Road curvature estimation system

• US 7,522,091 B2
• Filed: 12/24/2004
• Issued: 04/21/2009
• Est. Priority Date: 07/15/2002
• Status: Expired due to Fees

• Current Assignee: TK Holdings Incorporated (Ningbo Junsheng Investment Group Co., Ltd.)
• Original Assignee: Automotive Systems Laboratory, Incorporated (Ningbo Junsheng Investment Group Co., Ltd.)
• Inventors: Shen, Shi, Cong, Shan, Hong, Lang
• Primary Examiner(s): Alsomiri; Isam
• Application Number:
  US11/022,265
• Publication Number:
  US 20050225477A1
• Time in Patent Office: 1,579 Days
• Field of Search: 342 70- 74, 342/90, 342/107, 342113-115, 342/133, 342/159, 342160-162, 342/189, 342/195, 342/196, 701/300, 701/301
• US Class Current: 342/70
• CPC Class Codes: B60K 31/0008 including means for detecting potential obstacles in vehicle path
• CPC Class Codes: B60K 31/0066 responsive to vehicle path curvature
• CPC Class Codes: B60K 31/0083 responsive to centrifugal force acting on vehicle due to the path it is following
• CPC Class Codes: B60T 2210/24 Curve radius
• CPC Class Codes: B60T 2260/08 Coordination of integrated systems
• CPC Class Codes: B60W 2050/0052 Filtering, filters
• CPC Class Codes: B60W 2520/14 Yaw
• CPC Class Codes: B60W 2552/20 Road profile, i.e. the change in elevation or curvature of a plurality of continuous road segments
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/4042 Longitudinal speed
• CPC Class Codes: B60W 2554/4043 Lateral speed
• CPC Class Codes: B60W 2554/803 Relative lateral speed
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G01S 13/58 Velocity or trajectory determination systems; Sense-of-movement determination systems
• CPC Class Codes: G01S 13/723 by using numerical data
• CPC Class Codes: G01S 13/86 Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/932 using own vehicle data, e.g. ground speed, steering wheel direction
• CPC Class Codes: G01S 2013/9325 : for inter-vehicle distance regulation, e.g. navigating in platoons
• CPC Class Codes: G01S 2013/93271 : in the front of the vehicles
• CPC Class Codes: G01S 7/411 : Identification of targets based on measurements of radar reflectivity G01S7/415 takes precedence

Abstract

A processor using a first Kalman filter estimates a host vehicle state from speed and yaw rate, the latter of which may be from a yaw rate sensor if speed is greater than a threshold, and, if less, from a steer angle sensor and speed. Road curvature parameters are estimated from a curve fit of a host vehicle trajectory or from a second Kalman filter for which a state variable may be responsive to a plurality of host state variables. Kalman filters may incorporate adaptive sliding windows. Curvature of a most likely road type is estimated with an interacting multiple model (IMM) algorithm using models of different road types. A road curvature fusion subsystem provides for fusing road curvature estimates from a plurality of curvature estimators using either host vehicle state, a map database responsive to vehicle location, or measurements of a target vehicle with a radar system.

Claims

1. A road curvature estimation system, comprising:

a. a plurality of road curvature estimation subsystems selected from a first road curvature estimation subsystem adapted to estimate a first set of at least one first curvature parameter responsive to a measure of longitudinal speed of a host vehicle on a roadway and a measure of yaw rate of said host vehicle, a second road curvature estimation subsystem adapted to estimate a second set of at least one second curvature parameter responsive to a measure of said second set of at least one second curvature parameter from a map database responsive to a measure of location of said host vehicle, and a third road curvature estimation subsystem adapted to estimate a third set of at least one third curvature parameter responsive to a radar measurement of a target vehicle traveling on said roadway; and

b. a processor adapted to fuse at least two of said first set of at least one first curvature parameter, said second set of at least one second curvature parameter, and said third set of at least one third curvature parameter so as to generate a fourth set of at least one fourth curvature parameter as an estimate of curvature of said roadway.

2. A road curvature estimation system as recited in claim 1, wherein said second road curvature estimation subsystem comprises:

a. a vehicle navigation system adapted to provide said measure of location of said host vehicle;

b. said map database adapted to provide said measure of said second set of at least one second curvature parameter responsive to said measure of location; and

c. a Kalman filter adapted to estimate said second set of at least one road curvature parameter.

3. A road curvature estimation system as recited in claim 1, wherein said third road curvature estimation subsystem comprises:

a. a radar sensor adapted to provide a measure of a trajectory of said target vehicle;

b. an extended Kalman filter adapted to provide a target state vector responsive to said measure of said trajectory of said target vehicle; and

c. a curvature filter adapted to generate said estimate of said third set of at least one third curvature parameter responsive to a measure of said third set of at least one third curvature parameter responsive to said target state vector.

4. A road curvature estimation system as recited in claim 1, wherein said processor generates said at least one fourth curvature parameter from a weight combination of at least two of said first set of at least one first curvature parameter, said second set of at least one second curvature parameter, and said third set of at least one third curvature parameter.

5. A road curvature estimation system as recited in claim 1, wherein said processor generates and error covariance associated with said at least one fourth curvature parameter from a weight combination of at least two of a first error covariance associated with said first set of at least one first curvature parameter, a second error covariance associated with said second set of at least one second curvature parameter, and a third error covariance associated with said third set of at least one third curvature parameter.