Comparison of Two Different Radar Concepts for Pedestrian

Protection on Bus Stops

E. Streck

, R. Herschel

, P. Wallrath

, M. Sunderam

and G. Elger

Faculty Electrical Engineering and Computer Science, Technische Hochschule Ingolstadt,

Esplanade 10, Ingolstadt, Germany

Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Wachtberg, Germany

Fraunhofer Institut für Verkehrs- und Infrastruktursysteme IVI, Ingolstadt, Germany

{Mohan.Sunderam, Gordon.Elger}@ivi.fraunhofer.de.

Keywords: Sensor Data Fusion, Radar Sensor, Multiple-Sensor Systems. Machine Learning.

Abstract: This paper presents the joint work from the “HORIS” project, with a focus on pedestrian detection at bus-

stops by radar sensors mounted in the infrastructure to support future autonomous driving and protecting

pedestrians in critical situations. Two sensor systems are investigated and evaluated. The first based on single

radar sensor phase-sensitive raw data analysis and the second based on sensor data fusion of cluster data with

two radar sensors using neural networks to predict the position of pedestrians.

1 INTRODUCTION

Nowadays, in automotive and infrastructure radar

sensors, LiDAR sensors and camera-based solutions

are used to increase the safety of the traffic and enable

smart city solutions (Kumar, 2021). To increase the

security level especially for vulnerable road users

(VRU’s) like pedestrians or cyclists, sensors are used

in driver assistance systems in the car and in future

also in infrastructural applications, e.g., automatic

traffic light management systems. Every sensor has

its advantages and drawbacks. In contrast to camera,

whose strength lies in the classification, the strengths

of the radar sensor are in the accuracy of the distance

measurement and the extraction of the velocity

directly from the utilization of the Doppler Effect. On

the other hand, the strength of the LiDAR sensor is in

between, as it can be used as output for a good

classification due to its dense point cloud and it can

provide a very precise spatial resolution of the point

cloud (Yeong, 2021). Nevertheless, the LiDAR

sensor is currently relatively expensive compared to

cameras and radar sensors. Another advantages of

radar sensors are that they have high reliability in bad

weather conditions (e.g., rain, fog, snow, etc.) as well

as in night detection. In addition, radar data are

uncritical regarding privacy: No sensitive personal

data are measured, i.e., the data are completely

anonymous in contrast to camera data. For this

reason, radar sensors are an integral part of a wide

variety of applications and therefore the focus in this

paper is on the pedestrian detection using radar

sensors in the infrastructure, but the presented use

case could also be carried out by Camera or LiDAR.

In previous related works on pedestrian detection the

localization and classification are carried out by some

state-of-the-art methods like Micro-Doppler (Lam,

2016), methods based on doppler spectrum and range

profiles (Rohling, 2010), utilization of the range

azimuth map to estimate the dimensions of an object

(Toker, 2020), etc. In this paper, two different

approaches using the variance by utilizing the raw

data and using neural networks by utilizing the high-

level data, with different sensor systems for detecting

pedestrians on bus-stops with high accuracy in a joint

Fraunhofer project “HORIS” will be presented. First,

the project and the used sensor systems will be

described. Next, the two sensor systems are discussed

in more detail and the working principle of the

algorithms is presented. Finally, the performance of

the detection capability of the two systems is

evaluated and compared.

2 PROJECT PRESENTATION

HORIS

At this point the project HORIS is presented in which

the results for this paper were generated. Project

Streck, E., Herschel, R., Wallrath, P., Sunderam, M. and Elger, G.

Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops.

DOI: 10.5220/0010777100003118

In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 89-96

ISBN: 978-989-758-551-7; ISSN: 2184-4380

HORIS stands for "High Resolution Radar Sensors in

the Infrastructure", which is a joint Fraunhofer project

(sponsored by CCIT-COMMs) of the following

institutes:

 Fraunhofer FHR,

 Fraunhofer IIS,

 Fraunhofer IVI.

