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CTMJ | traditionalmedicinejournals.com                                    Chinese Traditional Medicine Journal | 2021 | Vol4 |Issue3 

 
 
 

 

Chinese Traditional Medical Journal 

Design and implementation of an Intelligent Mobility Solution 

with Adaptive Cruise Control 

Zhangi Aipingh, Ji Aiyingji, 

Department of Obstetrics and Gynecology, Zhengzhou Hospital of Traditional Chinese Medicine, 

Zhengzhou 

Department of Digestion, The First Affiliated Hospital of Henan University of Traditional Chinese 

Medicine, Zhengzhou China, 

 

 

 

 

 

 

 

 

 

 

 

 

I. INTRODUCTION 
 

It's exhausting to be behind the wheel 

when there's a lot of traffic. In order to 

react swiftly to unexpected conditions, 

the driver must be focused at all times. 

Driving may be made more enjoyable with 

the use of Adaptive Cruise Control (or 

ACC). It is possible for ACC to alleviate the 

stress of driving by adjusting the vehicle's 

speed to match the flow of traffic. [1] The 

driver specifies the desired speed and the 

 Abstract 

The primary aim for vehicle automation research is to enhance driver comfort. As the 

number of cars on the road continues to rise, traffic congestion is reaching new 

heights. Driving in an urban environment may be a gruelling experience. A lifesaver 

for motorists whose survival depends on their ability to move across town will be an 

effective traffic management system. Driver assistance systems that monitor the 

vehicle's speed were first introduced with the introduction of the cruise control (CC) 

[1]. Additional radars [11] and cameras were added to the original CC system, 

resulting in the more advanced Adaptive Cruise Control (ACC) [1]. When the 

automobile is in difficult-to-maneuver situations, even the control system itself may 

take over control of the vehicle. A new kind of control system was introduced when 

the ACC was upgraded to Cooperative Adaptive Cruise Control (CACC). String 

Stability and GPS [15] for precise movement have enhanced vehicle density while 

reducing road accidents dramatically thanks to the platoon concept and its associated 

inter-vehicle communication system. The paper sheds light on CACC from the 

perspective of ACC. 

Keywords: ACC, CACC, Vehicle Following, Vehicle-Vehicle Communication 

 

 

 

 

Keywords: ACC, CACC, Vehicle Following, Vehicle-Vehicle Communication 
 



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distance to be maintained from the car 

ahead of them. [1] It is possible to adjust 

this distance based on the driving 

circumstances and personal style. A radar 

sensor with a long range is used to keep 

tabs on traffic conditions directly in front 

of the vehicle 

 

Fig. 1: Long Range Radar [8] 

 

When a vehicle's sensor system sends out 

radar waves, they are reflected back by the 

objects in front of it. Using this 

information, the ACC can identify vehicles 

ahead and determine factors such as 

distance, direction, and speed relative to 

the item in front. ACC maintains the set 

desired speed when the road ahead is clear. 

There is an Electronic Stability Program 

(ESP) [1] and engine control or brake 

system interaction that guarantees that the 

established safety distance is maintained in 

the event of an approaching vehicle. [1] 

As soon as the road ahead is clear, the 

vehicle's ACC system accelerates 

automatically to the driver-specified speed. 

For example, ACC supports both harmonic 

and discordant traffic flow on the 

roadways. Even if the ACC is on, the 

driver is still responsible for keeping an 

eye on the distance and speed of the car in 

front of him or her at all times. The drivers 

have the priority to turn-off or override the 

ACC function at any moment. Drivers 

driving cross-country highways or on 

freeways may benefit from the assistance 

of the standard ACC, which can be used at 

speeds up to 200mph. At speeds below 30 

kilometres per hour, the ACC's stop and go 

version is also operational. Even at low 

speeds, the system is able to maintain a 

specified distance from the preceding 

vehicle and can even bring the car to a full 

stop. The ACC car will automatically drive 

ahead if the vehicle in front does so within 

a short period of time. To return to ACC 

mode after a period of inactivity, the driver 

merely has to apply a little amount of 

throttle. To those drivers who spend most 

of their time on congested roadways, 

"ACC stop and go" [1] is a godsend. One 

long-range radar sensor can be used to 

implement both ACC versions. By 

incorporating more video or radar sensor 

technologies into the vehicle, ACC 

functionalities may be further improved 

and enhanced.. Increased dynamics while 

moving forward from a stop are made 

possible by increasing the ACC speed 

range from 200kmph to 250kmph. [1] 

As ACC continues to evolve, Cooperative 

Adaptive Cruise Control is the next step 

(CACC). An inter-vehicle communication 

system is used as an add-on to the Cruise 

Control system in order to increase the 

maximum speed and decrease traffic 

congestion. This document describes the 

process of moving from ACC to CACC. 

