LASER-BASED ADAPTIVE CRUISE CONTROL FOR

INTELLIGENT VEHICLES

Miguel Ángel Sotelo, IEEE Member, David Fernández

Department of Electronics, University of Alcalá, Alcalá de Henares, Madrid, Spain

Eugenio Naranjo, Carlos González, IEEE member, Ricardo García, Teresa de Pedro, Jesús Reviejo

Department of Industrial Computer Science, Industrial Automation Institute, CSIC, Arganda del Rey, Madrid, Spain

Keywords: Intelligent Transportation Systems; Laser-based Adaptive Cruise Control; Fuzzy Logic; Vehicle

Automation

Abstract: Vehicle and highway automation is believed to reduce the risk of accident, improve safety, increase

capacity, reduce fuel consumption and enchance overall comfort and performance for drivers. One of the

most important research topics in the field of Intelligent Transportation Systems (ITS) is Adaptive Cruise

Control (ACC), aiming at adapting the vehicle speed to a predefined value while keeping a safe gap with

regard to potential obstacles. For this purpose, a laserscanner system provides the distance between the ego

vehicle and the preceding vehicle on the road. The complete system can be understood as a Laser-based

ACC controller, based on Fuzzy Logic, which assists the vehicle velocity control offering driving strategies

and actuation over the throttle of a car. This controller is embedded in an automatic driving system installed

in two testbed mass-produced cars operating in a real environment. The results obtained in these

experiments show a good performance of the Laser-based gap controller, which is adaptable to all speeds

and safe gap selections.

1 INTRODUCTION

Recent studies estimate that some 5.5 million

European Union accidents resulted in 42200

fatalities in 1998. The United States that same year

claimed another 42000 lives, and 9000 more were

lost in Japan. Overall, the cost to these nations

totalled some €682 billion, as described in (Marsh,

2003). As a result, a lot of money has been spent in

order to make roads safer. One of the applications of

ITS is to provide assistance to the control of some

vehicle elements, like the throttle pedal and

consequently, the speed-control assistance. A Cruise

Control (CC) system is a common application of

these techniques. It consists of maintaining the

vehicle speed at a user (driver) pre-set speed. These

kind of systems are already mass installed in top of

the line-end vehicles. A second and more

sophisticated step in the development of the speed

assistances is Adaptive Cruise Control (ACC)

(Crosse, 2000). ACC is similar to conventional

cruise control in that it keeps the vehicle pre-set

speed. However, unlike conventional cruise control,

this new system can automatically adjust speed in

order to maintain a proper headway distance (gap)

between vehicles in the same lane (STARDUST,

2002). In the current work this is achieved through a

laserscanner headway sensor, a PC, and a fuzzy-

logic speed controller. This paper addresses the

integration of a lasersacnner system, mechatronics,

and fuzzy control techniques in order to get robotic

aids to driving cars. The present application includes

a car computer throttle control powered by a fuzzy

logic controller, with the capability of performing a

Laser-based Adaptive Cruise Control either in an

unmanned/manual driving. The work described in

this paper was done at the Instituto de Automática

Industrial (IAI), a part of the Spanish Council for

Scientific Research (CSIC), in collaboration with the

Department of Electronics of the University of

Alcalá. The experiments were made in a private test

circuit located at the Instituto de Automática

Industrial, using two automated mass-produced

vehicles (Citroën Berlingo) as depicted in figure 1.

398

Ángel Sotelo M., Fernández D., Naranjo E., González C., García R., de Pedro T. and Reviejo J. (2004).

LASER-BASED ADAPTIVE CRUISE CONTROL FOR INTELLIGENT VEHICLES.

In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics, pages 398-401

DOI: 10.5220/0001128603980401

 SciTePress

Figure 1: Citroën Berlingo commercial prototypes.

2 LASER-BASED OBSTACLE

DETECTION

A laserscanner (SICK LMS) is mounted onboard the

vehicle in order to provide reliable obstacle

detection in a range that can reach up to 90 m,

depending on weather conditions. Using the

information obtained from the laserscanner, a map

of the environment around the vehicle can be

constructed and thus, vehicle velocity can be

modified so as to track the preceding vehicle in an

adaptive cruise control manner (ACC) or to produce

an emergency stop in case of inminent collision.

