AccidentGPT: Large Multi-Modal Foundation Model for Trafﬁc

Accident Analysis

Kebin Wu

, Wenbin Li

and Xiaofei Xiao

Technology Innovation Institute, Abu Dhabi, U.A.E.

Keywords:

Trafﬁc Accident Analysis, Multi-Modal Model, Video Reconstruction, Vehicle Dynamics, Multi-Task,

Multi-Modality.

Abstract:

Trafﬁc accident analysis is pivotal for enhancing public safety and developing road regulations. Traditional

approaches, although widely used, are often constrained by manual analysis processes, subjective decisions,

uni-modal outputs, as well as privacy issues related to sensitive data. This paper introduces the idea of Ac-

cidentGPT, a foundation model of trafﬁc accident analysis, which incorporates multi-modal input data to

automatically reconstruct the accident process video with dynamics details, and furthermore provide multi-

task analysis with multi-modal outputs. The design of the AccidentGPT is empowered with a multi-modality

prompt with feedback for task-oriented adaptability, a hybrid training schema to leverage labelled and unla-

belled data, and a edge-cloud split conﬁguration for data privacy. To fully realize the functionalities of this

model, we proposes several research opportunities. This paper serves as the stepping stone to ﬁll the gaps in

traditional approaches of trafﬁc accident analysis and attract the research community’s attention for automatic,

objective, and privacy-preserving trafﬁc accident analysis.

1 INTRODUCTION

The rapid and accurate trafﬁc accident analysis is crit-

ical in enhancing public safety and shaping effective

road regulations. The tasks of the trafﬁc accident

analysis, varying from accident process reconstruc-

tion, responsibility attribution to trafﬁc management

and emergency response, are multifaceted and com-

plex. Conventional approaches (Mohammed et al.,

2019), relying on eyewitness testimonies, ofﬁcial po-

lice documentation, and footage from surveillance

cameras (if any), have been the core of the accident

analysis for decades. However, these approaches are

constrained by intensive manual labor nature, suscep-

tibility to subjective biases, restricted uni-modal out-

puts, and the privacy concerns emerging from the han-

dling of sensitive data (Al-ani et al., 2023).

The advent of machine learning techniques have

begun to boost the ﬁeld of the trafﬁc accident analy-

sis, presenting enhanced precision and insights. Mod-

els are built by learning vast datasets including video

footage, sensor data, and textual reports to achieve

speciﬁc tasks such as accident detection (Ali et al.,

https://orcid.org/0000-0003-4492-4152

https://orcid.org/0000-0002-7836-0052

2021), accident prediction (Chand et al., 2021), cause

identiﬁcation (Najaﬁ Moghaddam Gilani et al., 2021).

Focusing on the accident process reconstruction, from

numerical modelling to software simulations of the

collisions (Duma et al., 2022) are applied to deter-

mine sliced elements (e.g., pre-collision speed, trav-

eled distance, trajectory) during the accident process.

Nevertheless, these works are often uni-modal pro-

viding useful but fragmented information, while lack-

ing the capacity to integrate and interpret diverse data

sources cohesively to reconstruct all details (e.g., pro-

cess video, vehicles’ dynamics) of the accident and

automate the post-accident management such as in-

jury assessment, emergency response, report gener-

ation, and insurance claim. Furthermore, these traf-

ﬁc applications have been limited in their adaptabil-

ity, often requiring extensive customization for each

speciﬁc use case.

As a step further, the recent emergence of the large

language models (LLMs) such as LLaMa2 (Touvron

et al., 2023) and large multi-modal models (LMMs)

such as GPT-4V (Wu et al., 2023b) not only demon-

strate the capability to handle multi-modal inputs and

outputs, but also underscores a paradigm shift towards

task-agnostic learning frameworks, which generate

insights across a myriad of tasks without the necessity

Wu, K., Li, W. and Xiao, X.

AccidentGPT: Large Multi-Modal Foundation Model for Trafﬁc Accident Analysis.

