- Email: [email protected]
Fuzzy drive expert system for an automobile
NORTH-HOLLAND Fuzzy Drive Expert System for an Automobile MIKIO
MAEDA
Depariment Technology,
of Computer Engineering, Faculty of Engineering, l-l, Sensui-cho, Tobata, Kitalcyushu 804, Japan
Kyvshu Institute of
ABSTRACT This paper deals with a fuzzy drive expert system for an auto-cruise car. This system consists of four rule sets such as environment recognition rules, driving control rules, learning-evaluation rules, and management. meta-rules. The structure c’f those rules is hierarchical. The fuzzy drive expert system is structured with five units; the distance extraction and image processing unit, the environment recognition unit, the control unit, the learning-evaluation unit, the I/O unit, the knowledge rule base, and the man-machine interface. Each unit drives the fuzzy production rules which are described by sentence and symbols based on the if-then type format. Antecedent parts and consequent parts of those rules include the fuzzy words such as big, positive, wide, short, and so on. On the basis of the recognized result of environment., the control unit manipulates the steering and the throttle valve (or fuel injectors, brake pressure) for direction control and speed control of vehicle. Th.e vehicle drive controls on the straight road and the corner are simulated on the digital computer. The overtaking control, the tracking control, and the
avoid,snce control of obstacles are successful and smoothable.
1.
INTRODUCTION
Recently, the rate of diffusion of automobiles has become remarkably high. In such a situation, the number of automobile accidents and deaths a year has been increasing. The cause of the accidents includes nap driving and look-away driving. Also, chances of long-time driving have been increasing as the network of super highways has been progressing today. Since a load that a driver receives from long-time driving is heavy, fatigue of the driver has gotten worse and worse. To reduce accidents and the fatigue, some investigations and the developments of the auto-cruise car are have been performed by the RCA and
INFORMATION SCIENCES 4, 29-48 (1995) @ Elsevier Science Inc., 1995
655 Avenue of the Americas, New York, NY 10010
0020-0255/95/$9.50 SSDI 0020-0255(94)00076-N
30
M. MAEDA
GM Companies since about 1960. An initial development of the cruise system has an auto steering control unit for driving on inductive cable built-in road. In 1964, an adaptive speed control system [l] was proposed by the Bendix Company. The feature of this system is that a safe following distance can be maintained with respect to a lead vehicle located in the same traffic lane, and also the vehicle speed control unit has proportional and differential control elements with respect to the input variable. The above following distance is obtained from a radar or an optical sensor equipped automobile. Later, a nonlinear control of vehicle speed was proposed by Toyota Motor [2] and a PID control [3] and an optimal control [4] were proposed by Kyushu Institute of Technology. These systems need the following conditions: i) Dynamic response is given, ii) Dynamic response does not change, and iii) Control system has only a small disturbance. These conditions are essential to control the vehicle speed as well as for steering control [5]. We have therefore designed a fuzzy logic controller [6] which can provide robust control and logic control. The fuzzy controller is constructed with fuzzy linguistic control rules which are relationally described by control knowledge between a throttle valve (or braking action) and the vehicle speed (or following distance) based on an “if . . then. .” format type. The vehicle speed and the following distance are controlled by the fuzzy control system which includes the fuzzy controller and man-machine interface. In this paper, furthermore, we propose a fuzzy drive expert system for speed control, following distance control, and steering control with fuzzy logic to reduce the load of the driver and avoid accidents because of nap driving and look-away driving. This fuzzy drive expert system (FDES) is constructed with the man-machine interface, speed and steering control unit, recognition unit of road environments, fuzzy reasoning engine (i.e., control unit), and so on. In each control, we use a fuzzy logic controller to which a simplified fuzzy reasoning is applied [7]. This controller consists of a speed control part and a steering control part. Those are respectively divided into three subparts such as straight driving control, curve driving control, and obstacle avoidance control. These parts use some of the speed control rule sets such as constant speed, tracking control (the following distance control), and risk avoidance control, and some of the steering control rule sets such as straight driving rules, curve driving rules, and obstacle avoiding control rules. When the facts for antecedent of those rules are obtained, some rules fire with the matching
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
31
Throttle or
(distances)
Fig. 1. Speed-steering
control system.
degree between the facts and rule conditions. By a unification of results of all rules, the control forces of vehicle, the recognized information around the vehicle, and the learning-evaluation result are obtained. Our system will gradually become the best from repeatedly evaluating the control results and the cognition effects and learning the knowledge rules. As an application to the fuzzy drive control by the FDES, the tracking and the outrunning control and the curve course driving are simulated on straight courses on a computer. Also, how to evaluate and optimize our system are briefly described. Finally, we discuss the usefulness of the FDES from the simulation results. 2.
SPEED-STEERING
CONTROL
SYSTEM
Figure 1 shows the fuzzy control system for speed control and steering control of an automobile. The control system consists of the fuzzy logic controller and the controlled system (automobile). The fuzzy logic controller, which consists of the control unit and driving rules, has the speed control part, the steering control part, the knowledge filter, predictive control, and the fuzzy reasoning part. This controller obtains the desired speed (set point), the actual speed of the vehicle, and the location of obstacles and vehicles. After that, to decide the steering angle of a handle of the automobile and the manipulated variable of the throttle valve or fuel injectors, the simplified fuzzy reasoning is done with drive knowledge rules. 3. 3.1.
FUZZY DRIVE EXPERT SYSTEM
SYSTEM
ARCHITECTURE
Figure 2 shows the fuzzy drive expert system architecture. This system is constructed from a CCD camera, a laser radar, the image processing
M. MAEDA
32 Driver
,\\
Dis;ydce
T
I
Learning evaluation .A
and unit _... c:-_..... ‘-->
extraction
nmage
pr ocessi ng
*
+
J
Rec;gn;ition
uni t
r
I
Laser radar CC0 camera
%iYO’
Driving rules Speed control Steering control Predictive control knowledge filter Recognition Learning & Evaluation
1
rules rules
1
J,
Fuzzy drive expert
system
Vehicle
Fig. 2. Structure of fuzzy drive expert system.
and distance calculation unit, the environment recognition rule base, the control rule base, the drive knowledge rule, the learning-evaluation rule base, and the man-machine interface. To obtain external information, the CCD camera and the laser radar are used in the I/O unit. Note that since there is no small, highly efficient, high-speed image processing unit, the simulations of speed control and steering control are performed with only the laser radar in this paper. Five directions of laser radar in all are therefore used to improve the correctness of recognition. Figure 3 shows the directions that the laser radar scans and the obstacle images obtained from that. Also, several distances (Id, rd, di, CL, and CR) in Figure 3 are results calculated by a trigonometrical survey. The distance data and the image data from the laser radar and the CCD camera are processed in the image processing and distance calculation unit, respectively, and sent to the environment recognition unit. The environment recognition unit then recognizes the road situation, that is, the pattern of road shapes (a straight road or a curve), the angle between the automobile and the walls (or road edges), the distance to the walls, the width of the road, the presence of obstacles (or preceding vehicle), the distance between the automobile and the obstacles, the distance between the obstacles and the walls on either side of the road, and the speed of the obstacles. The road shapes consist of seven patterns, with three patterns in the curve to the right, three patterns in the curve to the left, and one pattern in the straight road. The road shapes are fuzzily divided into these
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
33
Angle(degree)
Fig. 3. Laser radar and obstacle search.
