Special Issue

Technologies supporting the achievement of Furukawa Electric Group
Medium-term Management Plan 2022 - 2025
Contribution to the achievement of SDGs
by Furukawa Electric Group

Development of Automotive Application
Using Peripheral Monitoring Radar MMR2
Yasushi Aoyagi*1, Yoshihiro Nagare* 1, Yoshiyuki Ishida* 1
Shigeru Hosoda* 1, Zsolt Selmeczi* 2, Robert Hajdu* 2
The Advanced Driver Assistance Systems (ADAS) are becoming increasingly
popular. It is specifically important to develop applications that utilize a peripheral
monitoring radar that is installed at the four corners of the vehicle. In addition to the conventional
blind spot detection and the advanced backward approach warning, a support for other various
applications (e.g., lane change decision aid, free space detection, approaching collision warning,
Emergency Lane Keeping (ELK), etc.) using the radar-acquired object location information isrequired. In order to enable these functions, a software structure that adapts to theevolution of vehicle networks is necessary, and the dummy evaluation that emphasizes the
reproducibility and the support for simulation-based virtual experiments is also important in orderto verify these functions. In this paper, we introduce the case study of an application development using the MMR2, which is a peripheral monitoring radar developed by us.

ABSTRACT1.

INTRODUCTION

The Radio Detection And Ranging (RADAR) is a type of
sensor device that can detect the position and the relative speed of objects using radio waves (Table 1). In
recent years, a widespread adoption of the ADAS to prevent vehicle accidents by installing such sensor devices
and to detect surrounding objects has begun. The sensor
requirement for the ADAS includes its adequacy to a relative speed detection function to detect the risk of collisionand to poor visibility conditions such as rain, fog, nighttime, etc.
Table 1Comparison of environment perception sensors
capabilities.

Method

RADAR

LiDAR

Ultra-sonic

CameraRelative speed
detection

○

×

×

×

Weather resistance(fog, rain)

○

×

×

△

Night (darkness)

○

○

○

△

○ : Suitable △ : Possible × : Impossible

We have focused our attention on the superiority ofradars and have developed a peripheral monitoringradar 1), 2). This paper introduces the development of applications for the second-generation radar MMR2. Figure 1
shows the shape view of the MMR2, and Table 2 shows
its specifications.
*1*2

Engineering Div. 3, Furukawa Automotive Systems Inc.
Furukawa Electric Institute of Technology Ltd.

Figure 1
Table 2

Shape of the MMR2.

Specification.
Item

Specification value (reference)

Modulation method

Pulse Doppler

Angle measurement method

Digital Beam Forming

Operating frequency

24.05 – 24.25 GHz

External dimensions

W131 × D129 × H23 mm

External Communication I/F

CAN-FD, HS-CAN

Currently, bands for automotive radars are in the quasimillimeter wave band (24.05 –24.25 GHz) and in the millimeter wave band (76 –77 GHz and 77–81 GHz). Table 3
shows characteristics of each frequency band. The radio
waves in the quasi-millimeter wave band have a wavelength about three times longer than those in the millimeter wave band, so they are less affected by rainfall, etc.
and have less spatial propagation loss. Furthermore, thenumber of paint coatings and the effect of bumpers at
each frequency also cause larger differences in the charFurukawa Electric Review, No. 54 2023

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Development of an Application Using Peripheral Monitoring Radar MMR2

acteristics at higher frequencies. In particular, the reflectivity is involved in the multipath generated between bumper, vehicle body, and radar, which increases the detection angle error of objects. Thus, a robust sensing is possible in the quasi-millimeter wave band because theeffects of the paint and the material of the bumper are
small. The disadvantage is that the distance resolution ispoor due to the narrow-allocated bandwidth of 200 MHz.
The greatest advantage of the millimeter wave band is its
wide allocated bandwidth, which enables a high-resolution ranging. Furthermore, when the wide bandwidth isshared amongst radars, the mutual interference can be
reduced 3).
Table 3Comparison of frequency band characteristics in theradar.
Quasi-millimeterwave band

millimeterwave band

24.05–24.25 GHz

76–77 GHz

1.42

5.65

0.40

2.23

Bumper permeability(triple coating, round trip) 5)

1 dB

7 dB

Bumper reflection
characteristics (triple coating) 5)–10 dB–3 dB

Frequency
Heavy rain
Rainfall attenuation 10 mm/h
4)coefficient
Weak rain
γ (dB/km)
3 mm/hIn this development, the quasi-millimeter-wave band
was selected because of its weather immunity and its
ease of installation.