The applicational focus on this paper is on a bus-

stop, where the sensors are mounted in a fixed

distance to each other on the opposite side of the road

and observed will be a crowd of people. A trigger

event is provided if a person starts crossing the street

and enters the danger zone e.g., to catch the bus on

the opposite side of the road. In this scenario, the

objects are roughly in 5-15m distance away from the

sensor system. Once a person entering the danger

zone, a message via Car2X communication can be

sent to alert the surrounding vehicles. However, the

paper focuses on the radar technology, whereas the

communication via Car2X is not discussed any

further. A schematic representation of the use case

can be seen in Figure 1.

Figure 1: Radar sensors, locating on the opposite side of the

road, detects a pedestrian, which is leaving the static group

while crossing the street. Car2X message is sent out from

central unit to warn the surrounding traffic.

Two different sensor approaches, which operate on

80 GHz radar technology were used. The first

approach uses a radar sensor based on the TI chipset,

which can quickly detect the smallest movements

with a very high frame rate with the help of phase-

sensitive raw data analysis. Especially the

investigation of the correlation degree of the

movement patterns of a crowd of people with the full

utilization of the raw data is done and will be

discussed in more detail in section 4. The second

approach uses two commercial radar sensors from the

automotive industry built into the infrastructure,

which will be fused based on neural networks (NNs).

For the second approach, it will be investigated,

whether the high-level cluster data output by the

sensor results in an improvement of the detection

accuracy with two radar sensors in contrast to one

sensor that works based on a state-of-the-art tracking

algorithm based on "density-based spatial clustering

of applications with noise" (DBSCAN) (Dingsheng

Deng, 2020). The NN approach will be discussed in

more detail in section 5. The reason of using two

instead of a single radar sensor with NNs is that such

conditions were defined in this 6-month project

before and the results of one sensor will be presented

in a separate work. The sensors are operated with

Robot Operating System (ROS), since this

framework is well suited for data fusion, real time

processing and visualization. The data collection and

data acquisition for the development of the signal

processing, the tracking, and the classification, as

well as for the training, validation and test of the

neural networks is done by an optical-based

localization system with an accuracy of 1mm and

using PTP software synchronization with an accuracy

of ∆t ≤ 0.5ms provided by Fraunhofer IIS.

3 RADAR TECHNOLOGY AND

DATA PROCESSING

Since the “Frequency Modulated Continuous Wave”

FMCW radar (Skolnik, 1990) is the most used

scheme in automotive today, the HORIS project also

uses radar sensors, based on the FMCW technology,

with which it is possible to achieve good spatial

resolution with a comparatively lower transmission

power in comparison to a pulsed radar sensor. The

following section briefly explains how the FMCW

radar works (Engels, 2021). The frequency bands for

the used sensors are in the range of 77-81 GHz, which

defines one of the most important allowed frequency

bands for automotive. The FMCW radar sweeps wide

radar frequency (RF) bandwidth (in GHz), while

keeping the intermediate frequency (IF) bandwidth

small (in MHz) and this working principle is shown

in Figure 2.

Figure 2: The radar sweeps with a defined bandwidth B for

a chirp duration T on the carrier frequency 𝑓



, which is in

the range of 77-81 GHz. Multiple chirps are generated with

this sawtooth sweep principle and send out by a frame

containing the total N chirps.

A simple schematic block diagram of such a FMCW

radar can be seen in Figure 3.

SENSORNETS 2022 - 11th International Conference on Sensor Networks

Figure 3: The Tx antenna sends the radio wave out, which

will be reflected by a static or moving object and the

delayed signal will be received back by the Rx antenna. The

frequency mixer subtracts the frequencies of the received

signal from the generated signal, which results in the

intermediate frequency (IF) of each transmitter & receiver

pair.

Since the IF is proportional to the radial distance

between radar sensor and object, the distance can be

calculated using the first dimension FFT of the

received IF signal. For moving objects, the velocity

can be calculated using the phase change across

multiple chirps and therefore a second dimension FFT

is performed to determine the phase change and thus

the velocity of objects in form of a e.g., range velocity

image. For the angle estimation of detected objects,

the received signal is registered by multiple antennas.