seven patterns by the pattern decision rules such as, “If (& cos 30’ - Ck) is positive and (Ck - Lk cos 30’) is positive, then the possibility of right pattern 2 is high,” where Rk equals FR and LI, equals FL. Figure 4 shows the pattern of road shapes. The recognition result in the environment recognition rule base is sent to the control rule base. The control rule base consists of the speed control rule set and the steering control rule set. The fuzzy reasoning with the drive knowledge rule is done based on the recognition result, the speed of the automobile, and the location of it and the manipulated variables of the throttle valve and the steering angle are decided. The control rule base also includes the knowledge filter. The output values of the control rule base are adjusted through the knowledge filter to eliminate vibrations [8]. The output values are given to the automobile and, as a result, the automobile speled and the steering angle are fed back to the controller. The drive knowledge rule is roughly divided into three rule sets: the speed control rule set, the steering control rule set, and the predictive control rule set.
M. MAEDA
34
Ck
Rk
Cr Lh
Car
Cr
Lk
Rk
Car
Car
?T
I,;
Curve to right 1
Curve to right 2 Lk
Lk
Curve to left 2 Fig.
Straight
Cr Rk
+I!$ Car
Clf ?!T
to left 3
Curve to right 3 L*
Ck Rk
Curve
-P Lr
RI.
4. Patterns
Curve to left 1 of road
shape.
Now, the predictive control rule set has only a single rule for adjusting the steering angle and the vehicle speed based on the relation between speed and steering to avoid skid of the automobile. This rule set will include a rule set to predict the motion of the automobile from output values and adjust the values in the future. The changes of reference values for driving and other things are exchanged between the user (or the driver) via the interface. The criteria for evaluation of my system are considered the comfortability for running and the performance such as “overshoot,” “reaching time” (i.e., the time which the control state attains a reference value is employed instead of “settling time”), and “amplitude” of control response [9]. This system is assessed using fuzzy values for those criteria. That is, the learning-evaluation rule base evaluates the recognition results and the drive control results, and learns the knowledge rules from those results on the basis of these criteria. Then it adjusts the drive knowledge rules and parameters [9, lo]. But for the improvement of recognition rules, it employs the error of cognition as a criterion. Now, we suppose that an optimization of this system is accomplished by an optimizing subsystem such as the control unit, recognition unit, I/O unit, and knowledge base, which consist of knowledge rules. However, although this system is not optimized analytically because of using the
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
35
heuristic rules (learning and evaluation rules) for the fuzzy evaluation of the control performance and learning of knowledge rules, it will become nearly the best by repeatedly learning little by little.
3.2.
SPEED
CONTROL
Speed control is performed in three areas that are classified in accordance with the presence of obstacles (or preceding vehicle) and the distance to a front wall: obstacle avoiding area, straight area, and curve area. The straie;ht and curve areas are divided according to the distance to the wall in front under no obstacle. When there is a preceding vehicle or obstacle, the obstacle avoiding area is considered. The structure of the speed control rules is hierarchical, as shown in Figure 5. Figure 6 shows the fuzzy partitions on the straight area and curve area. The setting of a partition parameter d, depends on the width of the road.
Curve driving rule set Speed
Curve
control
Risk avoidance rules
Passing control rules
_
Obstacle avoiding
Tracking control rules
Risk avoidance
Fig. 5. Hierarchical speed control rule sets.
M. MAEDA
36
d.
Distance(m]
Fig. 6. Straight area and curve area.
In the straight area, speed control is done Straight Driving Control. in the free area and the dangerous area that are fuzzily classified according to the speed of the automobile and the distance to a wall in front of the automobile. These two areas are shown in Figure 7. The partition parameter is set by Equation (1): dt = 0.002~’ + 0.1~ + 0.75 [m]
(I)
w: vehicle speed.
In the free area, the vehicle speed is controlled to keep a constant speed [6]. On the other hand, in the dangerous area, the speed is controlled to avoid danger. Each area is shown as follows: 1) Free area. Since a wall and obstacle in front of an automobile do not exist in the free area, the automobile keeps constant speed based on a long-time reference speed with a constant speed driving rule. The long-time reference speed is set by the user or driver through the man-machine interface. It is also the reference speed of the entire control system. The constant speed driving rules are shown as
/ 0
I dl
\ Distance(m]
Fig. 7. Free area and dangerous area
FUZZY DRIVE EXPERT follows:
SYSTEM FOR AUTOMOBILE
37
If e, is P,l, then duT is P,, If e, is N,l, then dUT is N,l If de, is PVz, then dUT is P,z If de, is Nv2, then duT is N,2
(2)
If d2ev is Pv3, then duT is PU3 If d2ev is N,,s, then duT is NUs where e, , de,, and d2ev : the control error (= reference-vehicle speed) and its first and second differences; P,,i, N,i: the fuzzy labels (positive and negative), i = 1,2,3; Puiy Nui: the real values, i = 1,2,3; UT: the throttle (or fuel injector) variable The antecedent parts of these rules are described by fuzzy labels; the consequent parts have nonfuzzy (real value) and fuzzy labels which are characterized by singleton and arctangent functions, which are called membership functions. By fuzzy reasoning on Equation (2), the throttle variable duT is given as dUT = f(ev, dev, d2ev) where the function f (.) is expressed by a linear sum of the arctangent type.
area. The dangerous area represents the area where an obstacle or a wall in front of the automobile is within a dangerous distance to the automobile if the automobile keeps up its speed. If the obstacle or wall in front exists within the dangerous distance, the speed is reduced to avoid collision with a risk avoidance rule set. The amount by which speed is reduced is adjusted according to whether or not the change of distance to the wall and the obstacle in front is positive. One of the risk avoidance rules is indicated as, “if the deviation of distance to obstacle is positive small, then reduce speed a little.” On the basis of the degree of the reduce command, the throttle valve or brake pressure is controlled.
2) Dangerous
Curve Driving Control. Also in the curve area, speed control is done in the dangerous area and the free area, respectively, as shown in Figure 7.
