This presentation provides an overview of autonomous driving and examines how AI enables vehicle perception, decision-making, safety, and connectivity across various global approaches, including the USA, Europe, and China. It discusses key technical components, infrastructure, ethical and legal considerations, and the major challenges of reliability and uncertainty. Additionally, the presentation explores the economic impact on the automotive industry, emerging business models, job market changes, market disruptions, and future AI-driven trends and innovations.

1. Autonomous Driving
How AI Shapes Technical Challenges,
Industry Impact, and Future Trends
Velibor Ilić, PhD
The Institute for Artificial Intelligence Research and Development of Serbia, Fruškogorska 1, 21000 Novi Sad, Serbia
DSC Europe 25, Data Science Conference
Metropol Palace Hotel, Belgrade
17-21. November 2025

2. Overview of Presentation
• What is Autonomous Driving?
• Key components of autonomous vehicles
• Challenges of autonomous driving
• Conclusion
Timeline of key developments in autonomous driving and major milestones
Autonomous Driving - USA, Europe, and China approach
Sensors on autonomous vehicles
Control Systems of Vehicles
Connectivity in autonomous vehicles
Hardware Infrastructure of Autonomous Vehicles
Software Infrastructure – Autonomous Driving Tasks
Subsystems contained in a self-driving vehicle
Autonomous Vehicles and Safety
Technical challenges, reliability, decision-making under uncertainty
Autonomous Vehicles and Ethical Considerations
Autonomous Vehicles and Legal Considerations
Economic Impact on the Automotive Industry
New business models in the automotive industry
Impact on the Automotive Industry
The impact of autonomous driving on jobs
Market Disruptions
Future Trends and Innovations
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3. What is Autonomous Driving?
Autonomous driving refers to the technology enabling vehicles to
operate without human intervention by using sensors, cameras, and
artificial intelligence to navigate and make decisions. These vehicles
can vary in their level of automation, from partial assistance to full
self-driving capability where no human input is required at any time.

4. Timeline of key developments in autonomous driving and major milestones
Major milestones of autonomous driving
1960s and 1970s - Early Experiments:
(Stanford Cart)
1980s - ALV and Navlab Projects: The
Autonomous Land Vehicle (ALV)
project in Carnegie Mellon University
2000s - DARPA Challenges (2004-2007)
2010s - Google Self-Driving Car Project
2016 - Tesla Autopilot
2018 - Waymo’s Commercial Ride-Hailing
Service
2020s - Expansion and Integration

5. Autonomous Driving - USA, Europe, and China approach
United States
• Regulatory Framework: The federal
government provides guidelines, while
states can develop their own regulations for
testing and deploying autonomous vehicles.
• Industry Leadership: Silicon Valley serves
as a significant hub for innovation and
development. Tesla, Waymo, and Uber.
• Public Roads Testing: Extensive testing of
autonomous vehicles on public roads is
allowed in many states, with California being
notably active. Companies are required to
report performance and incident data to
state authorities.
Europe
• Harmonized Legislation: Europe tends to
emphasize more uniform regulations across its
member states, facilitated by bodies such as
the European Union.
• Focus on Safety and Privacy: European
regulations strongly emphasize safety, data
protection, and privacy, reflecting broader
societal values. The General Data Protection
Regulation (GDPR) impacts how companies
collect and use data from autonomous
vehicles.
• Collaborative Projects: European efforts
often involve public-private partnerships, with
projects like L3Pilot and Hi-Drive involving
multiple stakeholders, including automotive
manufacturers, technology firms, and research
institutions, focusing on advancing
autonomous driving technologies and
infrastructure.
China
• Government Support: The Chinese
government has robust national plans and
supports for autonomous driving, aiming to
become a world leader in AI and
autonomous vehicle technology. The
government facilitates infrastructure
developments and provides significant
funding and policy support.
• Rapid Deployment: China is quickly
deploying autonomous vehicles in both
closed environments (like campuses and
industrial parks) and open urban settings.
Baidu, AutoX.
• Integrated Approach: China’s approach
integrates autonomous vehicles with broader
smart city initiatives, which includes the use
of extensive surveillance and data gathering
to enhance vehicle communication with
urban infrastructure.