Basic functions

In this paper, we describe the application installed with
the MMR2, explain the software design and the actual
verification to achieve it, and furthermore, introduce ourefforts for the virtual verification, which is an important
future step.

2.

The application development is the process of selecting
an application extracting the vehicle information (vehicle
speed, steering, yaw rate, brake, gear, blinker status, etc.)that is adapted, and built in the in-vehicle system anddisplaying the requested information (presence of danger,
object position and speed information, road geometry,etc.) from the sensing results under each condition.
Figure 2 shows an example of the operation of lanechange decision support, which is a radar-only warning
application, and Figure 3 shows the installation position
and the system configuration. The radar is located at left
and right rear ends of the vehicle, and the application isconsisting of the following operations (1) to (3), while
addressing left and right radars.
(1) The radar feeds with vehicle information, and theapplication is activated when it is determined thatthe vehicle is traveling at or above a predetermined
speed and that the curve radius of the traveled roadis larger than a predetermined radius.
(2) The system monitors the adjacent lane behind thevehicle from directly beside the vehicle and deter-

Functions for each application

Measure position andspeed of objects

Thresholdjudgment

Calculate the“risk level”

True

Warning to driver（LED, buzzer）

False

Control cycles

Figure 2

APPLICATION STUDIES ADAPTED TO
THE MARKET

Basic operation in radar-based preventive safety systems.

HMI
LED

Vehicle CAN

LED

PrivateCAN

Radar
MasterRadar

Vehicle CAN

LED

Slave

Radar

Private CAN

PowerBattery
Figure 3

Ignition Key

Fuse
1)

Configuration of radar system .

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Development of an Application Using Peripheral Monitoring Radar MMR2

mines the degree of danger if the vehicle changes
lanes.
(3) When the predetermined danger level is exceeded,
the system judges the vehicle to be dangerous andnotifies of the danger through the Human MachineInterface (HMI) to the driver. HMI includes LEDs indoor mirrors and in the instrument panel display.
In complex environments such as highways, urban
areas, parking lots, etc., structures and various targets
must be separated and detected accurately and simultaneously, and in the MMR2, these are achieved with its
high resolution using the pulse method in addition to its
high-speed resolution.
2.1 Applications to be Achieved With the Peripheral
Monitoring Radar
Here, we introduce some examples of applications towhich the MMR2 can be applied.
2.1.1 Rear side application
The Lane Change Decision Aid System (LCDAS) 6) warns
the possibility of collision when changing lanes to thedriver (Figure 4 (a)). It has two functions: Blind Spot
Warning (BSW), which detects the location of the target
vehicle in the adjacent area, and Closing Vehicle Warning(CVW), which detects the proximity of the target vehicle in
the rear area.
The Rear Cross Traffic Alert (RCTA) is a function that
detects vehicles approaching from the rear on left andright when backing up in a parking lot (Figure 4 (b)).
（a）

CVW

（b）

BSW

ognizes the shape of guardrails and other objects aroundthe vehicle. The free space information is used as an auxiliary information to safely evacuate the vehicle to theshoulder in the event of an abnormality that makes it difficult for the driver to continue driving, such as a heart
attack or stroke, while driving on an expressway or inother situations where an emergency stop is difficult.

Figure 5

Free Space Detection.

2.2 Linked Operation With Other Sensors
There are applications that are not based on the peripheral monitoring radar information alone, but are adapted inconjunction with other sensors such as cameras.
The ELK is a function that automatically returns to theoriginal lane when a vehicle veers out of its lane and thefollowing dangers are anticipated (Figure 6 is for a lanechange).
(1) The vehicle drifts across the center line and collides
with an oncoming vehicle.
(2) The vehicle changes lanes while being overtaken
and collides with another vehicle.