38
M. MAEDA
TABLE 1 Short Time Speed Setting Rules
Lc
W
zero
positive
wide
about 25
zero
positive
narrow
about 20
RC
STR
positive
positive
wide
about 30
positive
positive
narrow
about 25
positive
zero
wide
about 35
positive
zero
narrow
about 30
negative
positive
wide
about 30
negative
positive
narrow
about 30
negative
zero
wide
about 25
negative
zero
narrow
about 20
negative
negative
wide
about 30
negative
negative
narrow
about 25
zero
negative
wide
about 35
zero
negative
narrow
Rc: RK cos 30’ - CK, Lc: CK - LK road, STR: short reference speed.
about 30 cos
30’, W:
width
of
In the free area, a short-time reference speed is set and speed control is done so that the automobile keeps its reference speed. As a rule for speed control, the constant speed driving rule set in the straight area is used after the reference speed is changed in the setting rule. The shorttime reference speed is the reference speed that is currently set in the fuzzy logic controller so that the automobile runs safely so as not to slip in a curve. The short-time reference speed is decided according to the pattern of road shapes as shown in Figure 4 and the width of the road, and their setting rules are shown in Table 1. If the decided short-time reference speed is more than the long-time reference speed, the automobile keeps constant speed on running at the long-time reference speed. If the distance to the wall in front is shorter than the predictive dangerous distance between the wall and the automobile in the curve, the speed is reduced to avoid collision with the risk avoidance rules discussed in the previous section. The speed control rule set (curve rules) in the curve area consists of short-time reference speed setting rules and constant speed control rules. Obstacle Avoidance Control. If an obstacle exists in front of an automobile and the obstacle is running fairly fast, the obstacle is recognized as the preceding car and the automobile follows the preceding car using the tracking control rules [ll]. Also, if the speed of the obstacle is much less
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
VI
39
Speed[Km/h]
Fig. 8. Trackingarea and passingarea. than the reference speed or the obstacle stops, the automobile passes the obstacle using the outrunning drive rules (passing control rules). In this way, the speed control is done in the passing area and the following area whic.h are classified according to the speed of the obstacle. Figure 8 shows the passing area and the tracking area. In the following area, the automobile keeps the reference distance between the automobile and the preceding car, which is derived from the speed of the preceding car, and the automobile follows the preceding car. But if the speed of the preceding car is more than the long-time reference speed and the short-time reference speed in a curve, or if the distance between the automobile and the preceding car is more than the limit-following distance between them df, the automobile stops following the preceding car and then the speed is controlled under the curve driving rules. Also, if the distance to the preceding car is less than the dangerous distance between the t,wo automobiles dt, the speed is reduced with the risk avoidance rule. Thes:e speed controls are done in three subareas: dangerous area, tracking
Speed
( km / h)
Fig. 9. Three areas in tracking.
M. MAEDA
40
area, and free area as shown in Figure in Equation (3):
If
e&
is zero,
then
9. Tracking
dW
=
If e& iS TtO72.2H-0, then dUT =
control
f (edk y dedk
f (e&,de&,
rules are shown
, d%k),
(3)
d2e&),
where e&, dedk, d’edk: the error between reference distance and real disdifferences, tance to wall or road edge, its first and second respectively; duT: the change of throttle; zero, nonzero: membership functions
of an exponential
type.
In the passing area, judging by the distance between the obstacle and the walls on either side of the road, if the distance to either the right wall or the left wall is so wide that the automobile can run through, the speed is kept. If the automobile dan go through and the distance from the obstacle to either the right wall or the left wall is narrow, the speed is reduced. If the distance to both walls is too narrow for the automobile to pass through, the speed is reduced and stopping the automobile is performed. Equation (4) expresses the passing control rules: If ow is wide,
then duT = f(de,,
d2e,),
If ow is narrow, then duT = negative,
(4)
where ow: the distance between wall (road edge) and obstacle; wide, narrow, negative: fuzzy labels. function with a linear sum of In Equation (4), f(.) im pl ies a nonlinear arctangent types (see [S]) which are expressed by simplified fuzzy reasoning [7] of the “if then” rule in the “then part” a priori. Note that df, dt are a linear and a second-order function, respectively, and the neighboring areas of the linear line and curve line are fuzzy areas. 3.3.
STEERING
Steering
control
CONTROL
is performed
in the following
1) Straight Area-A wall and obstacle mobile in this area.
three areas:
do not exist in front of the auto-
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
41
Straight-steering rule set
Parallel driving lll1e.s
Steering
Curve-steering rules
control
Obstacle avoiding
I
Obstacle avoidance
rules
Fig. 10. Hierarchical steering control rule sets.
211Curve Area-A wall exists in front of the automobile in this area. 3) Danger Avoiding Area-An obstacle exists in front of the automobile in this area. If’an obstacle exists in front of the automobile, the area is classified into the passing area and the tracking area as shown in Figure 8. In passing control, the obstacle avoidance rules for steering are used. On the other hand, in tracking control, the straight-steering rule set and the curve-steering rule:3 are used. If the obstacle does not exist, the curve area and straight area. as shown in Figure 6 are classified according to the distance to the walls, and steering control is performed in these areas. Straight Driving Control. In a straight road where an obstacle and wall do not exist in front of the automobile, steering control is performed so that. the automobile runs parallel to the walls on either side of the road in the middle of the road. The straight-steering rule set consists of a distance rule set and a parallel rule set. The structure of steering control rules is hierarchical, as shown in Figure 10. The distance rule set is one that the automobile runs in the middle of t.he road under the steering control. The steering control is performed so that the distance to the walls on either side of the roads is the same as the reference distance. This means that the car runs at the reference trajectory. The automobile is controlled to run in the middle of the road by making the reference distance to each wall the same. If one of the walls on either side of the road does not exist or cannot be detected, the reference distance is only the distance to the wall that exists. If neither wall on
42
M. MAEDA
TABLE 2 Parallel Driving Rules
de
8
duff
positive
positive
positive big
positive
negative
positive small
negative
positive
negative small
negative
negative
positive big
0, de: angle from vehicle direction to road edge line and its difference. dUH: steering angle.
either side of the road exists or can be detected with radar, the automobile keeps the steering angle. The distance rule set consists of the rules for keeping the constant distance to the correct wall. These rules are shown in Equation (5) : If left wall is not detected, then duH = f (edr, dedr, d’ed,), If right wall is not detected, then dUH = f (cdl, de&, d’edl), If both side walls are detected, then duH =
[f (edr,
dedr,
d2edT)
+
f (cdl,
dedl,
(5)
d’edl)]/&
where duH: the steering angle; e&, dedrr d2edr: the control error between the reference distance and measured distance to the wall, its first and second differences, respectively; e,-J,dedl, d2edl: the control error between the reference distance and measured distance to the wall, its first and second differences, respectively. The parallel rule set is one where the automobile runs in parallel to the walls on either side of the road and the angle between the automobile and the walls is zero. The parallel rule set is used to avoid snaking of the automobile. Table 2 shows the parallel rules. Curve Driving Control. In a curve, the manipulated variable of the handle is adjusted according to the pattern of road shapes, as shown in Figure 4 in Section 3.1. An example of a rule is “If (RI, cos 30’ - Ck) is positive and (Ck - LI, cos 30’) is positive, then duH is positive medium” [12]. The curve driving rule set does not only have a rule in the case where the automobile goes around the curve. Only under this rule set, the automobile
FUZZY
DRIVE
EXPERT
SYSTEM
FOR AUTOMOBILE
snakes sharply after passing through the curve. The straight road driving rule set is therefore used early to fuzzily cut off quickly by rotating the radar according to the steering angle so that the radar catches a situation ahead. Obstacle Avoidance Control [13]. If an obstacle exists, passing control or tracking control is performed in the passing area and the tracking area, respectively. If the obstacle is the preceding automobile that is running at fairly high speed, tracking control is performed with the straight road and curve driving rule set. If the obstacle stops or runs at much lower speed, passing control is done with the obstacle avoidance rule set. The obstacle avoidance rule set is one where the automobile goes to the location where the distance between the obstacle and each wall on either side of the road is wider, so that the automobile runs in the middle between the obstacle and the wall. This rule set includes the straight-steering rule set and the reference trajectory deciding rules. In this set, the reference trajectory of the automobile is temporarily changed so that the automobile runs in the middle between the obstacle and the wall. Equation (6) indicates the trajectory deciding rule: If rR is greater than lo, then Tr = rR/2 and Tl = W - rR/2, If rR is less than IL, then Tr = W - 1~12 and Tl = 1~12,
(6)
where rR, 1~: the distances between the obstacle and the right wall and between the obstacle and the left wall, respectively; Tr, Tl: the reference distances to the right and left walls, respectively; W: the width of road. 4.