6. Global autonomous transportation availability
World Europe

7. Key components of autonomous vehicles
Sensors: Camera, Radar, LiDAR, Ultrasonic Sensors
Artificial Intelligence (AI) and Machine Learning:
Perception, Prediction, Decision Making
Control Systems: including steering, braking, and throttle.
Software and Computing Hardware: Onboard computers,
operating systems and software
Navigation and Mapping Systems: High-definition maps
and GPS
Connectivity: V2X (vehicle-to-everything) communications
Safety Systems: redundant hardware for critical functions,
fail-safe operational procedures

8. Sensors on autonomous vehicles

9. Sensor Fusion
Cameras
Camera 360
Lidar
Sensor Fusion
Ultrasound
Short range radar
Long range radar

10. Data at Autonomous Vehicles

11. Control Systems of Vehicles
1. Engine Control Unit (ECU)
2. Transmission Control Unit (TCU)
3. Brake Control System,
Anti-lock Braking System (ABS), Electronic Brakeforce
Distribution (EBD), and Brake Assist (BA)
4. Steering Control
Electric Power Steering (EPS)
5. Suspension Control
6. Traction Control System (TCS) and Electronic
Stability Control (ESC)
7. Driver Assistance Systems
Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA),
and Automated Emergency Braking (AEB)
8. Autonomous Vehicle Control

12. Connectivity in autonomous vehicles
1. Vehicle-to-Vehicle (V2V)
Communication
2. Vehicle-to-Infrastructure (V2I)
Communication
3. Vehicle-to-Network (V2N)
Communication
4. Vehicle-to-Pedestrian (V2P)
Communication
5. Vehicle-to-Everything (V2X)
Communication

13. Hardware Infrastructure of Autonomous Vehicles
1.Sensors
1. Cameras
2. Radar
3. LiDAR (Light Detection and Ranging)
4. Ultrasonic Sensors
2.Computational Hardware
1. ECUs (Electronic Control Units)
2. GPUs (Graphics Processing Units)
3. CPUs (Central Processing Units)
3.Connectivity Modules
1. Telematics Units
2. GPS Modules
4.Power Systems
1. Power Distribution Units
2. Battery Systems (for electric vehicles)
5.Actuators
1. Steering Actuators
2. Brake Actuators
3. Throttle Actuators
6.Safety and Redundancy Systems
1. Backup Systems
2. Diagnostic Systems

14. Software Infrastructure – Autonomous Driving Tasks
• Perception Module
• Localization Module
• Control Module
• Path Planning Module
• Decision Making Module
• Prediction Module
• Sensor Fusion Module
• Communication Module
• User Interface Module
• Diagnostic and Monitoring Module
• Software Management Module

15. Algorithms for autonomous driving

16. Subsystems contained in a self-driving vehicle

17. Autonomous Vehicles and Safety
Scope of ISO 26262
ISO 26262 covers all aspects of the safety lifecycle of electronic and
electrical safety-related systems in passenger cars. This includes the
management of functional safety, development process at both the system
and hardware/software levels, production, operation, service, and even
decommissioning.
Relevance to Autonomous Vehicles
Autonomous vehicles integrate complex software and hardware such as
sensors, actuators, control units, and connectivity solutions that must operate
without fail to ensure safety.
The standard provides a framework for:
• Hazard Analysis and Risk Assessment: Determining potential hazards
caused by malfunctioning behavior of electrical/electronic systems.
• Safety Lifecycle Management: Ensuring safety throughout the entire
lifecycle of automotive electronic/electrical systems.
• System-Level Safety Requirements: Specifying and designing systems
to achieve acceptable risk levels.
• Verification and Validation: Testing and proving that the safety
requirements are met.
Autonomous Vehicle Safety Integrity Levels (ASIL)
ISO 26262 categorizes safety requirements by Automotive Safety Integrity
Levels (ASILs), ranging from ASIL A (lowest) to ASIL D (highest), based on
the severity, exposure, and controllability of hazards.

18. Autonomous Vehicles and Safety

19. Application of artificial intelligence and machine learning in autonomous vehicles
• Perception and Sensing: Machine learning algorithms help vehicles perceive
and understand their environment through data collected from various sensors like
cameras, radar, and LiDAR.
• Decision Making: Once a vehicle understands its surroundings, machine learning
is used to make decisions about how to navigate.
• Map Updates and Localization: Self-driving vehicles use AI to interpret data
from various sources to update maps in real time and precisely localize the vehicle.
• Interaction with Pedestrians and Other Drivers: AI is used to
predict and interpret the behavior of pedestrians, cyclists, and other drivers.
• Optimized Energy Usage: This ensures the vehicle uses its battery life
efficiently, extending the range and reducing the need for frequent recharging.
• Autonomous Navigation: AI systems process data from sensors to interpret
the surroundings, detecting obstacles, recognizing signs, and understanding road
conditions.
• Predictive Maintenance: Machine learning can predict vehicle maintenance
needs by analyzing data from vehicle sensors and usage patterns.
• Enhanced Safety Features: Machine learning enhances safety through
features like collision avoidance systems, which can predict potential collisions and take
action to prevent them.
• Driver Monitoring and Assistance: Machine learning algorithms can
monitor driver behavior and alertness to provide warnings or take control if the driver is

