# IntelliCAT: Intelligent Machine Translation Post-Editing with Quality
Estimation and Translation Suggestion

Dongjun Lee  Bering Lab, Republic of Korea  Junhyeong Ahn  Bering Lab,
Republic of Korea  Heesoo Park  Bering Lab, Republic of Korea  Jaemin Jo
Sungkyunkwan University, Republic of Korea

###### Abstract

We present IntelliCAT, an interactive translation interface with neural models
that streamline the post-editing process on machine translation output. We
leverage two quality estimation (QE) models at different granularities:
sentence-level QE, to predict the quality of each machine-translated sentence,
and word-level QE, to locate the parts of the machine-translated sentence that
need correction. Additionally, we introduce a novel translation suggestion
model conditioned on both the left and right contexts, providing alternatives
for specific words or phrases for correction. Finally, with word alignments,
IntelliCAT automatically preserves the original document’s styles in the
translated document. The experimental results show that post-editing based on
the proposed QE and translation suggestions can significantly improve
translation quality. Furthermore, a user study reveals that three features
provided in IntelliCAT significantly accelerate the post-editing task,
achieving a 52.9% speedup in translation time compared to translating from
scratch. The interface is publicly available at
https://intellicat.beringlab.com/.

##  1 Introduction

Existing computer-aided translation (CAT) tools incorporate machine
translation (MT) in two ways: post-editing (PE) or interactive translation
prediction (ITP). PE tools (Federico et al., 2014; Pal et al., 2016) provide a
machine-translated document and ask the translator to edit incorrect parts. By
contrast, ITP tools (Alabau et al., 2014; Green et al., 2014a; Santy et al.,
2019) aim to provide translation suggestions for the next word or phrase given
a partial input from the translator. A recent study with human translators
revealed that PE was 18.7% faster than ITP in terms of translation time (Green
et al., 2014b) and required fewer edits (Do Carmo, 2020). However, many
translators still prefer ITP over PE because of (1) high cognitive loads
(Koehn, 2009) and (2) the lack of subsegment MT suggestions (Moorkens and
O’Brien, 2017) in PE.

In this paper, we introduce IntelliCAT111A demonstration video is available at
https://youtu.be/mDmbdrQE9tc, a hybrid CAT interface designed to provide PE-
level efficiency while retaining the advantages of ITP, such as subsegment
translation suggestions. To mitigate the cognitive loads of human translators,
IntelliCAT aims to automate common post-editing tasks by introducing three
intelligent features: (1) quality estimation, (2) translation suggestion, and
(3) word alignment.

Quality estimation (QE) is the task of estimating the quality of MT output
without reference translations (Specia et al., 2020). We integrate QE into the
CAT interface so that the human translator can easily identify which machine-
translated sentences and which parts of the sentences require corrections.
Furthermore, for words that require post-editing, our interface suggests
possible translations to reduce the translators’ cognitive load. Finally,
based on word alignments, the interface aligns the source and translated
documents in terms of formatting by transferring the styles applied in the
source document (e.g., bold, hyperlink, footnote, equation) to the translated
document to minimize the post-editing time. Our contributions are:

  * •

We integrate state-of-the-art sentence-level and word-level QE (Lee, 2020)
techniques into an interactive CAT tool, IntelliCAT.

  * •

We introduce a novel words and phrases suggestion model, which is conditioned
on both the left and right contexts, based on XLM-RoBERTa (Conneau et al.,
2020). The model is fine-tuned with a modified translation language modeling
(TLM) objective (Lample and Conneau, 2019).

  * •

We conduct quantitative experiments and a user study to evaluate IntelliCAT.

The experimental results on the WMT 2020 English-German QE dataset show that
post-editing with the proposed QE and translation suggestion models could
significantly improve the translation quality ($-$6.01 TER and $+$6.15 BLEU).
Moreover, the user study shows that the three features provided by IntelliCAT
significantly reduce post-editing time (19.2%), which led to a 52.6% reduction
in translation time compared to translating from scratch. Finally, translators
evaluate our interface to be highly effective, with a SUS score of 88.61.

Figure 1:  The IntelliCAT Interface. After a document (i.e., an MS Word file)
is uploaded, A sentences from the original document (source) and B the initial
MT output for each sentence (target) are shown side-by-side. C Formatting tags
indicate where a specific style (identified by an integer style id) is applied
and D are automatically inserted at the proper position of the MT output based
on word alignments. E The interface shows the quality of each machine-
translated sentence based on sentence-level QE. F Potentially incorrect words
and G locations of missing words are highlighted based on word-level QE. When
the user selects a sequence of words in the MT output, H the corresponding
words in the source sentence are highlighted with a heat map, and I up to five
alternative translations are recommended.

##  2 Related Work

#### CAT Tool and Post-Editing

In the localization industry, the use of CAT tools is a common practice for
professional translators (Van den Bergh et al., 2015). As MT has improved
substantially in recent years, approaches incorporating MT into CAT tools have
been actively researched (Alabau et al., 2014; Federico et al., 2014; Santy et
al., 2019; Herbig et al., 2020). One of the approaches is post-editing in
which the translator is provided with a machine-translated draft and asked to
improve the draft. Recent studies demonstrate that post-editing MT output not
only improves translation productivity but also reduces translation errors
(Green et al., 2013; Aranberri et al., 2014; Toral et al., 2018).

#### Translation Suggestion

Translation suggestions from interactive translation prediction (ITP) (Alabau
et al., 2014; Santy et al., 2019; Coppers et al., 2018) are conditioned only
on the left context of the word to be inserted. Therefore, ITP has intrinsic
limitations in post-editing tasks where the complete sentence is presented,
and the right context of the words that need correction should also be
considered. We propose a novel translation suggestion model in which
suggestions are conditioned on both the left and right contexts of the words
or phrases to be modified or inserted to provide more accurate suggestions
when post-editing the complete sentence.

#### Cross-Lingual Language Model

Cross-lingual language models (XLMs), which are language models pre-trained in
multiple languages, have led to advances in MT (Lample and Conneau, 2019) and
related tasks such as QE (Lee, 2020), automatic post-editing (Wang et al.,
2020; Lee et al., 2020), and parallel corpus filtering (Lo and Joanis, 2020).
Accordingly, our QE and translation suggestion models are trained on top of
XLM-R (Conneau et al., 2020), an XLM that shows state-of-the-art performance
for a wide range of cross-lingual tasks. To the best of our knowledge,
IntelliCAT is the first CAT interface that leverages XLM to assist human post-
editing for MT outputs.