DOI: 10.5220/0012422100003636

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2024) - Volume 3, pages 943-950

ISBN: 978-989-758-680-4; ISSN: 2184-433X

943

for task-speciﬁc training. The intrinsic versatility of

these models is manifested in their capability to gen-

eralize learned knowledge and skills across complex

multi-task output scenarios. Although most of LMMs

focus on dealing with image and text inputs and out-

puts, recent work (Zhang et al., 2023), (Wu et al.,

2023b) shows the possibility to bridge extended list of

modalities (e.g., image, text, video, audio, video, etc.)

as inputs and produce corresponding outputs of multi-

modalities as a response to the prompt. In the context

of trafﬁc accident analysis, the technical foundation

of such LMM models and techniques brings forward

the possibility to build a foundation model to take into

account multi-modal inputs and generate outputs for

a multiplicity of trafﬁc accident analysis tasks.

However, while the incorporation of multi-

modality in trafﬁc analysis presents a promising fron-

tier, it also brings to light signiﬁcant challenges that

have yet to be fully addressed speciﬁc to the ﬁeld:

• Quality and Integrity of Data from Various

Sources: In trafﬁc analysis, data can come from

a variety of sources, including dashcams, trafﬁc

cameras, eyewitness reports, vehicle sensors, and

more. The quality and integrity of this data can

vary greatly, impacting the accuracy and reliabil-

ity of the analysis. The quality and integrity of

the data from various sources are to be ensured

for desired model performance;

• Complexities with Seamless Interpreting and Rea-

soning: The complexities associated with seam-

less interpreting and reasoning from diverse trafﬁc

accident data and modalities are substantial;

• Model Training and Task-Speciﬁc Outputs with

Multi-Modal Inputs: The model training and the

alignment of task-speciﬁc outputs with multi-

modal inputs are challenging, which often require

intricate customization and tuning;

• Ethical and Privacy Concerns: Ethical and privacy

concerns, especially related to the handling and

processing of sensitive and personal data, have

also been inadequately addressed.

In this work, we propose the idea of Accident-

GPT - a foundation model to transform the domain

of trafﬁc accident analysis by integrating multi-modal

inputs not only to automatically reconstruct acci-

dent scenario details but also delivers comprehensive

multi-task analysis with a variety of output modali-

ties. The idea extends the existing LLM and LMM

solutions with a multi-modality prompt coupled with

a feedback mechanism for adaptive task optimization,

a hybrid training schema leveraging both labelled and

unlabelled data for enhanced model generalization

and performance, and a edge-cloud split conﬁgura-

tion for data privacy. This paper seeks to tackles the

gaps in conventional solutions and unveil the poten-

tial of an automated, fast responding, objective, and

privacy-preserving trafﬁc accident analysis solution.

The rest of the paper is organized as follows: sec-

tion 2 discusses the gaps inherent in existing trafﬁc

accident analysis approaches. Section 3 outlines our

idea of AccidentGPT, highlighting its multi-modal in-

puts and outputs and multi-task features. Section

4 presents the corresponding research opportunities.

Section 5 concludes the paper.

2 GAPS IN CURRENT TRAFFIC

ACCIDENT ANALYSIS

The traditional approaches and contemporary ma-

chine learning techniques, while contributory, present

several gaps and challenges that limit their applicabil-

ity. These gaps highlight the urgent need for a sys-

tematic approach to trafﬁc accident analysis.

2.1 Data Integration and Analysis

Manual Efforts: The traditional approaches (Mo-

hammed et al., 2019) involve substantial manual ef-

forts in post-accident data collection, processing, and

analysis. This labor-intensive process is prone to

bias of human judgment and thus can lead to incon-

sistencies and errors impacting the reliability of the

analysis. Furthermore, the manual process is time-

consuming and the lag in analysis undermines the im-

mediacy of response, impacting emergency services,

trafﬁc management, and subsequent investigative pro-

cesses. Automate the process with timeliness and sys-

tematic analysis is one of the key challenge to tackle.

