PRODUCTIVE AND ACCIDENT-FREE DRIVING MODES FOR A VEHICLE

• US 20190041228A1
• Filed: 08/01/2017
• Published: 02/07/2019
• Est. Priority Date: 08/01/2017
• Status: Active Grant

• Current Assignee: NIO Technology Company Limited (Shanghai Anbin Technology Co., Ltd.)
• Original Assignee: Nio Usa Inc.
• Inventors: Singhal, Abhishek
• Granted Patent: US 10,837,790 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2040/0881 Seat occupation; Driver or passenger presence
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: G01C 21/3407 specially adapted for specific applications
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/021 specially adapted to land vehicles
• CPC Class Codes: G05D 1/0217 in accordance with energy consumption, time reduction or distance reduction criteria

Abstract

The systems and methods described herein can be applied to a vehicle equipped with a sensor suite that can observe information about a user, for example, a driver or passenger, within a vehicle. The system can determine a action being conducted by a user or an event associated with the user. Then, the navigation system can augment or learn what type of route or conditions allows the user to conduct the action or event. From the learned information, the navigation system can determine a route that best assists with the action or event regardless of whether the route is the shortest or quickest. As such, the vehicle tailors the chosen navigation route based on the user'"'"'s conditions or circumstances rather than the driving environment.

Claims

1-20. -20. (canceled)

21. A vehicle, comprising:

a first set of sensors to sense an environment surrounding the vehicle and to gather environment information about a location in which the vehicle is within physical proximity;

a second set of sensors to sense an inside of the vehicle and to gather occupant information;

a vehicle control system to operate the vehicle in a high or a full automation level;

wherein the vehicle control system and/or a remote route engine, based on occupant information gathered by the second set of sensors identifies, by occupant, an action currently being performed or to be performed by the respective occupant, selects, from among multiple possible routes from the location and a selected destination, a route from the location to the selected destination, the selected route, relative to the other possible routes, being optimal, while the vehicle control system is operating in the high or full automation level, for one or more actions currently being performed or to be performed by one or more of the vehicle occupants.

22. The vehicle of claim 21, wherein the sensed occupant information comprises, for each occupant, one or more of a corresponding user identifier and device identifier and a plurality of an action description for a respective action currently being performed by the respective occupant, a status identifier indicating a current status of the respective occupant, and an event description of an event that may be upcoming or is currently in progress associated with the respective occupant, wherein the vehicle control system and/or route engine select the route based on mapping a set of rules to the sensed occupant information, and wherein each rule in the set of rules is weighted, relative to other rules, in accordance with predetermined preferences of one or more occupants in selecting the route.

23. The vehicle of claim 21, wherein the sensed occupant information comprises, for each occupant, one or more of a corresponding user identifier and device identifier and a plurality of an action description for a respective action currently being performed by the respective occupant, a status identifier indicating a current status of the respective occupant, and an event description of an event that may be upcoming or is currently in progress associated with the respective occupant, wherein the vehicle control system and/or route engine select the route based on mapping a set of rules to the sensed occupant information, and wherein each occupant is ranked, relative to one or more other occupants, in mapping a set of rules to the sensed occupant information in selecting the route.

24. The vehicle of claim 21, wherein the action comprises one or more of conducting a telephone call, operating a computational device, sleeping, and reading a publication and wherein a set of maneuvers for the route to be executed by the vehicle control system while operating in the high or full automation level is optimal for the action.

25. The vehicle of claim 21, wherein the second set of sensors determines a number of occupants currently in the vehicle and wherein the vehicle control system and/or a remote route engine, based on environment information gathered by first sets of sensors of multiple different vehicles, a number of occupants currently in the vehicle, and the selected destination, selects one or more of (a) from among multiple possible routes from the location and the selected destination, a second route from the location to the selected destination, the selected second route, relative to the other possible routes, being optimal for high or full automation level operation of the vehicle and (b) a set of maneuvers for the selected route to be executed by the vehicle control system while operating in the high or full automation level along the selected second route, the set of maneuvers being optimal relative to a selected characteristic for the second route.

