Method and apparatus for estimating a road traffic condition and method and apparatus for controlling a vehicle running characteristic

• US 5,566,072 A
• Filed: 08/10/1994
• Issued: 10/15/1996
• Est. Priority Date: 08/10/1993
• Status: Expired due to Term

• Current Assignee: Mitsubishi Jidosha Kogyo Kabushiki Kaisha
• Original Assignee: Mitsubishi Jidosha Kogyo Kabushiki Kaisha
• Inventors: Yoshida, Hiroaki, Ito, Masayoshi, Momose, Nobuo, Sano, Yoshiaki, Taira, Masahito, Tani, Masanori
• Primary Examiner(s): Teska, Kevin J.
• Assistant Examiner(s): NGUYEN, TAN QUANG
• Application Number:
  US08/288,039
• Time in Patent Office: 797 Days
• Field of Search: 364/436, 364/437, 364/438, 364/426.04, 364/424.01, 364/424.05, 364/446, 395/900, 395/905, 395/930, 395/21, 395/913, 180/140, 180/141, 180/142, 180/143, 123/399
• US Class Current: 701/117
• CPC Class Codes: B60G 2400/104 lateral or transversal with regard to vehicle
• CPC Class Codes: B60G 2400/106 longitudinal with regard to vehicle, e.g. braking
• CPC Class Codes: B60G 2400/204 Vehicle speed
• CPC Class Codes: B60G 2400/34 Accelerator pedal position
• CPC Class Codes: B60G 2400/41 Steering angle
• CPC Class Codes: B60G 2400/823 Obstacle sensing
• CPC Class Codes: B60G 2400/824 Travel path sensing; Track monitoring
• CPC Class Codes: B60G 2400/91 Frequency
• CPC Class Codes: B60G 2600/12 Sampling or average detecting; Addition or substraction
• CPC Class Codes: B60G 2600/172 Weighting coefficients or factors
• CPC Class Codes: B60G 2600/1878 Neural Networks
• CPC Class Codes: B60G 2600/188 Spectral analysis; Transformations
• CPC Class Codes: B60G 2800/70 Estimating or calculating vehicle parameters or state variables
• CPC Class Codes: B60G 2800/702 Improving accuracy of a sensor signal
• CPC Class Codes: B60G 2800/704 predicting unorthodox driving conditions for safe or optimal driving
• CPC Class Codes: B60G 2800/91 Suspension Control
• CPC Class Codes: B60G 2800/92 ABS - Brake Control
• CPC Class Codes: B60G 2800/96 ASC - Assisted or power Steering control
• CPC Class Codes: B60G 2800/97 Engine Management System [EMS]
• CPC Class Codes: B60T 8/174 characterised by using special control logic, e.g. fuzzy logic , neural computing
• CPC Class Codes: B60W 2050/0057 : Frequency analysis, spectral techniques or transforms
• CPC Class Codes: B60W 2520/125 : Lateral acceleration
• CPC Class Codes: B60W 2530/00 : Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
• CPC Class Codes: B60W 2540/30 : Driving style
• CPC Class Codes: B60W 2554/00 : Input parameters relating to objects
• CPC Class Codes: B60W 2554/406 : Traffic density
• CPC Class Codes: B60W 40/04 : Traffic conditions
• CPC Class Codes: B60W 40/06 : Road conditions
• CPC Class Codes: B60W 40/09 : Driving style or behaviour
• CPC Class Codes: B62D 6/007 : adjustable by the driver, e.g. sport mode
• CPC Class Codes: B62D 7/159 : characterised by computing methods or stabilisation processes or systems, e.g. responding to yaw rate, lateral wind, load, road condition
• CPC Class Codes: F16H 2059/003 : Detecting or using driving style of a driver, e.g. for adapting shift schedules
• CPC Class Codes: F16H 2059/605 : Traffic stagnation information, e.g. traffic jams
• CPC Class Codes: F16H 2061/0081 : Fuzzy logic
• CPC Class Codes: F16H 2061/0084 : Neural networks
• CPC Class Codes: F16H 59/00 : Control inputs to control units of change-speed-, or reversing-gearings for conveying rotary motion
• CPC Class Codes: F16H 59/66 : Road conditions, e.g. slope, slippery
• CPC Class Codes: F16H 61/66 : specially adapted for continuously variable gearings control of exclusively fluid gearing F16H61/38
• CPC Class Codes: Y10S 706/90 : Fuzzy logic
• CPC Class Codes: Y10S 706/905 : Vehicle or aerospace
• CPC Class Codes: Y10S 706/913 : Vehicle or aerospace

