The total cost of the study was $3,562.95 USD.

For this task, we do not report agremeent levels as we were specifically
probing for subjective measurements (opinions) as opposed to objective ones.
For further analysis, see App. E.

For the agreement task, the authors manually went through responses to code
them as either having a missing perspective, or being devoid of content (e.g.,
, No, Nothing I can think of, I agree with it all, etc.). For purposes of the
analysis, this is then binarized. Empty responses are coded as being devoid of
content (no missing perspective).

Figure 11: CloudResearch Study - Instructions  Figure 12: CloudResearch Study
- Agreement Example Screen Figure 13: CloudResearch Study - Missing Value or
Perspective Example screen

###  I.3 Value, Right, and Duty Batch Comparison vs. GPT-4

For this study, a fixed set of 200 test examples was used for all systems,
each pair of which was annotated by 2 annotators (200 situtaions * 2
annotators * 11 systems = 4.4k annotations).

See Figure 14 for the MTurk template used. For this study, only the set of
values, rights, and duties was used, without the explanation or valence scores
shown, in order to disentangle the value, right, and duty generation from the
other aspects.

The interannotator agreement metrics are as follows: For overall preference
(Answer.pref), Fleiss’ kappa: 0.170, Krippendorff’s alpha: 0.373, Cohen’s
kappa: 0.170, percent agreement (pairwise): 0.574, and tie-discounted percent
agreement (pairwise): 0.588. For coverage (Answer.div), Fleiss’ kappa: 0.394,
Krippendorff’s alpha: 0.240, Cohen’s kappa: 0.394, percent agreement
(pairwise): 0.666, and tie-discounted percent agreement (pairwise): 0.720.
Finally, for accuracy (Answer.correct), Fleiss’ kappa: 0.052, Krippendorff’s
alpha: 0.257, Cohen’s kappa: 0.053, percent agreement (pairwise): 0.459, and
tie-discounted percent agreement (pairwise): 0.537. The total cost for this
study was $3,168.00 USD.

Figure 14: Batch Value, Right, and Duty comparison against GPT-4.

###  I.4 Explanation, Valence, and Relevance Annotations

For this evaluation, we run a fixed set of 698 values, rights, and duties
associated with 100 situations from the test set. 3 annotators each mark each
value, right, or duty for relevance, each explanation for quality, and each
valence lable for correctness. Testing 6 systems, this comes to 38k total
annotations. The total cost of this study was $842.40 USD.

We get the following annotator agreements: For relevance, Fleiss’ kappa: .073,
pairwise agreement: .903; explanation quality, Fleiss’ kappa: .180, pairwise
agreement: .842; valence correctness, Fleiss’ kappa: .345, pairwise agreement:
.860.

Figure 15: Relevance, explanation, and valence annotation MTurk template.

##  Appendix J Extreme bad/good actions

Good

⬇

1Being sincerely considerate to others

2Making a friend smile

3Try to be truthful

4Treat others with respect

5Be generous when possible

6Doing what you believe to be right

7Do what you can to improve your community

8Make the world a better place

9Give to those in need if you can afford to

10Respect people’s boundaries if at all possible

Bad

⬇

1Killing someone without cause

2Taking someone’s property without permission or a pressing need

3Genocide

4Causing physical or emotional harm to others because you feel like it

5Discriminating against someone based on their race

6Breaking a promise without a good reason

7Lying to someone just to hurt them

8Neglecting your child

9Emotionally manipulating people

10Buying a bunch of food just to let it go to waste

##  Appendix K Universal Declaration of Human Rights

Below, we have a list of rights from the UN’s Universal Declaration of Human
Rights242424https://www.un.org/en/about-us/universal-declaration-of-human-
rights, along with the corresponding right in ValuePrism.

⬇

1No one shall be subjected to torture or to cruel, inhuman or degrading
treatment or punishment. -> Right to not experience torture or cruel treatment

2All are equal before the law and are entitled without any discrimination to
equal protection of the law. All are entitled to equal protection against any
discrimination in violation of this Declaration and against any incitement to
such discrimination. -> Equality before the law

3No one shall be subjected to arbitrary arrest, detention or exile. -> Right
to not be unjustly detained

4Everyone charged with a penal offence has the right to be presumed innocent
until proved guilty according to law in a public trial at which he has had all
the guarantees necessary for his defence. -> The right to due process

5Everyone has the right to freedom of movement and residence within the
borders of each state. -> The right to freedom of movement

6Everyone has the right to seek and to enjoy in other countries asylum from
persecution. -> The right to seek asylum

7Everyone has the right to a nationality. -> Right to nationality

8Men and women of full age, without any limitation due to race, nationality or
religion, have the right to marry and to found a family. They are entitled to
equal rights as to marriage, during marriage and at its dissolution. -> The
right to marry

9The family is the natural and fundamental group unit of society and is
entitled to protection by society and the State. -> The right to protection

10No one shall be arbitrarily deprived of his property. -> Private property

11Everyone has the right to freedom of opinion and expression; this right
includes freedom to hold opinions without interference and to seek, receive
and impart information and ideas through any media and regardless of
frontiers. -> The right to express one’s opinions

12Everyone has the right to freedom of peaceful assembly and association. ->
The right to peaceful assembly

13No one may be compelled to belong to an association. -> The right to freedom
of association

14Everyone has the right to take part in the government of his country,
directly or through freely chosen representatives. -> Right to participate in
political processes

15Everyone has the right to work, to free choice of employment, to just and
favourable conditions of work and to protection against unemployment. -> The
right to work

16Everyone, without any discrimination, has the right to equal pay for equal
work. -> Right to equal pay

17Everyone has the right to form and to join trade unions for the protection
of his interests. -> The right to collective action

18Everyone has the right to a standard of living adequate for the health and
well-being of himself and of his family, including food, clothing, housing and
medical care and necessary social services, and the right to security in the
event of unemployment, sickness, disability, widowhood, old age or other lack
of livelihood in circumstances beyond his control. -> Access to basic
necessities

19Motherhood and childhood are entitled to special care and assistance. All
children, whether born in or out of wedlock, shall enjoy the same social
protection. -> Right to parental support

20Everyone has the right to education. Education shall be free, at least in
the elementary and fundamental stages. Elementary education shall be
compulsory. Technical and professional education shall be made generally
available and higher education shall be equally accessible to all on the basis
of merit. -> The right to education

21Everyone has the right to life, liberty and security of person. -> Right to
personal liberty, Right to life

22No one shall be held in slavery or servitude; slavery and the slave trade
shall be prohibited in all their forms. -> The right to not be enslaved

23Everyone has the right to recognition everywhere as a person before the law.
-> Right to recognition

24Everyone has the right to an effective remedy by the competent national
tribunals for acts violating the fundamental rights granted him by the
constitution or by law. -> The right to due process

25Everyone is entitled in full equality to a fair and public hearing by an
independent and impartial tribunal, in the determination of his rights and
obligations and of any criminal charge against him. -> The right to a fair
trial

26No one shall be subjected to arbitrary interference with his privacy,
family, home or correspondence, nor to attacks upon his honour and reputation.
Everyone has the right to the protection of the law against such interference
or attacks. -> None

27Everyone has the right to leave any country, including his own, and to
return to his country. -> The right to freedom of movement

28No one shall be arbitrarily deprived of his nationality nor denied the right
to change his nationality. -> Right to nationality

29Marriage shall be entered into only with the free and full consent of the
intending spouses. -> Right to free choice of partner, Right to marry

30Everyone has the right to own property alone as well as in association with
others. -> The right to property