Scan data provided by the laserscanner are converted

to x-y coordinates with respect to the vehicle frontal

part. Other vehicles can then be easily detected by

selecting the appropriate ROI in each situation. To

gain a better understanding on how the real system

works in real circumstances an example is presented

in which the ego-vehicle is driving along a straight

section of road, 62m behind the preceding vehicle,

as depicted in figure 2a. The angular resolution of

the SICK LMS was set to 0.25

in this case, as long

as ACC is the desired feature. Distances measured

by the laserscanner at different angles are shown in

figure 2b, while the x-y coordinates of the detected

obstacles are depicted in figure 2c.

3 LASER-BASED ACC

The Laser-based Adaptive Cruise Control (ACC) is

based on a fuzzy Adaptive Cruise Control System.

Although a detailed description of the fuzzy ACC

can be found in (Naranjo et al, 2003), a brief

summary is provided.

Figure 2: Vehicle detected on the same lane. a) Scene

picture, b) Distances measured by the laserscanner, c) x-y

coordinates of detected obstacles.

The ACC control system is based on the fuzzy

CC described in (Naranjo et al, 2003), with its

objective being to maintain a safe gap with the

vehicle ahead in the same lane of the road. Two new

input fuzzy variables, a new rule and two rule

modifications were added to the controller in order

to perform the ACC. The output is the same as that

in the CC controller: the actuation over the throttle

pedal. The pursuer car will be the automatically

driven vehicle that adapts its speed to the preceding

one. At this point, we shall define the new input

variables:

Time_Gap_current: it is the time headway, the

time it takes the pursuer vehicle to reach the point

where the pursued vehicle is at present speed, in

seconds. The mathematical representation is (1).

ursued Pursuer

current

Pursuer

Time Gap

−

(1)

where x

Pursued

and x

Pursuer

are the x coordinate of the

pursued and the pursuer cars over the reference line,

in meters, and v

Pursuer

is the speed of the pursuer car

in meters per second.

Time_Gap_target: it is the time-headway set by

the user to be maintained by the ACC system from

the preceding vehicle. In commercial vehicles it

should be between 1 and 2 seconds.

Derivative of Time_Gap: is the variation of the

Time-Gap_current with time. Its mission is to

smooth the actuation in the same way that the

Acceleration smoothes the Speed_Error. The

LASER-BASED ADAPTIVE CRUISE CONTROL FOR INTELLIGENT VEHICLES

399

equation used to calculate this variable for the

control iteration i is (2).

Time Gap Time Gap

dTimeGap

−

∆

(2)

Time_Gap_Error: it represents the time-gap

error, the difference between the user-defined target

time-gap and the current time-gap. Then, the input

value for the gap fuzzy controller is represented in

(3) and measured in seconds.

arg

__ _ - _

current t et

Time Gap Error Time Gap Time Gap=

(3)

We added a new rule and we also modified two

CC previous rules. The new set is as follows:

IF Speed_Error MORE THAN null THEN

Accelerator up

IF Speed_Error LESS THAN null AND

Time_Gap_Error MORE THAN near THEN Accelerator

down

IF Acceleration MORE THAN null THEN

Accelerator up

IF Acceleration LESS THAN null AND

Time_Gap_Error far THEN Accelerator down

IF Time_Gap_Error near AND d_Time_Gap negative

THEN Accelerator up

The aim of these rules is to maintain the Cruise

Control and to keep a safe distance. To do this, the

gap control only actuates when the pursuer car is

near the pursued one and it inhibits itself in other

cases, the control thus becoming the classical CC.

Let us see in detail the modified acceleration rules.

IF Speed_Error LESS THAN null AND

Time_Gap_Error MORE THAN near THEN Accelerator

down

The throttle signal decreases when the pursuer

car is near the pursued one so it will never accelerate

enough to crash with the other car.

IF Acceleration LESS THAN null AND

Time_Gap_Error far THEN Accelerator down

This rule allows stepping on the throttle only

when the pursuer car is far from the pursued one.

IF Time_Gap_Error near AND d_Time_Gap negative

THEN Accelerator up

With this rule, the control steps off the throttle

when the safe distance is near. The stabilization of

the system is the reason for the inclusion of the

derivative in this rule.

