Explore the Application Status of Automatic Driving and Legal

Gaps

Runze Wang

The Business, Computer Science and Applied Technologies Division,

De Anza College 21250 Stevens Creek Blvd, Cupertino, CA 95014, U.S.A.

Wangrunze2.fhda.edu

Keywords: Automatic Driving, Law, Current Situation.

Abstract: With the development of AI, autonomous driving technology is becoming more and more mature. This article

will explore the advantages and challenges of autonomous driving and explore the differences between

autonomous driving and traditional driving. This article concludes that most traffic accidents are caused by

subjective factors of the driver, such as drinking and lack of concentration. Autonomous driving can

undoubtedly reduce the incidence of cases where these subjective factors lead to traffic accidents. However,

although there have been many studies on the safety of autonomous driving, the discussion on ethical issues

and legal responsibilities is still insufficient. In most of the cases of traffic accidents related to autonomous

driving in China, most of them still cause controversy, and there are still many gaps in the formulation of legal

provisions on autonomous driving. In summary, the significance of this article is to summarize the advantages

and challenges of autonomous driving and combine the data of WHO to emphasize that the current legal

provisions related to autonomous driving are still imperfect, to promote further research and improvement in

this field.

1 INTRODUCTION

At present, the global road safety situation remains

grim. According to the World Health Organization

(WHO, 2023), about 1.19 million people are killed

and another 20 to 50 million are injured in traffic

accidents each year, with children and young people

aged 5 to 29 accounting for a significant proportion.

Human factors such as speeding, fatigue driving,

drunk driving, and distracted driving are still the main

causes of traffic accidents. With the advancement of

artificial intelligence and machine learning

technologies, autonomous driving has gradually

become an important development direction in the

global transportation field. Autonomous driving

technology is highly anticipated, and many people

believe that it can reduce human driving errors,

improve driving efficiency, and ultimately reduce

traffic accident rates. Therefore, the development of

autonomous driving technology is very important (Xu,

2019).

https://orcid.org/0009-0006-3924-3589

As the global smart car industry is developing

rapidly, advances in computing technology and

communication mechanisms have significantly

promoted the development of autonomous driving

technology. Autonomous vehicles rely on perception

results based on multiple sensors to make automatic

decisions and control vehicle operations. The key to

the success of these systems is the ability to make

reliable decisions in real time (Liu et al., 2021).

However, some scholars have pointed out that the

current autonomous driving technology in terms of

high-precision maps still faces seven major

challenges, including map models, high-precision

positioning, three-dimensional reconstruction, fusion

updates, data security, rapid review, and standard laws

and regulations (Yang et al., 2023) .To overcome

these challenges, the newly developed Transformer

has achieved remarkable results in the field of natural

language processing due to its powerful remote

modeling and parallel computing capabilities. When

extended to the multimodal trajectory prediction task

of autonomous driving, it also shows excellent

230

Wang, R.

Explore the Application Status of Automatic Dr iving and Legal Gaps.

DOI: 10.5220/0013681500004670

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

In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 230-234

ISBN: 978-989-758-765-8

performance and effectively copes with related

challenges. In addition, the application of the

Transformer model in the multimodal trajectory

prediction task shows stronger generalization ability

and interpretability, which makes it have broad

application prospects in this field (Huang et al., 2023).

The purpose of this paper is to explore the impact

of autonomous driving technology on road safety and

analyze its potential in reducing traffic accidents as

well as the current challenges. The paper first

introduces how autonomous driving technology can

reduce accident rates through intelligent decision-

making and environmental perception, and analyzes

key factors that affect driving safety, such as speed,

distraction, and environmental conditions. Then, the

article discusses the legal gaps facing autonomous

driving, including the division of responsibilities and

regulatory adaptability. Finally, the future research

directions of autonomous driving are summarized,

focusing on how to optimize the legal framework and

human-machine collaboration to further improve

road safety.

2 SAFETY ANALYSIS OF

AUTONOMOUS DRIVING

Autonomous driving technology has significant

advantages in improving road safety. According to

statistics from the World Health Organization (WHO,

2023), about 1.19 million people die in road traffic

accidents each year. In addition, about 20 million to

50 million people suffer traffic accident-related

injuries, a considerable number of whom are children

and young people aged 5-29. Speeding and traffic

violations are the main causes of road traffic deaths,

and autonomous driving systems can effectively

improve the overall safety level by accurately

controlling vehicle speed and reducing human errors.

The occurrence of traffic accidents is affected by

a variety of risk factors, among which speeding is

closely related to the probability and severity of

collisions. For example, for every 1% increase in

vehicle speed, the risk of fatal collisions increases by

4% and the risk of serious collisions increases by 3%

(WHO, 2023). In addition, the increase in vehicle

speed directly affects the risk of pedestrian mortality.