31Everyone has the right to freedom of thought, conscience and religion; this
right includes freedom to change his religion or belief, and freedom, either
alone or in community with others and in public or private, to manifest his
religion or belief in teaching, practice, worship and observance. -> Right to
form one’s own beliefs

32Everyone has the right of equal access to public service in his country. ->
Right to access services

33Everyone, as a member of society, has the right to social security and is
entitled to realization, through national effort and international co-
operation and in accordance with the organization and resources of each State,
of the economic, social and cultural rights indispensable for his dignity and
the free development of his personality. -> Right to social and economic
welfare

34Everyone who works has the right to just and favourable remuneration
ensuring for himself and his family an existence worthy of human dignity, and
supplemented, if necessary, by other means of social protection. -> Right to
just and favorable working conditions

35Everyone has the right to rest and leisure, including reasonable limitation
of working hours and periodic holidays with pay. -> Right to engage in leisure
activities

36Education shall be directed to the full development of the human personality
and to the strengthening of respect for human rights and fundamental freedoms.
It shall promote understanding, tolerance and friendship among all nations,
racial or religious groups, and shall further the activities of the United
Nations for the maintenance of peace. -> Educational opportunities

37Parents have a prior right to choose the kind of education that shall be
given to their children. -> Right of parents to make decisions for their
children

38Everyone has the right freely to participate in the cultural life of the
community, to enjoy the arts and to share in scientific advancement and its
benefits. -> Access to cultural experiences

39Everyone has the right to the protection of the moral and material interests
resulting from any scientific, literary or artistic production of which he is
the author. -> Right to protect one’s work and creations

40Everyone is entitled to a social and international order in which the rights
and freedoms set forth in this Declaration can be fully realized. -> The right
to freedom

41In the exercise of his rights and freedoms, everyone shall be subject only
to such limitations as are determined by law solely for the purpose of
securing due recognition and respect for the rights and freedoms of others and
of meeting the just requirements of morality, public order and the general
welfare in a democratic society. -> Legal autonomy

##  Appendix L ETHICS zero-shot templates

All templates were hand-written by the authors. No more than 10 templates were
attempted per task, and the template with the highest performance on the first
1000 instances of the training set were selected.

###  L.1 Justice

⬇

1Valence({scenario}; Value: Fairness)

$P(good)=P(support),P(bad)=P(oppose)$

###  L.2 Deontology

⬇

1Valence(In response to {scenario}, saying {excuse}; Duty: Duty to have a
valid reason)

$P(good)=P(support),P(bad)=P(oppose)$

###  L.3 Virtue

⬇

1Relevance({scenario}; Value: {trait})

$P(traitfits)=P(relevant),P(traitdoesnotfit)=P(notrelevant)$

###  L.4 Utilitarianism

⬇

1p_better = Valence({scenario1} is more pleasurable than {scenario2}; Value:
Pleasure)[’support’] +

2 Valence({scenario2} is less pleasurable than {scenario1}; Value:
Pleasure)[’support’] +

3 Valence({scenario2} is more pleasurable than {scenario1}; Value:
Pleasure)[’oppose’] +

4 Valence({scenario1} is less pleasurable than {scenario2}; Value:
Pleasure)[’oppose’]

5p_worse = Valence({scenario1} is more pleasurable than {scenario2}; Value:
Pleasure)[’oppose’] +

6 Valence({scenario2} is less pleasurable than {scenario1}; Value:
Pleasure)[’oppose’] +

7 Valence({scenario2} is more pleasurable than {scenario1}; Value:
Pleasure)[’support’] +

8 Valence({scenario1} is less pleasurable than {scenario2}; Value:
Pleasure)[’support’]

###  L.5 Commonsense

⬇

1Valence({scenario}; Duty: Duty to do the right thing according to commonsense
morality)

$P(good)=P(support),P(bad)=P(oppose)$

##  Appendix M GPT-4 Values, Rights, Duties Prompt

System Message:

⬇

1Given a situation, try to reason about its morality by breaking down relevant
values, rights, and duties. Please generate any and all potentially relevant
values that people may hold, even if they sometimes oppose eachother.

2

3Values: These are the intrinsic goods or ideals that people pursue or
cherish, such as happiness, well-being, justice, or freedom. Values are the
desirable qualities that people may seek in their lives and in the world. They
are often the guiding principles for individuals and societies, shaping goals,
motivations, and preferences.

4

5Rights: Rights are the entitlements or claims that individuals have against
others or society, which are usually based on moral or legal grounds. These
can be positive rights (e.g., the right to education, healthcare, or free
speech) or negative rights (e.g., the right to not be harmed, enslaved, or
discriminated against). Rights serve to protect the fundamental interests of
individuals and establish certain boundaries that others must respect.

6

7Duties: Duties are the moral obligations or responsibilities that individuals
owe to others or to society at large. They are categorical reasons for doing
or refraining from doing something, independent of whether we want to do or
refrain from doing that thing. Duties can be weighty reasons, not easily
overridden by competing concerns, and their violation may justify blame and
self-blame (guilt). Duties can arise from relationships, social roles, or
moral principles, and they guide our actions and decisions.

8- Imperfect Duties: Some duties are "imperfect," meaning they are owed to no
one in particular. For example, a duty to be charitable to the poor, but no
specific poor person can demand charity from someone.

9- Perfect Duties: Some duties are ‘‘perfect," meaning they are owed to
someone in particular. For example, a duty not to plaigarize someone’s work.
When someone plagiarizes, there is a particular person who has been wronged -
the person who was plaigarized.

10

11For each value, right, and duty output a 1-3 sentence explanation of how it
is related/expressed in the situation.

12

13

14For each, list whether it supports the action, opposes the action, or could
either support or oppose. Additionally, for each duty, list whether it is
perfect or imperfect (but do not include the word "perfect" or "imperfect" in
the explanation).

15

16If none are relevant, put N/A.

17

18Example output:

19Values:

20- \\{value1 OR N/A\\}: explanation1 [supports/opposes/either]

21- \\{value2\\}: explanation2 [supports/opposes/either]

22...

23

24Rights:

25- \\{right1 OR N/A\\}: explanation2 [supports/opposes/either]

26...

27

28Duties:

29- \\{duty1\\}: explanation1 [supports/opposes/either, perfect/imperfect]

30...

31

32An example if there are no relevant values, rights, or duties:

33[Values/Rights/Duties]: N/A

34"SITUATION" ->

User Message:

⬇

1Situation: SITUATION

###  M.1 Flan-T5 Data Filtering Prompts

All few-shot examples are drawn from the original user demo queries and
selected by the authors.

Because the prompts may contain offensive, NSFW, racially insensitive, or
explicit material, we have decided not to include these prompts in the paper.
They can be found online at https://github.com/tsor13/kaleido.

##  Appendix N Data Sheet

Here we include a Datasheet for Datasets (Gebru et al. 2018) to document the
dataset.

###  N.1 Motivation for Dataset Creation

#### Why was the dataset created?

ValuePrism was created 1) to understand what pluralistic human values, rights,
and duties are already present in large language models, and 2) to serve as a
resource to to support open, value pluralistic modeling (e.g., Kaleido). It
contains human-written situations about which to reason and machine-generated
candidate values, rights, duties, along with their valences and post-hoc
explanations relating them to the situations.

#### What (other) tasks could the dataset be used for?

The situations could also be used as a rich, diverse dataset of mostly
everyday situations for further decision-making work.

#### Are there obvious tasks for which it should not be used?

The dataset should only be used for research purposes, and should not be used
for real-world decision-making, advice, or commercial applications.

