On-Road Testing of Radar-based Blind Spot Monitoring System

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 68

201103-5858-IJMME-IJENS © June 2020 IJENS I J E N S

On-Road Testing of Radar-based Blind Spot

Monitoring System M. Nor1, Mz Hassan2, N. Ab Wahab2, S. M. Najib1*, Ir. Dr. Khairil Anwar Abu Kassim3, Aqbal

Hafeez Ariffin3, N M Yatim4 1Faculty of Electrical and Electronic Engineering Technology, Universiti Teknikal Malaysia Melaka, Hang

Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 2Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia

Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3Malaysian Institute of Road Safety Research, 43000 Kajang Selangor, Malaysia

4Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

*Corresponding author: [email protected]

Abstract— Recognition of the vehicle existence within the

blind spot area efficiently is an essential issue for active safety

in the automotive industry. To reduce the cases of car

crashing caused by the changing lane maneuver, the features

of the blind spot system need to be upgraded periodically. To

fulfill this requirement, this paper presents the performance

of the developed radar-based blind spot system. The

performance of the system will be evaluated through on-road

testing using HONDA CRV on the two selected regions which

were Selangor and Wilayah Persekutuan Kuala Lumpur. The

developed system has been designed to detect any vehicles

exist within the blind spot area by using a recorded video

based on OpenMV camera and 24GHz radar sensor for real

time sensor detection. By taking video frames and converting

the images to the one-dimensional image, the information

regarding the number of vehicles being detected can be

located and analyzed.

Index Term— ISO 17387:2008(E), Vision-based blind spot

detection

I. INTRODUCTION

Car accidents on the road are a big factor of death and

can cause severe injuries. As reported by Blomberg [1]

which cited it from World Health Organization (WHO),

Malaysia ranked among the emerging countries with the

highest death rate in road fatality. There are many reasons

for road traffic accidents such as rapid urbanization, poor

safety standards, lack of enforcement, speeding, and failure

to wear seatbelt or helmet. To reduce the car accident

caused by the vehicle itself has been proposed by many

researchers by improving current features of the blind spot

system occasionally.

It is essential to predict other vehicles on the road to

prevent crashing [2]. However, the side collision of the

vehicles might happen during lane change maneuvering

due to the unsighted region around the vehicle itself. This

phenomenon is called a blind spot [3].

As the driver, limitation in recognizing the visibility of

other vehicles within the blind spot region makes the blind

spot become the vital aspects. The major cause of the

accident due to blind spot occurs during lane changing

maneuvers especially the motorcyclist [4]. Blind spot area is

different for different driver’s height and vehicle size. The

widened and the lengthy vehicle tends to have a broader

blind spot, for instance, truck (Fig. 1(a)) and bus (Fig. 1(b)).

The objective of this research is to develop a radar-based

blind spot system and implement it on the road which will

capture the vehicle visibility and warn the driver about the

existence of the vehicles within the blind spot region. The

development process has been discussed in the previous

paper by Nor et al. (2020) [5]. On the other hand, this paper

will discuss the performance of the system which has been

developed.

(a)



(b)

Fig. 1. Blind spot region for (a) Truck and (b) Bus. [6]

II. SYSTEM DEVELOPMENT

This blind spot system has been developed based on a

24GHz radar sensor and OpenMV M7 camera for

accurate blind spot monitoring in the urban area. A radar

sensor has been utilized to detect the obstacles around the

vehicle. Unlike the ultrasonic sensor, radar uses

electromagnetic waves to detect the obstacles.

Electromagnetic waves react faster compared to

ultrasonic waves. However, like the ultrasonic, once it

detects the obstacles, the waves will be bounced back to

acknowledge the radar transmitter. To develop this

system, a radar sensor is chosen as it less affected by

temperature, powder, dust, and uneven surfaces which

will increase the accuracy of the vehicle’s detection in the

blind spot region.

Fig. 2(a) shows the development of a radar-based blind

spot system. This system consists of a radar sensor and

camera as a monitoring and detecting component,

Arduino nano as a controller and indicators (light and

buzzer), and a personal computer as an output

component. The process flowchart of the vehicle

detection and image gathering can be seen in Fig. 2(b).

The presence of the vehicle will be captured by the radar

sensor and the camera. These obstacles will be analyzed

whether it is located in the blind spot region of the

vehicle.

Both data from the radar sensor and camera will be

processed to visualize the location of the obstacle located

in the blind spot area. This detection process will be

repeated until any vehicle exists within the blind spot

area. Once the vehicle exists, the indicators will be

triggered to alert the driver.

Fig. 3 illustrates the hardware positioning assembled on

the car. The radar sensor has been set up on the rear-left

corner and rear-right corner of the vehicle while

OpenMV camera is positioned at the rear-center of the

vehicle. The detection range of the system is set to 3 to 6

meters from the rear side of the vehicle. Fig. 4 shows the

illustration of the rear-right side of the blind spot area.