（c）Steering
operation

Figure 4

Adopted application of the peripheral monitoringradar.

CameraEgo car

2.1.2 Front side application
The Front Cross Traffic Alert (FCTA) is a function that
detects approaching objects such as vehicles, bicycles,
pedestrians, etc. in poor visibility conditions, such as
crossing collision (Figure 4 (c)).
2.1.3 Front and rear application
The Free Space Detection (Figure 5) is a function that rec-

Radar

Figure 6

Adjacent lanevehicleAn example of the ELK function.

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Development of an Application Using Peripheral Monitoring Radar MMR2

As shown in Figure 7, in addition to the radar, theMMR2 even includes a module that operates the steering
wheel using the lane information detected by the camera.
The MMR2 contributes as a sensor to detect whether avehicle is approaching or is existing at a predetermined
position in the adjacent lane, i.e., auxiliary information forthe danger prediction of (2) in the rear lateral side.

Steering

Figure 7

Steering
controlLane
information

Camera＋Decision-making
Target position
part
information

with a minimum of effort. The detailed configuration of the
AUTOSAR is shown in Figure 10. In addition, a vulnerability diagnosis was conducted during the verification inorder to ensure a high level of safety. These supports
allow to provide secure products to OEMs.
Application layerFlashbootloader

Middleware layer
Driver layer

Radar

Microcontroller

Figure 8

Structure of the conventional software.

System configuration of the ELK.

Application layer
AUTOSAR runtime environment (RTE)3.

For the accurate operation of applications introduced sofar, it is important to adapt the communication function toreceive vehicle body information such as vehicle speed,
yaw rate, and gear information, and to transmit information on objects captured by the radar. On the other hand,the amount of data from Electronic Control Units (ECUs)
has been increasing year by year as vehicles are becoming more sophisticated. The MMR2 also needed tochange the in-vehicle network standard to the Controller
Area Network Flexible Data rate (CAN-FD), which has ahigher maximum transmission speed and an increasingtransmission data length size change, due to the increasein the amount of data used.
Table 4 shows a comparison of communication specifications between CAN-FD and the conventional mainstream High-Speed Controller Area Network (HS-CAN).
Table 4Services layer

SOFTWARE STRUCTURE ADAPTED TO
THE APPLICATION DEVELOPMENT

Comparison of the in-vehicle network
communication specifications.
ProtocolHS CANCAN FD

Max. communication speed

1 Mbps

5 MbpsTransmitted data length

8 bytes

64 bytesFigure 8 shows the conventional software structure,and Figure 9 shows the structure of the MMR2.
Specifically,
T he AUTomotive Open System ARchitecture(1) (AUTOSAR) 7) that was introduced,(2) The application was divided into data processing andfunctional parts and was allocated to software components (SW-C),(3) A driver was created as a basic software Complex
Device Driver (CDD) for the hardware-dependent partof the MMR2,(4) The application was designed in a manner that makethe software modular and easier to reuse.
The introduction of the AUTOSAR has made it possible
to respond to changes in in-vehicle network standards

ECU abstraction layer

Complexdrivers

Microcontroller abstraction
layer (MCAL)

Flash
bootloader

Microcontroller

Figure 9

AUTOSAR software structure adapted to the MMR2.
SWC 2

SWC 1

SWC 3

RTE (runtime environment)
SYS
OS

DIAG

MEM

CRYPTOCOM
CAN

Microcontroller abstraction
layer (MCAL)

EXT

LIBS

Flash
bootloader

Complexdrivers

Microcontroller

Figure 10 Details of the AUTOSAR software structure.

4.