The distances of the reflected wave to the receiver

antennas are now different with respect to the angle

of arrival for each Rx. This results in a phase change,

which can be estimated using the third dimension

FFT and finally the angle of arrival can be extracted.

Since the 1D FFT processing is done inline in the

active transmission time of the chirps, the 2D & 3D

FFT is processed “offline” in the inter-frame time.

This information output is the so-called radar cube

and is shown in Figure 4.

Figure 4: The columns for the FMCW radar cube are filled

with range, the rows with doppler velocity and the depth

with the angle information (Sturm, 2016).

After this data has been processed with the help of the

so-called constant false alarm rate (CFAR) algorithm

(Finn and Johnson, 1968), which calculates an

adaptive threshold value due to the estimated noise

floor to reduce the number of false detections, clutter

and noise, the remaining data is also referred to as the

so-called point cloud data. The sensor approach,

presented in section 4, is based on 3D voxels in

Cartesian coordinates, whereas the sensor system

presented in section 5 is based on processed point

cloud data, the so-called “cluster data”, using similar

algorithms to DBSCAN. The radar sensor presented

there has a limited data transfer rate, since it is

operating with CANBUS instead of using a high

speed ethernet interface. Since the bandwidth of the

cluster data is very reduced (few MB/min) in

comparison to the whole radar cube (few GB/min),

both sensor systems, which are based on phase

sensitive raw data analysis and cluster data analysis

using machine learning (ML) techniques, will be

presented in the upcoming sections.

4 FHR RADAR SENSOR AND IT’S

ALGORITHM

4.1 The Sensor

For the measurements, an integrated MIMO radar

sensor from TI was used. This includes 3 transmitters

and 4 receivers. Only 2 transmitters were used

resulting in 8 antenna combinations forming a single

line in azimuth. This allows an azimuth resolution of

15 degrees. To get access to the raw data the

AWR2243 BOOST board from TI was combined

with a DCA1000 as shown in Figure 5.

Figure 5: MIMO radar module used for people detection.

The radar supports 4 GHz bandwidth. However only

380 MHz bandwidth were used to be able to support

a framerate of 2 kHz to monitor people with a high

frequency to detect small movements. The raw data

was received over Ethernet online processed on the

PC and the result published using a ROS interface.

Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops

4.2 Signal Processing

The signal processing included two major steps. First,

the scene was captured with people standing at a

defined distance from the radar. In that initialization

step it was crucial to determine the position of the

person in the scene. For test measurements people

were standing 5m from the radar sensor.

Figure 6: Range-Azimuth map of the measured scene based

on the reflected power (left) and the variance of the

reflection over time (right).

As one can clearly see in Figure 6 it is hard to see the

persons in the scene if only the magnitude of the

reflectivity is considered. Static reflections from the

bus stop are dominating the image. That significantly

changes if the variance over time is taken over 5s.

Since variance calculation includes the subtraction of

the mean value, static targets are well suppressed. As

shown on the right, all three persons can be seen

standing beside each other. That allowed to monitor

the signal phase at the voxels of relevance. However,

the movement of each person also lead to a significant

variance at further range bins, caused by moving

shadows. Therefore, the phase of various voxels was

monitored. From that range-azimuth map, it can be

hard to distinguish individual persons. However, the

movement within each voxel can be taken as an

alternative feature to identify different individually

moving objects. This is currently a subject under

investigation.

Figure 7: Temporal phase development for range bins 7-11

for azimuth:9. Movement of pedestrian can be extracted by

range bins 7 and 8.

Figure 7 shows the phase of several selected voxels.

To only select one person, only a single azimuth bin

was chosen. The two closest range bins appear to be

the best monitor for the movement of the person.

Before starting to walk fluctuations can be seen

caused by gesticulations or moving at the same

position. Even vital parameters such as pulse and

respiration can be extracted if the phase fluctuation is

filtered accordingly (Rudrappa, 2020). Signals from

range bins behind the person are far weaker, so that

their phase fluctuation is dominated by receiver noise.

