Personalize self-driving cars

Abstract

A method to personalize the operation of a self-driving automobile is disclosed that improves the applicability and user appreciation of a self-driving automobile by acquiring and applying the user preference data set and the user profile data set, incorporating individual user choice of preferred driving behaviors on different scenarios and user preferred driving styles, and/or the moral or ethics into the control of the automobile operation.

Description

TECHNICAL FIELD

Artificial intelligence, self-driving cars, and robot.

BACKGROUND

AI (denotes artificial intelligence hereby and hereafter in this disclosure) based driving automation has evolved now to a stage of heavy premarketing road test by several self-driving car manufacturers. Among other issues, accidents are still occasionally reported calling for more improvements. A self-driving car could be viewed as if a robot sits on a conventional car, though it does not take the shape of what is commonly presented or perceived, comprising a sensing sub-system, an AI control sub-system and an activation sub-system, and the conventional car should be altered significantly for a better integration, as illustrated in FIG. 1. A self-driving car drives itself from one start point to a destination set by a user or a remote controller through a wireless communication system or an electronic media device and guided by a GPS navigational system with or without involving a user in the car. It can contain one or more passengers or no passengers, for example when it is sent for a passenger. The robot conducts real-time scene analysis of roadway and traffic events, interprets and applies the traffic rules wherever it is driving, and synchronizes activation subsystem to make the driving for a user (denotes an owner or a passenger who rides or uses the self-driving car hereby and hereafter in this disclosure). A robot on the self-driving car has been trained in the factory and learned the general skills and rules of a car operation. However, driving as a human activity has many quality attributes than just moving or transportation, such as safety, comfort, exercise, sport and so on, which are valued according to each user's experiences, favors, moral and ethics traits among other things. A personalized self-driving car could do better to satisfy a user's needs, and a method for its customer design is hereby introduced in this disclosure.

SUMMARY OF THE INVENTION

A method is disclosed to personize a self-driving car's driving behavior to reflect user's preferred driving styles, and/or moral or ethics traits in handling normal traffic and emergency scenarios, based on acquiring and analyzing user preference data and user profile data to start with and a continuing leaning by the robot during the driving.

BRIEF DISCUSSION OF DRAWINGS

FIG. 1 Illustration of a functional structure of a self-driving car.

FIG. 2 Illustration of categorized response time interval to roadway and traffic events, the shaded area around T1 and T2 indicate it should be considered as a zone with a boundary varying from model to model, and from time to time.

FIG. 3 Illustration of personalizing a self-driving car procedures.

FIG. 4 Table 1, an example of impact on operation by personalized user preference data.

FIG. 5 Illustration of how to apply user data on roadway and traffic scenarios.

DETAILED DESCRIPTION OF THE INVENTION

The robot of a self-driving car keeps monitoring and detecting roadway and traffic conditions by its sensing sub-system, and any events prompting for a responding adjustment of its driving will be analyzed to fall into one of the three categorized response time intervals, taking into account the distance of an involved object to and the speed of the car, the time needed for the robot to run algorithms and activation sub-system, and for the activation to take effect, as illustrated in FIG. 2, wherein the actual parameter values could be different from one car model to another, since each model usually has a performance features of maneuverability as designed by its manufacturer. The interval between time 0 to T1 is referred to as The Blinking Zone, wherein the robot can virtually do little or nothing to address the event or avoid an accident but could act somehow to minimize the damages to the user or the car and send out alarms if there is an accident. The interval between T1 to T2 is referred to as The Emergency Zone, wherein actions could be taken to address the events or avoid an accident or let an accident happen in one way or another that would put different risks of damages to the driver, the car of the driver and/or a third party who is involved in the accident, such as a vehicle or a pedestrian who happens to share the roadway. The interval from T2 beyond is referred to as the Cruise Zone, wherein the roadway and traffic events are easily manageable and chance of an accident is very small. Corresponding to each interval, there are pre-acquired personalized sets of data for each user reflecting the user's choices of behaviors in different scenarios, preferred driving styles, and/or moral or ethics traits, which will be used by the robot in its control of the operations, a process hereby referred to as a personalized self-driving and is detailed below.