Fig. 2: Driver assistance systems [1] 



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II. LITERATURE REVIEW 

Autonomous driving is becoming an 

integral aspect of car technology. For 

automotive drivers, artificial intelligence 

using neural networks is assuming the role 

of intelligent driving. To avert calamities, 

there are instances where human intellect 

must outperform artificial intelligence's 

computer power. Many types of 

autonomous manoeuvring have been 

created to aid the driver in avoiding 

weariness as a result of long distance 

driving.HIL (Hardware in the Loop) [2] is 

the first of its type as a control for a fleet 

of automobiles.Following a successful test 

of platooning cars for Cooperative 

Adaptive Cruise Control (CACC), wireless 

technology has now advanced to a 

sufficient level. There were always issues 

when an algorithm was performed between 

cars in a lane because of the decentralised 

nature of the technique. For example, each 

car in the fleet is able to talk to one 

another. After some time, this is now a 

dynamically-connected network system. 

Because of this, developing an asymptotic 

network control system is a time-

consuming endeavour for developers. The 

development of an algorithm with String 

Stability [2] is currently being pursued.The 

dynamics of traffic will differ based on the 

location, such as highways and urban 

centres. As the algorithm is designed, the 

CACC-equipped cars will react 

accordingly. Not only does this strategy 

not work because of the varying road 

conditions, but it also doesn't work 

because of the variable vehicle densities at 

different sites. Depending on the age and 

aggressiveness of the driver, the reaction 

time might range from 0.8 seconds to 1 

second. To compensate for this disparity, a 

flexible strategy is essential. [3]FAST 

(flexible agent-based simulator of traffic) 

[3] is a simulator model to study the 

Adaptability of CACC-equipped cars in 

varied traffic situations. Different road 

conditions with changeable traffic are part 

of this object-oriented microsimulation 

running on a Java platform. In this they 

employed a dedicated lane known as HOV 

(high-occupancy vehicle) for cars 

equipped with CACC. As a consequence, 

traffic flow increased significantly while 

congestion was reduced to a minimum. [3] 

If your car is equipped with adaptive 

cruise control (ACC), you may select a 

speed and the vehicle will maintain that 

speed and modify automatically based on 

the vehicle ahead of you. In the event of 

cars equipped with CACC, the speed and 

spacing between the vehicles are 

determined by the vehicle in front, and the 

platoon will be updated with the vehicle 

characteristics and other traffic factors to 

ensure a safe and efficient movement with 

the least possible congestion.The goal of 

algorithm development is to get the output 

of a closed loop system to a zero error 

level. In cruise control systems and 

adaptive cruise control systems, PID 

controllers [13] are often used. The two 

controls vary in that they rely on different 

types of settings and operate in different 

ways. 

 

Traffic flow must be taken into account 

while driving ACC-equipped vehicles. 



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Slide mode is used to do this. An extra S 

variable will be introduced such that the 

feedback loop's output is always zero error 

probability and does so with the least 

amount of time delay possible. This allows 

the vehicle to respond quickly and adapt to 

changing conditions. [4]It's not a simple 

process to develop cruise control 

controllers. During acceleration and 

braking, the vehicle's dynamics will be 

different. Depending on the car's gearbox, 

the throttle response will be different. In 

the field of vehicle automation, the idea of 

a hybrid controller that can change its 

mode of operation quickly and easily in 

response to changing conditions is gaining 

traction. The FOC (fractional-order 

control) [5] modified version of the 

ordinary PID controller has acquired 

prominence in the design of CACC 

because it can transition between 

controllers based on dynamics. 

ACC IN BRIEF 

III.CACC has two subsystems: ACC and 

CACC. The ACC in the CACC system 

controls the vehicle's braking system as 

well as the steering angle and yaw rate 

sensors, which allow it to determine the 

vehicle's current lane and recognise road 

bends. The second CACC subsystem, the 

communication subsystem, is responsible 

for transferring data among the pool's cars. 