Abstract

An apparatus for estimating a road traffic condition and for controlling a vehicle running characteristic includes a controller having a fuzzy inference function and a neural network function. The controller carries out frequency analysis on vehicle driving parameters such as vehicle speed, steering angle, accelerator opening degree, and longitudinal acceleration and lateral acceleration of a vehicle, to thereby determine a mean value and variance of each parameter. It implements fuzzy inference based on a traveling time ratio, an average speed, and an average lateral acceleration, which are obtained from vehicle speed and/or steering angle, to thereby calculate road traffic condition parameters, including a city area degree, a jammed road degree, and a mountainous road degree. According to the neural network function, the controller further determines an output parameter, indicative of the vehicle maneuvering state, by subjecting the mean value and variance of the vehicle driving parameters and the weighted total sum of the road traffic condition parameters to nonlinear conversion. Then, it variably controls the operating characteristic of a vehicle-mounted apparatus such as a rear-wheel steering controlling unit in accordance with the output parameter, thereby variably controlling the vehicle running characteristic.

Claims

1. A road traffic condition estimating method for estimating a traffic condition of a road on which a vehicle is traveling, comprising the steps of:

(a) calculating a traveling time ratio of the vehicle based upon a detected speed of the vehicle, the traveling time ratio being a ratio of traveling time of the vehicle to a total time including the traveling time and a stopping time of the vehicle;

(b) calculating an average speed of the vehicle; and

(c) estimating the road traffic condition based on a relationship between said calculated traveling time ratio and said calculated average speed.

2. The road traffic condition estimating method according to claim 1 wherein said step (c) includes estimating a city area degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving in a city, as the road traffic condition.

3. The road traffic condition estimating method according to claim 2, wherein said step (c) includes determining that said city area degree is high when both said traveling time ratio and said average speed are at one of a low and medium level.

4. The road traffic condition estimating method according to claim 2, wherein said step (c) includes carrying out fuzzy inference to determine said city area degree based on a plurality of fuzzy rules, said plurality of fuzzy rules including a fuzzy rule by which said city area degree is determined to be high when both said traveling time ratio and said average speed are at a low level, and a fuzzy rule by which said city area degree is determined to be high when both said traveling time ratio and said average speed are at a medium level.

5. The road traffic condition estimating method according to claim 2, wherein said step (c) includes estimating an expressway degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of an expressway, based on a value which is obtained by subtracting said city area degree from a maximum calculated value of said city area degree, said maximum calculated value being subject to a predetermined maximum value.

6. The road traffic condition estimating method according to claim 1, wherein said step (c) includes estimating a jammed road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of a traffic jam, as said road traffic condition.

7. The road traffic condition estimating method according to claim 6, wherein said step (c) includes determining that said jammed road degree is high when at least one of said traveling time ratio and said average speed are at a low level.

8. The road traffic condition estimating method according to claim 6 wherein said step (c) includes carrying out fuzzy inference to determine said jammed road degree based on a plurality of fuzzy rules, said plurality of fuzzy rules including a fuzzy rule by which said jammed road degree is determined to be high when said traveling time ratio is at a low level, and a fuzzy rule by which said jammed road degree is determined to be high when said average speed is at a low level.