##  3 System Description

###  3.1 Overview

IntelliCAT is a web-based interactive interface for post-editing MT outputs
(Figure 1). Once loaded, it shows two documents side-by-side: the uploaded
original document (an MS Word file) on the left and the machine-translated
document on the right. Each document is displayed as a list of sentences with
formatting tags inserted, tags that show the style of the original document,
including text styles (e.g., bold, italic, or hyperlinked) and inline contents
(e.g., a media element or an equation).

The user can post-edit MT outputs on the right using the following three
features: (1) sentence-level and word-level QE, (2) word or phrase suggestion,
and (3) automatic tagging based on word alignments. The sentence-level QE
shows the estimated MT quality for each sentence, and word-level QE highlights
the parts of each machine-translated sentence that need correction. When the
user selects a specific word or phrase, the top-$5$ recommended alternatives
appear below, allowing the user to replace the selected words or insert a new
word. Finally, the system automatically captures the original document style
and inserts formatting tags in machine-translated sentences at the appropriate
locations. After post-editing, the user can click on the export button to
download the translated document with the original style preserved. A sample
document and its translated document without human post-editing is presented
in Appendix A.

###  3.2 Machine Translation

Our system provides MT for each sentence in the input document. We build our
NMT model based on Transformer (Vaswani et al., 2017) using OpenNMT-py (Klein
et al., 2017). As training data, the English-German parallel corpus provided
in the 2020 News Translation Task (Barrault et al., 2020) is used. We use
unigram-LM-based subword segmentation (Kudo, 2018) with a vocabulary size of
32K for English and German, respectively, and the remaining hyperparameters
follow the base model of Vaswani et al. (2017).

###  3.3 Quality Estimation

Quality estimation (QE) is the task of estimating the quality of the MT
output, given only the source text (Fonseca et al., 2019). We estimate the
quality at two different granularities: sentence and word levels. Sentence-
level QE aims to predict the human translation error rate (HTER) (Snover et
al., 2006) of a machine-translated sentence, which measures the required
amount of human editing to fix the the machine-translated sentence. By
contrast, word-level QE aims to predict whether each word in the MT output is
OK or BAD and whether there are missing words between each word.

Figure 1 demonstrates the use of QE in our interface. Based on the sentence-
level QE, we show the MT quality for each machine-translated sentence computed
as $1-(predicted\>HTER)$. In addition, based on word-level QE, we show words
that need to be corrected (with red or yellow underlines) or locations for
missing words (with red or yellow checkmarks). To display the confidence of
word-level QE predictions, we encode the predicted probability of the color of
underlines and checkmarks (yellow for $P_{BAD}>0.5$ and red for
$P_{BAD}>0.8$).

For QE training, we use a two-phase cross-lingual language model fine-tuning
approach following Lee (2020), which showed the state-of-the-art performance
on the WMT 2020 QE Shared Task (Specia et al., 2020). We fine-tune XLM-RoBERTa
(Conneau et al., 2020) with a few additional parameters to jointly train
sentence-level and word-level QEs. We train our model in two phases. First, we
pre-train the model with a large artificially generated QE dataset based on a
parallel corpus. Subsequently, we fine-tune the model with the WMT 2020
English-German QE dataset (Specia et al., 2020), which consists of 7,000
triplets consisting of source, MT, and post-edited sentences.

###  3.4 Translation Suggestion

As shown in Figure 1, when the user selects a specific word or phrase to
modify or presses a hotkey (ALT+s) between words to insert a missing word, the
system suggests the top-$5$ alternatives based on fine-tuned XLM-R.

#### XLM-R Fine-Tuning

For translation suggestion, we fine-tune XLM-R with a modified translation
language modeling (TLM) objective (Lample and Conneau, 2019), which is
designed to better predict the masked spans of text in the translation.
Following Lample and Conneau (2019), we tokenize source (English) and target
(German) sentences with the shared BPE model (Sennrich et al., 2016), and
concatenate the source and target tokens with a separation token (</s>).
Unlike the TLM objective of Lample and Conneau (2019), which randomly masked
tokens in both the source and target sentences, we only mask tokens in target
sentences since the complete source sentence is always given in the
translation task. We randomly replace $p$% ($p\in[15,20,25]$) of the BPE
tokens in the target sentences by <mask> tokens and train the model to predict
the actual tokens for the masks. In addition, motivated by SpanBERT (Joshi et
al., 2020), we always mask complete words instead of sub-word tokens since
translation suggestion requires predictions of complete words. As training
data, we use the same parallel corpus that is used for MT training.

#### Inference

To suggest alternative translations for the selected sequence of words, we
first replace it with multiple <mask> tokens. The alternative translations may
consist of sub-word tokens of varying lengths. Hence, we generate $m$ inputs,
where $m$ denotes the maximum number of masks, and in the $i^{th}$ input
($i\in[1,...,m]$), the selected sequence is replaced with $i$ consecutive
<mask> tokens. In other words, we track all cases in which alternative
translations consist of $1$ to $m$ sub-word tokens. Then, each input is fed
into the fine-tuned XLM-R, and <mask> tokens are iteratively replaced by the
predicted tokens from left to right. In each iteration, we use a beam search
with a beam size $k$ to generate the top-$k$ candidates. Finally, all mask
prediction results from $m$ inputs are sorted based on probability, and the
top-$k$ results are shown to the user.

###  3.5 Word Alignment and Automatic Formatting

To obtain word alignments, we jointly train the NMT model (section 3.2) to
produce both translations and alignments following Garg et al. (2019). One
attention head on the Transformer’s penultimate layer is supervised with an
alignment loss to learn the alignments. We use Giza++ (Och and Ney, 2003)
alignments as the guided labels for the training. As sub-word segmentation is
used to train the NMT model, we convert the sub-word-level alignments back to
the word-level. We consider each target word to be aligned with a source word
if any of the target sub-words is aligned with the source sub-words.