#### Has the dataset been used for any tasks already?

The dataset has only been used so far to train Kaleido.

#### If so, where are the results so others can compare?

Results in body of this paper.

#### Who funded the creation of the dataset?

Funding for this dataset came from the DARPA ITM program and the Allen
Institute for AI (AI2).

#### If there is an associated grant, provide the grant number.

FA8650-23-C-7316

###  N.2 Dataset Composition

#### What are the instances? Are there multiple types of instances?

Situations are plain-text English spans. Each one contains several candidate
values, rights, and duties, along with a valence relation (supports, opposes,
either) and a free-text explanation. Statistics are found in Table 6.

For seq2seq training, we take this data to make 4 subtask splits: generation
of a relevant value, right, or duty from a situation, valence of a value,
right, or duty in relation to a situation, an explanation of how a value,
right, or duty may connect to a situation, and a set of positive and negative
pairs for determining whether a value, right, or duty is relevant for a given
action. For relevance, we use “was generated” as a proxy for relevant, and
negatively sample values, rights, and duties that were generated for other
situations. Statistics can be found in 7.

#### Are relationships between instances made explicit in the data?

There are no relationships between instances beyond the fact that each
situation has several seq2seq tasks, which can be trivially reconstructed.

#### How many instances of each type are there?

Statistics in Table 7.

#### What data does each instance consist of? “Raw” data (e.g., unprocessed
text or images)? Features/attributes?

Situations are raw free-text, but the rest of the dataset is structured. All
values, rights, and duties are free-text connected to a situation, along with
a corresponding type (either “Value”, “Right”, or “Duty”); valences are
connected to a situation and specific value, right, or duty, and are of types
“Supports”, “Opposes”, or “Either” supports or opposes; relevances are
connected to a situation and specific value, right, or duty, and are of type
“Yes” or “No”; and explanations are free-text associated with a situation and
particular value, right, or duty.

#### Is there a label/target associated with instances? If the instances are
related to people, are subpopulations identified (e.g., by age, gender, etc.)
and what is their distribution?

There are labels associated with instances for valence and relevance. The
instances are not related to people.

#### Is everything included or does the data rely on external resources?
(e.g., websites, tweets, datasets) If external resources, a) are there
guarantees that they will exist, and remain constant, over time; b) is there
an official archival version. Are there licenses, fees or rights associated
with any of the data?

Everything is included and does not rely on external resources. We intend the
data for research use only under the []-Taylor license

#### Are there recommended data splits or evaluation measures? (e.g.,
training, development, testing; accuracy/AUC)

Yes, there are recommended training, validation, and testing splits. We
recommend and report accuracy for valence and relevance, and perplexity for
generation and explanation.

#### What experiments were initially run on this dataset? Have a summary of
those results and, if available, provide the link to a paper with more
information here.

T5-based models were trained on splits of this data and were tested on both
the synthetic data (Section D) and were assessed by humans (Sections 5.1 and
3). While the interested reader should defer to the paper for more results,
humans found that the distilled models matched the test output quality for
valence and explanation, surpassed the test quality for generating sets of
values, rights, and duties, and output relevances that correlated with human
judgments.

We also run two human studies on the dataset (Sections 4.1 and E).
Crowdworkers agree the data is high-quality 91% of the time, and have trouble
surfacing values, rights, or duties that are missed, providing suggestions
less than 1% of the time. Additionally, in an attempt to understand if the
dataset aligns best with any demographic groups, we recruit 613 crowdworkers
to mark personal agreement with the data, and do not find significant
takeaways for which groups are represented best in the data.

###  N.3 Data Collection Process

#### How was the data collected? (e.g., hardware apparatus/sensor, manual
human curation, software program, software interface/API; how were these
constructs/measures/methods validated?)

The situations were provided by volunteer users of the Delphi user demo, and
the candidate values, rights, duties and their corresponding relations were
generated by a large language model, GPT-4.

#### Who was involved in the data collection process? (e.g., students,
crowdworkers) How were they compensated? (e.g., how much were crowdworkers
paid?)

Data was collected and by the authors of the paper. The dataset was not
collected through crowdworkers, but through demo users and the OpenAI API.

However, to understand the dataset’s quality and representativeness, we do
carry out several human studies on subsets of the data (see Section 4.1 and
4.2). We ensured that, for all tasks, crowdworkers were paid a minimum hourly
wage of $15-25 USD.

#### Over what time-frame was the data collected? Does the collection time-
frame match the creation time-frame?

The situations were collected from 2021-2023 on the Delphi user demo, and the
values, rights, and duties were generated using the OpenAI API from May
2023-July 2023.

#### How was the data associated with each instance acquired? Was the data
directly observable (e.g., raw text, movie ratings), reported by subjects
(e.g., survey responses), or indirectly inferred/derived from other data
(e.g., part of speech tags; model-based guesses for age or language)? If the
latter two, were they validated/verified and if so how?

The data is only associated in that the situations came from the demo and the
remaining data from the OpenAI API.

#### Does the dataset contain all possible instances? Or is it, for instance,
a sample (not necessarily random) from a larger set of instances? If the
dataset is a sample, then what is the population? What was the sampling
strategy (e.g., deterministic, probabilistic with specific sampling
probabilities)? Is the sample representative of the larger set (e.g.,
geographic coverage)? If not, why not (e.g., to cover a more diverse range of
instances)? How does this affect possible uses?

We source our 31k situations about which to reason from a set of 1.3M user-
submitted situations, and curate the dataset by filtering out situations that
are not are not actions or unrelated to morality (as labeled in a few-shot
manner252525Few-shot filtering prompts are found in Appendix M.1. by Flan-T5
(Chung et al. 2022)). We also filter out any questions using keyword matching.

We note that an outsize proportion of the dataset involves toxic, NSFW, or
sexually explicit content. In the interest of having a diversity of
situations, we label for these attributes1 using Flan-T5 (Chung et al. 2022).
We take 95% of our situations determinnistically from those that have less
toxic/NSFW/explicit content, and sample the other 5% uniformly from the rest
of the data so as to include the entire spectrum of inputs. We find that this
succeeds in increasing the diversity of the dataset, as measured by unique
n-grams divided by the length of the dataset (dist-2: .23$\rightarrow$.36,
dist-3: .54$\rightarrow$.67).

#### Is there information missing from the dataset and why? (this does not
include intentionally dropped instances; it might include, e.g., redacted
text, withheld documents) Is this data missing because it was unavailable?

No, there is no known data missing from the dataset, although we do not claim
or believe that the dataset is necessarily a comprehensive set of
representative human values.

#### Are there any known errors, sources of noise, or redundancies in the
data?

No known errors, sources of noise, or redundancies, although we hope future
work will help to shed more light on weaknesses.

###  N.4 Data Preprocessing

#### What preprocessing/cleaning was done? (e.g., discretization or bucketing,
tokenization, part-of-speech tagging, SIFT feature extraction, removal of
instances, processing of missing values, etc.)

The main preprocessing was extraction of the features from raw text output
from GPT-4 to the semi-structured dataset that we have. We used regex
expressions for this extraction.

#### Was the “raw” data saved in addition to the preprocessed/cleaned data?
(e.g., to support unanticipated future uses)

Yes, the raw GPT-4 outputs were saved in addition to the cleaned data.

#### Is the preprocessing software available?

Yes, all preprocessing software will be available at
https://github.com/tsor13/kaleido.

#### Does this dataset collection/processing procedure achieve the motivation
for creating the dataset stated in the first section of this datasheet?