Privacy Concerns: Machine learning based ap-

proaches (Najaﬁ Moghaddam Gilani et al., 2021) in-

tegrates sensitive data sources (e.g., dashcam footage

and bystander videos) and raise corresponding pri-

vacy and ethical concerns (Butt et al., 2019). These

challenges have constrained the scope and depth of

accident analysis, leaving a wealth of potentially in-

sightful data untapped. Ensuring the privacy of sen-

sitive data directly improves the effectiveness of the

trafﬁc accident analysis.

2.2 Model Modality and Generalization

Model Specialization: Current machine learning

models in the ﬁled of trafﬁc accident analysis are of-

ten specialized and task-speciﬁc (Chand et al., 2021).

These models excel in their designated tasks but face

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

944

challenges when exposed to scenarios or data that are

different from their training environments. The gen-

eralization capability of these models is limited, and

this specialization hinders their adaptability and ﬂex-

ibility, reducing their applicability to a diverse range

of accident scenarios and conditions. There exists a

signiﬁcant gap in developing models endowed with

task-agnostic learning mechanisms that can seam-

lessly adapt and perform across a variety of tasks and

conditions without the need for retraining or extensive

customization.

Uni-Modal Analysis: Automatic trafﬁc accident

analysis on speciﬁc tasks (Ali et al., 2021) predomi-

nantly relies on uni-modal data sources, such as tex-

tual reports or image evidence. These uni-modal ap-

proaches lack the capacity to provide a holistic view

of accident scenarios, often missing out on crucial

contextual and dynamic information that multi-modal

data can offer. The lack of versatility to adapt to dif-

ferent data types and analysis requirements leads to a

fragmented and compartmentalized understanding of

accident scenarios. There is a pressing need for mod-

els that can assimilate diverse data sources, under-

stand the intricate interplay of dynamic factors, and

provide a comprehensive analysis.

Output Limitations: The outputs of existing

models (Duma et al., 2022) for trafﬁc accident analy-

sis is typically limited in solo modality (e.g., respon-

sibility, text report) as well. The uni-modality re-

stricts the detailed insights that stakeholders, includ-

ing investigators, trafﬁc planners, and victims, can ex-

tract from the outputs. Furthermore, the lack of in-

teroperability between different analysis systems and

technologies can hinder comprehensiveness and intu-

itiveness of accident analysis across machine learning

models. Models are expected to produce multi-modal

outputs (e.g., visual representation, numerical dynam-

ics, text reports and news) especially in a multi-task

scenario in order to meet diverse stakeholders’ re-

quirements (e.g., responsibility attribution, video re-

construction) for a trafﬁc accident analysis system.

In the light of these gaps and challenges, this pa-

per introduces AccidentGPT as a multi-modal foun-

dation model capable of automatically interpreting a

diverse range of data modalities and delivering com-

prehensive, multi-faceted outputs on multiple trafﬁc

accident analysis tasks.

3 AccidentGPT OVERVIEW

The general idea of the AccidentGPT is depicted in

Figure 1, and the model core follows a preprocess-

ing&encoding, alignment&fusion, and decoding pro-

cess. To revolutionize the ﬁeld of trafﬁc accident anal-

ysis, the joint of use of data from diverse sources is

critical to provide robust and insightful analyses. The

model inputs can include a) pre- and post-accident

site photos, b) CCTV camera recordings, c) dash-

cam footage, d) statements about the accident process

from the involving parties (e.g., drivers, witness) , e)

information from Inertial Measurement Units (IMUs)

of the movement dynamics during the accident, f) the

contextual data containing the accident’s related GPS

location, time, road signal and condition (e.g., wet,

dry, icy), historical trafﬁc data of the trafﬁc sites, and

the details related to vehicles and their insurances, and

g) most importantly, the task-oriented prompt to in-

struct AccidentGPT on the desired analysis and out-

puts. AccidentGPT does not expect the comprehen-

sive set of the input data in every accident analy-

sis, but dynamically adapt to the available data for

analysis with partial inputs as similar to the work

(Moon et al., 2023). The statements, context and

the prompt can be described in a multi-modal fash-

ion (e.g., speech, text, image, etc.), leveraging both

textual and non-textual data for a more holistic inter-

pretation.