We provide two features based on word alignment information. First, when the
user selects a specific word or phrase in the machine-translated sentence, the
corresponding words or phrases in the source sentence are highlighted using a
heatmap. Second, formatting tags are automatically inserted at the appropriate
locations in the machine-translated sentences. We use two types of tags to
represent the formatting of the document: paired tags and unpaired tags.
Paired tags represent styles applied across a section of text (e.g., bold or
italic). To retain the style applied in the source sentence to the MT, we
identify the source word with the highest alignment score for each target word
and apply the the corresponding source word’s style to the target word. By
contrast, unpaired tags represent inline non-text contents such as media
elements and equations. To automatically insert an unpaired tag in the MT, we
identify the target word with the highest alignment score with the source word
right before the tag and insert the corresponding tag after the target word.

| (With Predicted QE) | (With Oracle QE)
---|---|---
Model | TER$\downarrow$ | BLEU$\uparrow$ | TER$\downarrow$ | BLEU$\uparrow$
Baseline (MT) | 31.37 | 50.37 | 31.37 | 50.37
XLM-R |  |  |  |
(Conneau et al., 2020) |  |  |  |
Top-1 | 30.28 (-1.09) | 50.78 (+0.41) | 26.57 (-4.80) | 56.02(+5.65)
Top-3 | 29.47 (-1.90) | 50.89 (+0.52) | 24.10 (-7.27) | 60.28 (+9.91)
Top-5 | 28.75 (-2.62) | 51.85 (+1.48) | 22.78 (-8.59) | 62.40 (+12.03)
Proposed
Top-1 | 29.04 (-2.33) | 51.93 (+1.56) | 24.26 (-7.11) | 59.38 (+9.01)
Top-3 | 26.69 (-4.68) | 54.70 (+4.33) | 19.08 (-12.29) | 67.51 (+17.14)
Top-5 | 25.36 (-6.01) | 56.52 (+6.15) | 17.30 (-14.07) | 70.50 (+20.13)
Table 1: TER and BLEU for machine-translated sentences (Baseline) and post-
edited sentences (XLM-R and Proposed) based on word-level QE and translation
suggestion.

##  4 Experiments

###  4.1 Model Evaluation

#### Experimental Setup

To evaluate the performance of translation suggestions, we measure MT quality
improvement when a sentence is corrected with the suggested words or phrases.
We introduce two selection conditions (Oracle QE and Predicted QE) and two
suggestion methods (XLM-R and Proposed). The selection conditions locate the
words that need to be corrected in a sentence; in Oracle QE condition, the
ground truth word-level QE label is used as a baseline, and in Predicted QE
condition, our word-level QE model is used to identify the target words. The
suggestion methods determine the words that the selected words should be
replaced with. We test two suggestion models, the pre-trained
XLM-R222https://pytext.readthedocs.io/en/master/xlm_r.html and the proposed
model, fine-tuned with the modified TLM objective, with three different
suggestion sizes: top-1, top-3, and top-5.

Each of the QE and translation suggestion models was trained using two Tesla
V100 GPUs. As an evaluation dataset, we use the WMT 2020 English-German QE dev
dataset (Specia et al., 2020). As evaluation metrics, we use the translation
error rate (TER) (Snover et al., 2006) and BLEU (Papineni et al., 2002).

#### Experimental Result

Table 1 shows the translation quality of (1) MT sentences (baseline), (2)
post-edited sentences with XLM-R-based translation suggestion, and (3) post-
edited sentences with the proposed translation suggestion model. When MT
sentences are post-edited based on QE prediction with the top-1 suggestion,
TER and BLEU are improved over the baseline by $-$2.33 and $+$1.56,
respectively. This result suggests that our QE and translation suggestion
models can be used to improve MT performance without human intervention. When
the top-5 suggestions are provided, TER and BLEU are improved by $-$6.01 and
$+$6.15, respectively, for the QE prediction condition and improved by
$-$14.07 and $+$20.13, respectively, for the oracle QE condition. These
results imply that post-editing based on translation suggestions can
significantly improve the translation quality. Finally, the proposed model
significantly outperforms XLM-R in all experimental settings, showing that
fine-tuning XLM-R with the modified TLM objective is effective for the
suggestion performance.

###  4.2 User Study

We conducted a user study to evaluate the effectiveness of IntelliCAT.

#### Tasks and Stimuli

We asked participants to translate an English document to German using the
given interface. As stimuli, we prepared three English documents, each with 12
sentences and 130, 160, and 164 words. The documents included 22, 18, and 20
styles, respectively (e.g., bold, italic, or a footnote), and participants
were also asked to apply these styles in the target document.

#### Translation Interfaces

We compared three translation interfaces: MSWord, MT-Only, and Full. In
MSWord, the participants were asked to translate documents using a popular
word processor, Microsoft Word. In this baseline condition, two Microsoft Word
instances were shown side-by-side: one showing an English document (source)
and the other showing an empty document where one could type the translated
sentences (target). In MT-Only, participants started with a machine-translated
document on IntelliCAT without QE, translation suggestion, and word alignment;
they had to edit incorrect parts and transfer styles by themselves. In Full,
the participants could use all the features of IntelliCAT.

#### Participants and Study Design

We recruited nine participants (aged 23–31 years). All participants majored in
German and were fluent in both English and German. We adopted a within-subject
design; each participant tested all three interfaces and three documents.
Thus, our study consisted of nine (participants) $\times$ 3 (conditions) = 27
trials in total. The order of interfaces and documents was counterbalanced
using a $3\times 3$ Latin square to alleviate the possible bias of learning
effects or fatigue. For each trial, we measured the translation completion
time.

#### Procedure

Participants attended a training session for ten minutes, where they tried
each interface with a short sample document. Subsequently, they performed
three translation tasks with different interfaces. We allowed them to look up
words for which they did not know the translation before starting each
translation task. Upon completing the three tasks, participants responded to a
system usability scale (SUS) questionnaire (Brooke, 1996), and we gathered
subjective feedback. The entire session took approximately 90 min per
participant.

Figure 2:  SUS Feedback. The usability of IntelliCAT was evaluated as an excellent level with a score of 88.61±7.82. Interface | Avg. time (s)
---|---
MSWord | 1178.78 $\pm$ 280.41
MT-Only | 688.00 $\pm$ 175.02
Full | 555.66 $\pm$ 200.81
Table 2:  Translation completion time. The differences between the three
interface conditions are statistically significant.

#### Result and Discussion

Table 2 summarizes the result of the user study. A repeated measures ANOVA
with a Greenhouse-Geisser correction found a significant difference in
completion time between the three translation interfaces
($F(1.306,10.449)=56.398$, $p<0.001$). Post hoc tests using the Bonferroni
correction revealed that All (555.66 ± 200.81 s) was significantly faster than
MT-Only (688.00 ± 175.02 s) ($p=0.013$) and MT-Only was significantly faster
than MSWord (1,178.78 ± 280.41 s) ($p<0.001$). These results suggest that our
QE, translation suggestion, and word alignment features could further
accelerate post-editing (a 19.2% speedup) (All vs. MT-Only), and our system
could reduce the translation time by more than half (52.9%) compared to
translating from scratch (All vs. MSWord).

We could not find a significant difference between documents
($F(1.964,15.712)=0.430$, $ns$) with the same statistical procedure, which
suggests that the translation difficulties of the three English documents were
not statistically different.