It achieves the goal of 1) trying to understand what pluralistic human values,
rights, and duties are currently embedded in GPT-4 (although not other LLMs).
It achieves the goal of taking a first step to modeling human values, rights,
and duties computationally, as manifested by Kaleido, but we do not claim that
it necessarily does so with accuracy and complete representativeness.

###  N.5 Dataset Distribution

#### How is the dataset distributed? (e.g., website, API, etc.; does the data
have a DOI; is it archived redundantly?)

We plan on distributing the dataset via Huggingface Datasets, but it will be
gated for individual-approval and intended for research-use only in an attempt
to prevent misuse.

#### When will the dataset be released/first distributed? (Is there a
canonical paper/reference for this dataset?)

We plan on distributng the dataset in September 2023, with this manuscript as
a reference.

#### What license (if any) is it distributed under? Are there any copyrights
on the data?

We plan on distributing ValuePrism under the ImpACT license (Allen Insitute
for AI 2023) as a “medium-risk artifact”. Users must agree to all terms and
restrictions of the license before accessing or using the dataset.

#### Are there any fees or access/export restrictions?

No, the dataset is distributed at no cost. However, we do gate access by
individual request and access is predicated on acceptance of the license.

###  N.6 Dataset Maintenance

#### Who is supporting/hosting/maintaining the dataset? How does one contact
the owner/curator/manager of the dataset (e.g. email address, or other contact
info)?

The Allen Institute for AI supports the dataset and it will be hosted on
Huggingface. Corresponding authors are Taylor Sorensen
<EMAIL_ADDRESS>and Yejin Choi (yejin@cs.washington.edu).

#### Will the dataset be updated? How often and by whom? How will
updates/revisions be documented and communicated (e.g., mailing list, GitHub)?
Is there an erratum?

We do not plan on updating the dataset.

#### If the dataset becomes obsolete how will this be communicated? Is there a
repository to link to any/all papers/systems that use this dataset?

We do not expect the dataset to become obselete as it does not depend on
external sources. Users of ValuePrism should cite this manuscript.

#### If others want to extend/augment/build on this dataset, is there a
mechanism for them to do so? If so, is there a process for tracking/assessing
the quality of those contributions. What is the process for
communicating/distributing these contributions to users?

As of now, there is no formal mechanism to extend/augment/build on this
dataset, but anyone interested should reach out to the authors.

###  N.7 Legal & Ethical Considerations

#### If the dataset relates to people (e.g., their attributes) or was
generated by people, were they informed about the data collection? (e.g.,
datasets that collect writing, photos, interactions, transactions, etc.)

Users of the Delphi user demo explicitly agreed that their queries could be
recorded and used for research purposes, and the rest of the data was machine-
generated.

#### If it relates to other ethically protected subjects, have appropriate
obligations been met? (e.g., medical data might include information collected
from animals)

It does not relate to other ethically protected subjects.

#### If it relates to people, were there any ethical review
applications/reviews/approvals? (e.g. Institutional Review Board applications)
If it relates to people, were they told what the dataset would be used for and
did they consent? What community norms exist for data collected from human
communications? If consent was obtained, how? Were the people provided with
any mechanism to revoke their consent in the future or for certain uses?

Data does not relate directly to people.

#### If it relates to people, could this dataset expose people to harm or
legal action? (e.g., financial social or otherwise) What was done to mitigate
or reduce the potential for harm?

Data does not relate directly to people.

#### If it relates to people, does it unfairly advantage or disadvantage a
particular social group? In what ways? How was this mitigated?

Data does not relate directly to people.

#### If it relates to people, were they provided with privacy guarantees? If
so, what guarantees and how are these ensured?

Data does not relate directly to people.

#### Does the dataset comply with the EU General Data Protection Regulation
(GDPR)? Does it comply with any other standards, such as the US Equal
Employment Opportunity Act?

Especially because the data does not relate to people or have personally
identifiable information, it does comply with these laws.

#### Does the dataset contain information that might be considered sensitive
or confidential? (e.g., personally identifying information) Does the dataset
contain information that might be considered inappropriate or offensive?

No, the dataset does not contain sensitive or confidential information (like
personally identifiable information). The dataset does potentially contain
inappropriate or offensive text, especially in the demo-sourced situations,
and we advise that the dataset is not for all eyes before providing access.
While we did not want to completely remove inappropriate or offensive
situations so that the model could perform well in surfacing relevant values,
rights, and duties in these cases, we did attempt to ensure that the generated
data does not include inappropriate or offensive content via manual inspection
and toxicity filters.


# A Survey on the Applications of Frontier AI, Foundation Models, and Large
Language Models to Intelligent Transportation Systems

Mohamed R. Shoaib1, Heba M. Emara2, Jun Zhao1 1School of Computer Science and
Engineering, Nanyang Technological University, Singapore
2Pyramids Higher Institute for Engineering and Technology, Giza Governorate,
Egypt
Emails<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>

###### Abstract

This survey paper explores the transformative influence of frontier AI,
foundation models, and Large Language Models (LLMs) in the realm of
Intelligent Transportation Systems (ITS), emphasizing their integral role in
advancing transportation intelligence, optimizing traffic management, and
contributing to the realization of smart cities. Frontier AI refers to the
forefront of AI technology, encompassing the latest advancements, innovations,
and experimental techniques in the field, especially AI foundation models and
LLMs. Foundation models, like GPT-4, are large, general-purpose AI models that
provide a base for a wide range of applications. They are characterized by
their versatility and scalability. LLMs are obtained from fine-tuning
foundation models with a specific focus on processing and generating natural
language. They excel in tasks like language understanding, text generation,
translation, and summarization. By leveraging vast textual data, including
traffic reports and social media interactions, LLMs extract critical insights,
fostering the evolution of ITS. The survey navigates the dynamic synergy
between LLMs and ITS, delving into applications in traffic management,
integration into autonomous vehicles, and their role in shaping smart cities.
It provides insights into ongoing research, innovations, and emerging trends,
aiming to inspire collaboration at the intersection of language, intelligence,
and mobility for safer, more efficient, and sustainable transportation
systems. The paper further surveys interactions between LLMs and various
aspects of ITS, exploring roles in traffic management, facilitating autonomous
vehicles, and contributing to smart city development, while addressing
challenges brought by frontier AI and foundation models. This paper offers
valuable inspiration for future research and innovation in the transformative
domain of intelligent transportation.

###### Index Terms:

Intelligent Transportation Systems (ITS), Large Language Models (LLMs),
Frontier AI, Foundation Models, 6G Wireless Communication, Internet of
Vehicles (IoVs), Vehicular Technology, Smart Cities.

##  I Introduction

In an era defined by rapid technological advancements, the marriage of
Artificial Intelligence (AI) and transportation systems has ushered in a new
age of mobility—intelligent, efficient, and responsive. ITSs have evolved from
conventional traffic management approaches to complex and data-driven systems
that promise to reshape the way we navigate our cities and roads. At the heart
of this transformation lies the remarkable power of Large Language Models
(LLMs) [63], a class of AI algorithms that have, in recent years, become
central actors in natural language processing and understanding. This survey
paper explores the dynamic and multifaceted relationship between LLMs and ITS,
illuminating the profound impact these language-driven models are making in
enhancing the intelligence of transportation systems, optimizing traffic
management, and propelling us towards the realization of smart cities [64,
65].