9. The road traffic condition estimating method according to claim 1, further including the steps of:

(d) calculating an average lateral acceleration of the vehicle; and

(e) determining a mountainous road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving on a mountain road, based said calculated average lateral acceleration.

10. The road traffic condition estimating method according to claim 9, wherein said step (e) includes determining said mountainous road degree based on a stored map having a characteristic in which said mountainous road degree increases with an increase in said calculated average lateral acceleration.

11. The road traffic condition estimating method according to claim 1, wherein said step (c) estimates one of a city area degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving in a city, expressway degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of an expressway, and jammed road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of a traffic jam as said estimated road traffic condition.

12. The road traffic condition estimating method according to claim 11, further including the steps of:

(d) calculating an average lateral acceleration of the vehicle; and

(e) determining a mountainous road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving on a mountain road, based said calculated average lateral acceleration.

13. The road traffic condition estimating method according to claim 1, further comprising:

(d) performing frequency analysis on at least one vehicle operating parameter indicative of an operating state of the vehicle; and

(e) processing output of said steps (c) and (d) using a neural network to determine a desired vehicle maneuvering state.

14. The road traffic condition estimating method according to claim 13, wherein said step (d) performs frequency analysis on at least one of an accelerator depression amount, a longitudinal acceleration of the vehicle, and a lateral acceleration of the vehicle.

15. A method for controlling a characteristic of a vehicle in which a characteristic of a vehicle is variably controlled by variably controlling an operating characteristic of an apparatus mounted on the vehicle, comprising the steps of:

(a) calculating a traveling time ratio of the vehicle based upon a detected speed of the vehicle, the traveling time ratio being a ratio of traveling time of the vehicle to a total time including the traveling time and a stopping time of the vehicle;

(b) calculating an average speed of the vehicle;

(c) estimating a road traffic condition based on a relationship between said calculated traveling time ratio and said calculated average speed; and

(d) variably controlling the operating characteristic of the apparatus mounted on the vehicle in accordance with the estimated road traffic condition.

16. The method of claim 15, wherein the apparatus mounted on the vehicle is a rear-wheel steering apparatus, and said step (d) includes setting target rear-wheel steering angle by multiplying one of a detected front-wheel steering value and a detected value of a vehicle behavior by a coefficient, and variably controlling said coefficient in accordance with said estimated road traffic condition.

17. The method of claim 15, wherein the apparatus mounted on the vehicle is a power steering unit and said step (d) includes variably controlling a ratio of a steering reaction force and vehicle speed characteristic of the power steering unit in accordance with said estimated road traffic condition, and changing a steering reaction force in accordance with vehicle speed.

18. The method of claim 15, wherein the apparatus mounted on the vehicle is an automatic transmission and said step (d) includes variably setting a speed change map in accordance with said estimated road traffic condition, the map being based on vehicle speed and throttle opening degree.

19. The method of claim 15, wherein the apparatus mounted on the vehicle is an engine output control unit and said step (d) includes variably controlling a ratio of an accelerator operation and an engine output characteristic of the engine output control unit in accordance with said estimated road traffic condition.

20. The road traffic condition estimating method according to claim 15, further including the steps of:

(e) calculating an average lateral acceleration of the vehicle; and

(f) determining a mountainous road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving on a mountain road, based said calculated average lateral acceleration; and

wherein

said step (d) variably controls said operating characteristic in accordance with the estimated road traffic condition and output of step (f).

21. The road traffic condition estimating method according to claim 15, wherein said step (c) estimates one of a city area degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving in a city, expressway degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of an expressway, and jammed road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of a traffic jam as said estimated road traffic condition.

22. The method of claim 15, wherein

the apparatus mounted on the vehicle is a rear-wheel steering apparatus;

and

said step (d) sets a rear-wheel steering angle based on said estimated road traffic condition.