Our interface received a mean SUS score of 88.61 ($\sigma=7.82)$, which is
slightly higher than the score for an “Excellent” adjective ratings (85.58,
Bangor et al. (2008)). Eight out of nine participants reported that QE was
useful for proofreading purposes; P2 stated, “With QE, I could double-check
the words that are possibly wrong.” All participants evaluated the translation
suggestions to be useful; P7 mentioned “Translation suggestion was very
convenient. It might significantly reduce the dependence on the dictionary.”

Overall, the user study results demonstrated the effectiveness of IntelliCAT
both quantitatively and qualitatively, and we found that human translators
could streamline their post-editing process with the three features provided
in IntelliCAT.

##  5 Conclusion and Future Work

In this paper, we introduce IntelliCAT, an intelligent MT post-editing
interface for document translation. The interface provides three neural
network-based features to assist post-editing: (1) sentence-level and word-
level QEs, (2) alternative translation suggestions for words or phrases, and
(3) automatic formatting of the translated document based on word alignments.
The model evaluation shows that post-editing based on the proposed QE and
translation suggestion models can significantly improve the quality of
translation. Moreover, the user study shows that these features significantly
accelerate post-editing, achieving a 52.9% speedup in translation time
compared to translating from scratch. Finally, the usability of IntelliCAT was
evaluated as an “excellent” level, with a SUS score of 88.61.

In future work, we will build a pipeline that continuously improves the
performance of neural models based on automatically collected triplets
consisting of source, MT, and post-edited sentences. We will implement an
automatic post-editing (Chatterjee et al., 2020) model to continuously improve
MT performance and apply online learning to QE models to continually enhance
QE performance.

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##  Appendix A Sample Document Translation

Figure 3 shows a sample document and the translated document using IntelliCAT
without human intervention.

Figure 3:  A sample document (left) and the translated document (right)
without human intervention.


# Rapid Detection of Aircrafts in Satellite Imagery based on Deep Neural
Networks
††thanks: * Research Center For Modeling and Simulation (RCMS), NUST

Arsalan Tahir  * RCMS, NUST
Islamabad, Pakistan
<EMAIL_ADDRESS>Muhammad Adil  RCMS, NUST
Islamabad, Pakistan
<EMAIL_ADDRESS>Arslan Ali  RCMS, NUST
Islamabad, Pakistan
<EMAIL_ADDRESS>
###### Abstract

Object detection is one of the fundamental objectives in Applied Computer
Vision. In some of the applications, object detection becomes very challenging
such as in the case of satellite image processing. Satellite image processing
has remained the focus of researchers in domains of Precision Agriculture,
Climate Change, Disaster Management, etc. Therefore, object detection in
satellite imagery is one of the most researched problems in this domain. This
paper focuses on aircraft detection. in satellite imagery using deep learning
techniques. In this paper, we used YOLO deep learning framework for aircraft
detection. This method uses satellite images collected by different sources as
learning for the model to perform detection. Object detection in satellite
images is mostly complex because objects have many variations, types, poses,
sizes, complex and dense background. YOLO has some limitations for small size
objects (less than$\sim$32 pixels per object), therefore we upsample the
prediction grid to reduce the coarseness of the model and to accurately detect
the densely clustered objects. The improved model shows good accuracy and
performance on different unknown images having small, rotating, and dense
objects to meet the requirements in real-time.

###### Index Terms:

Deep Learning, Satellite Images, YOLO

##  I Introduction

Object detection is a fundamental challenge in computer vision and also a key
part of active research. The purpose of object detection is to find an
instance in a specific location by drawing a bounding box [1]. Many high-level
vision tasks are also solvable using object detection like segmentation,
activity recognition and event capturing. Traditional machine learning
techniques are not suitable to perform in realtime for object detection. With
invent of deep learning, the computer is capable to understand the visual
imagery like a human. In satellite imagery, object detection is a very
complicated task due to low-resolution pixel and have densely clustered
objects. In deep learning three frameworks which give solution for object
detection are Faster RCNN [2], YOLO [3] and SSD [4]. YOLO has the greatest
inference speed and score on the Pascal VOC dataset [5]. The main problem is
to locate aircraft from the large searching area of the image in an efficient
manner.

Figure 1: Bounding boxes in above image show the detection of aircrafts.

For this purpose, a complete autonomous UAVs application are required, which
used classification and localization in real-time. Authors used YOLO based
deep neural network to solve this problem. This paper presents object
detection of an aircraft for satellite images based on YOLO real-time
detector.

This paper is divided into six sections. First section covers the introductory
part and the second section covers related work after the introduction. After
the second section network architecture for object detection is discussed. The
methodology and results are covered in the fourth and fifth section and
conclusion are highlighted in the last section.

##  II Related Work

The frequently used techniques of object detection suggested by national and
international academics are split primarily into three categorizations: based
on motion data, based on the extraction of features, and based on the matching
of templates. Qinhan et al. [6] used multiple windows that have the highest
chances of objects and then apply SVM and HOG techniques for the generation of
proposals, but the disadvantage of it used fixed-size windows. Cheng et al.
[7] used subtraction and registration techniques for the identification of
objects in satellite images. Lee et al. [8] applied RCNN for the detection of
objects in images taken by UAVs. Azevedo et al. [9] used median background
techniques for the identification of objects in aerial imagery. J. Khan et al.
[10] proposed automated target detection for satellite images using edge boxes
algorithm. Junyan Lu et al. [11] propose a method for the detection of
vehicles using YOLO deep learning framework. Douillard [12] proposed a deep
learning method for detection of an object in satellite images. They used
RetinaNet architecture on COCW dataset based on Faster RCNN. Lu Zhang et al.
[13] present a hierarchical oil tank detector with deep surrounding features
for high-resolution optical satellite imagery. The proposed method is divided
into three modules named candidate selection, feature extraction and
classification. Marcum et al. [14] propose a method to localize the surface to
air-missile (SAM) sites using sliding window approach for satellite images. If
the object is hundreds of meters then this approach performs better results
rather than small objects which is computationally expensive. For small
objects, millions of sliding window cutouts generate over 10-meter area in
Digital Globe image. With the arrival of deep learning and GPU technology, the
development becomes fast and efficient in the field of computer vision
especially when we are solving problems of pattern recognition and image
processing and these are more robust rather than traditional techniques. Deep
learning techniques plays a very important part in the field of object
detection because it can extract features from an image automatically. Deep
learning provides excellent accuracy in the field of object detection. If we
want to detect category (a horse, a cat, a dog) then first collect the large
dataset and start training on this dataset. After training when we give the
image for prediction and then output is produced in the form of vector scores
for each category. We define an objective function that calculates the error
between desired output and vector output score. For this computation machine
sets internal parameters (weights or real number) to minimize the error. In
deep learning, millions of weights are used for training. So the gradient
vector is used for each weight vector and tells what amount of error is
decreases and increases. The gradient vector is then adjusted in the opposite
direction of the weighted vector. So many practitioners use stochastic
gradient descent to minimizing the loss function with randomly [15] [16] [17].
Therefore this paper uses YOLO deep learning method to obtain real-time
detection and performance in satellite images.