The model inputs encompass a variety of modal-

ities, including audio, image, video, text, spatial

and/or temporal tabular data, and other modali-

ties such as remote sensing spectrum. Each in-

put modality is subject to modality-speciﬁc prepro-

cessing steps and encoders (e.g., CLAP (Wu et al.,

2023c) for audio, DinoV2 (Oquab et al., 2023) for im-

age, AnyMAL-Video (Moon et al., 2023) for video,

IMU2CLIP (Moon et al., 2022) for spatial/temporal

series). During the model inference, the preprocess-

ing and encoding process is to be carried out on the

users’ edge devices for the sake of privacy, and the

decoding process is to be performed by the Acci-

dentGPT model on cloud server. The edge machine

learning techniques (Li et al., 2023) can be applied to

the encoders for computational efﬁciency and perfor-

mance; in the case that speciﬁc encoders remain sig-

niﬁcantly demanding in computational resources after

model compression, split learning (Vepakomma et al.,

2018) can be leveraged by executing only the initial

layers of the encoder on the edge devices, while the

remaining layers can be ofﬂoaded and ran in the cloud

environment.

The alignment among different modalities (Gird-

har et al., 2023) harmonizes different data sources and

ensures diverse modalities are properly integrated and

correlated for accurate and cohesive analysis. In this

edge-cloud split conﬁguration, the alignment can be

ﬂexibly executed in the edge devices and/or the cloud

environment. This provides adaptability based on the

AccidentGPT: Large Multi-Modal Foundation Model for Trafﬁc Accident Analysis

945

Figure 1: AccidentGPT Overview.

speciﬁc requirements of tasks, the computational re-

sources available at the edge, and the desired response

time. For simpler alignments on edge, the involving

parties can quickly access the data and indicate the

temporal and spatial properties of each data item (e.g.,

pre-accident, in-accident, post-accident). Conversely,

for precise alignments involving multiple modalities

and the complete input data, the cloud environment

can be leveraged with superior computational capa-

bilities to encapsulate the intricate cross-modal inter-

actions among individual components spanning vari-

ous modalities via representation fusion, coordination

and ﬁssion (Liang et al., 2023).

After preprocessing, encoding and alignment, the

data are fed into the AccidentGPT for modality-

speciﬁc decoding to automatically generate outputs

corresponding to multiple tasks. AccidentGPT targets

the following outputs of trafﬁc accident analysis:

• Reconstructed Video: this output creates a vi-

sual 2D or 3D representation of the complete acci-

dent process. The resulting representation offers a

temporal and spatially accurate depiction, provid-

ing investigators with a sequential understanding

of the events leading up to, during, and after the

accident.

• Dynamics of Vehicles: The dynamics of vehicles

are associated with each video frame containing

the following information of the involved vehi-

cles: coordinates, velocity, direction, actions of

each vehicle involved (i.e., braking, acceleration,

turning, no action) and the point of impact and the

damage descriptions.

• Accident Report & News: The accident report

servers as the ofﬁcial documentation and details

the sequence of events, involved parties, identiﬁed

causes, and potential preventive measures. Based

on the report, an accident news is tailored for dis-

semination to news agencies for public awareness.

• Responsibility Attribution: This output method-

ically identiﬁes and attributes responsibilities to

involved parties.

• Insurance Claims: This output automates the in-

surance claim assessments by providing a data-

driven breakdown of the accident, which high-

lights damages, identiﬁes potential policy viola-

tions, and offers estimations of repair costs based

on the severity and nature of the damages.

