage time.

9 . The method for learning travel characteristics according to claim 1 , further comprising:
when a magnitude of variation of the driving characteristics in the travel data being learned is equal to or larger than a third threshold, setting a timing of the variation as an end time of learning target data indicating the driving characteristics before an occurrence of the variation; and setting the timing of the variation as a start time of learning target data indicating the driving characteristics after an occurrence of the variation.

10 . The method for learning travel characteristics according to claim 9 , wherein the travel characteristics are an inter-vehicular distance between the vehicle and a proceeding vehicle.

11 . The method for learning travel characteristics according to claim 9 , wherein the travel characteristics are a braking deceleration rate of the vehicle.

12 . The method for learning travel characteristics according to claim 9 , wherein the travel characteristics are a starting acceleration rate of the vehicle.

13 . A travel assistance device that learns driving characteristics of a driver from travel data during manual driving by a driver and applies a learning result to travel control of autonomous-driving, in a vehicle capable of switching manual driving by a driver and autonomous-driving, the travel assistance device comprising:
a processor; memory in electronic communication with the processor; instructions stored in the memory, the instructions being executable to implement a method comprising:
determining continuity of the driving characteristics based on the travel data and sets a start time and an end time of learning target data, being a learning target of the driving characteristics of the travel data, by using a determination result of the continuity.

Assignees

• Nissan Motor

Inventors

• Jang Hwaseon
• Hiramatsu Machiko
• Sunda Takashi

Classifications

• G08G1/166

  for active traffic, e.g. moving vehicles, pedestrians, bikes · CPC title

• G08G1/096775

  where the origin of the information is a central station · CPC title

• G08G1/096741

  where the source of the transmitted information selects which information to transmit to each vehicle · CPC title

• G08G1/096708

  where the received information might be used to generate an automatic action on the vehicle control · CPC title

• B60W2555/60

  Traffic rules, e.g. speed limits or right of way · CPC title