##  III Network Architecture

Redmon et.al proposed a method YOLO [3], is a real-time object detector based
on a convolutional neural network. After some time Joseph Redmon and Ali
Farhadi released a new version YOLOv2, which has good performance and speed
[18]. Now the latest version is YOLOv3 proposed by Joseph Redmon and Ali
Farhadi with the increment of layers in architecture to improve speed and
accuracy [19]. YOLO has many advantages rather than traditional algorithms
because of its architecture. The traditional method used region proposal
networks for the generation of proposals and then implement the CNN on these
proposals for feature extraction. These methods are slow and not real-time due
to their two-stage detection architecture for satellite imagery. YOLO takes an
image with resolution 416 × 416 × 3 and divides the input image into S × S
grid. If the center of the object falls into the grid then that grid is
responsible for predicting the object. Each cell in grid predicts the bounding
boxes (B) and confidence scores related to those boxes. YOLO v1 has a large
positioning mistake and low recall rate compared to the region-based proposal
technique such as Fast R-CNN. The primary improvements of YOLOv2 are therefore
to improve the rate of recall, batch normalization, anchor boxes, and
multiscale training. Batch normalization is a popular method to normalize the
data at the time of training and also used to increase the speed with mean 0
and variance 1, which can prevent the gradient descent for vanishing. Batch
normalization also helps to make network convergence faster. Faster RCNN used
to add fully connected layers to predict bounding boxes directly after the
convolutional layers but YOLO uses anchor boxes, which improves the speed and
recall rate.

Figure 2: YOLO CNN Architecture

During training, YOLO adjusts the input after every 10 epoch to make the model
performed well at the test time on the multiscale images. The CNN architecture
using in this paper has 24 convolutional layers followed by two fully
connected layers. To reduce the coarseness model uses prediction grid is 26
$\times 26$ and downsample the factor by 16. This architecture shows the
highest speed and accuracy. Figure 2 displays the whole architecture of CNN
and also preferred due to computational speed and accuracy. The final layer is
used for the classification of objects with probability in between 0 and 1.

##  IV Dataset

First, we collect datasets from DigitalGlobe satellite and apply some
preprocessing and data augmentation techniques. In preprocessing we converted
all image in 550 × 350 resolution to reduce the training time. After that we
used labeling tools for tagging of images. After tagging we converted the data
into standard architecture using python language.Datasets have played very
important role in the area of object detection. It is most important factor
for measuring and analysis of performance of different algorithms and also
pushing this field towards challenging and complex problems. The internet
makes it possible to capture diversity and richness of objects in large images
with large number of categories. The increase in large scale datasets with
millions of images has played important role and opened unprecedented
performance in object detection.

The classifiers show poor results on satellite images due to the effect of
different conditions.

  * •

Spatial Resolution

    * –

Objects are very small and densely clustered in satellite images rather than
the prominent and large object and for small objects like cars, the object is
only ~15 pixels in high-resolution images.

  * •

Rotation Invariance

    * –

Objects in satellite imagery have many orientations (for example ships have
any orientation ranging from 0 to 360 degree).

  * •

Training example frequency

    * –

There is relative dearth of data in satellite imagery and objects are not
clearly visible in shape.

  * •

Ultra-high resolution

    * –

Images are of very high resolution (hundreds of megapixels) but most
algorithms take input images with few hundreds of pixels. Upsampling the image
means object of interest becomes large, dispersed and not feasible for
standard architecture and downsampling the image can change the object shape.

  * •

Temporal (time of day/season/year)

    * –

Seasonal differences and time of day also effect on satellite images.

Therefore it is difficult for the classifier to detect objects from
conventional datasets due to mentioned reasons on satellite images. For this,
we need a specialized kind of data for satellite images for the processing
which is computationally less expensive and time-efficient. Here the some
datasets, which are using for Aerial images. VEDAI Dataset Razakarivony et.al
[20] made a dataset VEDAI (Vehicle Detection in Aerial Images) collected from
public Utah ARGC database. The images have three RGB channel and one infrared
channel. The authors split the images into 1024 × 1024 RGB channel and perform
downsampling to convert the images into 512 × 512 pixels and ignore the
infrared channel. The authors used just RGB channels and also set the GSD
(Ground Sample Distance) is 12.5 cm. This dataset consist of nine vehicle
classes and total images are 1250 (“plane”, “boat”, “camping car”, “tractor”,
“van”, “pick- up” and “other”). The annotation of images has five parts:
Object class and four coordinates of objects. Mundhenk et al. [21] made a
dataset COWC(Cars Overhead with Context) and collected from six different
locations. The image size of images is 2000 × 2000 pixels and the total number
of images is 53 with the format of TIFF. They covered areas of six locations
namely Columbus, Utah (United States), Selwyn (New Zealand), Postdam
(Germany), Tornoto (Canada) and Vaihingen. The images of Columbus and
Vaihingen are in grayscale while remaining are in the RGB channel. The object
size in the image is 24 pixel with GSD of 15 cm per pixel. They annotate the
32,716 images with car object and annotation includes object class and four
coordinates of objects. DOTA Dataset Guisong et al. [22] made a dataset DOTA
(Dataset for object detection in Aerial images) of aerial images and collected
from different sources like google earth an airplane or ship. We used Bbox
Labeling tool for tagging of aircraft. and sensors. The GSD of images is
diversified and characterized by multiresolution and multi-sensor. DOTA images
are 4000 ×4000 pixels and classes are 15 with annotations namely (“plane”,
“storage tank”, “swimming pool”, “ship”, “harbor”, “bridge”, “helicopter”, and
“other”). The annotation of images has five parts: Object class and four
coordinates of objects. All above mentioned datasets belong to aerial imagery.
There are many reasons behind the creation of dataset. First the dataset of
satellite imagery are not commonly available. Second two or three datasets are
available and those datasets have less number of objects. For this purpose, we
collected images from DigitalGlobe and convert according for standard
architectures by applying some preprocessing techniques. Data annotation is
process of labelling the data of specific instance like human, car etc. which
is understandable for machines. Data annotation is performed manually by human
using the annotation tool and stored large amount of data for machine
learning. The area of objects is cropped through bounding boxes and
coordinates of objects are stored in file for learning of machines. We used
open-source Bounding Box Label tool for ground truth boxes of aircrafts in the
dataset [23].