In addition to the RADAR sensor, the 

ACC subsystem has a LIDAR [12], 

stereoscopic camera, tyre speed sensor, 

and brake pad monitor sensor. The 

RADAR sensor measures the vehicle's 

speed and distance. As long as the 

computed distance between two cars is 

higher than the intended one, one of them 

will be sped up to make up for lost time, 

and as long as the calculated distance is 

smaller, one of them will be slowed down 

by decreasing throttle, lowering gear, or 

even applying brakes. The maximum 

deceleration potential of the brake system 

(full braking) is limited to roughly 25% in 

order to minimise unexpected jerks and to 

provide a comfortable journey. 

IV.There will be noise in the RADAR 

signals that are received, and as a result, 

there is a considerable risk of signal loss. 

Rain, sleet, and slush can have an impact 

on radar performance. It becomes more 

difficult to keep track of what's going on 

since the reflected signal from the previous 

car must be processed and filtered. Stereo 

vision cameras and their image processing 

capability are employed here as a support 

for RADAR. This aids with lane 

maintenance and identifies the precise 

vehicle with which manoeuvring is 

required for a safe trip through road bends. 

In metropolitan locations, where stop-and-

go traffic is the norm, the CACC function 

will be very useful. When stuck in traffic, 

technology has improved to the point 

where it is now possible to entirely halt the 

automobile in order to save gasoline. 

When traffic starts moving again, the 

system restarts the vehicle and follows the 

automobile in front. As a result, the 

driver's weariness will be reduced by using 

this form of control action. Active braking 

may be necessary to prevent a collision in 

certain traffic scenarios. 

V.HARDWARE ARCHITECTURE OF 

CACC 

 



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Figure 3 depicts the CACC hardware 

architecture. A stereoscopic camera, 

RADAR, and LIDAR make up the front 

end. A digital signal processor (DSP) 

manages the navigation units, which can 

process data in real time. There are 

tachometers attached to each tyre that 

measure the speed of the vehicle in real-

time. via the CAN Bus [10], the 32-bit 

MCU [14] receives the signal from the 

DSP and tyre speed controller.System and 

sensor data may be exchanged via the 

CAN Bus, a serial protocol. Up to 1Mbps 

can be transmitted and received over a 

single or dual-wire networked data bus 

when using CAN. In figure-3, the CAN 

bus is shown by a black arrow. The system 

must update the brake pad information on 

a frequent basis in order to have total 

control over the vehicle's movement. In the 

event of an emergency, the ACC can 

calculate the amount of time it will take to 

decelerate and come to a complete stop 

from the predetermined speed. As a result, 

a CAN bus connects the brake pad 

controller to the MCU. 

 

 

Fig. 3: Cooperative Adaptive Cruise 

Control (CACC) Hardware Layout. 

VI. The distinction between ACC and 

CACC is that communication mechanisms 

are present. There will be a GPS system 

for satellite navigation. Vehicle-to-vehicle 

communication is handled by a separate 

wireless system. The protocol utilised for 

string stability in the HIL system will be 

improved for vehicle usage, as there is no 

central unit. 

VII. WORKING OF CACC 

Automated driver assistance systems 

(CACCs) are designed to help alleviate 

driver fatigue during long drives. The 

ACC and OTA (On the Air) [17] wireless 

communication systems in CACC may 

function together as a group or as ACC on 

its own to communicate with the rest of 

the system. Figure-4 depicts the CACC 

workflow diagram. The fluidic 

performance of a vehicle equipped with 

the CACC system is dependent on three 

parameters from the preceding vehicle: 

distance, speed, and position. Long-range 

radars are used to determine the distance 

and speed of the vehicle in front of the 

driver. The Doppler shift effect can be 

used to determine if the preceding vehicle 

is accelerating or decelerating based on the 

time difference between the transmitted 

and reflected signals at the radar sensor. 

The previous vehicle's relative angle must 

be determined in order to ascertain the 

vehicle's location (relative position). With 

the use of a 3-beam radar, the ratio of 

signal amplitudes of the broadcast and 

received signals can be calculated, and this 

may be used to establish a car's location in 

relation to other vehicles. 



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The CACC system log will be updated as a 

result of the sensors in the vehicle 

subsystem monitoring the various system 

parameters. In addition, the ACC system's 

subsidiaries are screened for compliance. 

After the feature is activated in the car, the 

initialization process will be completed in 

2 seconds. At first, the system will 

function as a stand-alone ACC. Having 

modulated the vehicle's manoeuvrability 

using the ACC in accordance with the pre-

set speed restriction to the ACC console in 

the driver cabin, the vehicle prepares its 

communication subsystem for platooning 

so that CACC may be activated. 