26. The vehicle of claim 25, wherein the vehicle control system and/or a remote route engine selects option (a), wherein, when the vehicle control system operates the vehicle in the high or full automation level, the vehicle control system executes driving operations autonomously, the driving operations comprising steering, accelerating the vehicle, decelerating the vehicle, and braking the vehicle, wherein the optimal set of maneuvers comprises a lane change, a turn, an acceleration, and a deceleration, wherein the optimal set of maneuvers are selected are predetermined, and wherein the optimal set of maneuvers define an optimal driving pattern for the selected route.

27. The vehicle of claim 26, wherein the selected characteristic for the selected second route is travel time and/or estimated time of arrival and wherein the gathered environment information comprises traffic light locations along the selected route, for each traffic light along the selected second route, one or more of traffic light cycle time to change from a first light color to a second light color, and a duration of illumination of a selected traffic light color, and a plurality of a number of vehicles in each lane along a length of the selected second route, a vehicle occupancy requirement for each lane along a length of the selected second route, and a toll related rule for a length of the selected second route.

28. The vehicle of claim 25, wherein the vehicle control system and/or a remote route engine selects option (b), wherein when the vehicle operates in the high automation level, a human driver is separated from controlling all vehicle operations, and wherein a processor of the vehicle:

identifies the location within the environment;

collects metadata associated with the location; and

sends the collected metadata to a navigation source, wherein the metadata includes a characteristic about the location.

29. The vehicle of claim 27, wherein the location is an intersection, wherein the metadata comprises a number of lanes and, for each lane, a plurality of a type of lane, vehicle occupancy requirements to use the lane, how long a turn signal is active for each lane, and how many cars are in the lane, and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles and wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers.

30. The vehicle of claim 26, wherein the collected metadata is associated with a traffic light at the intersection and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles, wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers, wherein the collected metadata is a cycle time for the traffic light and/or a length of a green light, wherein the navigation source receives, from each of the multiple vehicles, collected metadata in association with a unique identifier of the corresponding vehicle, an identifier of the location associated with the collected metadata, a type of location of the identified location, a type of the metadata collected, and a date and timestamp of metadata collection and aggregates the collected metadata from the multiple vehicles by identified location, and wherein a first portion of the collected metadata is a time to clear the intersection, a number of vehicles that clear the intersection during a green light, a number of vehicles that turn during a turn signal, and/or a number of vehicles in a lane during at particular time of day and/or day and a second portion of the collected metadata is associated with the high occupancy vehicle (HOV) lane.

31. A method, comprising:

a first set of sensors to sense an environment surrounding the vehicle and to gather environment information about a location in which the vehicle is within physical proximity;

a second set of sensors to sense an inside of the vehicle and to gather occupant information;

a vehicle control system to operate the vehicle in a high or a full automation level;

wherein the vehicle control system and/or a remote route engine, based on occupant information gathered by the second set of sensors identifies, by occupant, an action currently being performed or to be performed by the respective occupant, selects, from among multiple possible routes from the location and a selected destination, a route from the location to the selected destination, the selected route, relative to the other possible routes, being optimal, while the vehicle control system is operating in the high or full automation level, for one or more actions currently being performed or to be performed by one or more of the vehicle occupants.

32. The method of claim 31, wherein the sensed occupant information comprises, for each occupant, one or more of a corresponding user identifier and device identifier and a plurality of an action description for a respective action currently being performed by the respective occupant, a status identifier indicating a current status of the respective occupant, and an event description of an event that may be upcoming or is currently in progress associated with the respective occupant, wherein the vehicle control system and/or route engine select the route based on mapping a set of rules to the sensed occupant information, and wherein each rule in the set of rules is weighted, relative to other rules, in accordance with predetermined preferences of one or more occupants in selecting the route.