• Trafﬁc Management: This output primarily fo-

cuses on the immediate and long-term implica-

tions of trafﬁc ﬂow and infrastructure. Post-

accident, the output provides real-time recom-

mendations on trafﬁc rerouting, crowd control,

and area isolation to ensure minimal disruption

and prevent secondary accidents. In the long term,

based on recurrent patterns, the output identiﬁes

weaknesses in current infrastructure and trafﬁc

regulations, and suggest interventions to ensure

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

946

smoother and safer trafﬁc ﬂow in the future.

• Emergency Response: Emphasizing immediate

post-accident actions, this output assists in de-

termining the severity of injuries, potential haz-

ards (e.g., fuel leaks), and the requirement of

specialized resources such as medical teams, ﬁre

brigades, or specialized rescue units. Addition-

ally, the output provides essential information to

ﬁrst responders, like the number of vehicles in-

volved, hazards, and the access to the accident

site. This ensures that the response is not only

swift but also tailored to the speciﬁc needs of the

incident, minimizing harm and damage.

• Other Tasks: The adaptability and expansive-

ness of the AccidentGPT’s design based on multi-

modality and multi-task modelling makes the

model suited for additional task-speciﬁc outputs

not covered in the primary list. Such ﬂexibility

ensures that the model remains relevant and scal-

able, accommodating evolving trafﬁc safety needs

and technology advancements.

Furthermore, the model provides avenues for

multi-modal prompt based on reinforcement learn-

ing with human feedback (RLHF) (Christiano et al.,

2017) or AI feedback (RLAIF) (Bai et al., 2022),

ensuring a continuous loop of learning and reﬁne-

ment to improve the model performance with the task-

oriented and multi-modal prompt.

4 RESEARCH OPPORTUNITIES

4.1 Opportunity 1: Multi-Modal Trafﬁc

Data Collection and Integration

Gathering and integrating a comprehensive dataset for

trafﬁc accident analysis is essential for pretraining.

Collection and Standardization: Similar to the

paradigm shift in the computer vision domain with

the introduction of ImageNet (Deng et al., 2009), the

trafﬁc accident analysis ﬁeld expects a transforma-

tion through the establishment of a comprehensive

and standardized multi-modal dataset. However, the

complexity and multifaceted nature of trafﬁc acci-

dents, along with the discrepancies in data collection

methods across regions, make this endeavor challeng-

ing. The collaboration and standardization of the data

gathering to consolidate such data require synchro-

nized efforts from various stakeholders, in order to

ensure the analysis uniformity and solution scalabil-

ity. Alternatively, leveraging simulation software for

autonomous driving such as (Cognata, 2023) can pro-

duce standardized datasets with controlled variables,

which can act as a base for model training across dif-

ferent scenarios and conditions.

Data Preprocessing: Real-world trafﬁc data can

be noisy due to various interference sources like

weather conditions affecting sensors or low-quality

trafﬁc cameras, and thus necessitate extensive propro-

cessing efforts (e.g., cleaning, ﬁltering). On the other

hand, collecting high-quality supervised data can be

expensive and, at times, unfeasible. Although semi-

supervised learning approaches resort to leveraging

unlabeled or weakly labeled data, they still demand

specialized ﬁltering procedures (Radenovic et al.,

2023). In a multi-modal scenario, the challenges com-

pound even more, as each modality has its own inher-

ent noise and discrepancies. The integration of varied

data streams necessitates not only modality-speciﬁc

preprocessing but also meticulous alignment and syn-

chronization. This is essential to guarantee that inputs

from disparate sources accurately represent a singular

event. Additionally, an inter-modality harmonization

mechanism (Wu et al., 2023a) is crucial to ensure that

the composite representation holistically encapsulates

the phenomenon under study, with no single modality

disproportionately inﬂuencing the analysis.

