Intelligent driving system with an embedded driver model

• US 10,286,900 B2
• Filed: 04/15/2015
• Issued: 05/14/2019
• Est. Priority Date: 09/23/2014
• Status: Active Grant

• Current Assignee: Beijing Institute of Technology
• Original Assignee: Beijing Institute of Technology
• Inventors: Xi, Junqiang, Wang, Wenshuo
• Primary Examiner(s): Marc, McDieunel
• Application Number:
  US15/512,987
• Publication Number:
  US 20170297564A1
• Time in Patent Office: 1,490 Days
• CPC Class Codes: B60W 2050/0029 Mathematical model of the driver
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2050/0083 Setting, resetting, calibration
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2520/06 Direction of travel
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2540/229 Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2540/24 Drug level, e.g. alcohol
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/40 Coefficient of friction
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/40 Dynamic objects, e.g. animals, windblown objects
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2710/226 Damping
• CPC Class Codes: B60W 2720/10 Longitudinal speed
• CPC Class Codes: B60W 2720/106 Longitudinal acceleration
• CPC Class Codes: B60W 2720/14 Yaw
• CPC Class Codes: B60W 30/025 : related to comfort of drivers or passengers
• CPC Class Codes: B60W 30/18145 : Cornering
• CPC Class Codes: B60W 40/09 : Driving style or behaviour
• CPC Class Codes: B60W 50/0098 : Details of control systems ensuring comfort, safety or stability not otherwise provided for
• CPC Class Codes: G06N 20/00 : Machine learning

Abstract

An intelligent driving system with an embedded driver model. The system includes a driver model module that adjusts vehicle performances according to driving characteristics of a driver and road environment. A driver'"'"'s visual and tactile information may be taken into account when driving a vehicle, so as to tune vehicle performances to allow the vehicle to adapt itself to the individual driver.

Claims

1. An intelligent driving system with an embedded driver model, comprising:

a road environment detection module, a driver operation detection module, a complete vehicle system module, and a driver model module;

wherein,

the road environment detection module is used for detecting the road environment information that comprises visual information related to the driver'"'"'s operations on the vehicle;

the driver operation detection module is used for detecting driver operation data that comprise tactile information related to the driver'"'"'s operations on the vehicle;

the complete vehicle system module is used for outputting vehicle state parameters;

the driver model module is used for

     1) collecting the road environment information detected by the road environment detection module, the driver operation data detected by the driver operation detection module, and the vehicle state parameters output by the complete vehicle system module,

     2) analyzing the road environment information and the vehicle state parameters to obtain an expected parameter value required for driving,

     3) analyzing the output data collected by the driver operation detection module to obtain the driving characteristics of the driver,

     4) comparing the expected parameter value with the driving characteristics of the driver to obtain the driver'"'"'s demands for the vehicle performance, and

     5) tuning corresponding parameters of the vehicle according to the driver'"'"'s demands.

2. The system according to claim 1, characterized by further comprising a driver module, wherein the driver module is used for changing a motion state of the vehicle according to the operation of the driver.

3. The system according to claim 1, characterized in that the road environment detection module comprises a distances-measuring radar and a camera;

the road environment information comprises the visual information related to the driver'"'"'s operations on the vehicle; and

the visual information comprises a road curvature, a road width, an adhesion coefficient of road surface, and a traffic flow.

4. The system according to claim 1, characterized in that the tactile information comprises operation parameter data on a steering wheel, a brake pedal, an accelerator pedal, a clutch and a transmission.

5. The system according to claim 1, characterized in that the vehicle states parameters output by the complete vehicle system module comprise a vehicle speed, a longitudinal acceleration and a yaw velocity.

6. The system according to claim 1, characterized in that the road environment comprises curve road, at a certain time, a road curvature radius ρ

, an actual steering wheel angle δ

_s and a longitudinal velocity ν

_x of the vehicle are detected by the road environment detection module, the driver operation detection module, and the complete vehicle system module, respectively, and an expected steering wheel angle

7. A vehicle, characterized in that the vehicle comprises the system according to claim 1.

8. A vehicle driving method, characterized in that the vehicle adopts the system according to claim 1.

9. The method according to claim 8, characterized in that the method further comprises the following steps:

1) automatically detecting current states of the vehicle, current states of the driver and states of the road ahead before starting the vehicle;

2) detecting a road curvature radius ρ

(t) at the current time t, determining whether the vehicle is about to enter a curve road, i.e., if ρ

(t) =∞

, it indicates that the vehicle is running in a straight lane and calculating a road curvature radius ρ

(t+Δ

t) at a time t+Δ

t;

if ρ

(t) <

∞

,it indicates that the vehicle is about to enter or has entered the curve road, and calculating a road radius curvature ρ

(t+Δ

t) at the time t+Δ

t;

3) detecting the actual steering wheel angle δ

_s δ

_s applied by the driver;

4) detecting a longitudinal velocity ν

_x of the vehicle;

5) calculating an expected steering wheel angle δ

_sr for passing the curve road, wherein