(a)

(b)

Fig. 2. Development of radar-based blind spot system (a) Hardware

requirement (b) System flowchart

Start

Obstacles data from radar sensor +

image from Open MV camera

Vehicle detected

Light and buzzer will be triggered

End

No vehicle

detected

No

Yes

Processing



Fig. 3. Hardware positioning of the blind spot system

Fig. 4. Rear-right side of the blind spot region

III. DATA COLLECTION

To analyze system performance, on-road experiment

has been done. HONDA CRV has been selected to run

the test. On the selected region, three different routes at

four time frames have been chosen based on peak hour

and non-peak hour. Three different routes were namely

Road 1, Road 2 and Road 3 while the time selected

within the range of 8:00 to 10:00, 11:00 to 13:00, 16:00-

18:00 and 19:00 to 21:00. Two regions have been chosen

for on-road experiment which were Selangor and

Wilayah Persekutuan Kuala Lumpur which the latter is

commonly known as a busiest city in Malaysia. Fig. 5 to

Fig. 10 illustrate the respective routes.

A. Selangor, Road 1

Road 1 for Selangor was started from Petronas, Persiaran

Pekeliling, Seksyen 3 Bandar Baru Bangi, 43650 Bandar

Baru Bangi, Selangor heading to SMKA Maahad Hamidiah,

Jalan Sungai Ramal, Sungai Ramal Luar, 43000 Kajang,

Selangor. Location details for Road 1 with a total distance

of 8.0 km is illustrates in Fig. 5.

Fig. 5. Road 1 for Selangor

B. Selangor, Road 2

The second route for Selangor was selected from SMKA

Maahad Hamidiah, Jalan Sungai Ramal, Sungai Ramal

Luar, 43000 Kajang, Selangor to Malaysian Institute of

Road Safety

Research (MIROS), 125-135, Jalan TKS 1, Taman Kajang

Sentral, 43000 Kajang, Selangor. Fig. 6 shown location

details for Road 2 with a total distance of 5.4 km.

Indicator

Camera

Radar

Sensor




C. Selangor, Road 3

Malaysian Institute of Road Safety Research (MIROS),

125-135, Jalan TKS 1, Taman Kajang Sentral, 43000 Kajang, Selangor heading to Petronas, Persiaran Pekeliling,

Seksyen 3 Bandar Baru Bangi, 43650 Bandar Baru Bangi,

Selangor was chosen as a route for Road 3 in Selangor with

a total distance of 12.5 km (Fig. 7).


D. Wilayah Persekutuan Kuala Lumpur, Road 1

Road 1 was selected from KLCC, Kuala Lumpur City

Centre, 50450 Kuala Lumpur, Federal Territory of Kuala

Lumpur to Chow Kit, Kuala Lumpur, Federal Territory of

Kuala Lumpur. Location details for Road 1 with a total road

distance of 2.6 km is shown in Fig.8.

Fig. 8. Road 1 for Wilayah Persekutuan Kuala Lumpur

E. Wilayah Persekutuan Kuala Lumpur, Road 2

For Road 2, the path was from Chow Kit, Kuala Lumpur,

Federal Territory of Kuala Lumpur to Berjaya Times



Square, Imbi, 55100 Kuala Lumpur, Federal Territory of

Kuala Lumpur with a total road distance of 3.6 km (Fig. 9).

Fig. 9. Road 2 for Wilayah Persekutuan Kuala Lumpur

F. Wilayah Persekutuan Kuala Lumpur, Road 3

The route from Berjaya Times Square, Imbi, 55100

Kuala Lumpur, Federal Territory of Kuala Lumpur to

Ambassador Row Hotel Suites by Lanson Place, 1, Jalan

Ampang Hilir, Taman U Thant, 55000 Kuala Lumpur,

Federal Territory of Kuala Lumpur was chosen as Road 3

with a total road distance of 6.6 km as shown in Fig. 10.

Fig. 10. Road 3for Wilayah Persekutuan Kuala Lumpur

IV. RESULTS AND DISCUSSION

The important part of this project is by verifying the

output of the radar-based blind spot system that was used to

detect the blind spot detection area. In order to verify the

performance of the system, multiple image frames have

been analyzed. As mentioned earlier, the main detection

sensor of this blind spot system is a 24GHz radar sensor and

OpenMV camera. The latter is used to capture the vehicle

detected in the blind spot area. This camera produces 30 fps

(frame per second) video with an image resolution of

720x480 pixels. Video sequences were captured for about

30 minutes of driving. By taking video frames and

converting the images to the one-dimensional image, the

information regarding the number of vehicles being detected

has been located and analyzed. There are numerous types of

vehicles that are capture by the camera including sedans,

SUVs, vans, trucks, and buses which located in the blind

spot area.

Figures below show several sample frames of test

sequences in various situations. Fig. 11 and Fig. 12 consists

of two sample frames for urban area and highway,

respectively. These images show that the system was

successfully captured by the camera which exists in the



blind spot area.

Fig. 11. Sample frame for urban area

Fig. 12. Sample frame for highway area

Fig. 13 to Fig. 14 show the total vehicle detected by this

bind spot system at Selangor and Wilayah Persekutuan

Kuala Lumpur.