Studies have shown that for every 10 km/h increase

in speed, the risk of pedestrian death increases

significantly. For example, when the speed increases

from 50 km/h to 65 km/h, the risk of pedestrian death

increases by 4.5 times. At the same time, not wearing

a seat belt will also increase the risk of death for

occupants in the car. At a speed of 65 km/h, the risk

of death for occupants in the car increases by 85%.

The application of autonomous driving helps

reduce traffic accidents. Its main features include

intelligent speed control, predictive driving, and

reducing driver fatigue.

The autonomous driving system can adjust the

speed according to the real-time road environment to

avoid speeding; it predicts dangerous situations

through historical data and real-time perception

capabilities to avoid potential risks on time. In

addition, autonomous driving reduces dependence on

manual driving, thereby reducing accidents caused by

fatigue, driving, or distraction.

In addition to driving behavior, other factors also

have an important impact on road safety. For example,

alcohol and psychoactive substances can reduce the

driver's judgment and reaction speed, greatly

increasing the risk of fatal traffic accidents. WHO

(2023) pointed out that even if the blood alcohol

concentration (BAC) is low (such as 0.02 g/dl), the

risk of traffic accidents is still significantly increased.

When the BAC exceeds 0.04 g/dl, the risk of

accidents increases further. In addition, drivers who

take psychoactive drugs are five times more likely to

have an accident than those who do not take drugs

(WHO, 2023).

The use of safety equipment also affects road

safety. Correctly wearing seat belts can reduce the

risk of death for passengers by 50% and the risk of

death for children by 71% (WHO, 2023). In addition,

the use of helmets has a significant impact on the

survival rate of motorcycle accidents, reducing the

risk of death for passengers by 63% and the risk of

brain damage by 74%.

Distracted driving has become a major traffic

safety issue in the modern driving environment, such

as using mobile phones and in-car entertainment

systems. Studies have shown that the likelihood of a

collision while driving with a mobile phone is about

four times that of normal driving (WHO, 2023).

Although autonomous driving can reduce the impact

of human distraction, the human-machine interface

still needs to be improved to prevent drivers from

over-relying on the autonomous driving system and

causing distraction.

The current road traffic safety issues are mainly

affected by multiple subjective factors of drivers,

including speeding, driver fatigue, distracted driving,

the influence of alcohol and psychoactive substances,

and insufficient use of safety equipment, etc. These

factors significantly increase the probability of traffic

accidents and threaten road safety.

Explore the Application Status of Automatic Driving and Legal Gaps

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In this context, autonomous driving technology

can be seen as a key means to improve road safety.

Through intelligent speed control, predictive driving,

and effective intervention in driver fatigue and

distraction, autonomous driving systems can

effectively reduce human errors and reduce traffic

accident rates. In addition, advances in technologies

such as V2X (Vehicle-to-Everything) communication,

AI decision-making algorithms, and real-time

environmental perception enable autonomous driving

systems to more accurately judge road conditions and

optimize driving strategies, thereby further improving

driving safety.

Therefore, with the continuous development and

optimization of autonomous driving technology, its

role in improving road safety will become more and

more significant.

3 CASES AND LEGAL GAPS

RELATED TO AUTONOMOUS

DRIVING

3.1 Advantages and limitations of

autonomous driving technology

Although autonomous driving technology has shown

great advantages in reducing human driving errors

and optimizing driving safety with its precise

environmental perception and AI decision-making

capabilities, it still cannot prevent traffic accidents.

Especially in extreme weather (such as fog and heavy

rain) or in cases of system perception errors (such as

insufficient prediction of pedestrian behavior), the

autonomous driving system may not be able to make

the best response, thereby increasing the risk of

accidents.

Therefore, when discussing the legal liability of

autonomous driving, it is necessary to clarify the

division of responsibilities when an accident occurs

and the criteria for its determination. At present, the

legal framework for the determination of autonomous

driving responsibilities in various countries is still

being improved, and one of the key discussion points

is the definition of the concept of "driver".

3.2 Definition of legal liability

When an autonomous driving traffic accident occurs,

it is particularly important to clarify the concept of

"driver". Autonomous driving technology is usually

divided into six levels according to its autonomous

control capabilities, from L0 (fully manual driving) to

L5 (fully autonomous driving). Each level represents

the degree of dependence of the vehicle on human

drivers during driving. L0-level vehicles need to be

driven entirely by humans, while L5-level vehicles

can be driven completely autonomously in any

situation without human intervention.

At present, L4 and L5 autonomous driving

technologies are under intense discussion and

controversy. The main feature of these two levels is

that the vehicle can make driving decisions

autonomously under specific environments and

conditions, while the intervention of human drivers is

limited. Due to the continuous development of

technology, the laws and regulations of various

countries have not yet fully adapted to this change,

which has led to large differences in the determination

of legal liability. Different countries have

significantly different regulations on the identity of

"driver", which directly affects the attribution of

liability after an accident.