SIMULATIONS
To verify the usefulness of the fuzzy logic controller in the speed and steering control of an automobile, these controls are simulated with a personal computer. Here, complex image processing is not performed, and the distance data from a laser radar are used. The model of the automobile, whi.ch is the controlled system, consists of a speed control model and a steering-location model. The sampling interval in the simulation is 0.1 second. Figures 11-14 show simulation results of the speed and steering control. The simulation in Figure 11 was done on a course consisting of a right angle corner, a composite corner, a gentle curve, and a cranky course. In the esc,ape from the sharp corners, such as the right angle corner, turning the
M. MAEDA
44
a) Location
0
Time(s)
~ig~,s--___________________-----_--_____P A
60. 5
.n
Time(s)
_
,~,,‘--____-__-_-__--_____-----_________
0
ww~vAu^u Y-Y+ V’10
20
30
40
Time(s)
50
b) Speed, Steering Angle, and Throttle Value Fig. 11. Course driving.
45
FUZZY DRIVE EXPERT SYSTEM FOR AUTOMOBILE
Vehicle
a) Location
Vehicle
t--
oL------
Time(s)
A.* ATARI-----___________---________________
F *
‘~o-te--Y j;
I
Time(s)
-
left------------------___----__--________ ” -
Yi ------------------__-----________.____ 5 E -\I I 1 I 0 20 10 b)
30
40
1
50
Time(s)
Speed, Steering Angle, and Throttle Value Fig. 12. Obstacle avoidance control.
Except at this point, the performance of the cutoff is good. As for the manipulated variable of the throttle valve, the useless manipulation is still found compared with the driver, and the vibration of the automobile is pretty much eliminated. The reduction of speed in the corners is good too. The negative value of the throttle implies the braking action. Tracking control, passing control, and obstacle avoidance control on a straight road were also simulated. Figure 12 shows the result of obstacle avoidance control. In the narrow area of the road width, the vehicle speed
steering wheel is late.
M. MAEDA
46
Vehicle
a) Location J Preceding car Z”------__/____________________-________ g
_-
%f W cz
_ -
Vehicle
I
0
I Time(s)
right--------_-________--_-_______________ 07 _ .c
-1-
Time(s)
IeR---------__-_-____--_________________ gQ) --------____-__-___-_-________________ E
:,J 0
10
20
I
30
b) Speed, Steering Angle, and
40
Throttle
I
1 50
Time(s)
Value
Fig. 13. Tracking drive control when the preceding car is accelerating. is well reduced,
and then in the wide area, the reference speed (43 km/h) is kept. Figure 13 shows the results of tracking control in the case where It is found that the vehicle follows the the preceding car is acclerating. preceding car well. The passing control results were also good, as shown in Figure 14. Note that the effectiveness of the fuzzy learning-evaluation has been shown in [7-g], and is not described in this paper. Furthermore, the fuzzy evaluation of a comfortability for driving is not used in the above simulations. But in practical use of this system, the comfortability (for example,
FUZZY DRIVE EXPERT
47
SYSTEM FOR AUTOMOBILE
a)
Location
~so__-_-__-____-_---____--_________-_---
5 __I -c
_ _
al Cl 4:
Vehicle Preceding car
-
I 0 rightA__-_--_----__-_-_--_----__-_---_-__-_‘I=” ,; I - I p a etn ,~~,~_~~_~~_~____~__~~_~______~_~_~____~
0
10
20
30
Time(s)
Time(s)
40
50
Time(s)
b) Speed, Steering Angle, and Throttle Value Fig. 14. Passing control.
its, measure may alternatively employ some aspects such as the feeling for speedup and slowdown, and the vibration of vehicle) and the operation ability are important aspects ofsystem evaluation.
5.
CONCLUSIONS
We have constructed the fuzzy drive expert system with the fuzzy logic controller. Since there is no highly-efficient image processing unit, complex
48
M. MAEDA
image processing is not performed, and only the distance data from a laser radar are used in the recognition of a situation in computer simulation. In order to apply this expert system, there remain problems. One of them is on the equipment of control rules to apply the expert system. Also, a highly-efficient image processing unit needs to be developed. We are sure that the method that is proposed in this paper will be needed and found useful in the future if such problems are resolved without delay. REFERENCES 1. 2.
3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
R. W. Cafp et al., Adaptive speed control for automobiles, Bendix Technical Journal 46-56 (1969). T. Ochiai, Speed control and electronics, Journal of SAE of Japan 32(2):136-142 (1978). S. Murakami et al., Optimal speed control of automobile, in Preprints of SICE’77, 16th Lecture Meeting, 1977, pp. 499-500. S. Murakami and M. Maeda, Optimal speed control of automobile using microprocessor, nans. of SICE of Japan 19(7):51-55 (1983). T. Yatabe, et al., Application of microprocessors to automated vehicle, Journal of SAE of Japan 34(2):158-163 (1980). M. Maeda and S. Murakami, Speed control of automobile by fuzzy logic controller, nuns. of SICE of Japan 21(9):984-989 (1985). M. Maeda et al., Fuzzy drive control of an autonomous mobile robot, Fuzzy Sets and Systems (39):195-204 (1991). M. Maeda, Fuzzy expert system and its applications to control and diagnosis, Doctoral dissertation, Tokyo Institute of Technology, 1988. M. Maeda and S. Murakami, A self-tuning fuzzy controller, Fuzzy Sets and Systems 51(1):29-40 (1992). H. Takahashi et al., Application of a self-tuning fuzzy logic system to automatic speed control devices, in Preprints of SICE’87, Hiroshima, 1987, pp. 1241-1244. M. Maeda and S. Murakami, Automobile tracking control with a fuzzy logic, in Pr-oc. of the 3rd FUZZYSystem Symposium of Japan, 1987, pp. 61-66. M. Maeda and S. Murakami, Steering control and speed control of an automobile with a fuzzy logic, in PTOC. of the 3rd IFSA Congress, 1989, pp. 75-78. M. Maeda, Y. Takada, and S. Murakami, Automobile speed and steering control with a fuzzy logic, in Preprints of the 33rd Japan Joint Automatic Control Conference, 1990, pp. 463-464.