VERIFICATION AND PERFORMANCEDEVELOPMENT

In the radar development, the most important element isthe evaluation of the actual driving to confirm the feasibility of applications. The performance development should
be conducted based on evaluation criteria such as undetected alarms, false positives, and timing (delay, fluctuation), taking into consideration of their impacts on drivers
and targets which are the objects to be detected. Figure 11
shows the performance development flow that we implemented.
(1) Formulate various running scenarios based on application specifications.
(2) Analyze data obtained from field tests, determine
whether there are any issues, and if so, list them andregister them in a database.
(3) Based on issues, study countermeasures, formulate
an operation model, and predict effects and sideeffects.
(4) Firmware and parameters are determined by incorporating countermeasures.
(5) Conduct the driving evaluation again, and continue
the process from (2) until the issue is resolved.
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Development of an Application Using Peripheral Monitoring Radar MMR2

Application speciﬁcation

Countermeasure studies

Detection model change

Scenario

Virtual test
Firmware,Parameters

Issue List
Data Analysis

Measurement
data

Global Vehicle Target (GVT: Vehicle dummy target), theGuided Soft Target (GST: a robotic platform for GVT
dummy transfer) and other various dummies are available,
reproducible evaluations can be performed (Figure 13).
Figures 14 and 15 show examples of actual test results.
The test under reproducible conditions has beenachieved by using the vehicle installed with the radar andcontrolling the GVT with a robot.

Field test
(test course, actual road)
Figure 11 Flow of the application performance development.

In this performance development flow, there are several
ways of handling the tests, depending on the locationand target specifications. Table 5 shows the classificationof actual tests.
The following is a description of the test course driving
and virtual test activities.
Table 5GST

GVT

DummiesFigure 13 An example of the evaluation equipment.

Test type for the performance development.

Classification

Explanation

Static tests

Evaluate under the condition of the static
vehicle.

Test course driving

Evaluate using a test course that canoccupy dedicated lanes.

Field tests

Evaluate by driving on actual publicroads and highways.

Virtual tests

Set up a virtual space, reproduce theradar operation model and the radio
wave reflection characteristics fromsimulations, and evaluate the feasibilityof applications in virtual reality.

4.1

Example of Test Course Driving Performance
Development
The verification using a test course is being conducted at
the ZalaZONE (AVL ZalaZONE, area: 250 hectares) 8)(Figure 12). The test course was newly established inwestern Hungary. It has a test course exclusively for the
ADAS. Since the evaluation tools such as a steering operation robot to ensure the driving reproducibility, a pedal
robot to control the accelerator and brake including theLeft side

Front side Right side

Vehiclemovingparallel

Rear side
Vehiclemoving parallel
detection results

Figure 14 When the target is a dummy car + GST (parallelrunning).

Left
side

Front
side

right
side

Rear
side
Rear
side

Overtaking vehicle
detection results

Overtaking vehicle
Figure 15 Overtaking test with an actual vehicle as the target.

4.2 Virtual Testing Approach
In order to investigate the reproducibility of scenarios andthe effects of subtle positional relationships, not only
actual evaluations but also virtual tests utilizing simulations are important. In the MMR2, a radar model that canoperate in this simulation environment has been developed (Figure 16).
ADAS PLATFORM

Figure 12 ADAS test area in ZalaZONE.

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Development of an Application Using Peripheral Monitoring Radar MMR2

For driving scenarios
commercial simulator

Reﬂection data

Radar modelVeriﬁcation
Detection
results

Figure 16 Flow of the simulation development.

The radar model (Figure 17) is designed to work in
combination with a commercial simulator, and simulation
results can be obtained in the same data format as actual
vehicle measurement data (point data, object markers,
and warning output). Therefore, the accumulated data
and the simulation results can be subjected to a common
data analysis, so that this structure allows an easy understanding of correlations, etc.
Detectiondata

Reﬂected
waveforms
data
Antenna model

Detection signal processing

Figure 17 Configuration of the radar model.