##  V Methodology

We performed two steps in methodology in which, first we make a dataset for
standard architecture and second configure the parameters for training to
obtain results. We collect a dataset of satellite images from different
sources and manually annotate the images and draw anchor boxes on desired
objects. There are two parts in the dataset: images, which are in JPEG format
and labels, which are in text format. Evert text file is saved according to
images, which contain the annotation of objects and the format of the
annotation is:

$\displaystyle<object-class><x,y,w,h>$ (1)

Where x and y are the center points of object and w and h are the width and
height of object correspondence to the image and name of object class. The
input dimension of YOLO is 416 × 416 ×3 for training but you should care about
the image size should not large may lose the useful information. The basic
information of publically available datasets of aerial imagery is described in
Table II.

We process our dataset and convert in the form of standard architecture using:

  * •

Center points

$\displaystyle x=(x_{max}+x_{min})/2$ (2)

$\displaystyle y=(y_{max}+y_{min})/2$ (3)

  * •

Width and Height

$\displaystyle w=(x_{max}+x_{min})$ (4)

$\displaystyle h=(y_{max}+y_{min})$ (5)

The batch training passes the dataset through learning algorithms and save the
weights. Batch size represents the training examples in one forward pass. The
learning rate is used for optimization and minimizing the loss function of
neural network and loss function maps values of variables onto real number and
also show the associated cost with values. During training of neural network
it is common to use decay because after each update weights are multiplied by
value less than 1 and also prevents the weights from growing too large.
Momentum is used to improve both training speed and accuracy. Our network
consist of 26 × 26 grid and was tested on one object class and returned 26× 26
× 11 tensor. We used batch_size =64 and filter =30 for training.

Figure 3: Testing output of YOLO

Figure 3 shows YOLO generates number of bounding boxes when the input images
is given and it use non max suppression technique to find the correct bounding
boxes around the object with maximum intersection over Union.

##  VI Results

In this paper we used NVIDIA GPU Geforce GTX 1060 for training. Authors
changed the architecture of the model according to object size ($\sim$10
pixels per object) and performed training on the custom dataset. The detection
results of unknown images are shown in figure 4. Figure 4 (left) shows that
our model has good results and perform well on small objects. In middle figure
also shows good results but in Figure 4 (right) there is one object, which is
not detected. But the overall model gives good results and also detects
objects within milliseconds. Our model detects more than 96 % of aircrafts.
Results showed in table I that YOLO was able to identify “aircraft” objects in
the dataset with 90.20% accuracy.

TABLE I: Test results on unseen testing images

Indicator | accuracy | Precision | F1-score | fps
---|---|---|---|---
value | 94.20% | 99% | 96 % | 55

Figure 4: Test Results on Unseen Images TABLE II: Comparison with other
Satellite Imagery (Aircraft) datasets

Sr. | Name | Number Of Objects | Type | Description
---|---|---|---|---
1 | NWPU-RESISC45 Dataset | 700 | Aircraft | (class,0,1)
2 | NWPU VHR-10 Dataset | 800 | Aircraft | (class,0,1)
3 | Custom Dataset | 2213 | Aircraft | (class,x,y,w,h)

##  VII Conclusion

In this paper rapid aircraft detection based on YOLO deep learning is
presented. We used 2200 objects for training with tuning the parameters to
calculate the anchors for a good intersection over the union. The improved
model has good results on unknown images of small and densely clustered
objects and also meet the real-time requirements. Results show this approach
is fast and robust for aircraft detection in dense airports. Next, we will
increase the number of objects and classes to achieve good performance and
accuracy.

## Conflicts of Interests

The author of this paper shows no conflicts of interest.

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#  CHARTOPOLIS: A Small-Scale Labor-art-ory for Research and Reflection on
Autonomous Vehicles, Human–Robot Interaction, and Sociotechnical Imaginaries

Sangeet Sankaramangalam Ulhasa, Aditya Ravichandera, Kathryn A. Johnsonb,
Theodore P. Pavlicc,
Lance Gharavid, and Spring Bermana This work was supported by NSF EAGER Award
#2146691 and the ASU Center for Human, Artificial Intelligence, and Robot
Teaming (CHART).All authors are with Arizona State University (ASU), Tempe, AZ
85287.a S. S. Ulhas, A. Ravichander, and S. Berman are with the ASU School for
Engineering of Matter, Transport and Energy
<EMAIL_ADDRESS>K. A. Johnson is with the ASU
Department of Psychology<EMAIL_ADDRESS>T. P. Pavlic is with the
ASU School of Computing and Augmented Intelligence, the School of
Sustainability, and the School of Complex Adaptive Systems<EMAIL_ADDRESS>L.
Gharavi is with the ASU School of Music, Dance and Theatre
<EMAIL_ADDRESS>
###### Abstract

CHARTOPOLIS is a multi-faceted sociotechnical testbed meant to aid in building
connections among engineers, psychologists, anthropologists, ethicists, and
artists. Superficially, it is an urban autonomous-vehicle testbed that
includes both a physical environment for small-scale robotic vehicles as well
as a high-fidelity virtual replica that provides extra flexibility by way of
computer simulation. However, both environments have been developed to allow
for participatory simulation with human drivers as well. Each physical vehicle
can be remotely operated by human drivers that have a driver-seat point of
view that immerses them within the small-scale testbed, and those same drivers
can also pilot high-fidelity models of those vehicles in a virtual replica of
the environment. Juxtaposing human driving performance across these two
contexts will help identify to what extent human driving behaviors are
sensorimotor responses or involve psychological engagement with a system that
has physical, not virtual, side effects and consequences. Furthermore, through
collaboration with artists, we have designed the physical testbed to make
tangible the reality that technological advancement causes the history of a
city to fork into multiple, parallel timelines that take place within
populations whose increasing isolation effectively creates multiple
independent cities in one. Ultimately, CHARTOPOLIS is meant to challenge
engineers to take a more holistic view when designing autonomous systems,
while also enabling them to gather novel data that will assist them in making
these systems more trustworthy.

##  I Introduction

Figure 1: CHARTOPOLIS, a reconfigurable, modular testbed for urban autonomous-
vehicle research.

We are developing _CHARTOPOLIS_ , a small-scale traffic testbed that serves as
a research laboratory, an art installation, and a boundary object [1] that is
intended to increase integration between engineering, psychology,
anthropology, and ethical philosophy. This mind–machine–motor nexus
(M3X)111NSF M3X program: https://beta.nsf.gov/funding/opportunities/mind-
machine-and-motor-nexus-m3x “labor-art-ory” consists of a model physical
driving environment with small robotic vehicles (Fig. 1), a driving station
for remote control of the vehicles by human participants (Fig. 3), and a
driving simulator that serves as a high-fidelity, virtual replica of the
physical environment (Fig. 4).