For example, in China and the European Union,

the law requires that drivers still bear certain legal

responsibilities in autonomous driving at levels L3

and above. This means that even in highly automated

situations, drivers are still responsible for the

operation of the vehicle and must be able to take over

control at any time. This regulation reflects the

emphasis on the ability of human drivers to intervene

in emergencies to ensure safety.

In contrast, the National Highway Traffic Safety

Administration (NHTSA) made a different judgment

in 2022, believing that L4 and L5 autonomous driving

systems themselves can be regarded as "drivers". This

change in position means that as technology advances,

autonomous driving systems will gain greater

independence and responsibility in law. This not only

changes the understanding of the legal liability of

autonomous vehicles, but may also affect future

legislation and policy-making processes.

In general, how to clarify and define the concept

of "driver" in different levels of autonomous driving

is not only a challenge that the legal community needs

to face, but also an important issue that countries must

solve in the process of promoting the development of

autonomous driving technology. Solving this problem

will play a key role in the popularization and

application of autonomous driving technology, and it

can also provide protection for future traffic safety.

3.3 The challenge of the social public

opinion and the legal framework

In contrast to research based on existing laws, Jamy

Li and colleagues at Stanford University surveyed

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120 adult US citizens through two online experiments

about their views on the legal liability of self-driving

cars in traffic accidents. The results show that in the

same accident scenario, the public views self-driving

cars as less at fault than human drivers, and is more

likely to believe that legal responsibility should be

borne by automakers and governments, because self-

driving cars themselves are not seen as independent

moral agents. In addition, the study also found that

the public generally believes that ethicists and

automobile manufacturers should be responsible for

developing ethical and legal norms for autonomous

vehicles (Li et al., 2016). In line with this study, the

fatal Uber self-driving vehicle accident in 2018 also

exposed the current deficiencies in the identification

of legal liability.

On March 18, 2018, an Uber self-driving test

vehicle in Arizona, the United States, hit a pedestrian,

Elaine Herzberg, who was crossing the road during a

road test, killing her on the spot. This accident

became the world's first case of a self-driving car

causing death and quickly triggered widespread

discussion on the legal liability of self-driving cars.

Alexander Hevelke believes that autonomous

driving is different from traditional driving. When an

accident occurs, if the driver cannot take over the car

quickly, then the driver has no obligation to bear

responsibility (Hevelke et al., 2015). However, this

article only analyzes traffic accidents caused by

autonomous driving at a moral level and has no legal

basis. Siming Zhai's article also uses experimental

tests to show that when an autonomous driving

accident occurs, the driver often has no way to take

over the car in time to avoid the accident. Siming Zhai

also believes that the driver often faces great social

pressure after an accident, so the user may not be

responsible (Zhai et al., 2024).

At present, the legal status of autonomous

vehicles is not clear, and there are certain obstacles in

the application of traditional tort liability rules, which

greatly restricts the development of the entire industry.

In order to conform to the inherent law of the

development of rights, autonomous vehicles should

be endowed with independent legal personality, and

the corresponding tort liability rules should be

formulated according to the purpose of their use

(

Zhang, J., & Xiao, G., (2019)).

Nick Belay studied the different roles and division

of responsibilities of manufacturers, individuals,

insurance companies and legislatures in terms of legal

liability in autonomous vehicle accidents. He pointed

out that before driverless cars are officially put on the

market, the legal system must clearly define the

boundaries between "control rights" and "driving

rights".

This process requires the revision of relevant legal

provisions in the Criminal Law, the Road Traffic

Safety Law, the Insurance Law, etc., to clarify the

responsibilities of all parties and provide legal

guarantees for the legal operation of self-driving cars

on the road. On this basis, he further proposed that the

future legal framework should take the protection of

the rights and interests of car owners and passengers

in the vehicle as the core, ensuring that the clarity of

the law does not become an obstacle to the promotion

of self-driving technology (Belay et al., 2015).

4 CONCLUSION

This paper looks forward to the future development

direction of autonomous driving technology and puts

forward suggestions for further research. Future

research can focus on how to optimize human-

machine interaction to improve the safety of

autonomous driving, how to improve the legal system

to adapt to the development of autonomous driving

technology, and how to improve the decision-making

ability of autonomous driving systems through smart

infrastructure and V2X technology. Existing research

shows that autonomous driving has great potential in

reducing accident rates, but there is still a need to

improve human-machine collaboration and

emergency response capabilities.

This article believes that autonomous driving will

effectively reduce the incidence of traffic accidents in

the future, but it still faces many challenges such as

imperfect laws and imperfect high-precision map

technology. After overcoming these challenges,

autonomous driving will usher in a new era. Future

research should focus on the mixed environment of

autonomous driving and traditional driving and

explore how to optimize traffic management and laws

and regulations related to autonomous driving to

further improve road safety.

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