However, range bin 8 shows the movement of the

person with smaller latency. Since the person is first

moving forward with its upper body before moving

the leg (usually being in front) that is not unexpected.

A fixed threshold was defined to cause an interrupt

for all channels. Since the monitor of all voxels was

combined by a logical AND the first movement

caused the alarm, it did not make a difference where

the first movement occurred.

After the first alarm, a second alarm was defined

at a predefined range bin. In that case, no detection

was required. The same approach for phase detection

was used.

Figure 8: Phase tracking at position of the monitored person

(red) and at the range bin defined to be critical (yellow) in

comparison to the position of the person measured with an

optical marker (blue).

In Figure 8 the red curve shows the movement

detected on the voxel where the person was detected

during the initialization phase. It shows a very strong

correlation to the movement detected by an optical

Qualisys system. Differences are likely to be caused

by the different parts of the body monitored with

optical and radar system, since the marker was fixed

on the helmet of the person. The correlation ends as

soon as the person left the voxel under test. After

entering the second range bin which defines the

transition to a critical area the yellow curve follows

the movement of the person. Now, the second alarm

is caused before the person leaves the monitored

voxel. For a constant monitoring the position of the

person must be tracked so that always the correct

SENSORNETS 2022 - 11th International Conference on Sensor Networks

voxel is chosen. This has not been required in that

scenario but was also realized to measure the vital

parameters of walking persons in as separate work

(Rudrappa, 2020).

5 TWO RADAR SENSOR ML

APPROACH

In this section the prediction of the localization of

pedestrians with two commercial Conti ARS408-21

automotive radar sensors, operating on raw untracked

detections, using a neural network (NN) based on

high-level cluster data is presented. First, the data

processing for the training is discussed. Later the NN

structure is presented, and the training results are

discussed. Finally, a comparison of the localization

capability of the two-radar sensor ML approach with

respect to one single radar sensor operating with a

state-of-the-art tracking algorithm, like the Hungarian

(Kuhn, 2012) algorithm modified with a clustering

DBSCAN algorithm is presented.

5.1 Data Processing

To train a NN model, it is necessary to use prepared

data in an appropriate format as input to speed up the

training and save computational resources. The frame

rate of the radar sensors is approx. 14 fps and the

cluster data are from the following shape: position

coordinates, radial velocity, and Radar Cross Section

(RCS). Since the internal software on the radar

prioritize moving detections before static ones and

therefore several static clusters will be filtered out, the

approach presented here covers only dynamic objects,

since for the application it is necessary to detect

pedestrians entering the danger zone. In (Streck,

2021) a possible solution also for static objects is

presented. For a general use case it is reasonable to

use a radar sensor, which has also a good static object

detection. To obtain a proper data format for the

training process first, a coordinate transformation for

both radar sensors in a common coordinate frame

(chosen as center of mass of both radar sensors) is

made. Second, since the update rates for both sensors

are not exactly coinciding a time synchronization for

both sensors was performed, whereas every frame of

sensor A should be assigned to the time nearest frame

of sensor B. Because both radar sensors are seeing the

same scene from different perspectives with a

different number of reflections, it is sometimes

necessary to throw out one frame of sensor A or

sensor B to achieve a proper assignment of frames.

With this simple time synchronization method, the

maximum delay between two frames from both radars

can be estimated to 35.7ms, which leads to an

uncertainty of around 6cm. With this software

synchronization algorithm, the results are acceptable

and could be further improved using a hardware

synchronization. Since the focus lies on pedestrian

detection it is reasonable to filter those cluster out,

which contributes to noise. For the training those

cluster of non-characteristic RCS as for pedestrians

are omitted. In general, it was found out by

experimental measurements that the range of the RCS

to detect pedestrians is between [-30,5] dBm². Using

this method, the total amount of clusters could be

reduced by roughly 40%.