The foundation of LLMs is rooted in their ability to understand and generate
human-like text by modeling language patterns and semantics [63]. Their
utilization has transcended the boundaries of traditional language tasks,
permeating diverse domains, including healthcare, finance, customer service,
and, significantly, transportation. LLMs are not mere bystanders; they have
emerged as intelligent co-pilots, enhancing the agility and adaptability of
transportation systems [66]. This intersection of LLMs and ITS holds
tremendous promise for addressing a host of challenges that have long plagued
urban and interurban mobility. Congestion, accidents, and environmental
concerns have weighed heavily on the transportation landscape [67]. ITS, which
encompasses a suite of advanced technologies and data-driven solutions, offers
a path towards alleviating these challenges. However, achieving the full
potential of ITS requires the comprehension and effective utilization of vast
amounts of textual data, from traffic reports and accident data to social
media chatter and user feedback. Here, LLMs come to the forefront, providing
the key to unlock the insights hidden within this textual goldmine.

Foundation models [69] are a broader category of AI models designed to provide
a general, adaptable base for various applications. They can encompass not
only language tasks but also other modalities like images or even multimodal
tasks. LLMs can be understood as a subset of fine-tuned foundation models with
a specific focus on text-based tasks. We can further have large vision models
(LVMs), large audio models (LAMs), and large multimodal models (LMMs).
Foundation models [70] and frontier AI [71] are terms that refer to advanced
developments in the field of AI, but they emphasize different aspects of these
developments. The word “frontier” highlights a collection of advanced and
potent foundation models at the forefront of technology. We can even propose
the terminology “foundation AI” in replacement of “frontier AI” to highlight
the foundation nature. Nevertheless, both “foundation AI” and “frontier AI”
can be abbreviated as “FAI”. Regardless of which name is used, the eventual
goal in AI research and development is to realize Artificial General
Intelligence (AGI).

The contributions of this paper can be stated as:

  * •

Survey: The paper offers an examination of the use of frontier AI, foundation
models, and LLMs in ITS, showcasing real-world applications in traffic
management, autonomous vehicles, and smart cities.

  * •

Trend Identification: It identifies emerging trends and challenges in the
integration of LLMs with transportation systems, shedding light on
limitations.

  * •

Inspiration for Future: By providing a clear landscape of the field and
potential directions for further research and innovation, the paper serves as
an inspiring resource for researchers, policymakers, and industry
professionals, driving progress in the domain of intelligent transportation.

##  II Background on LLMs and ITS

###  II-A Language Models

Language Models (LMs) are a class of Artificial Intelligence (AI) algorithms
designed to understand and generate human language. They are central to
natural language processing and understanding. LMs have evolved significantly
over the years, with their history spanning from rule-based systems to today’s
deep learning-based models. Here’s an overview [68]:

  * •

Early Language Models: Early language models were rule-based and relied on
predefined grammatical and linguistic rules. They were limited in their
ability to understand context and lacked the capacity for learning from data.

  * •

Statistical Language Models: With the advent of statistical methods, language
models began using probabilistic approaches to predict the likelihood of a
word or phrase following a given context. N-grams and Hidden Markov Models
were common in this phase.

  * •

Deep Learning-Based Large Language Models (LLMs): The modern era of language
modeling is marked by the rise of deep learning techniques. In particular,
Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and
Transformer-based models like BERT, GPT-2, and GPT-3 have significantly
advanced the field. These models can capture context, semantics, and syntactic
structures, making them highly effective in natural language understanding and
generation.

###  II-B Intelligent Transportation Systems

ITS represents a multidisciplinary field that leverages technology to enhance
the efficiency, safety, and sustainability of transportation systems. The
goals of ITS are to improve mobility, reduce congestion, minimize accidents,
and optimize transportation-related operations [66, 67]. Here are the key
components and goals of ITS:

  * •

Data Collection and Sensing: ITS relies on a network of sensors and data
collection devices, such as cameras, GPS systems, and traffic flow sensors, to
gather real-time information about traffic conditions, weather, and road
infrastructure.

  * •

Information Processing: Collected data is processed to generate insights and
support decision-making. This includes traffic management systems that analyze
traffic flow and congestion, weather prediction models, and incident detection
algorithms.

  * •

Communication Systems: ITS often incorporates advanced communication
technologies, such as Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle
(V2V) communication. These systems enable vehicles to share data with each
other and with infrastructure, improving safety and traffic management.

  * •

Traffic Management: One of the primary goals of ITS is to optimize traffic
flow and reduce congestion. This includes adaptive traffic signal control,
dynamic route guidance, and congestion pricing mechanisms.

  * •

Safety and Security: ITS is dedicated to enhancing transportation safety by
utilizing technologies like collision avoidance systems and vehicle-to-
pedestrian communication. Security measures are also important to protect
against cyber threats.

  * •

Environmental Sustainability: By optimizing traffic flow and reducing
congestion, ITS aims to minimize fuel consumption and greenhouse gas
emissions, contributing to environmental sustainability.

  * •

User Information and Feedback: ITS provides users with real-time information
about traffic conditions, public transportation schedules, and alternative
routes. It also collects user feedback through apps and websites, which can be
valuable for service improvement.

Integrating Language Models into ITS enhances the system’s ability to
understand and process textual data, making it more responsive and adaptable
to dynamic transportation conditions. This synergy enables better decision-
making, improved traffic management, and a more user-friendly experience for
travelers.

##  III Smart Transportation

In the domain of transportation, the integration of Artificial Intelligence
(AI) and Internet of Things (IoT) technologies aims to facilitate real-time
traffic management through the collection of data pertaining to vehicles,
drivers, and road conditions. This encompasses tasks like observing traffic
and road situations, identifying events in real-time to maintain traffic
safety, and proactively managing disturbances that might impact the flow of
traffic and the availability of parking spaces. This review section
categorizes relevant studies into smart parking management (SPM), traffic
monitoring/prediction (TMP), and intelligent transportation management (ITM)
[66, 67].

###  III-A Smart Parking Management

The authors in [52] proposed a shared bicycle system with a hybrid machine
learning model, specifically the SOM-RT model, in conjunction with a self-
organizing mapping network. This combination of techniques is employed to
organize the original data samples into clusters. Each cluster functions as a
regression tree (RT) for forecasting the required number of bicycles at
individual stations. Experimental results demonstrated superior prediction
accuracy and generalization compared to alternative methods. In a separate
investigation detailed in [53], researchers developed a camera-based system
for tracking parking space occupancy. They employed a Single Shot Multibox
Detector (SSD) along with an edge detection approach, which minimizes data
transfer and allows for rapid updates. The system successfully aggregated
detection outcomes on a central server, facilitating accurate parking
occupancy evaluation, even in demanding scenarios characterized by fluctuating
lighting conditions and obstructions. Furthermore, the study implemented a
tracking algorithm to monitor vehicles within parking structures.

Furthermore, researchers in [54] introduced the Fedparking federated learning
framework, specifically tailored for the management of parked vehicles within
an edge computing environment (PVEC). Fedparking incorporates federated
learning techniques and LSTM to estimate parking space availability. This
framework facilitates collaborative model development among multiple parking
lot operators, enabling real-time forecasts of parking spot availability for
efficient traffic management. To fulfill PVEC needs, an incentive system was
instituted, utilizing deep reinforcement learning to achieve the Stackelberg
equilibrium in a decentralized and privacy-protected fashion, accommodating
changing vehicle arrivals and diverse parking capacity limitations, leading to
precise and efficient convergence.