Personalize self-driving starts by an initialization process, which takes place before the car is started or moved for a roadway driving, using an interactive interface to communicate between the user and the robot of a self-driving car as illustrated in FIG. 3. A user should be identified first for the robot to acquire user preferences data set of preferred behaviors of a self-driving car on a collection of roadway and traffic scenarios. There are many ways to identify a user by state of the art technologies, and a user ID/password combination could be an easy one. The robot would present a collection of roadway and traffic conditions scenarios one by one, and invite the user inputting his or her opinions on a preferred handling behavior by selecting an answer among multiple choices or answering to a yes or no question. Since it is very difficult and lengthy to cover all possible scenarios, some generalization and categorization of scenarios are necessary, and a numeric value within a normalized range of indicating a percentage degree of a consent or discontent to a choice of answer is optionally used. The interactive interface between the robot and the user could be of a visual media such as a touch screen panel for display and input, or an audio media such as a speaker announcement combined with a microphone and a speech recognition module to take the inputs, or a combination thereof, for users without vision or hearing disabilities. For user with disabilities, however, an assistant to the user could help with the initialization to use the above common interface mechanisms for the communication, or an adaptive device could be designed and installed. The acquired preference data will then be stored in a data structure named the user preference data set, which has an entry for each user of the self-driving car categorized according to the above described three segments of response time intervals.

In addition to a user preference data set, a second data set named the user profile data set is also acquired, based on information provided by a user and/or through a research by the robot through a wireless communication system or an electronic media device, which comprises the age, gender, profession, education level and other personal and/or public information available such as marriage status, living areas, driving, credit, insurance, health and criminal records. The acquisition of user profile data set could take place between the robot and a user using an interactive interface at the time of purchasing or requesting a service of a self-driving car. After a user provides related information, the robot runs a background check using a wireless communication system or an electronic media device. Alternatively, these data could be acquired prior to purchasing or using the service of a self-driving car between a user and a vender or service provider and delivered to the robot of a self-driving car later.

The robot will then analyze these two sets of user data to determine and profile preferred driving style, and/or moral or ethics traits of a user and infer the proper behavior for the self-driving car in a variety of difficult roadway and traffic scenarios based on data from behavior modeling, factory tests and user statistics and related algorithms, and store the results in a data structure in the user profile data set. Alternatively, the two sets of data could also be analyzed using resources elsewhere and the results are delivered to the robot later. An illustration how to apply these data for real time operation of a self-driving car in a user's personal way can be found in FIG. 5. In the first place, a self-driving should follow the traffic rules and other laws regarding a vehicle operation. Within that restraint, one recommendation is to run scenario matching first, and find the closest match between the current scenario and a scenario used in the collection for acquisition of the user preference data, and apply the user preference data operating the car if the match is close enough. However, when a close enough match could not be found, the robot should then refer to the user preferred driving style and/or the moral or ethics traits to generate a suggestion how to handle an unexpected scenario and find an optimal solution by considering it together with other options generated by AI control subsystem. So, there is a clear difference between how to use these two sets of user data. Certain restrictions are applied as a default settings for the self-driving cars in general. For example, since this disclosure is not concerned about the application to use the driver-less technology as a battle vehicle in a war or as a vehicle for law enforcement, the self-driving car is recommended to be inhibited to be engaged in any offensive action against any third parties, including pedestrians, other vehicles etc. It should also be barred from any self-destruction behavior such as running out of a cliff or against a road barrier or walls of a building, unless the AI control sub-system of the robot determines such a move is necessary for reducing the seriousness of an otherwise unavoidable accident and the user has optioned such a choice in the user preference data set. Although in general, applying the user preference data and user's preferred driving styles, and/or moral or ethics traits is intended to satisfy the user's expectation, there are exceptions on the contrary, for example, if a user riding the car is found to be drunk by an alcoholic sensor, or to be a habitual reckless driving offender, certain functions such as user overriding the robot for operating the car should be restricted.

when multiple users are riding the car, it is optional to select the user preference data and the user profile data of one of the riders in assisting the operation of the car. In case there is no passenger riding the car, a self-driving car will follow its factory settings or use a pre-acquired designated user preference data set and user profile data set.

An example of impact on operation by personalized user preference data is illustrated in FIG. 4 Table 1. Since how to handle emergency between T1 and T2 is most critical and controversial to the safety behavior of a self-driving car, some examples are designed and given below as an illustration of scenarios and preference data pairs.

EXAMPLE 1

A self-driving car is driving on a roadway at a normal speed approaching an intersection with a green light, a bicycle suddenly runs red light from one side of the roadway appearing in front of the self-driving car. The robot finds braking the car is too late to avoid the accident, but the car to the left or right might have a chance, which would violate the traffic rules by running into a wrong lane and have a chance to damage the self-driving car, what would be the user's opinion? The choices for the answer are:

• • A. Brake the car
  • B. Swing the car

EXAMPLE 2

When a self-driving car entering a potential accident involving another party that might have the liability for causing the accident, to what degree of risk between 0 and 1 would you take to avoid the accident, if the self-driving car has been following the traffic rules?

EXAMPLE 3

When a collision between the self-driving car and another vehicle is not avoidable, which of the following you would choose?