20. AI System for Autonomous Vehicle Fleet Coordination
Real-time Optimization: AI optimizes routing, task allocation,
and resource management for better fleet efficiency.
Predictive Maintenance: AI predicts maintenance needs,
reducing downtime and preventing malfunctions.
Demand Prediction: AI forecasts high-demand areas to
redistribute vehicles efficiently.
Automatic Task Scheduling: AI automates scheduling,
minimizing service disruptions.
Dynamic Pricing and Incentives: AI adjusts pricing and
incentivizes vehicle relocation to high-demand zones.
Route Optimization for EV Fleets: AI optimizes EV charging,
factoring in battery levels, charger availability, and bookings.
Vehicle Performance Monitoring: AI monitors performance,
automatically blocking faulty vehicles.
Smart Fleet Allocation: AI ensures vehicles are in the right
place at the right time.
Autonomous Fleet Management: AI automates the entire fleet
management process, from booking to maintenance.
AI-driven Reporting and Compliance: AI automates
compliance reporting, ensuring accurate data for stakeholders.

21. Challenges of autonomous driving
Challenges of
autonomous driving

22. Challenges in integration and real-time processing in autonomous vehicle
1. Sensor Fusion Complexity: Autonomous vehicles rely on
multiple sensors that collect vast amounts of diverse data
types (visual, radar, LiDAR, ultrasonic, etc.).
2. High Data Volumes: The sensors on autonomous vehicles
generate enormous volumes of data every second.
3. Latency Issues: Any delay in processing and reacting to
sensor data can be critical.
4. Software and Hardware Integration: Integrating software
with diverse hardware components while ensuring they
operate harmoniously is challenging.
5. Reliability and Fault Tolerance: The systems must be
exceptionally reliable.
6. Scalability of Systems: As autonomous technology evolves,
the systems must be scalable to integrate new sensors, more
advanced computational hardware, and improved algorithms
without extensive redesigns.
7. Cybersecurity Risks: Protecting autonomous vehicles from
hacking and cyber-attacks is crucial.
8. Energy Consumption: The computational and sensor
systems in autonomous vehicles are energy-intensive.
9. Environmental Adaptability: Autonomous vehicles must
operate reliably under a wide range of environmental
conditions, including various weather conditions and lighting.
10.Regulatory and Compliance Issues: Complying with diverse
and evolving regulatory frameworks across different regions
can complicate the integration and deployment of autonomous
vehicles.

23. Technical challenges, reliability, decision-making under uncertainty
Reliability in autonomous vehicles refers to the consistent
performance of the vehicle’s systems under various operating
conditions.
• Sensor Reliability
• Software and Hardware Failures
• System Integration
• Long-Term Operation
Decision-Making Under Uncertainty
Autonomous vehicles must make decisions in environments
where all variables cannot be predicted or controlled.
• Dynamic Environments
• Limited Sensor Data
• Predictive Modelling
• Algorithm Robustness
• Ethical Decision Making

24. Autonomous Vehicles and Ethical Considerations
1.Decision-Making Algorithms: How should an
autonomous vehicle be programmed to act in scenarios where harm
is unavoidable? This raises questions about prioritizing the lives of
passengers versus pedestrians, known as the "trolley problem" in
ethics.
2.Privacy: Autonomous vehicles collect vast amounts of data,
including personal preferences, travel habits, and possibly even
conversations. This raises concerns about data privacy and the
potential misuse of personal information.
3.Security: With increased connectivity, there is a heightened risk
of hacking and other cyber threats. Ensuring the security of
autonomous vehicles is crucial to protect against unauthorized control
of the vehicle and data breaches.
4.Transparency: There is a need for transparency about how
autonomous vehicles operate, how decisions are made, and how
data is used. Users and the public should understand the technology
to trust and accept it.
5.Accountability: When an autonomous vehicle is involved in an
accident, determining accountability can be complex. Is it the
manufacturer, software developer, the owner for maintenance lapses,
or the vehicle itself at fault?

