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)
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 69
201103-5858-IJMME-IJENS © June 2020 IJENS I J E N S
(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
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 70
201103-5858-IJMME-IJENS © June 2020 IJENS I J E N S
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
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Fig. 6. Road 2 for Selangor
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).
Fig. 7. Road 3 for Selangor
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
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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
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 73
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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.
International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:20 No:03 74
201103-5858-IJMME-IJENS © June 2020 IJENS I J E N S
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.
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