33. The method of claim 31, wherein the sensed occupant information comprises, for each occupant, one or more of a corresponding user identifier and device identifier and a plurality of an action description for a respective action currently being performed by the respective occupant, a status identifier indicating a current status of the respective occupant, and an event description of an event that may be upcoming or is currently in progress associated with the respective occupant, wherein the vehicle control system and/or route engine select the route based on mapping a set of rules to the sensed occupant information, and wherein each occupant is ranked, relative to one or more other occupants, in mapping a set of rules to the sensed occupant information in selecting the route.

34. The method of claim 31, wherein the action comprises one or more of conducting a telephone call, operating a computational device, sleeping, and reading a publication and wherein a set of maneuvers for the route to be executed by the vehicle control system while operating in the high or full automation level is optimal for the action.

35. The method of claim 31, wherein the second set of sensors determines a number of occupants currently in the vehicle and wherein the vehicle control system and/or a remote route engine, based on environment information gathered by first sets of sensors of multiple different vehicles, a number of occupants currently in the vehicle, and the selected destination, selects at least one of (a) from among multiple possible routes from the location and the selected destination, a second route from the location to the selected destination, the selected second route, relative to the other possible routes, being optimal for high or full automation level operation of the vehicle and (b) a set of maneuvers for the selected route to be executed by the vehicle control system while operating in the high or full automation level along the selected second route, the set of maneuvers being optimal relative to a selected characteristic for the second route.

36. The method of claim 35, wherein the vehicle control system and/or a remote route engine selects option (b), wherein, when the vehicle control system operates the vehicle in the high or full automation level, the vehicle control system executes driving operations autonomously, the driving operations comprising steering, accelerating the vehicle, decelerating the vehicle, and braking the vehicle, wherein the optimal set of maneuvers comprises a lane change, a turn, an acceleration, and a deceleration, wherein the optimal set of maneuvers are selected are predetermined, and wherein the optimal set of maneuvers define an optimal driving pattern for the selected route.

37. The method of claim 36, wherein the selected characteristic for the selected second route is travel time and/or estimated time of arrival and wherein the gathered environment information comprises traffic light locations along the selected route, for each traffic light along the selected second route, one or more of traffic light cycle time to change from a first light color to a second light color, and a duration of illumination of a selected traffic light color, and a plurality of a number of vehicles in each lane along a length of the selected second route, a vehicle occupancy requirement for each lane along a length of the selected second route, and a toll related rule for a length of the selected second route.

38. The method of claim 35, wherein the vehicle control system and/or a remote route engine selects option (a), wherein when the vehicle operates in the high automation level, a human driver is separated from controlling all vehicle operations, and wherein a processor of the vehicle:

identifies the location within the environment;

collects metadata associated with the location; and

sends the collected metadata to a navigation source, wherein the metadata includes a characteristic about the location.

39. The method of claim 37, wherein the location is an intersection, wherein the metadata comprises a number of lanes and, for each lane, a plurality of a type of lane, vehicle occupancy requirements to use the lane, how long a turn signal is active for each lane, and how many cars are in the lane, and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles and wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers.

40. The method of claim 36, wherein the collected metadata is associated with a traffic light at the intersection and a navigation source, in communication with the route engine, aggregates metadata associated with the location received in temporal proximity from multiple vehicles, wherein the metadata is used by the vehicle control system and/or route engine to select the optimal route and/or set of maneuvers, wherein the collected metadata is a cycle time for the traffic light and/or a length of a green light, wherein the navigation source receives, from each of the multiple vehicles, collected metadata in association with a unique identifier of the corresponding vehicle, an identifier of the location associated with the collected metadata, a type of location of the identified location, a type of the metadata collected, and a date and timestamp of metadata collection and aggregates the collected metadata from the multiple vehicles by identified location, and wherein a first portion of the collected metadata is a time to clear the intersection, a number of vehicles that clear the intersection during a green light, a number of vehicles that turn during a turn signal, and/or a number of vehicles in a lane during at particular time of day and/or day and a second portion of the collected metadata is associated with the high occupancy vehicle (HOV) lane.