Received 2 January 1994; revised 12 March 1994 and 12 August 1994
MAEDA
Depariment Technology,
of Computer Engineering, Faculty of Engineering, l-l, Sensui-cho, Tobata, Kitalcyushu 804, Japan
Kyvshu Institute of
ABSTRACT This paper deals with a fuzzy drive expert system for an auto-cruise car. This system consists of four rule sets such as environment recognition rules, driving control rules, learning-evaluation rules, and management. meta-rules. The structure c’f those rules is hierarchical. The fuzzy drive expert system is structured with five units; the distance extraction and image processing unit, the environment recognition unit, the control unit, the learning-evaluation unit, the I/O unit, the knowledge rule base, and the man-machine interface. Each unit drives the fuzzy production rules which are described by sentence and symbols based on the if-then type format. Antecedent parts and consequent parts of those rules include the fuzzy words such as big, positive, wide, short, and so on. On the basis of the recognized result of environment., the control unit manipulates the steering and the throttle valve (or fuel injectors, brake pressure) for direction control and speed control of vehicle. Th.e vehicle drive controls on the straight road and the corner are simulated on the digital computer. The overtaking control, the tracking control, and the
avoid,snce control of obstacles are successful and smoothable.
1.
INTRODUCTION
Recently, the rate of diffusion of automobiles has become remarkably high. In such a situation, the number of automobile accidents and deaths a year has been increasing. The cause of the accidents includes nap driving and look-away driving. Also, chances of long-time driving have been increasing as the network of super highways has been progressing today. Since a load that a driver receives from long-time driving is heavy, fatigue of the driver has gotten worse and worse. To reduce accidents and the fatigue, some investigations and the developments of the auto-cruise car are have been performed by the RCA and
INFORMATION SCIENCES 4, 29-48 (1995) @ Elsevier Science Inc., 1995
655 Avenue of the Americas, New York, NY 10010
0020-0255/95/$9.50 SSDI 0020-0255(94)00076-N
30
M. MAEDA
GM Companies since about 1960. An initial development of the cruise system has an auto steering control unit for driving on inductive cable built-in road. In 1964, an adaptive speed control system [l] was proposed by the Bendix Company. The feature of this system is that a safe following distance can be maintained with respect to a lead vehicle located in the same traffic lane, and also the vehicle speed control unit has proportional and differential control elements with respect to the input variable. The above following distance is obtained from a radar or an optical sensor equipped automobile. Later, a nonlinear control of vehicle speed was proposed by Toyota Motor [2] and a PID control [3] and an optimal control [4] were proposed by Kyushu Institute of Technology. These systems need the following conditions: i) Dynamic response is given, ii) Dynamic response does not change, and iii) Control system has only a small disturbance. These conditions are essential to control the vehicle speed as well as for steering control [5]. We have therefore designed a fuzzy logic controller [6] which can provide robust control and logic control. The fuzzy controller is constructed with fuzzy linguistic control rules which are relationally described by control knowledge between a throttle valve (or braking action) and the vehicle speed (or following distance) based on an “if . . then. .” format type. The vehicle speed and the following distance are controlled by the fuzzy control system which includes the fuzzy controller and man-machine interface. In this paper, furthermore, we propose a fuzzy drive expert system for speed control, following distance control, and steering control with fuzzy logic to reduce the load of the driver and avoid accidents because of nap driving and look-away driving. This fuzzy drive expert system (FDES) is constructed with the man-machine interface, speed and steering control unit, recognition unit of road environments, fuzzy reasoning engine (i.e., control unit), and so on. In each control, we use a fuzzy logic controller to which a simplified fuzzy reasoning is applied [7]. This controller consists of a speed control part and a steering control part. Those are respectively divided into three subparts such as straight driving control, curve driving control, and obstacle avoidance control. These parts use some of the speed control rule sets such as constant speed, tracking control (the following distance control), and risk avoidance control, and some of the steering control rule sets such as straight driving rules, curve driving rules, and obstacle avoiding control rules. When the facts for antecedent of those rules are obtained, some rules fire with the matching
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
31
Throttle or
(distances)
Fig. 1. Speed-steering
control system.
degree between the facts and rule conditions. By a unification of results of all rules, the control forces of vehicle, the recognized information around the vehicle, and the learning-evaluation result are obtained. Our system will gradually become the best from repeatedly evaluating the control results and the cognition effects and learning the knowledge rules. As an application to the fuzzy drive control by the FDES, the tracking and the outrunning control and the curve course driving are simulated on straight courses on a computer. Also, how to evaluate and optimize our system are briefly described. Finally, we discuss the usefulness of the FDES from the simulation results. 2.
SPEED-STEERING
CONTROL
SYSTEM
Figure 1 shows the fuzzy control system for speed control and steering control of an automobile. The control system consists of the fuzzy logic controller and the controlled system (automobile). The fuzzy logic controller, which consists of the control unit and driving rules, has the speed control part, the steering control part, the knowledge filter, predictive control, and the fuzzy reasoning part. This controller obtains the desired speed (set point), the actual speed of the vehicle, and the location of obstacles and vehicles. After that, to decide the steering angle of a handle of the automobile and the manipulated variable of the throttle valve or fuel injectors, the simplified fuzzy reasoning is done with drive knowledge rules. 3. 3.1.
FUZZY DRIVE EXPERT SYSTEM
SYSTEM
ARCHITECTURE
Figure 2 shows the fuzzy drive expert system architecture. This system is constructed from a CCD camera, a laser radar, the image processing
M. MAEDA
32 Driver
,\\
Dis;ydce
T
I
Learning evaluation .A
and unit _... c:-_..... ‘-->
extraction
nmage
pr ocessi ng
*
+
J
Rec;gn;ition
uni t
r
I
Laser radar CC0 camera
%iYO’
Driving rules Speed control Steering control Predictive control knowledge filter Recognition Learning & Evaluation
1
rules rules
1
J,
Fuzzy drive expert
system
Vehicle
Fig. 2. Structure of fuzzy drive expert system.
and distance calculation unit, the environment recognition rule base, the control rule base, the drive knowledge rule, the learning-evaluation rule base, and the man-machine interface. To obtain external information, the CCD camera and the laser radar are used in the I/O unit. Note that since there is no small, highly efficient, high-speed image processing unit, the simulations of speed control and steering control are performed with only the laser radar in this paper. Five directions of laser radar in all are therefore used to improve the correctness of recognition. Figure 3 shows the directions that the laser radar scans and the obstacle images obtained from that. Also, several distances (Id, rd, di, CL, and CR) in Figure 3 are results calculated by a trigonometrical survey. The distance data and the image data from the laser radar and the CCD camera are processed in the image processing and distance calculation unit, respectively, and sent to the environment recognition unit. The environment recognition unit then recognizes the road situation, that is, the pattern of road shapes (a straight road or a curve), the angle between the automobile and the walls (or road edges), the distance to the walls, the width of the road, the presence of obstacles (or preceding vehicle), the distance between the automobile and the obstacles, the distance between the obstacles and the walls on either side of the road, and the speed of the obstacles. The road shapes consist of seven patterns, with three patterns in the curve to the right, three patterns in the curve to the left, and one pattern in the straight road. The road shapes are fuzzily divided into these
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
33
Angle(degree)
Fig. 3. Laser radar and obstacle search.