23. The method of claim 22, wherein said step (d) comprises:

(d1) inputting at least one vehicle operating parameter indicative of an operating condition of the vehicle;

(d2) inputting at least one front-wheel steering parameter indicative of an operating condition of a front-wheel steering apparatus;

(d3) determining a rear-wheel steering mode based on the vehicle operating parameter;

(d4) determining a road surface condition based on the vehicle operating parameter and the front-wheel steering parameter; and

(d5) setting the rear-wheel steering angle based the rear-wheel steering mode, the road surface condition, and the estimated road traffic condition.

24. The method of claim 15, wherein

the apparatus mounted on the vehicle is a power steering unit;

and

said step (d) controls power steering force of the power steering unit based on at least the vehicle speed and the estimated road traffic condition.

25. The method of claim 24, wherein said step (d) reads the power steering force from a map based on the vehicle speed, and modifies the map based on the estimated road traffic condition.

26. The road traffic condition estimating method according to claim 25, further comprising:

(e) performing frequency analysis on at least one vehicle operating parameter indicative of an operating state of the vehicle; and

(f) processing output of said steps (c) and (e) using a neural network to determine a desired vehicle maneuvering state; and

wherein

said step (d) reads the power steering force from a map based on the vehicle speed, and modifies the map based on the estimated road traffic condition and the desired vehicle maneuvering state.

27. The method of claim 15, wherein

the apparatus mounted on the vehicle is an automatic transmission;

and

said step (d) includes the steps of,

(d1) determining at least one shift change parameter indicative of whether the automatic transmission should effect a shift change;

(d2) adjusting the shift change parameter based on the estimated road traffic condition; and

(d3) shifting the automatic transmission based on the adjusted shift change parameter.

28. The method of claim 27, wherein

said step (d1) reads the shift change parameter from a map based on the vehicle speed and a throttle degree opening of a throttle for an engine of the vehicle.

29. The road traffic condition estimating method according to claim 27, further comprising:

(e) performing frequency analysis on at least one vehicle operating parameter indicative of an operating state of the vehicle; and

(f) processing output of said steps (c) and (e) using a neural network to determine a desired vehicle maneuvering state; and

wherein

said step (d2) adjusts the shift change parameter based on the estimated road traffic condition and the desired maneuvering state.

30. The method of claim 15, wherein

the apparatus mounted on the vehicle is an engine output control unit which controls output of an engine of the vehicle;

and

said step (d) adjusts an amount of engine output set by the engine output control unit based on the estimated road traffic condition.

31. A road traffic condition estimating apparatus for estimating a traffic condition of a road on which a vehicle is traveling, comprising:

traveling time ratio detecting means for calculating a traveling time ratio of the vehicle based upon a detected speed of the vehicle, the traveling time ratio being a ratio of traveling time of the vehicle to a total time including the traveling time and a stopping time of the vehicle;

average speed detecting means for calculating an average speed of the vehicle; and

road traffic condition estimating means for estimating the road traffic condition based on a relationship between said calculated traveling time ratio and said calculated average speed.

32. The road traffic condition estimating apparatus according to claim 31, wherein said road traffic condition estimating means estimates a city area degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving in a city, as the road traffic condition.

33. The road traffic condition estimating apparatus according to claim 32, wherein said road traffic condition estimating means determines that said city area degree is high when both said traveling time ratio and said average speed are at one of low and medium level.

34. The road traffic condition estimating apparatus according to claim 32, wherein said road traffic condition estimating means carries out fuzzy inference to determine said city area degree based on a plurality of fuzzy rules, said plurality of fuzzy rules including a fuzzy rule by which said city area degree is determined to be high when both said traveling time ratio and said average speed are at a low level, and a fuzzy rule by which said city area degree is determined to be high when both said traveling time ratio and said average speed are at a medium level.