4 EXPERIMENTAL RESULTS

After installing the above defined controller in the

testbed cars, some experiments were made in order

to demonstrate its performance. These tests were

done at the CSIC’s Instituto de Automática

Industrial in Arganda del Rey, Madrid. The

experiment set consists of the combination of laser-

based safe gap maintenance and stop-and-go

capacity. Two testbed cars were used to make the

controller tests. Both of them are equipped with a

computer, an RTK-GPS receptor and Radio-

Ethernet, but only the pursuer has an onboard laser-

based ACC. The pursued is manually driven in order

to simulate real conditions in which the reactions of

the car ahead are unpredictable. The CC will be in

effect when the car is alone on the road. During the

156 seconds of this experiment, the pursued car runs

at some variable speed between 0, when the car is

stopped, and 30 Km/h. The target speed of the

pursuer car is always higher than the pursued one so,

the cruise control will only maintain this speed when

the pursuer is farther than the pre-selected safe gap.

The experiment was made in a circuit with a straight

lane with 2 Km of length, oriented from West to

East, yielding the results depicted in figure 3.

Figure 3: Fuzzy ACC performance.

0 102030405060708090100

Time (Sec)

Speed (Km/h)

Pursued Speed (manuall y driven)

Pursuer Speed (automati cal ly driven)

Headway Time (Sec)

Time-Headway

Inter-Vehicle Distance (m)

Headway Distance

0.2

0.4

0.6

0.8

Pedal Pressure Normalized

Throttl e Pressure Normalized

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400

We have also pre-set a minimum safe headway

gap in the pursuer car of 2 seconds. The reason for

selecting this value is that 2 seconds are enough to

fulfil the safety requirements in a limited

environment such as our circuit. The bottom graphic

in figure 3 shows the real speed of both cars for the

duration of the experiment. The third graphic is the

real-time headway time-gap between these testbed

cars. The second represents the inter-vehicle

distance, in meters, including the length of the

pursued car (4 m). The top graphic shows the

pressure on the throttle of the pursued car, meaning

0 foot quite off the pedal and 1 throttle fully pressed.

At the beginning of the experiment both cars are

stopped and separated by about 50 meters. The

driver of the pursued car starts slowly while the

automatic driver of the pursued car sets the target

speed at 8 Km/h. The time gap is initially very high,

because the speeds are too low, so as the pursuer car

speed increases, the gap reduces. After the first 16

seconds, the pursuer car gets to its target speed of 20

Km/h. Then, the gap reduces drastically until it

becomes about 2 seconds. At 40 seconds from the

beginning, the pursued car stops. In this case, the

pursuer car approaches the other car until the gap is

about 2 meters (6 in the graphic), when it stops too

(STOP). The reason for this change of units is that

when the pursuer speed tends to zero, the time-gap

tends to infinity and in this case it is not useful for

control, because the cars would crash. It can be seen

in the gap graphic around the 40th second. The

distance between the cars is never less than 2 meters.

In order to improve the safety at these low speeds it

is recommended to increase the minimum safe gap

to 3 or 4 seconds.

5 CONCLUSIONS

The alliance of laser technology, fuzzy logic, and

Global Navigation Satellite Systems (GNSS) can

generate powerful controllers for automatic driving

applications. The combination of ACC+Stop&Go is

a good solution in order to achieve safer driving,

from high workload roads to traffic jams. A SICK

LMS 221 is the key element to provide obstacle

detection for active safety. By selecting a

configurable Region of Interest (ROI), the detection

ability of the laser system can be adapted to quite

different driving situations such as Adaptive Cruise

Control (tracking of a preceding vehicle on the same

lane), overtaking manoeuvres, and intersection

navigation (giving way to other vehicles before

traversing the intersection). This makes the system a

very versatile one and allows to use it either on

highways or on urban scenarios. In our experiments,

one of the testbed vehicles is manually driven while

the second vehicle is autonomously driven using the

laser-based ACC system described in this work. The

real application of this kind of technology can be

grouped in the field of intelligent driving aids.

ACKNOWLEDGEMENT

This work has been granted by several Spanish

Foundations, being the last ones: Ministery of

Science CICYT DPI2002-04064-05-04 and

Ministery of Fomento (Transports).

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