seven patterns by the pattern decision rules such as, “If (& cos 30’ - Ck) is positive and (Ck - Lk cos 30’) is positive, then the possibility of right pattern 2 is high,” where Rk equals FR and LI, equals FL. Figure 4 shows the pattern of road shapes. The recognition result in the environment recognition rule base is sent to the control rule base. The control rule base consists of the speed control rule set and the steering control rule set. The fuzzy reasoning with the drive knowledge rule is done based on the recognition result, the speed of the automobile, and the location of it and the manipulated variables of the throttle valve and the steering angle are decided. The control rule base also includes the knowledge filter. The output values of the control rule base are adjusted through the knowledge filter to eliminate vibrations [8]. The output values are given to the automobile and, as a result, the automobile speled and the steering angle are fed back to the controller. The drive knowledge rule is roughly divided into three rule sets: the speed control rule set, the steering control rule set, and the predictive control rule set.
M. MAEDA
34
Ck
Rk
Cr Lh
Car
Cr
Lk
Rk
Car
Car
?T
I,;
Curve to right 1
Curve to right 2 Lk
Lk
Curve to left 2 Fig.
Straight
Cr Rk
+I!$ Car
Clf ?!T
to left 3
Curve to right 3 L*
Ck Rk
Curve
-P Lr
RI.
4. Patterns
Curve to left 1 of road
shape.
Now, the predictive control rule set has only a single rule for adjusting the steering angle and the vehicle speed based on the relation between speed and steering to avoid skid of the automobile. This rule set will include a rule set to predict the motion of the automobile from output values and adjust the values in the future. The changes of reference values for driving and other things are exchanged between the user (or the driver) via the interface. The criteria for evaluation of my system are considered the comfortability for running and the performance such as “overshoot,” “reaching time” (i.e., the time which the control state attains a reference value is employed instead of “settling time”), and “amplitude” of control response [9]. This system is assessed using fuzzy values for those criteria. That is, the learning-evaluation rule base evaluates the recognition results and the drive control results, and learns the knowledge rules from those results on the basis of these criteria. Then it adjusts the drive knowledge rules and parameters [9, lo]. But for the improvement of recognition rules, it employs the error of cognition as a criterion. Now, we suppose that an optimization of this system is accomplished by an optimizing subsystem such as the control unit, recognition unit, I/O unit, and knowledge base, which consist of knowledge rules. However, although this system is not optimized analytically because of using the
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
35
heuristic rules (learning and evaluation rules) for the fuzzy evaluation of the control performance and learning of knowledge rules, it will become nearly the best by repeatedly learning little by little.
3.2.
SPEED
CONTROL
Speed control is performed in three areas that are classified in accordance with the presence of obstacles (or preceding vehicle) and the distance to a front wall: obstacle avoiding area, straight area, and curve area. The straie;ht and curve areas are divided according to the distance to the wall in front under no obstacle. When there is a preceding vehicle or obstacle, the obstacle avoiding area is considered. The structure of the speed control rules is hierarchical, as shown in Figure 5. Figure 6 shows the fuzzy partitions on the straight area and curve area. The setting of a partition parameter d, depends on the width of the road.
Curve driving rule set Speed
Curve
control
Risk avoidance rules
Passing control rules
_
Obstacle avoiding
Tracking control rules
Risk avoidance
Fig. 5. Hierarchical speed control rule sets.
M. MAEDA
36
d.
Distance(m]
Fig. 6. Straight area and curve area.
In the straight area, speed control is done Straight Driving Control. in the free area and the dangerous area that are fuzzily classified according to the speed of the automobile and the distance to a wall in front of the automobile. These two areas are shown in Figure 7. The partition parameter is set by Equation (1): dt = 0.002~’ + 0.1~ + 0.75 [m]
(I)
w: vehicle speed.
In the free area, the vehicle speed is controlled to keep a constant speed [6]. On the other hand, in the dangerous area, the speed is controlled to avoid danger. Each area is shown as follows: 1) Free area. Since a wall and obstacle in front of an automobile do not exist in the free area, the automobile keeps constant speed based on a long-time reference speed with a constant speed driving rule. The long-time reference speed is set by the user or driver through the man-machine interface. It is also the reference speed of the entire control system. The constant speed driving rules are shown as
/ 0
I dl
\ Distance(m]
Fig. 7. Free area and dangerous area
FUZZY DRIVE EXPERT follows:
SYSTEM FOR AUTOMOBILE
37
If e, is P,l, then duT is P,, If e, is N,l, then dUT is N,l If de, is PVz, then dUT is P,z If de, is Nv2, then duT is N,2
(2)
If d2ev is Pv3, then duT is PU3 If d2ev is N,,s, then duT is NUs where e, , de,, and d2ev : the control error (= reference-vehicle speed) and its first and second differences; P,,i, N,i: the fuzzy labels (positive and negative), i = 1,2,3; Puiy Nui: the real values, i = 1,2,3; UT: the throttle (or fuel injector) variable The antecedent parts of these rules are described by fuzzy labels; the consequent parts have nonfuzzy (real value) and fuzzy labels which are characterized by singleton and arctangent functions, which are called membership functions. By fuzzy reasoning on Equation (2), the throttle variable duT is given as dUT = f(ev, dev, d2ev) where the function f (.) is expressed by a linear sum of the arctangent type.
area. The dangerous area represents the area where an obstacle or a wall in front of the automobile is within a dangerous distance to the automobile if the automobile keeps up its speed. If the obstacle or wall in front exists within the dangerous distance, the speed is reduced to avoid collision with a risk avoidance rule set. The amount by which speed is reduced is adjusted according to whether or not the change of distance to the wall and the obstacle in front is positive. One of the risk avoidance rules is indicated as, “if the deviation of distance to obstacle is positive small, then reduce speed a little.” On the basis of the degree of the reduce command, the throttle valve or brake pressure is controlled.
2) Dangerous
Curve Driving Control. Also in the curve area, speed control is done in the dangerous area and the free area, respectively, as shown in Figure 7.