35. The road traffic condition estimating apparatus according to claim 32, wherein said road traffic condition estimating means estimates an expressway degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of an expressway, based on a value which is obtained by subtracting said city area degree from a maximum calculated value of said city area degree, said maximum calculated value being subject to a predetermined maximum value.

36. The road traffic condition estimating apparatus according to claim 31, wherein said road traffic condition estimating means estimates a jammed road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of a traffic jam, as said road traffic condition.

37. The road traffic condition estimating apparatus according to claim 36, wherein said road traffic condition estimating means determines that said jammed road degree is high when at least one of said traveling time ratio and said average speed are at a low level.

38. The road traffic condition estimating apparatus according to claim 36, wherein said road traffic condition estimating means carries out fuzzy inference to determine said jammed road degree based on a plurality of fuzzy rules, said plurality of fuzzy rules including a fuzzy rule by which said jammed road degree is determined to be high when said traveling time ratio is at a low a level, and a fuzzy rule by which said jammed road degree is determined to be high when said average speed is at a low level.

39. The road traffic condition estimating apparatus according to claim 31, further including:

average lateral acceleration detecting means for calculating an average lateral acceleration of the vehicle; and

mountainous road degree estimating means for determining a mountainous road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving on a mountain road, based said average lateral acceleration.

40. The road traffic condition estimating apparatus according to claim 39, wherein said mountainous road degree estimating means determines said mountainous road degree based on a stored map having a characteristic in which said mountainous road degree increases with an increase in said calculated average lateral acceleration.

41. The road traffic condition estimating apparatus according to claim 31, wherein said road traffic estimating means estimates one of a city area degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving in a city, expressway degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of an expressway, and jammed road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of a traffic jam as said estimated road traffic condition.

42. The road traffic condition estimating apparatus according to claim 41, further including:

average lateral acceleration detecting means for calculating an average lateral acceleration of the vehicle; and

mountainous road degree estimating means for determining a mountainous road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving on a mountain road, based on said average lateral acceleration.

43. The road traffic condition estimating apparatus according to claim 31, further comprising:

frequency analysis means for performing frequency analysis on at least one vehicle operating parameter indicative of an operating state of the vehicle; and

a neural network processing output of said frequency analysis means and the estimated road traffic condition to determine a desired vehicle maneuvering state.

44. The road traffic condition estimating apparatus according to claim 43, wherein said frequency analysis means performs frequency analysis on at least one of an accelerator depression amount, a longitudinal acceleration of the vehicle, and a lateral acceleration of the vehicle.

45. An apparatus for controlling a characteristic of a vehicle in which a characteristic of a vehicle is variably controlled by variably controlling an operating characteristic of an apparatus mounted on the vehicle, comprising:

traveling time ratio detecting means for calculating a traveling time ratio of the vehicle based upon a detected speed of the vehicle, the traveling time ratio being a ratio of traveling time of the vehicle to a total time including the traveling time and a stopping time of the vehicle;

average speed detecting means for calculating an average speed of the vehicle;

road traffic condition estimating means for estimating a road traffic condition based on a relationship between said calculated traveling time ratio and said calculated average speed; and

characteristic controlling means for variably controlled controlling the operating characteristic of the apparatus mounted on the vehicle in accordance with the estimated road traffic condition.

46. The apparatus of claim 45, wherein the apparatus mounted on the vehicle is a rear-wheel steering apparatus, and wherein a target rear-wheel steering angle is set by multiplying one of a detected front-wheel steering value and a detected value of a vehicle behavior by a coefficient, said coefficient being variably controlled by said characteristic controlling means in accordance with said estimated road traffic condition.

47. The apparatus of claim 45, wherein the apparatus mounted on the vehicle is a power steering unit and said characteristic controlling means variably controls a ratio of a steering reaction force and vehicle speed characteristic of the power steering unit in accordance with said estimated road traffic condition, and changes a steering reaction force in accordance with vehicle speed.