5.2 Neural Network Structure and

Training

As mentioned above, the model respects only

dynamic objects, since static ones with a lower RCS

(especially for pedestrians, which are enveloped by

the bus-stop) cannot be detected in every frame

constantly. This lack of detection causes a problem

for the training. The input for the training is extracted

from the whole data set, which includes 160k samples

(static & dynamic objects) and is of the size of 23k

effective sample frames. Additional data was also

created by mirroring the data with respect to the x-

axis (radar coordinate frame). In the following,

TensorFlow 2.1 (Abadi, 2015) and Keras (Chollet,

2015) were used as the python library for the training

and evaluation of the NNs. Since the trained

cooperative sensor system will be operating in the

infrastructure mounted at a fixed place, without loss

of generality, a region of interest (ROI) was chosen

as a surface, spanned by 15m x 18m in the lateral and

longitudinal direction, respectively, which starts 1m

from the common sensor system coordinate frame.

The shape of the input and output data for the NN is

chosen as a pixel representation of the ROI, in which

the algorithm should perform for a variable number

of detected pedestrians. For simplicity the ROI is

divided into three different pixel size models. These

different models, together with the resolution for each

pixel cell, as well as its probability of the prediction

of a pedestrian using the test-set evaluation is shown

in table 1, whereas the NN structure achieving these

probabilities will be discussed later in this sub-

section. One can also see that the probabilities are

smaller the larger the pixel model gets, which is

reasonable, since with greater pixel models the

prediction of the exact pixel localization of a

pedestrian gets much harder.

Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops

Table 1: Overview of test probabilities of corresponding

pixel resolutions.

Model Pixel Size [m] Probabilit

5 x 6 3.0 0.90

15 x 18 1.0 0.73

75 x 90 0.2 0.36

The whole data used for the training is separated in

0.44, 0.22 and 0.33 for train-, validation- and test-set,

respectively. The features of the input vector for the

NN are defined as the frequency of occupancy of the

clusters for each pixel cell, the radial velocity of the

pixel cell and their corresponding RCS value. These

three features are representing a pixel, where its

values are divided into the color-coded range of

[0,255]. The output array for localization can be

represented as



⃗



𝑙

,

𝑙

,

⋯𝑙

,

𝑙

,

𝑙

,

⋯𝑙

,

⋮⋮⋱⋮

𝑙

,

𝑙

,

⋯𝑙

,

,

(1)

where 𝑙

,

is the pixel occupancy for sample i and

pixel j, counting from the left upper corner of the

pixel representation of the ROI, which is

𝑙

,



1 ,𝑖𝑓 𝑝𝑖𝑥𝑒𝑙

𝑗

𝑖𝑠 𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑

0 ,𝑒𝑙𝑠𝑒.

(2)

For further discussion of the architecture and the

training the (90x75)-model is selected, which defines

the size of the input image as total 6750 pixels plus

three-color channels for the input parameter, since

this representation is the most accurate one for the

presented use case. For this purpose, a DNN (Huang,

2016) architecture was used. The reason why for the

training a DNN instead a CNN was chosen is, since

the density of the cluster data is much lower than that

of a point cloud, the dynamic objects doesn’t show

such good shapes and features which could be

detected nicely by the CNN, therefore a simple fully

connected NN with more training parameters was

chosen. This model takes as input a picture of size

(90x75) with three color-channels and flatten these

inputs to get an array with the length of 20250. This

is the input for the next dense layer of size 6750,

which represents the total number of all pixels, since

the output in the end predicts the occupied pixels in

the ROI. After the dense layer a “tanh” (Hyperbolic

Tangent) (Nwankpa, 2018) activation function was

applied because it can integrate non-linearities into

the model much easier than the “relu” activation

function in comparison. Due to the enormously large

input for the last dense layer, the total number of

parameters increases up to 136,694,250.

Figure 9: Training (red) and validation (green) accuracy of

the trained dynamic model with following settings:

RMSprop as optimizer, batch normalization, batch size of

256 and learning rate of 0.000774 found using the

ReduceLROnPlateau.

Figure 9 shows the training performance, where a

modified MSE loss function was used, which

regulates the predictions based on the weighting for

neighbor predictions as a kind of penalty mechanism.