38
M. MAEDA
TABLE 1 Short Time Speed Setting Rules
Lc
W
zero
positive
wide
about 25
zero
positive
narrow
about 20
RC
STR
positive
positive
wide
about 30
positive
positive
narrow
about 25
positive
zero
wide
about 35
positive
zero
narrow
about 30
negative
positive
wide
about 30
negative
positive
narrow
about 30
negative
zero
wide
about 25
negative
zero
narrow
about 20
negative
negative
wide
about 30
negative
negative
narrow
about 25
zero
negative
wide
about 35
zero
negative
narrow
Rc: RK cos 30’ - CK, Lc: CK - LK road, STR: short reference speed.
about 30 cos
30’, W:
width
of
In the free area, a short-time reference speed is set and speed control is done so that the automobile keeps its reference speed. As a rule for speed control, the constant speed driving rule set in the straight area is used after the reference speed is changed in the setting rule. The shorttime reference speed is the reference speed that is currently set in the fuzzy logic controller so that the automobile runs safely so as not to slip in a curve. The short-time reference speed is decided according to the pattern of road shapes as shown in Figure 4 and the width of the road, and their setting rules are shown in Table 1. If the decided short-time reference speed is more than the long-time reference speed, the automobile keeps constant speed on running at the long-time reference speed. If the distance to the wall in front is shorter than the predictive dangerous distance between the wall and the automobile in the curve, the speed is reduced to avoid collision with the risk avoidance rules discussed in the previous section. The speed control rule set (curve rules) in the curve area consists of short-time reference speed setting rules and constant speed control rules. Obstacle Avoidance Control. If an obstacle exists in front of an automobile and the obstacle is running fairly fast, the obstacle is recognized as the preceding car and the automobile follows the preceding car using the tracking control rules [ll]. Also, if the speed of the obstacle is much less
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
VI
39
Speed[Km/h]
Fig. 8. Trackingarea and passingarea. than the reference speed or the obstacle stops, the automobile passes the obstacle using the outrunning drive rules (passing control rules). In this way, the speed control is done in the passing area and the following area whic.h are classified according to the speed of the obstacle. Figure 8 shows the passing area and the tracking area. In the following area, the automobile keeps the reference distance between the automobile and the preceding car, which is derived from the speed of the preceding car, and the automobile follows the preceding car. But if the speed of the preceding car is more than the long-time reference speed and the short-time reference speed in a curve, or if the distance between the automobile and the preceding car is more than the limit-following distance between them df, the automobile stops following the preceding car and then the speed is controlled under the curve driving rules. Also, if the distance to the preceding car is less than the dangerous distance between the t,wo automobiles dt, the speed is reduced with the risk avoidance rule. Thes:e speed controls are done in three subareas: dangerous area, tracking
Speed
( km / h)
Fig. 9. Three areas in tracking.
M. MAEDA
40
area, and free area as shown in Figure in Equation (3):
If
e&
is zero,
then
9. Tracking
dW
=
If e& iS TtO72.2H-0, then dUT =
control
f (edk y dedk
f (e&,de&,
rules are shown
, d%k),
(3)
d2e&),
where e&, dedk, d’edk: the error between reference distance and real disdifferences, tance to wall or road edge, its first and second respectively; duT: the change of throttle; zero, nonzero: membership functions
of an exponential
type.
In the passing area, judging by the distance between the obstacle and the walls on either side of the road, if the distance to either the right wall or the left wall is so wide that the automobile can run through, the speed is kept. If the automobile dan go through and the distance from the obstacle to either the right wall or the left wall is narrow, the speed is reduced. If the distance to both walls is too narrow for the automobile to pass through, the speed is reduced and stopping the automobile is performed. Equation (4) expresses the passing control rules: If ow is wide,
then duT = f(de,,
d2e,),
If ow is narrow, then duT = negative,
(4)
where ow: the distance between wall (road edge) and obstacle; wide, narrow, negative: fuzzy labels. function with a linear sum of In Equation (4), f(.) im pl ies a nonlinear arctangent types (see [S]) which are expressed by simplified fuzzy reasoning [7] of the “if then” rule in the “then part” a priori. Note that df, dt are a linear and a second-order function, respectively, and the neighboring areas of the linear line and curve line are fuzzy areas. 3.3.
STEERING
Steering
control
CONTROL
is performed
in the following
1) Straight Area-A wall and obstacle mobile in this area.
three areas:
do not exist in front of the auto-
FUZZY DRIVE EXPERT
SYSTEM FOR AUTOMOBILE
41
Straight-steering rule set
Parallel driving lll1e.s
Steering
Curve-steering rules
control
Obstacle avoiding
I
Obstacle avoidance
rules
Fig. 10. Hierarchical steering control rule sets.
211Curve Area-A wall exists in front of the automobile in this area. 3) Danger Avoiding Area-An obstacle exists in front of the automobile in this area. If’an obstacle exists in front of the automobile, the area is classified into the passing area and the tracking area as shown in Figure 8. In passing control, the obstacle avoidance rules for steering are used. On the other hand, in tracking control, the straight-steering rule set and the curve-steering rule:3 are used. If the obstacle does not exist, the curve area and straight area. as shown in Figure 6 are classified according to the distance to the walls, and steering control is performed in these areas. Straight Driving Control. In a straight road where an obstacle and wall do not exist in front of the automobile, steering control is performed so that. the automobile runs parallel to the walls on either side of the road in the middle of the road. The straight-steering rule set consists of a distance rule set and a parallel rule set. The structure of steering control rules is hierarchical, as shown in Figure 10. The distance rule set is one that the automobile runs in the middle of t.he road under the steering control. The steering control is performed so that the distance to the walls on either side of the roads is the same as the reference distance. This means that the car runs at the reference trajectory. The automobile is controlled to run in the middle of the road by making the reference distance to each wall the same. If one of the walls on either side of the road does not exist or cannot be detected, the reference distance is only the distance to the wall that exists. If neither wall on
42
M. MAEDA
TABLE 2 Parallel Driving Rules
de
8
duff
positive
positive
positive big
positive
negative
positive small
negative
positive
negative small
negative
negative
positive big
0, de: angle from vehicle direction to road edge line and its difference. dUH: steering angle.
either side of the road exists or can be detected with radar, the automobile keeps the steering angle. The distance rule set consists of the rules for keeping the constant distance to the correct wall. These rules are shown in Equation (5) : If left wall is not detected, then duH = f (edr, dedr, d’ed,), If right wall is not detected, then dUH = f (cdl, de&, d’edl), If both side walls are detected, then duH =
[f (edr,
dedr,
d2edT)
+
f (cdl,
dedl,
(5)
d’edl)]/&
where duH: the steering angle; e&, dedrr d2edr: the control error between the reference distance and measured distance to the wall, its first and second differences, respectively; e,-J,dedl, d2edl: the control error between the reference distance and measured distance to the wall, its first and second differences, respectively. The parallel rule set is one where the automobile runs in parallel to the walls on either side of the road and the angle between the automobile and the walls is zero. The parallel rule set is used to avoid snaking of the automobile. Table 2 shows the parallel rules. Curve Driving Control. In a curve, the manipulated variable of the handle is adjusted according to the pattern of road shapes, as shown in Figure 4 in Section 3.1. An example of a rule is “If (RI, cos 30’ - Ck) is positive and (Ck - LI, cos 30’) is positive, then duH is positive medium” [12]. The curve driving rule set does not only have a rule in the case where the automobile goes around the curve. Only under this rule set, the automobile
FUZZY
DRIVE
EXPERT
SYSTEM
FOR AUTOMOBILE
snakes sharply after passing through the curve. The straight road driving rule set is therefore used early to fuzzily cut off quickly by rotating the radar according to the steering angle so that the radar catches a situation ahead. Obstacle Avoidance Control [13]. If an obstacle exists, passing control or tracking control is performed in the passing area and the tracking area, respectively. If the obstacle is the preceding automobile that is running at fairly high speed, tracking control is performed with the straight road and curve driving rule set. If the obstacle stops or runs at much lower speed, passing control is done with the obstacle avoidance rule set. The obstacle avoidance rule set is one where the automobile goes to the location where the distance between the obstacle and each wall on either side of the road is wider, so that the automobile runs in the middle between the obstacle and the wall. This rule set includes the straight-steering rule set and the reference trajectory deciding rules. In this set, the reference trajectory of the automobile is temporarily changed so that the automobile runs in the middle between the obstacle and the wall. Equation (6) indicates the trajectory deciding rule: If rR is greater than lo, then Tr = rR/2 and Tl = W - rR/2, If rR is less than IL, then Tr = W - 1~12 and Tl = 1~12,
(6)
where rR, 1~: the distances between the obstacle and the right wall and between the obstacle and the left wall, respectively; Tr, Tl: the reference distances to the right and left walls, respectively; W: the width of road. 4.