In contrast to existing small-scale self-driving car testbeds, e.g. [2, 3, 4,
5, 6, 7, 8], CHARTOPOLIS is specifically designed to facilitate participatory
studies of human sensorimotor, behavioral, cognitive, and aesthetic responses
to diverse driving scenarios with the goal of enriching autonomous vehicles
with human-like behavioral profiles. Its matching virtual and physical
environments will enable safe, controlled experimental manipulation of both
typical driving conditions and unavoidable accidents that would be difficult
or hazardous to replicate with full-scale vehicles. Furthermore, by comparing
and contrasting human driving performance in the physical testbed to
performance in the high-fidelity simulator, we can identify commonalities
among human behaviors across the two participatory platforms (i.e., the
physical testbed and virtual replica) that are likely to extend to
hypothetical behaviors in full-scale vehicles. Thus, this comparative approach
aims to elucidate the underlying problems resulting in the Sim2Real gap rather
than attempting to find costly and risk-prone stopgap solutions to them, e.g.,
[9, 10]. More generally, juxtaposing the physical and virtual environments
enables us to investigate the minimal set of features (e.g., sensory stimuli,
dynamical characteristics, psychological association with outcomes in physical
space) required for a participatory driving testbed to effectively engage a
human operator in as realistic of a driving experience as possible. Finally,
CHARTOPOLIS doubles as an art installation that makes a statement about the
effect of technology and autonomy on the evolving history of a city.

##  II Goals: Human-Focused Design and Art

###  II-A Trustworthy Autonomy via Participatory CHARTOPOLIS

Much of the focus in developing autonomous vehicles (AVs) has been on
improving sensors and algorithms to enable accurate perception and enhance
driver safety. To that end, researchers and manufacturers have worked
intensely at designing AVs that emulate human driving behavior, but little
effort has been placed in determining _which_ humans to emulate. To what
extent are we able to account for the significant variance in human drivers’
personalities, temperaments, values, and moral priorities? Can we design AVs
that reflect the personality types and driving styles of their owners? The
CHARTOPOLIS testbed and driving simulator allow us to investigate this
variability in human driving performance in a safe environment.

####  II-A1 Mapping Parameters to Personality and Values

Gaining the trust and acceptance of human passengers and human drivers of
other vehicles will require fully autonomous vehicles to do more than be
competent at obeying the objective rules of the road; AVs will also have to
emulate human driving behaviors that are acceptable in terms of social and
ethical norms. Within any group of human drivers who all obey driving laws,
the remaining unconstrained degrees of freedom allow for significant
differences to emerge across driving preferences (e.g., following distance,
responsiveness to light changes, responsiveness to upcoming speed-limit
changes, etc.). These driving-style differences, in turn, reflect variability
in individual drivers’ motives, values, moral priorities, and other
psychological states/traits.

Consistent suites of different driving preferences can conspicuously identify
a driver’s “personality” as benevolent/careful/pessimistic/defensive or power-
oriented/egoistic/optimistic/aggressive, and the resulting behaviors can be
placed on a normative ethical scale. An AV’s programmer is free to choose
these driving parameters, possibly reflecting their own driving personality.
In optimal control theory [11], such remaining degrees of freedom might map to
some scalar functional (e.g., energy use or some proxy for physical driving
comfort) that can be optimized through an automated design process. In either
case, no _explicit_ characterization of the _ethical_ dimension of these
choices is incorporated into the design. A major motivation of CHARTOPOLIS is
to develop a framework for formalizing the currently cryptic ethical dimension
of sociotechnical systems’ modeling and control design.

####  II-A2 Beyond the Artificial Moral Dilemma

Prior attempts to characterize morality and ethical behavior in machine
decision-making, e.g. [12], implicitly assume that ethical stances are only
evident as the outcome of often contrived, singular, pathological driving
events. For example, Awad et al. [12] asked humans to judge hypothetical AV
decision-making by using a battery of questions about driving-related
dilemmas, such as whether an AV with a sudden braking failure should crash
into a wall (certainly killing its passengers) or continue driving into a
crowded crosswalk (certainly killing others on the road). Humans evaluating
these two options had significantly more time to deliberate on the correct
answer than the AV would have. Furthermore, some scenarios were only dilemmas
in the myopic perspective; for example, crashing into pedestrians in a
crosswalk does not guarantee that the brake-less AV will not immediately
afterward hit something else that will also kill everyone in the AV. Implicit
in these studies is that the ideal AV will have an explicit rule-based
(deontological) or utility-based (utilitarian) reasoning system, c.f. [13],
that will recognize emergent dilemmas and, at those instants, assert a
(hopefully acceptable) decision.

In contrast with those prior attempts, we recognize that ethical stances are
being made continually throughout the driving process. An “aggressive” driver
might be viewed by an observer as behaving “less ethically” than a “defensive”
driver despite neither of them actually being observed in a formal dilemma.
These ethical stances emerge from the non-trivial combination of the human’s
driving preferences, sensing and actuation dynamics of the human and the
vehicle, and the physical realities of the exterior world. In other words,
ethical stances are an ecological property of the system of the driver’s mind,
the motor (human sensing and actuation dynamics), and the machine (physical
dynamics of the vehicle and the surrounding environment). Engineers should
characterize the ethical stances that their AVs implicitly take as they
operate continually and also formally recognize how their technologies
modulate the ethical stances taken by their human operators.

###  II-B CHARTOPOLIS as Art Installation

CHARTOPOLIS will constitute a kind of multivalent work, not only in its
function as laboratory but also in a dual function as artwork and as a kinetic
installation whose collection of vehicles, buildings, roadways, signs, and
inhabitants serve as a boundary object [1] suggesting multiple
interpretations. It will serve as a site for both the practice of science and
for meditation on the worlds that science creates as well as a tool for
thinking through technology, specifically robots and AI, as a dynamic between
opposing imaginaries: salvation and damnation, utopia and dystopia, hope and
dread. Robots, in the differences we draw between _us_ and _them_ , become a
way to think about human values and the fantasy of automation as an ethically
immaculate source of labor. Robots serve for us the function that Viktor
Shklovsky [14] identifies for art: they make us strange.

Our ecological perspective of continually operating ethics in driving contexts
acts on both short time scales and on very long, sociotechnical evolutionary
time scales. The increased penetration of AVs in urban environments results in
the continuous operation of sensing technologies that are aware of features
familiar to human perception (e.g., visible light) as well as features that
are totally invisible to humans (e.g., electronic, subsonic, supersonic,
etc.). As urban infrastructure is altered to better enable the performance of
AVs, those in less AV-friendly areas of a city become hidden from those who
begin to depend on AVs. Moroever, with physical separation comes cultural and
historical separation.