Platooning requests are sent with their 

GPS locations and pre-set vehicle speeds. 

CACC-equipped cars in close proximity to 

the platoon may now join the pool and 

synchronise themselves with the vehicle in 

front of the platoon. The operation will be 

continued to any limit. Using the 

accelerator pedal, the driver can overdrive 

at any time and the system will activate 

once the driver stops accelerating. 

Fig. 4: CACC work-flow diagram 

VIII. Drivers in CACC can only control 

the ACC, which keeps tabs on the vehicle's 

performance metrics and makes 

adjustments to provide a stress-free travel. 

The platooning method is handled by a 

wireless communication subsystem that is 

not under the control of the driver. To 

initiate platooning, the first vehicle must 

be ACC-stabilized; otherwise, it may join 

a pool with CACC controls. At any point, 

the driver has the option to accept or refuse 

a platoon. If there are any objects present, 

the ACC will wait for a response from the 

driver. The ACC will begin the braking 

operation if the driver does not reply 

within the determined safe time, and the 

acoustic notice will be heard in the cabin. 

Accident-avoidance technology alerts the 

driver if the vehicle's brake actuation is 

insufficient to stop it from colliding with 

another object. An item is removed from 

the path of travel, and the ACC 

automatically accelerates to its pre-set 

speed. 

IX.For a CACC system the complete flow 

diagram is valid for its functioning. The 

add on in this situation is the following 

cars will be informed with the leading 

vehicle’s location, velocity, and 

acceleration so that the following vehicle’s 

ACC may optimise the settings in its 

plant(car) for a safe journey in platoon. 

Assign each vehicle's location, velocity 

and acceleration I = 0 -n) to zi, vi and ai; I 

= 1 is the lead vehicle and the rest are 

following. Vehicles that are too close 

together pile up, and this results in 

incorrect parameter computations. 

X. CONCLUSION 

There is still a long way to go in the 

development of vehicle intelligence. 

Although it isn't a replacement for human 

intellect, it may be used as an aid in some 

situations. Human intelligence will only be 

able to avert a collision with a plant in 

certain scenarios on the road. So the 

technology must be viewed merely as an 

assisting system. Due to the inherent 

limitations of wireless technology, there is 

the potential for data loss, packet 

corruption, and platooning delays. Even if 

the vehicle is equipped with CACC, the 



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driver should still be aware of their 

surroundings. 

REFERENCES 

 [1]http://www.bosch-mobility-

solutions.com/ 

 [2]Four people from Italy: [2] Giovanni 

Fiengo; [2] Manuela Tufo; [2] Stefania 

Santini; University of Palermo, Palermo, 

Italy: 22nd Mediterranean Conference on 

Control and Automation (MED), 2014, 

22nd Mediterranean Conference on 

Control and Automation (MED). The 

week of June 16-19, 2014. The city of 

Palermo in Sicily 

[3 Shannon Bowling, Georges M. Arnaout 

Cooperative Adaptive Cruise Control 

Deployment Strategy to Improve Traffic 

Dynamics. Automation and Computing 

11(1), February 2014; pages 10-18 in 

print. 

[4]Sliding-Mode-Control-Based Adaptive 

Cruise Controller [4] by Behnam Ganji; 

Abbas Z. Kouzani; Sui Yang Khoo; and 

Mojdeh Nasir, 2014 IEEE International 

Conference on Control & Automation 

(ICCA) June 18-20, 2014 in Taichung, 

Taiwan. 

[5]Experimental Application of Hybrid 

Fractional-Order Adaptive Cruise Control 

at Low Speed IEEE Transactions on 

Control Systems Technology, Vol. 22, No. 

6, November 2014. 

(6) Vicente Milanés, Steven E. Shladover, 

John Spring, Christopher Nowakowski, 

Hiroshi Kawazoe, and Masahide 

Nakamura. [6]. IEEE Transactions on 

Intelligent Transportation Systems, Vol. 

15, No. 1, February 2014, "Cooperative 

Adaptive Cruise Control in Real Traffic 

Situations" 

[6]http://forum.a8parts.co.uk/attachment.p

hp?attachmentid=4812&d=1348136382 

Car Cruise Control System by Karen Lie at 

Calvin College, page 8 of 10. [9] A 

sampled-data cooperative adaptive cruise 

control system for vehicles with sensor 

failures, IEEE Transactions on Intelligent 

Transportation Systems, Vol. 15, No. 6, 

December 2014, by Ge Guo and Wei Yue, 

IEEE members.