SIMULATIONS
To verify the usefulness of the fuzzy logic controller in the speed and steering control of an automobile, these controls are simulated with a personal computer. Here, complex image processing is not performed, and the distance data from a laser radar are used. The model of the automobile, whi.ch is the controlled system, consists of a speed control model and a steering-location model. The sampling interval in the simulation is 0.1 second. Figures 11-14 show simulation results of the speed and steering control. The simulation in Figure 11 was done on a course consisting of a right angle corner, a composite corner, a gentle curve, and a cranky course. In the esc,ape from the sharp corners, such as the right angle corner, turning the
M. MAEDA
44
a) Location
0
Time(s)
~ig~,s--___________________-----_--_____P A
60. 5
.n
Time(s)
_
,~,,‘--____-__-_-__--_____-----_________
0
ww~vAu^u Y-Y+ V’10
20
30
40
Time(s)
50
b) Speed, Steering Angle, and Throttle Value Fig. 11. Course driving.
45
FUZZY DRIVE EXPERT SYSTEM FOR AUTOMOBILE
Vehicle
a) Location
Vehicle
t--
oL------
Time(s)
A.* ATARI-----___________---________________
F *
‘~o-te--Y j;
I
Time(s)
-
left------------------___----__--________ ” -
Yi ------------------__-----________.____ 5 E -\I I 1 I 0 20 10 b)
30
40
1
50
Time(s)
Speed, Steering Angle, and Throttle Value Fig. 12. Obstacle avoidance control.
Except at this point, the performance of the cutoff is good. As for the manipulated variable of the throttle valve, the useless manipulation is still found compared with the driver, and the vibration of the automobile is pretty much eliminated. The reduction of speed in the corners is good too. The negative value of the throttle implies the braking action. Tracking control, passing control, and obstacle avoidance control on a straight road were also simulated. Figure 12 shows the result of obstacle avoidance control. In the narrow area of the road width, the vehicle speed
steering wheel is late.
M. MAEDA
46
Vehicle
a) Location J Preceding car Z”------__/____________________-________ g
_-
%f W cz
_ -
Vehicle
I
0
I Time(s)
right--------_-________--_-_______________ 07 _ .c
-1-
Time(s)
IeR---------__-_-____--_________________ gQ) --------____-__-___-_-________________ E
:,J 0
10
20
I
30
b) Speed, Steering Angle, and
40
Throttle
I
1 50
Time(s)
Value
Fig. 13. Tracking drive control when the preceding car is accelerating. is well reduced,
and then in the wide area, the reference speed (43 km/h) is kept. Figure 13 shows the results of tracking control in the case where It is found that the vehicle follows the the preceding car is acclerating. preceding car well. The passing control results were also good, as shown in Figure 14. Note that the effectiveness of the fuzzy learning-evaluation has been shown in [7-g], and is not described in this paper. Furthermore, the fuzzy evaluation of a comfortability for driving is not used in the above simulations. But in practical use of this system, the comfortability (for example,
FUZZY DRIVE EXPERT
47
SYSTEM FOR AUTOMOBILE
a)
Location
~so__-_-__-____-_---____--_________-_---
5 __I -c
_ _
al Cl 4:
Vehicle Preceding car
-
I 0 rightA__-_--_----__-_-_--_----__-_---_-__-_‘I=” ,; I - I p a etn ,~~,~_~~_~~_~____~__~~_~______~_~_~____~
0
10
20
30
Time(s)
Time(s)
40
50
Time(s)
b) Speed, Steering Angle, and Throttle Value Fig. 14. Passing control.
its, measure may alternatively employ some aspects such as the feeling for speedup and slowdown, and the vibration of vehicle) and the operation ability are important aspects ofsystem evaluation.
5.
CONCLUSIONS
We have constructed the fuzzy drive expert system with the fuzzy logic controller. Since there is no highly-efficient image processing unit, complex
48
M. MAEDA
image processing is not performed, and only the distance data from a laser radar are used in the recognition of a situation in computer simulation. In order to apply this expert system, there remain problems. One of them is on the equipment of control rules to apply the expert system. Also, a highly-efficient image processing unit needs to be developed. We are sure that the method that is proposed in this paper will be needed and found useful in the future if such problems are resolved without delay. REFERENCES 1. 2.
3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
R. W. Cafp et al., Adaptive speed control for automobiles, Bendix Technical Journal 46-56 (1969). T. Ochiai, Speed control and electronics, Journal of SAE of Japan 32(2):136-142 (1978). S. Murakami et al., Optimal speed control of automobile, in Preprints of SICE’77, 16th Lecture Meeting, 1977, pp. 499-500. S. Murakami and M. Maeda, Optimal speed control of automobile using microprocessor, nans. of SICE of Japan 19(7):51-55 (1983). T. Yatabe, et al., Application of microprocessors to automated vehicle, Journal of SAE of Japan 34(2):158-163 (1980). M. Maeda and S. Murakami, Speed control of automobile by fuzzy logic controller, nuns. of SICE of Japan 21(9):984-989 (1985). M. Maeda et al., Fuzzy drive control of an autonomous mobile robot, Fuzzy Sets and Systems (39):195-204 (1991). M. Maeda, Fuzzy expert system and its applications to control and diagnosis, Doctoral dissertation, Tokyo Institute of Technology, 1988. M. Maeda and S. Murakami, A self-tuning fuzzy controller, Fuzzy Sets and Systems 51(1):29-40 (1992). H. Takahashi et al., Application of a self-tuning fuzzy logic system to automatic speed control devices, in Preprints of SICE’87, Hiroshima, 1987, pp. 1241-1244. M. Maeda and S. Murakami, Automobile tracking control with a fuzzy logic, in Pr-oc. of the 3rd FUZZYSystem Symposium of Japan, 1987, pp. 61-66. M. Maeda and S. Murakami, Steering control and speed control of an automobile with a fuzzy logic, in PTOC. of the 3rd IFSA Congress, 1989, pp. 75-78. M. Maeda, Y. Takada, and S. Murakami, Automobile speed and steering control with a fuzzy logic, in Preprints of the 33rd Japan Joint Automatic Control Conference, 1990, pp. 463-464.
Received 2 January 1994; revised 12 March 1994 and 12 August 1994