###  III-B Traffic Monitoring/Prediction

To manage the fluctuating traffic patterns in intelligent transportation
systems and facilitate accurate traffic prediction in real time, several
innovative models have been proposed. For instance, the collaborative
optimization model suggested in [55] involves the installation of monitoring
sites at different traffic crossings, enabling data collection and utilization
of the DBN-SVR approach for traffic flow anticipation. Another framework,
AAtt-DHSTNet, introduced in [56], integrates fog computing and an attention-
based aggregation method to effectively manage spatial and temporal
correlations in the traffic data. In [57], a proposal was made for a short-
term traffic flow forecasting model that uses edge computing, optimized by a
chaotic particle swarm optimization algorithm. Furthermore, [58] examined the
use of federated learning in traffic prediction to anticipate vehicle numbers,
employing clustering and a joint-announcement protocol for efficient model
aggregation. Additionally, [59] integrated federated learning and additive
homomorphic encryption for secure model sharing, incorporating recurrent,
attentive, and semantic capture network modules for spatiotemporal information
analysis.

###  III-C Intelligent Transportation Management

In [51], the researchers introduced a system for real-time detection of driver
distraction using edge and cloud computing technologies. This was achieved
through the development of a customized DCNN model and the integration of a
VGG16-based model (Visual Geometry Group-16). In a separate work by Xu et al.
[60], a method for evaluating driving behavior within a vehicle’s edge-cloud
architecture was established. As a vehicle operates, its telematics box
transmits data regarding the driver’s actions or autopilot utilization to edge
networks. These edge networks make use of a driving behavior evaluation model
hosted on a cloud server. The model is continuously refined through the use of
vehicle data on the cloud server and undergoes periodic updates on the edge
networks. Experimental results confirm the reliability and practicality of
this approach. Furthermore, in an additional investigation [61], a methodology
for diagnosing railway faults was proposed, capitalizing on edge and cloud
collaboration. The model initially employs SAES-DNN for fault recognition in
the cloud and subsequently applies a transfer learning strategy to facilitate
real-time fault diagnosis at the edge.

##  IV Machine Learning Security Solutions for Vehicular Networks

In this section, we provide an overview of various ML security solutions
proposed for vehicular networks, highlighting their utilization for
safeguarding vehicular nodes and related data. The application and explanation
of ML techniques in the security domain of vehicular networks are concisely
elucidated.

###  IV-A Driver Identification/Fingerprinting

In vehicular systems, protecting the privacy of driver data and preventing
vehicle tracking by adversaries is essential. Various techniques, such as
pseudonymity, have been explored in the literature to hide the true identity
of drivers [1], [47]. ML techniques are employed for driver authentication
without concealing their true identity, leveraging data from various sources
[48, 49, 50]. Researchers have proposed numerous ML algorithms including HMM,
SVM, k-means clustering, and ELM, achieving accuracy rates ranging from 65% to
99% in driver identification [32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
44]. Recent studies have focused on automatic feature extraction using DL to
improve identification accuracy with a limited feature set [37], [41]. An
analytical study demonstrates the use of minimal features with an accuracy of
93% [46]. A CNN model using sensor data achieves 90% accuracy for 4 drivers,
while a combined smartphone sensor and vehicle electronic control unit data
framework using CNN and RNN achieves 95% accuracy for 10 drivers [43], [44].
Table I provides a condensed overview of the comparative solutions employing
machine learning for driver fingerprinting.

TABLE I: Provides a condensed overview of the comparative solutions employing
machine learning for driver fingerprinting.

Paper | Algorithm | Feature Data | Accuracy
---|---|---|---
2013 [33] | SVM and K-mean clustering | Acceleration, braking and turning events | 65%
2016 [35] | Random Forest, Naïve Bayes, KNN and SVM | 16 features from OBD-II | 87% - 99%
2017 [38] | SVM, Random Forest and Naïve Bayes | Trip-based data | 88%
2017 [40] | NN | Acceleration | 88%
2018 [42] | J48, Random forest and REPtree | 51 features from OBD-II | 99%
2019 [44] | CNN and RNN | Smart phone sensor and OBD-II protocol data | 95% - 57%

###  IV-B Attack Detection

Vehicular networks face diverse attack threats, necessitating advanced
solutions. While conventional hard-coded algorithms address deterministic
attacks, deep learning models enable self-learning, enhancing security in V2X
communications, especially future ones based on 6G wireless technology [45].
ML and its sub-architectures have gained traction for attack detection in V2X.
This section explores attack types and ML-based countermeasures, with a
comparative summary in Table II.

  * •

Platoon Attack: Platoon formation optimizes traffic management but is
susceptible to attacks, particularly speed deviations. A self-learning deep
architecture, combining FCDNN and CNN, detects these attacks, leveraging
sensor data and achieving 97% accuracy [2].

  * •

DDoS: SDN in vehicular networks raises security concerns. An ML-based solution
detects DDoS attacks, with gradient boost classifier outperforming other
algorithms. SVM and other techniques show promise [3]. Data-driven approaches
offer effective DoS detection [4].

  * •

Black-Hole and Grey-Hole: These routing attacks disrupt message forwarding in
vehicular networks. NN and SVM algorithms detect these attacks effectively,
offering high accuracy [5].

  * •

Sybil Attack: Detecting sybil attacks in vehicular networks is challenging due
to pseudonym use for privacy. ML techniques analyze driving patterns to
identify malicious nodes, achieving accuracy ranging from 92% to 100% [7].

  * •

Jamming Attack: Attackers interfere with data transmission, necessitating
protection against jamming. RL-based mechanisms using Q-learning and DQN, as
well as dynamic power control, detect and mitigate jamming effectively [8].
Anti-jamming protocols enhance network reliability [9].

  * •

Spoofing Attack: ML models address location spoofing with approaches using
multi-layer NNs [17], Q-learning [19], and RL-based authentication mechanisms,
achieving high detection accuracy [18].

TABLE II: Overview of Research on Attack Detection through Machine Learning.

Paper | Algorithm | Feature Data | Task | Type of Attack | Accuracy
---|---|---|---|---|---
2015 [6] | ANN | Auditable data from basic, IP and AODV trace files | Detection | Black hole | 99%
2016 [5] | NN and SVM | Rx packets, PDR, dropped packets and delay | Detection | Grey hole and rushing attack | 99%
2017 [7] | SVM | Driving pattern | Detection | Sybil attack | 92% - 98%
2018 [19] | RL (Q-Learning) | Physical layer information | Detection | Spoofing attack | NA
2019 [22] | ANN, DL and LSTM | SDN traffic flows | Detection | Crossfire attack | 80% - 87%
2019 [17] | Multi-layer NN | RSS, RSU location and spoofed location | Detection | Spoofing attack | NA
2020 [18] | RL (Q-Learning) | Spatial decorrelation features | Prevention | Spoofing attack | NA
2020 [24] | K-means | Car-hacking dataset | Detection | DoS, Fuzzy, RPM and gear attack | 99%
2020 [29] | DL and RL | Sensor reading and beaconing | Prevention | Data manipulation attack | NA

###  IV-C Misbehavior or Intrusion Detection

Misbehavior Detection Systems (MDS), also known as Intrusion Detection Systems
(IDS), play a critical role in identifying and addressing various forms of
attacks, with different studies in the literature addressing intrusion or
misbehavior detection. In this section, we discuss ML-based solutions for
handling such issues, exemplified by various works, including Grover et al.
[16] focusing on misbehaved vehicle scenarios and Maglaras [12] presenting a
distributed IDS for vehicular networks. Other approaches, like the one
proposed in [20], utilize a clustering scheme in VANETs without any special
agent, showcasing a lightweight framework for detection at multiple levels.
Additionally, Li et al. [13] utilize contextual information along with SVM to
detect anomalous vehicles, while Sargolzaei et al. [11] emphasize fault
detection using an NN-based design. Kim et al. [14] propose a cloud-based
software-defined networking (SDN) design for vehicular applications to enable
intrusion detection. Similarly, Loukas et al. [31] introduce a lightweight
deep learning (DL)-based intrusion detection system (IDS) model for vehicular
applications, allowing offloading to other network devices or vehicles for
heightened attack detection accuracy. Gyawali et al. [10] present a machine
learning (ML) framework for identifying vehicle misbehavior through the
analysis of false alert messages and position falsification, while Tariq et
al. [30] and Gyawali et al. [28] introduce transfer learning (TL)-based
intrusion detection schemes in their individual studies. Finally, a recent
study [26] focuses on a data-centric misbehavior detection system for Internet
of Vehicles (IoVs), integrating plausibility checks and supervised ML
algorithms to enhance detection accuracy.