4.2 Opportunity 2: Multi-Modal Model

Structure and Core Components

Although the general idea of the AccidentGPT fol-

lows the encoding, alignment, fusion, and decoding

process to generate multi-task multi-modal outputs,

no dominant design of model structure exists yet ei-

ther in existing vision-language pretraining (Liu et al.,

2023; Alayrac et al., 2022; Zhu et al., 2023a; Zhu

et al., 2023b) or the multi-modality works (Zhang

et al., 2023; Wu et al., 2023b), and no singular struc-

ture has been conclusively demonstrated to signiﬁ-

cantly outperform others. In addition to the model

structure, the large multi-modal and multi-task model

for trafﬁc accident analysis involves four fundamental

components that require further research innovations:

Alignment: Alignment deals with the synchro-

nization of data from various modalities to ensure that

they represent the same event or phenomenon. Al-

though emerging works (Girdhar et al., 2023) demon-

strate promising results, the extent and dimensions

where the trafﬁc accident data are shared across

modalities can lead to: a) non-uniformity across

modality alignment (e.g., one-to-one, one-to-many, or

not exist at all), and b) long-range dependencies that a

particular element from one modality corresponds to

an element in another modality that is temporally or

spatially distant. Effective alignment methods are ex-

pected for temporal matching, spatial calibration and

AccidentGPT: Large Multi-Modal Foundation Model for Trafﬁc Accident Analysis

947

semantic bridging across modalities.

Fusion: Once aligned, the fusion component form

a uniﬁed representation of the data from different

modalities and learn representations that capture the

interactions between individual elements spanning

various modalities (Man et al., 2023). Due to the

fact that multi-modal data are heterogeneous in char-

acteristics, distribution, carried information and rel-

evance toward speciﬁc tasks, this component is in-

trinsically challenging. In the ﬁled of trafﬁc accident

analysis, the fusion process becomes even more im-

portant due to the critical spatial-temporal relations

of the sequencing, timing, and positioning of events

and actions that lead up to, occur during, and follow

an accident.

Decoding: The decoding process produces

human-understandable outputs that reﬂect cross-

modal interactions and coherence. While certain

modality-speciﬁc decoders (e.g., text) are mature and

widely used, the AccidentGPT decoding components

do not merely construct raw outputs from model’s

internal representation, but also involves summariza-

tion of contents, translation between modalities and

creation of new contents (i.e., reconstruction of acci-

dent process video). Video generation, as a modality,

poses multiple challenges, especially when aiming for

high ﬁdelity and temporally coherent sequences. This

is yet one of the most challenging but popular re-

search direction. Recent advances (Xu et al., 2023)

offer potential solutions, but further research is es-

sential to enhance the granularity, accuracy, and re-

alism of generated video content, particularly in the

nuanced domain of trafﬁc accident analysis due to its

dynamics complexity, physical consistency and multi-

modal integration.

4.3 Opportunity 3: Multi-Modal

Reasoning

During the AccidentGPT’s entire process of trafﬁc ac-

cident analysis, reasoning with the fused represen-

tation is the key capability of to reconstruct the ac-

cident sequence, derive critical insights, and formu-

late logical conclusions about the incident’s dynam-

ics. The reasoning dimension is vast and complex for

AccidentGPT involving the culmination of a series of

events and interactions among multiple entities. The

reasoning function is expected to: a) determine and

learn the relationships and interactions within the ac-

cident scene, b) understand the contribution of each

multi-modal data within the reasoning sequence, c)

extrapolate increasingly abstract ideas from the in-

dividual pieces of multi-modal evidence. Existing

works show contradicting results (Stechly et al., 2023;

Figure 2: Framework for Pretraining.

Huang and Chang, 2023) on how well multi-modal

models can perform on reasoning tasks, and yet a fur-

ther step on the reasoning leveraging external large-

scale knowledge and components can yield signiﬁcant

advancements in accurate accident reconstruction and

understanding.

4.4 Opportunity 4: Data Efﬁcient

Training Paradigm

Data from different sources for trafﬁc accident analy-

sis can be categorized into three types: labeled, unla-

beled data, and weakly-labeled noisy data (even af-

ter preprocessing). Since the data related to trafﬁc

accident is scarce in general, it is worthwhile to in-

vestigate how to maximize the utilization of (pseudo)

supervision or priors in the multi-modal data.