Fig. 13.: Total vehicle detection based on different routes in Selangor

Fig. 14. Total vehicle detection based on different routes in Wilayah

Persekutuan Kuala Lumpur

While Fig. 15 and Fig. 16 show the total vehicles, which

have been detected in the blind spot area for both locations.

Based on The International Organization for

Standardization (ISO) standards (ISO 17387), Type I

coverage zone is referred in this study where this system is

intended to warn the subject vehicle driver of target vehicles

in the adjacent zones. The results show that the system is

capable of detecting the visibility of the vehicles within the

blind spot area especially during peak hours. As a result,

this system can reduce the possibility of the car crashing

during lane changing maneuver especially in the urban city

especially Wilayah Persekutuan Kuala Lumpur.

Fig. 15. Vehicle detection in Selangor for left and right division

Fig. 16. Vehicle detection in Wilayah Persekutuan Kuala Lumpur for left

and right division

V. CONCLUSIONS

This radar-based blind spot system has been developed

for active safety in automotive applications to warn the

driver about the presence of other vehicles within blind spot

area in the range of 3 to 6 meters. Hence this system will

greatly assist the driver in lane changing and overtaking

maneuvers. Meanwhile, image recognition can be added

into the system to increase the accuracy and processing time

of vehicle detection. Besides, image processing can also be

used to differentiate between vehicle and non-vehicle.



ACKNOWLEDGMENT

The authors would like to thank the support and guidance

given by Faculty of Mechanical and Manufacturing

Engineering Technology, Faculty of Electrical and

Electronic Engineering Technology, Universiti Teknikal

Malaysia Melaka (UTeM), Malaysian Institute of Road

Safety Research (MIROS) and ASEAN NCAP secretariat

through ASEAN NCAP Collaborative Holistic Research

(ANCHOR II) programme. This work had been carried out

through the grant of Antarabangsa-

ANCHOR/2019/FTKMP- AMC/A00019

REFERENCES

[1] R. Brand, “These Are the World’s Most Dangerous Roads,”

Bloomberg, 2017.

[2] C. Padmanabhan, S. Chellamuthu, “A Study on Blind Spot

Warning and Lane Change Warning in Vehicular

Communication,” J. Mach. Learn. Technol., vol. 3, no. 1, pp. 72–

75, 2013.

[3] M. Z. Hassan and H. I. Zainal Ariffin, “Development of vehicle

blind spot system for passenger car,” in Applied Mechanics and

Materials, 2013, vol. 393, pp. 350–353.

[4] M. H. Md Isa, “Blind Spot Technology Must Be Seen,” The

Sun(Malaysia), 2016.

[5] M. Nor, M. Hassan, N. A. Wahab, S. M. Najib, K. Anwar, and A.

Kassim, “Development of Smart Vehicle Blind Spot Detection

System Based on 24 GHz Radar Sensors,” Int. J. Eng. Adv.

Technol., vol. 9, no. 3, pp. 2726–2730, 2020.

[6] SAAQ, “Visibility Around Heavy Vehicles,” 2018. [Online].

Available: https://saaq.gouv.qc.ca/en/road-

safety/behaviours/blind-spots/visibility-around-heavy-vehicles/.

[Accessed: 31-Jan-2020].

Mohammad Nor Bin Mustaza is an undergraduate

student of Universiti Teknikal Malaysia Melaka.

From Faculty of Electrical and Electronic

Engineering Technology.

PM. Dr. Muhammad Zahir Hassan Qualification

in Ph.D. Engineering from University of Leeds.

Area of Interest in Innovative Automotive

Technology.

Ts. Dr. Norfariza Ab Wahab Qualification in Ph.D

in Engineering (Mechanical Engineering), Master

Engineering (Mechanical Engineering), Bachelor of

Degree Engineering (Mechanical Engineering),

Diploma (Electronic Mechanical Engineering). Area

of Interest in Manufacturing, Machining Process,

CAD/CAM, Green Technology. Has Won Gold in

UTeMEX19, Bronze in MIIX19, Gold in MIIX17

and Bronze in UTeMEX17 of Vacuum Clamping.

Dr. Suhaila Mohd Najib Qualification in

Ph.D. Engineering from Universitit Teknologi

Malaysia. Area of Interest in Process Tomography,

Tomographic, Image Processing, Instrumentation

System and Embedded System.

Ir. Dr. Khairil Anwar bin Abu Kassim is an adjunct

Professor in Research Officer at Malaysian Institute

of Road Safety Research. Area of Interest in Vehicle

Safety and Biomechanics.

Aqbal Hafeez Ariffin is a Research Officer at

Malaysian Institute of Road Safety Research. Area of

Interest in Road Safety, Pedestrian Protection and

Real-world Crash Investigation.

N M Yatim is a senior lecturer at Department of

Computer Engineering at Faculty of Electronic and

Computer Engineering, Universiti Teknikal Malaysia

Melaka (UTeM),Malaysia.

https://scholar.google.com/citations?hl=en&view_op=search_authors&mauthors=label:innovative_automotive_technology

https://scholar.google.com/citations?hl=en&view_op=search_authors&mauthors=label:innovative_automotive_technology

On-Road Testing of Radar-based Blind Spot Monitoring System

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