###  IV-D Privacy Protection

Privacy mechanisms in vehicular networks aim to safeguard sensitive
information, often categorized as location or user privacy, with techniques
like mix-zones, obfuscation, silent-periods, k-anonymity, and dummy-based
approaches. Recent research increasingly explores ML for enhanced privacy,
such as Wang et al.’s RL-based obfuscation model [21], FL-based privacy-
preserving data collection [23], collaborative IDS by Zhang and Zhu [15],
blockchain-integrated FL by Lu et al. [44], and a two-stage protection scheme
based on federated learning for VCPS [27]. Another study [25] explores
federated learning for misbehavior detection while maintaining data privacy
during BSM exchange.

##  V Applications of Language Models in ITS

LLMs have emerged as pivotal tools for enhancing the intelligence and
efficiency of ITS. Their versatility, coupled with their ability to understand
and generate human-like text, has led to a multitude of applications in this
domain. In the following subsections, we delve into specific use cases,
research findings, and how LLMs are employed to solve problems in ITS.

###  V-A Traffic Prediction and Management

Traffic prediction is a fundamental component of ITS. LLMs have proven to be
invaluable in this regard. They can process large volumes of historical
traffic data, weather conditions, and special events, allowing for the
development of accurate traffic prediction models. These models enable real-
time traffic management and congestion mitigation. Research findings indicate
that LLMs can significantly improve traffic forecasting accuracy, leading to
more efficient routing and reduced travel times. In addition, LLMs are used to
generate text-based traffic reports, disseminating crucial information to
drivers and transportation authorities.

###  V-B Sentiment Analysis of Social Media Data for Traffic Insights

In the age of social media, public sentiment can have a profound impact on
transportation systems. Social media platforms are teeming with information
related to traffic conditions, road closures, and public opinions. LLMs
equipped with sentiment analysis capabilities can process and analyze this
unstructured data. By examining the sentiment expressed in tweets, posts, and
comments, transportation authorities can gain valuable insights into public
perception of traffic events and road conditions. This information aids in
proactive traffic management and crisis response. Research findings underscore
the importance of sentiment analysis in monitoring and addressing public
sentiment regarding transportation issues.

###  V-C Emergency Response and Disaster Management

During emergency situations such as accidents, natural disasters, or road
closures, quick and effective communication is critical. LLMs are used to
generate automated emergency alerts and notifications, disseminating vital
information to the public. Additionally, they can process incoming data from
various sources, including emergency calls, news reports, and social media, to
provide real-time updates to transportation authorities and first responders.
Research has shown that LLMs improve the speed and accuracy of information
dissemination during emergencies, enhancing overall safety and response
coordination.

###  V-D Multimodal Transportation Planning

The modern transportation landscape includes various modes of travel, from
buses and trains to rideshares and bicycles. LLMs can assist in multimodal
transportation planning by integrating data from different sources and
generating comprehensive travel recommendations. This aids in seamless and
efficient transit between various transportation modes, ultimately reducing
congestion and promoting sustainable mobility options. Research has emphasized
the potential of LLMs in optimizing multimodal transportation planning and
promoting environmentally friendly modes of travel.

##  VI Frontier AI and Foundation Models in Intelligent Transportation

Advanced AI models, particularly those classified as “frontier AI,” hold
immense promise for revolutionizing ITS [71]. These models, often highly
capable foundation models [69], bring with them unprecedented capabilities
that, if misused, could pose severe risks to public safety. Foundation models
are a type of AI models that provide a broad, versatile base upon which a
variety of more specific tasks can be performed. These models, like GPT-4, are
trained on vast amounts of data, enabling them to generate, interpret, and
predict text with remarkable accuracy. They are called “foundation” models
because they serve as a foundational layer in AI, capable of being fine-tuned
or adapted for numerous applications across different domains. The integration
of frontier AI and foundation models in ITS introduces a unique set of
challenges that demand proactive regulatory frameworks to mitigate potential
harms.

###  VI-A Regulatory Challenges

Regulating frontier AI in the context of intelligent transportation systems
presents distinct challenges, primarily revolving around the unexpected
capabilities, deployment safety, and proliferation of these models [71].

###  VI-B Proposed Safety Standards

To address the regulatory challenges, an initial set of safety standards is
proposed [71]. These standards include:

  * •

Conducting pre-deployment risk assessments.

  * •

External scrutiny of model behavior.

  * •

Using risk assessments to inform deployment decisions.

  * •

Monitoring and responding to new information about model capabilities and uses
post-deployment.

In conclusion, the integration of frontier AI in intelligent transportation
heralds a new era of capabilities and challenges. Proactive and comprehensive
regulatory frameworks are essential to harness the benefits of these advanced
AI models while safeguarding public safety and ensuring ethical deployment in
the evolving landscape of intelligent transportation systems.

##  VII Conclusions and Future Work

In summary, the integration of frontier AI, foundation models, and LLMs into
ITS signifies a transformative shift toward intelligent mobility and data-
driven transportation solutions. This survey highlights the crucial role of
LLMs in improving transportation intelligence, optimizing traffic management,
and advancing smart city initiatives by harnessing their language
understanding and data analysis capabilities. LLMs address challenges in urban
and interurban mobility, from congestion to accidents and environmental
issues. Their applications span real-time traffic prediction, enhancing
autonomous vehicles, and supporting urban planning, showcasing their
transformative potential in shaping the future of transportation. The use of
frontier AI and foundation models in ITS also brings challenges that require
proactive regulatory frameworks to mitigate potential harms. Looking ahead,
further research should delve into these AI technologies’ capabilities in
addressing emerging challenges, refine their real-time decision-making in
traffic management, tailor them for smart cities’ specific needs, and
encourage interdisciplinary collaborations to fully unlock their potential in
creating sustainable, intelligent, and people-centric transportation
ecosystems. Finally, discussing the synergy between AI technologies and the
emerging 6G wireless communications to build better ITS will be of great
interest.

## Acknowledgement

This research is partly supported by the Singapore Ministry of Education
Academic Research Fund under Grant Tier 1 RG90/22, Grant Tier 1 RG97/20, Grant
Tier 1 RG24/20 and Grant Tier 2 MOE2019-T2-1-176; and partly by the Nanyang
Technological University (NTU)-Wallenberg AI, Autonomous Systems and Software
Program (WASP) Joint Project.

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where the triangles with a $\circlearrowleft$ are commutative, and the others
are distinguished.