One potential solution is to adopt a combined

loss that enables supervised learning for labeled data

(Dosovitskiy et al., 2020), self-supervised learning for

unlabeled data (He et al., 2022; Chen et al., 2020) and

weakly-supervised learning for weakly-labeled noisy

data (Radford et al., 2021). Without losing gener-

alization, illustrative examples are shown in Figure

2. In contrast to single strategy training, the hybrid

training paradigm is another research opportunity al-

lowing for the comprehensive exploitation of valuable

and diverse data information, offering a ﬂexible trade-

off between the cost of data collection and the perfor-

mance of the model.

4.5 Opportunity 5: Task-Oriented

Multi-Modal Prompt with Feedback

The concept of ”prompting” has demonstrated re-

markable utility in LLMs (Brown et al., 2020) and

LMMs (Lyu et al., 2023). By managing various

tasks with task-speciﬁc descriptive prompts, attach-

ing them to the input for downstream processing, and

then jointly feeding them into a pre-trained, frozen

foundational model, this approach offers a uniﬁed so-

lution for diverse tasks. However, the full poten-

tial of prompting within the realm of large multi-

modal models has yet to be fully explored. One of

the paramount challenges lies in the vast complex-

ity and diversity of multi-modal data. Unlike textual

data where prompts can be relatively straightforward,

ICAART 2024 - 16th International Conference on Agents and Artiﬁcial Intelligence

948

deﬁning an ideal prompt in multi-modal scenarios be-

comes intricate. And the alignment of task objectives

with the modality speciﬁcs make the process non-

trivial, as the misinterpretations or biases can have

signiﬁcant real-world consequences in the trafﬁc acci-

dent analysis, ensuring the accuracy, interpretability,

and contextual relevance of multi-modal prompts be-

comes absolutely critical.

Further, the process of feedback plays a vital role

in shaping the effectiveness of the prompt. While

RLHF (OpenAI, 2023) can provide nuanced insights

and guide the model towards desired outcomes, re-

lying solely on it can be costly and time-consuming.

On the other hand, recent work on RLAIF (Bai et al.,

2022) demonstrates that AI systems can potentially

self-regulate, reﬁne, and provide feedback only with

the help of human oversight in terms of a list of rules

or principles. This presents an intriguing paradigm

where multi-modal prompts can be self-optimized

and critiqued by a balance between human and AI

feedback. The potential evolution of a feedback-

driven prompting mechanism could pave the way for

more granular and context-aware prompts, thereby

enhancing the model’s efﬁcacy and responsiveness.

4.6 Opportunity 6: Validation Methods

and Reliability Metrics

The evolution of multi-modal models in trafﬁc acci-

dent analysis opens new avenues for research, partic-

ularly in the development of sophisticated validation

techniques. Future studies should focus on creating

methodologies that can accurately assess and ensure

the reliability of outputs from complex systems like

AccidentGPT. Another critical area of research is the

formulation of robust metrics tailored to multi-modal,

multi-task models in high-stake scenarios. These met-

rics would serve as benchmarks for evaluating the

trustworthiness of the model’s interpretations, which

is paramount in trafﬁc accident analysis.

5 CONCLUSION

In this paper, we have introduced AccidentGPT, an

innovative foundation model tailored for the intricate

domain of trafﬁc accident analysis, leveraging multi-

modal data streams and a multi-tasking paradigm.

AccidentGPT synthesizes these varied data streams

and processes them seamlessly through a uniﬁed an-

alytical framework, thereby enabling comprehensive

and insightful outputs that span multiple modalities

and tasks. The potential of this approach represents

a signiﬁcant paradigm shift, promising to revolution-

ize the methodologies and tools available for trafﬁc

accident analysis.

Our work marks a ﬁrst step towards an auto-

matic, systematic and privacy preserving trafﬁc acci-

dent analysis solution. Research efforts are required

to reﬁne these opportunities, fully realize their poten-

tial, and rigorously evaluate their performance in real-

world scenarios. Future work will focus on exploring

the related research opportunities and enhancing the

effectiveness of the proposed approach.

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