By configuring the firmware and parameters including
antenna characteristics incorporated in the embedded
version, the components of the radar model are designed
to operate in the same manner as the one in the actual
vehicle.
Figure 18 shows the verification flow using this radar
model. In addition to the radar reflection information, thevehicle information such as vehicle speed and yaw rate
can be input to the model, and the radar model uses thisinformation to run warning applications and make warning decisions in the same way as the actual radar. The
output of the model can output the same data as thatsent over the CAN in the actual radar. Furthermore, themodel can be used for the sensor fusion simulation byconnecting it to a model of a host ECU used for the radar
performance verification.
The model is available in two versions, which areadapted to either MATLAB/Simulink or C/C++, and adaptations to a variety of simulation platforms are being considered.

Input

Model operation

Output- Reﬂection data
- Vehicle information- Object detection- Alarm judgment- Object information- Alarm Judgment Result

Figure 18 Flow of verification using the radar model.

Figure 19 is a comparison of the virtual test using theroad data from the Metropolitan highway C1 and theactual evaluation data in detection results performed
using these models. The results are in good agreement,
indicating that the radar model and the map data are efficiently utilized.
Currently, in addition to the scenario evaluation using
dummies as stipulated in the New Car Assessment
Program (NCAP) 9), etc., a simulator that can simulate

Simulation results

Actual measurement results

Figure 19 Free space simulation results for the Metropolitan Highway C1.

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Development of an Application Using Peripheral Monitoring Radar MMR2

driving on public roads is being tested, and the application range of simulations is explored.
Since the field verification of radars needs to be conducted under many conditions, including complex environments such as urban areas and special environments
such as snowy areas, etc. the amount of acquired data isenormous when using only actual radar data. Therefore,
in order to take advantage of the simulations, as shown in
Figure 20, it is essential to understand the correlation
between the measurement results of actual radar and thesimulation results according to the verification items, tobuild a database based on statistical data such as scenario search, detection result analysis, and errors, and to
manage data including automation of data analysis,
etc.10). In the future, we aim to utilize the database integrating both data for the verification planning, and tocomplement the verification using the actual vehicles withthe simulation.
Actual radar measurement results

Databases

Simulation results- Search- Analysis- Statics

Figure 20 Database using the actual devices and simulation.

5.

REFERENCE
1) Yasushi Aoyagi: “Development of 24GHz Peripheral Monitoring
Radar”, Furukawa Electric Review, No. 137 (2018), 3.
2) Yasushi Aoyagi: “Development of a 24-GHz Band Vehicle
Peripheral Monitoring Radar”, IEICE Society Conference,
Sensing and Device Technologies for Automatic Driving, No.
CI-3-5, 2019.
3) A. Bourdoux et al.: “Phenomenology of mutual interference of
FMCW and PMCW automotive radars”, 2017 IEEE Radar
Conference (RadarConf), 2017.
4) Takada: “Basic theor y of radio propagation”, MWE 2005,(Referred on Dec.19, 2022).
		http://www.apmc-mwe.org/mwe2005/src/TL/TL05-01.pdf
		 (in Japanese)5) R. Lachner: “Development Status of Next Generation Automotive
Radar in the EU”, ITS Forum 2009, (Referred on Dec.19, 2022).
http://www.itsforum.gr.jp/Public/J3Schedule/P22/ lachner090226.pdf
		6) ISO: “ISO17387: Intelligent transport systems -- Lane change decision aid systems (LCDAS) -- Performance requirements and test
procedures performance requirements and test procedures”,
2008.
7) AUTOSAR organization, (Referred on Dec. 19, 2022).
		https://www.autosar.org/
8) Automotive Proving Ground Zala Ltd, (Referred on Dec.19, 2022).
		https://zalazone.hu/
9) National Agency for Automotive Society and Victims’ Aid,
(Referred on Dec. 19, 2022).
		 https://www.nasva.go.jp/mamoru/ (in Japanese)
10) Cabinet Office, “Recruitment of Monitor Participants for DIVP(R)”, (Nov.22, 2021), (Referred on Dec.19,2022).
		 https://www8.cao.go.jp/cstp/stmain/20211122sip. html
		 (In Japanese)

CONCLUSION

This paper introduced an overview of application developments using a peripheral monitoring radar. In the
future, we will continue to improve the reliability of theradar and expand its functions in order to support the
next-generation ADAS and automated driving.

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