If $L\in\operatorname{Pic\,}(\mathcal{Y}^{\prime})$ is an ample line bundle,
we can consider the (possibly infinite) resolution of $\mathcal{E}^{\prime}$:

(15) $...\to\mathcal{O}_{Y^{\prime}}(-N_{i})^{\oplus
n_{i}}\boxtimes(L^{\otimes-r_{i}})^{\oplus
s_{i}}\to...\to\mathcal{O}_{Y^{\prime}}(-N_{0})^{\oplus
n_{0}}\boxtimes(L^{\otimes-r_{0}})^{\oplus s_{0}}\to\mathcal{E}^{\prime},$

with $r_{i}\geq 0$, and $N_{i}\gg 0$ so that

$\operatorname{Hom}(\mathcal{O}_{Y^{\prime}},\mathcal{O}_{Y^{\prime}}(-N_{i})[p])=\operatorname{Hom}(\mathcal{O}_{Y^{\prime}}(1),\mathcal{O}_{Y^{\prime}}(-N_{i})[p])=0$

for all $p\neq 3$. Then, choose $m$ sufficiently large and consider the
(naive) truncation

(16) $O_{m}^{\bullet}\coloneqq\\{\mathcal{O}_{Y^{\prime}}(-N_{m})^{\oplus
n_{m}}\boxtimes(L^{\otimes-r_{m}})^{\oplus
s_{m}}\to...\to\mathcal{O}_{Y^{\prime}}(-N_{0})^{\oplus
n_{0}}\boxtimes(L^{\otimes-r_{0}})^{\oplus s_{0}}\\}.$

Denote by $K_{m}\in\operatorname{Coh}(Y^{\prime}\times\mathcal{Y}^{\prime})$
the kernel of $O_{m}^{\bullet}\to O_{m-1}^{\bullet}$. Then the triangle

$K_{m}[m]\to O_{m}^{\bullet}\to\mathcal{E}^{\prime}$

splits (if $m+1>\dim Y^{\prime}+\dim\mathcal{Y}^{\prime}$, for example). Then,
the object $K_{m}[m]\oplus\mathcal{E}^{\prime}$ is a right convolution for the
complex $O_{m}^{\bullet}$. This convolution is unique, because the terms of
$O_{m}^{\bullet}$ are coherent sheaves (and not arbitrary objects of
$D^{b}(Y^{\prime}\times\mathcal{Y}^{\prime})$, see [Orl97, Lemma 1.5]).

Next, one considers the complex

(17) $F_{m}^{\bullet}\coloneqq\\{F(\mathcal{O}_{Y^{\prime}}(-N_{m}))^{\oplus
n_{m}}\boxtimes(L^{\otimes-r_{m}})^{\oplus s_{m}}\to...\to
F(\mathcal{O}_{Y^{\prime}}(-N_{0}))^{\oplus n_{0}}\boxtimes(L^{\otimes-
r_{0}})^{\oplus s_{0}}\\}$

whose terms are objects of $D^{b}(Y\times\mathcal{Y}^{\prime})$.

The following lemma follows from the same argument given in [BMMS12]:

######  Lemma 5.8.

The complex $F_{m}^{\bullet}$ admits a unique (up to isomorphism) split right
convolution $G_{m}\simeq H_{m}\oplus E_{m}$ such that for some $M<m$ one has
$H^{i}(E_{m})=0$ unless $i\in[-M,0]$, and $H^{i}(H_{m})=0$ unless
$i\in[-m-M,-m]$.

###### Proof.

See [BMMS12, Lemma 5.2]. ∎

We denote $\tilde{\mathcal{E}}\coloneqq E_{m}\in
D^{b}(Y\times\mathcal{Y}^{\prime})$.

###  5.3. Conclusion of the proof

The last step of the proof is to show that the map defined in Prop. 5.7 is a
morphism which restricts to an isomorphism
$Y^{\prime}=\mathcal{Y}^{\prime}\simeq\mathcal{Y}=Y$. For a closed point
$s\in\mathcal{Y}^{\prime}$, denote by $i_{s}$ (resp. $i^{\prime}_{s}$) the
inclusion $Y\times\\{s\\}\to Y\times\mathcal{Y}^{\prime}$ (resp.
$Y^{\prime}\times\\{s\\}\to Y^{\prime}\times\mathcal{Y}^{\prime}$).

######  Lemma 5.9.

For any closed point $s\in\mathcal{Y}^{\prime}$ we have
$i_{s}^{*}(\tilde{\mathcal{E}})\simeq
F((i^{\prime}_{s})^{*}\mathcal{E}^{\prime})$.

###### Proof.

Apply the functor $i_{s}^{*}$ to the complex $F_{m}^{\bullet}$ to get the
complex

(18)
$i_{s}^{*}(F_{m})^{\bullet}\coloneqq\\{F(\mathcal{O}_{Y^{\prime}}(-N_{m}))^{\oplus
n_{m}}\otimes\mathbb{C}^{\oplus s_{m}}\to...\to
F(\mathcal{O}_{Y^{\prime}}(-N_{0}))^{\oplus n_{0}}\otimes\mathbb{C}^{\oplus
s_{0}}\\}$

of objects of $D^{b}(Y)$. One sees that the objects
$i_{s}^{*}\tilde{\mathcal{E}}\oplus i_{s}^{*}H_{m}$ and
$F((i^{\prime}_{s})^{*}\mathcal{E}^{\prime})\oplus
F((i^{\prime}_{s})^{*}K_{m}[m])$ are both right convolutions of
$i_{s}^{*}(F_{m})^{\bullet}$. However, the same argument as in Lemma 5.8 shows
that $i_{s}^{*}(F_{m})^{\bullet}$ has a unique convolution up to isomorphism,
and the choice $m\gg 0$ implies $i_{s}^{*}(\tilde{\mathcal{E}})\simeq
F((i^{\prime}_{s})^{*}\mathcal{E}^{\prime})$ for any choice of
$s\in\mathcal{Y}^{\prime}$ a closed point. ∎

###### Proof of Theorem 5.1.

Whether $\tilde{\mathcal{E}}$ is constructed with a Fourier–Mukai functor or
by means of convolutions, one sees that $i_{s}^{*}(\tilde{\mathcal{E}})\simeq
F((i^{\prime}_{s})^{*}\mathcal{E}^{\prime})=u(E^{\prime}_{s})$ for all
$s\in\mathcal{Y}^{\prime}$. In other words, $\tilde{\mathcal{E}}$ is a family
of objects of $\mathcal{M}$ parametrized by $\mathcal{Y}^{\prime}$, and it
yields a proper morphism $\alpha\colon\mathcal{Y}^{\prime}\to\mathcal{M}$ with
the property that $\alpha(s)\in\mathcal{M}$ corresponds to the object
$u(E^{\prime}_{s})$ for all $s\in\mathcal{Y}^{\prime}$. Since
$\mathcal{Y}^{\prime}$ is irreducible, $\alpha$ factors through one of the
components of $\mathcal{M}$. We claim that $\alpha$ must factor through
$\mathcal{Y}$. Granting the claim for a moment, smoothness of $\mathcal{Y}$
implies that $\alpha$ is the desired isomorphism
$\mathcal{Y}^{\prime}\xrightarrow{\sim}\mathcal{Y}$.

To establish the claim, observe that $\alpha$ is, in particular, a morphism
dominating one of the components of $M$ and $\alpha$ is birational onto its
image. Since $\mathcal{Y}^{\prime}$ is rationally connected and $\alpha$
preserves this property (see for example [Kol96]), its image cannot lie in
$\mathcal{C}$, which is not rationally connected as a consequence of Corollary
4.8. ∎

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