CHARTOPOLIS will capitalize on the narrative ability of physical and computer
simulations to illuminate these ethical dimensions of AV design and control,
which unroll over a wide range of operational and evolutionary time scales.
The testbed design draws on the utopian aesthetic of architectural models and
their vision of a clean, hopeful future, as well as on the weird fiction of
China Miéville [15]. The completed testbed will embody two cities, one visible
and one transparent, that represent two realities occupying the same space.
The “invisible city” will evoke those things that are erased and made
invisible, forgotten or ignored, left out of the fables of the past and
visions of the future.

Figure 2: CHARTOPOLIS testbed layout with dimensions. Figure 3: Computer
station for remote operation of robotic car. Monitor displays a video stream
from onboard camera. Figure 4: Driving simulator utilizing CARLA [16]
environment.

##  III Testbed Components

We have developed several iterations of the CHARTOPOLIS testbed. The first
[17] consisted of a grid of roadways with traffic lights at intersections and
several Pheeno [18] differential-drive robots, which were programmed with
autonomous driving functions such as lane tracking and traffic light
detection. The second version was created in conjunction with the Go-CHART
[19] miniature car robot, which emulates sensing and computation capabilities
of a full-size AV, and included traffic lights, signs, and scenery (grass and
trees). For this work, we also developed an initial version of our driving
station for remote control of the robots. Our most recent version of
CHARTOPOLIS (Fig. 1) enhances the versatility and portability of the testbed
and the onboard compute capability of the robotic cars. This version comprises
a customizable driving environment with roads, traffic lights and signs,
reconfigurable buildings, and adjustable lighting, and it uses a modified
version of the JetRacer Pro AI kit [20] (Fig. 5) as the robot car platform.

The following modifications were made to the Jetracer Pro AI Kit and its
control interface. Photo-interrupt encoders were coupled with the four-wheel
drivetrain’s shaft to obtain feedback for speed control. An Arduino Nano was
added to expand the I2C buses on the Jetson Nano and to facilitate the use of
prebuilt Arduino libraries that are not supported on the Jetson Nano. An IMU
sensor, which uses the expanded I2C bus, was mounted on the back of the robot
on an elevated Z-bracket in order to prevent damage to it from collisions. The
Donkey Car web-control interface [21] was modified to include the data from
the encoders and IMU sensor and to improve the accuracy of remote steering
control of the robot via the Logitech G920 steering wheel and pedals by
mapping its throttle and steering angle to the PWM pulses sent from the
PCA9685 I2C controller board.

In both the remote-control driving station (Fig. 3) and driving simulator
(Fig. 4), a human operator has a first-person-view of the roadway on a monitor
and drives the simulated or physical car using a Logitech G920 steering wheel
with accelerator and brake pedals. We are modeling different driving scenarios
in the driving simulator using the open-source virtual driving environment
CARLA [16]. This builds on our previous work developing a driving simulator
[22, 23] to obtain data for our earlier set of studies on human driving
responses (unpublished), described in Section IV. The environment in our
simulations is an exact replica of the road layout (Fig. 2) and buildings on
the CHARTOPOLIS testbed. The layout is replicated to scale in OpenDRIVE format
using RoadRunner [24], and the buildings are imported as assets through the
Unreal Engine editor. The complete simulated CHARTOPOLIS is finally packaged
as a portable CARLA distribution.

The control architecture of the CHARTOPOLIS testbed and simulator is
illustrated in Fig. 6. A single interface common to the physical and simulated
environments helps to characterize and mitigate the Sim2Real gap by allowing
for direct comparison of performance across the two environments. In the
physical testbed, the robot’s pose in a global coordinate frame is obtained
using an overhead camera; further experiments will determine whether a motion-
capture system is necessary to achieve a sufficiently accurate mapping of the
robot’s pose between the physical testbed and simulation. The robot measures
its velocity using its onboard encoder and obtains image data from its wide-
angle camera. The pose, velocity, and images are broadcast to the Jetson Nano,
which computes the robot’s control actions using this feedback. In the
simulator, the CARLA Server obtains the virtual vehicle’s state from simulated
GNSS and images from its onboard RBG sensor, and the Python Client uses these
data to compute the vehicle’s control actions.

Figure 5: Modified JetRacer AI Pro robotic car [20]. Figure 6: CHARTOPOLIS
control architecture, including components of the human-robot interface
(green), physical testbed (orange), and CARLA driving simulator (blue).

##  IV Experimental Procedures

In our planned studies, human participants will be given a battery of
questionnaires including: personality traits [25], values [26], moral
priorities [27], and driving style (e.g., positive vs. aberrant) [28]. These
same participants will then be invited to the lab, where they will experience
driving in both the simulator and through remote control of a robot car within
the physical testbed with a first-person video livestream (counterbalanced for
ordering effects).

Our data-analysis plan will be to map the pre-screened personality profiles
and self-reported driving styles of the human drivers onto the driver behavior
data collected in the virtual and physical environments. Our previous,
unpublished data show that individuals with power-oriented vs. benevolent
profiles are more likely to have self-reported aggressive vs. positive
(prosocial) driving styles, respectively. Further, the two driving styles are
highly predictive of the number of real traffic violations (power-oriented,
aggressive drivers having significantly more violations). We expect these
traits and driving styles to be evident in the driving behaviors in the
simulator and matching physical testbed. Ultimately, this information will
allow us to design controllers that mimic, at least these two, personality and
driving styles.

Gathered simulation data will include all state-variable and sensor data that
are necessary to reproduce aspects of the driving experience, including: the
vehicle’s ground-truth position, speed, and acceleration; data from the
onboard navigational sensors (camera, IMU, GNSS); and information about lane
changes and collisions. On the physical robot, speed and acceleration data
will be calculated using sensor fusion of data from a photo-interrupt shaft
encoder on the robotic drivetrain with IMU and positional data from an
overhead camera, and these data will be synchronously recorded. In both the
simulator and testbed, we will collect data on steering, pedal angle, and
braking.

##  V Conclusion and Open Challenges

The CHARTOPOLIS labor-art-ory is a work in progress, with further technical
and conceptual challenges to overcome. The many challenges of implementing
ethics in AVs are beyond the scope of this paper, but they warrant further
consideration [29, 13, 30]. One take-away message, however, is that all
stakeholders should be consulted in implementing machine ethics [31].

##  VI Acknowledgement

The authors thank Brunella Provvidente for helping with the design and
implementation of the physical testbed.

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