This controls somehow the maximum number of

predictions. As already stated in table 1, the final test

accuracy for the corresponding trainings accuracy of

0.87, is 0.36. Unfortunately, one can clearly see, that

the system was learned due to the clear signs of

overfitting. This problem will be overcome when

larger amount of data, as well as more general data

for the training will be measured. Also, a possible

improvement of the model might be the extension of

the input space. Nevertheless, this model is chosen for

the final evaluation of the results.

5.3 Localization Results

At this point the localization capability of the NN

approach in comparison with the single-radar sensor

system, based on DBSCAN algorithm, mentioned at

the beginning of this section, will be presented. For

the bus-stop use case, the mean absolute error was

used to analyse the localization accuracy. The

Figure 10: Evaluation of the mean absolute error of the

localization for the single radar system, using tracking

algorithm. In total 40796 frames were evaluated, and the

histogram was normalized due to this value.

SENSORNETS 2022 - 11th International Conference on Sensor Networks

evaluation of the single-radar sensor approach is

shown in Figure 10, whereas one can see, that approx.

44% of the total amount of predictions are within

0.4m accuracy.

The evaluation of the two radar NN approach is

shown in Figure 11 and one can see, that 66% of the

predictions are within 0.4m accuracy.

Figure 11: Evaluation of the mean absolute error of the

localization for the NN approach of total 7666 frames, and

the histogram was normalized due to this value.

Since the resolution of the used radar sensors is

anyway 0.4m, this value is used as a reference. In both

histograms one can also see that outliers for larger

deviations of 0.4m can occur. In Figure 10 these false

positives are the detections of incorrect objects by the

used tracking algorithm, while noise is likely to be

detected as an object that clearly doesn’t match the

reference data and therefore represents false

detections for the single radar system. In Figure 11

these deviations come from the forecast of the NN,

which sometimes predicts several ghost targets, since

the total number of pedestrians in the scene is

unknown for the NN.

6 COMPARISON & OUTLOOK

For the time evaluation of both presented sensor

models in section 4 and 5, a set of photoelectric

barriers was placed approx. 30cm in front of the

pedestrian, which defines start of movement into

danger zone and one in front of the entrance to the

danger zone. Figure 12 and 13 show the comparison

of the detection time delay of both sensor systems

w.r.t. the photoelectric barriers.

Figure 12: Comparison of the detection delays of the phase-

sensitive evaluation (red), NN approach (blue) and a single

sensor tracking algorithm (green) for the start of the motion

of a pedestrian.

Figure 13: Comparison of the detection delays of the phase-

sensitive evaluation (red), NN approach (blue) and a single

sensor tracking algorithm (green) for the entrance into the

danger zone.

In both measurements one can see that the

performance of the phase sensitive solution using raw

data performs around 1s better than the NN approach

with two commercial radar sensors, operating on

cluster data. The negative value in Figure 12 of the

FHR detection comes from the fact that for the

demonstration the light barrier was placed a bit too far

from the pedestrian which initialized the movement.

For the comparison also the time performance of the

single-radar solution is plotted, which for the entrance

into the danger zone performs around 300ms faster

and for the detection of the motion around 150ms

delayed in comparison to the NN solution. This is due

to the fact, that the single-radar approach works with

a tracking algorithm based on establishing the track

by comparing frames of the past. So, this system

needs an initialization time which is around 450ms to

track the pedestrian. To increase the performance of

the NN approach and to overcome the overfitting

problem and to achieve a better prediction accuracy,

the net could be retrained using more training data.

Also, for reduction of false positives more

Comparison of Two Different Radar Concepts for Pedestrian Protection on Bus Stops

generalized data should be collected, as well as the

setup parameters (e.g., angles and position of the

sensors with respect to each other) should be

calibrated better. Nevertheless, the NN approach

looks promising and should be further investigated

because even with the actual simple overfitting

model, location performance could be substantially

improved as opposed to a single radar approach.

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SENSORNETS 2022 - 11th International Conference on Sensor Networks