48. The apparatus of claim 45, wherein the apparatus mounted on the vehicle is an automatic transmission and said characteristic controlling means variably sets a speed change map in accordance with said estimated road traffic condition, the map being based on vehicle speed and throttle opening degree.

49. The apparatus of claim 45, wherein the apparatus mounted on the vehicle is an engine output control unit and said characteristic controlling means variably controls a ratio of an accelerator operation and an engine output characteristic of the engine output control unit in accordance with said estimated road traffic condition.

50. The road traffic condition estimating apparatus according to claim 45, further including:

average lateral acceleration detecting means for calculating an average lateral acceleration of the vehicle; and

mountainous road degree estimating means for determining a mountainous road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving on a mountain road, based on said average lateral acceleration; and

wherein

said characteristic controlling means variably controls the operating characteristic in accordance with the estimated road traffic condition and output of the mountainous degree estimating means.

51. The road traffic condition estimating apparatus according to claim 45, wherein said road traffic estimating means estimates one of a city area degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of driving in a city, expressway degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of an expressway, and jammed road degree, corresponding to a likelihood of travel of the vehicle under driving conditions typical of a traffic jam as said estimated road traffic condition.

52. The apparatus of claim 45, wherein

the apparatus mounted on the vehicle is a rear-wheel steering apparatus;

and

said characteristic controlling means sets a rear-wheel steering angle based on said estimated road traffic condition.

53. The apparatus of claim 45, wherein said characteristic controlling means inputs at least one vehicle operating parameter indicative of an operating condition of the vehicle and at least one front-wheel steering parameter indicative of an operating condition of a front-wheel steering apparatus, determines a rear-wheel steering mode based on the vehicle operating parameter, determines a road surface condition based on the vehicle operating parameter and the front-wheel steering parameter, and sets the rear-wheel steering angle based the rear-wheel steering mode, the road surface condition, and the estimated road traffic condition.

54. The apparatus of claim 45, wherein

the apparatus mounted on the vehicle is a power steering unit;

and

said characteristic controlling means controls power steering force of the power steering unit based on at least the vehicle speed and the estimated road traffic condition.

55. The apparatus of claim 54, wherein said characteristic controlling means reads the power steering force from a map based on the vehicle speed, and modifies the map based on the estimated road traffic condition.

56. The road traffic condition estimating apparatus according to claim 54, further comprising:

frequency analysis means for performing frequency analysis on at least one vehicle operating parameter indicative of an operating state of the vehicle;

a neural network processing output of said frequency analysis means and the estimated road traffic condition to determine a desired vehicle maneuvering state; and

wherein

said characteristic controlling means reads the power steering force from a map based on the vehicle speed, and modifies the map based on the estimated road traffic condition and the desired vehicle maneuvering state.

57. The apparatus of claim 45, wherein

the apparatus mounted on the vehicle is an automatic transmission;

and

said characteristic controlling means determines at least one shift change parameter indicative of whether the automatic transmission should effect a shift change, adjusts the shift change parameter based on the estimated road traffic condition, and shifts the automatic transmission based on the adjusted shift change parameter.

58. The apparatus of claim 57, wherein

said characteristic controlling means reads the shift change parameter from a map based on the vehicle speed and a throttle degree opening of a throttle for an engine of the vehicle.

59. The road traffic condition estimating apparatus according to claim 57, further comprising:

frequency analysis means for performing frequency analysis on at least one vehicle operating parameter indicative of an operating state of the vehicle;

a neural network processing output of said frequency analysis means and the estimated road traffic condition to determine a desired vehicle maneuvering state; and

wherein

said characteristic controlling means adjusts the shift change parameter based on the estimated road traffic condition and the desired maneuvering state.

60. The apparatus of claim 45, wherein

the apparatus mounted on the vehicle is an engine output control unit which controls output of an engine of the vehicle;

and

said characteristic controlling means adjusts an amount of engine output set by the engine output control unit based on the estimated road traffic condition.