• • A. Minimize the damage to yourself no matter what happens to the other party
  • B. Minimize the damage to yourself no matter what happens to the other party if the other party has the liability
  • C. Take some risk of damaging yourself depending the circumstances to reduce the damage to the other party

EXAMPLE 4

When an accident is not avoidable, which of the following you would choose?

• • A. Minimize the damage to the passenger sitting on the front-left seat
  • B. Minimize the damage to the passenger sitting on the back-right seat
  • C. Minimize the damage to myself no matter where I am sitting

EXAMPLE 5

Your preferred driving style in highway is:

• • A. Quick and fast
  • B. Steady and smooth

A continuing user adaptation by learning during the driving is illustrated in FIG. 3 by module 380, particularly if the user is a recurrent one such as an owner of the car, and data acquired in the initialization may not cover all roadway and traffic scenarios and the interpretation by the robot of the user preference data may not fully satisfy the user. The robot could prompt messages or make announcements, through visual or sound or other kind of media devices, about an unfamiliar or untrained or hazardous roadway and traffic condition, and asks for the user to input a guidance or command, and executes the operation accordingly upon receiving such a guidance or command, updating the user preference data set by taking the user's inputs in combination with the scenarios as a result. The user could also take over the driving physically when necessary and if it is feasible in the design, or take initiatives to direct or correct the driving behavior of the robot through an interactive interface. On the other hand, the robot could use a gaze, gesture or other monitoring techniques to detect and analyze the user's body languages reflecting his or her experiences during the driving, and tune its operation accordingly. Thereby, a satisfactory personalized match between the behavior of the robot of a self-driving car and the expectation of its user could be realized.

Claims

1. A method of personalizing a self-driving car comprising a robot of the car conducting steps of identifying a user; acquiring the user preference data set and/or the user profile data set; acquiring the preferred driving styles, and/or the moral or ethics traits of the user; and applying the user preference data set and/or the preferred driving style and/or the moral or ethics traits of the user in operating the car.

2. The method of claim 1, wherein acquiring the user preference data set comprising the robot identifying a user; presenting a collection of roadway and traffic conditions scenarios one by one, inviting the user inputting his or her choices on a preferred handling behavior, and inputting the scenario/choice pairs in an entry of the user preference data set through an interactive initialization process between the robot and the user at the time of purchasing or requesting the service of the car; or by receiving a previously acquired user preference data set of the user and confirming or updating the acquired data prior to or at the time of the self-driving being used in a public roadway.

3. The method of claim 1, wherein acquiring the user profile data set comprising the robot identifying a user; acquiring personal information provided by the user through an interactive interface between the robot and the user and/or by researching the public records through a wireless communication system or an electronic media device, or through receiving the user profile data set through a wireless communication system or an electronic media device.

4. The method of claim 1, wherein acquiring the preferred driving styles, and/or the moral or ethics traits of the user comprising extracting the preferred driving styles, and/or the moral or ethics traits of the user from the acquired user preference data set and/or the user profile data set or receiving the extracted preferred driving styles, and/or the moral or ethics traits of the user.

5. The method of claim 2, wherein the collection of roadway and traffic conditions scenarios are categorized into segments comprising the blinking zone, the emergency zone and the cruise zone based on the estimated response time of the car to roadway or traffic scenarios.

6. The method of claim 1, wherein applying the user preference data set and/or the preferred driving style and/or the moral or ethics traits of the user in operating the self-driving car comprising restraining the operating to be lawful; finding a closest match between a current scenario and a scenario used in the collection for acquisition of the user preference data set, and applying the user preference data operating the car if a match being found close enough; generating a suggestion how to handle a scenario based on the user preferred driving style and/or the moral or ethics traits if a close enough match not found, and achieving an optimal solution by considering the suggestion together with other options generated by AI control subsystem.

7. The method of claim 1, wherein the user preference data set and/or the preferred driving style and/or the moral or ethics traits comprising the data sets of one of the users riding the car, or of a designated user not riding the car or the factory settings.

8. (canceled)

9. The method of claim 2, wherein the user inputting his or her choice on a preferred handling behavior on a roadway and traffic conditions scenario comprising selecting an answer among multiple choices or answering to a yes or no question or entering a numeric value within a normalized range, indicating a percentage degree of a consent or discontent to a answer.

10. The method of claim 1, wherein the personalizing continues during the driving, comprising the robot executing guidance from a user in operation of the car and updating the user preference data set by the roadway and traffic scenario/guidance data pairs, through interactions between the robot and the user over roadway and traffic scenarios.

11. The method of claim 1, wherein the personalizing continues during the driving, comprising the robot automatically detecting and analyzing the facial and/or body languages of a user reflecting his or her sentiment to the behaviors of the car; tuning the operations of the car; extracting the user profile data and updating the user profile data set.