25. Autonomous Vehicles and Legal Considerations
1. Liability: Laws must define who is liable in the case of an
accident involving an autonomous vehicle.
2. Regulation and Standards: Establishing uniform legal
standards and regulations for the testing and deployment of
autonomous vehicles is necessary to ensure safety and
consistency across different regions and countries.
3. Insurance: New models for insurance might need to
consider the roles of software malfunctions, cyber threats, and
hardware failures in accidents.
4. Intellectual Property: With the advanced technology in
autonomous vehicles, intellectual property rights become a
significant issue.
5. Accessibility: Ensuring that autonomous vehicles are
accessible to all, including the disabled, elderly, and those
without smartphones or connectivity.
6. Cross-border Operation: The operation of
autonomous vehicles across state or national borders involves
navigating varying legal environments.
7. Compliance with Traffic Laws: Adapting existing
traffic laws to accommodate autonomous vehicles or creating
new laws to address specific needs of autonomous navigation.

26. Economic Impact on the Automotive Industry
1.Reduced Transportation Costs: Autonomous
vehicles could drastically reduce the costs associated with
transportation by eliminating the need for drivers, reducing
accidents (thereby lowering insurance costs and healthcare
expenses), and optimizing fuel efficiency.
2.Productivity Boost: The time that people currently spend
driving can be redirected towards more productive or enjoyable
activities. This could potentially increase overall economic
productivity.
3.Job Redistribution: While autonomous driving may reduce
the number of jobs in traditional areas such as driving and vehicle
insurance, it is also expected to create jobs in technology sectors,
including software development, data analysis, and cybersecurity.
4.Impact on Auto Industry: The automotive industry might
see a shift from individual car ownership to fleet management and
mobility services, affecting how vehicles are sold and managed
commercially.
5.Real Estate and Urban Planning: Reduced need for
parking spaces could transform urban landscapes, potentially
lowering real estate costs in city centers and influencing the design
of future infrastructure.

27. New business models in the automotive industry
1.Ride-Sharing and Mobility-as-a-Service (MaaS):
Autonomous vehicles enable the expansion of ride-sharing services,
making them more cost-effective and efficient. Companies: Uber and
Lyft
2.On-Demand Services: Autonomous vehicles can be used for
on-demand delivery services for goods and packages.
3.Subscription and Leasing Models: Car manufacturers
may shift from selling vehicles to offering them on a subscription or
leasing basis.
4.Data Monetization: Autonomous vehicles generate vast
amounts of data from their operations, which can be analyzed and
monetized.
5.Dynamic Pricing Models: Autonomous vehicle technologies
enable dynamic pricing models in ride-sharing service

28. Impact on the Automotive Industry
1.Product Development: Traditional car
manufacturers are increasingly partnering with technology firms
to incorporate advanced sensors, AI, and computing hardware
into their vehicles. This shift requires substantial investment in
research and development, as well as changes in the
manufacturing processes.
2.Safety and Efficiency: Autonomous vehicles are
expected to significantly reduce accidents caused by human
error, potentially lowering mortality rates and health care costs
related to road accidents. They also promise to optimize fuel
efficiency and reduce traffic congestion through more
coordinated movement.
3.Regulatory Adaptation: The industry must adapt to
new regulations and standards that are being developed to
ensure the safe integration of autonomous vehicles into
existing road networks. This includes changes in vehicle
testing, certification processes, and compliance with
international safety standards like ISO 26262.
4.Shift in Skill Demand: There is a growing demand
for professionals with expertise in robotics, artificial intelligence,
and data analytics, alongside a potential decline in some
traditional manufacturing and driving jobs.

29. The impact of autonomous driving on jobs
1.Displacement of Drivers: The most direct impact is on driving
jobs. Taxi drivers, truck drivers, delivery personnel, and others in driving-
related professions could see their jobs change significantly or even become
obsolete.
2.New Job Creation: While some jobs may be lost, new opportunities
will likely emerge in areas like vehicle monitoring, fleet management, data
analysis, and the development of autonomous technology.
3.Shift in Skill Requirements: As the demand for drivers
decreases, the demand for tech-savvy professionals capable of managing
and maintaining autonomous systems will increase.
4.Economic Redistribution: As autonomous vehicles potentially
lower the cost of transportation, there could be broader economic effects.
5.Training and Education: To mitigate job displacement, significant
investments in training and education will be necessary to equip the existing
workforce with new skills relevant to the changing job landscape.

30. Impact of Autonomous Vehicles on the Environment
1.Emissions Reduction: Autonomous vehicles are
likely to be predominantly electric, contributing to a reduction in
greenhouse gas emissions.
2.Decreased Congestion: Improved traffic
management and reduced congestion could lead to smoother
rides with less idling, which in turn would lower emissions and
reduce air pollution.
3.Land Use Efficiency: With fewer parking lots needed
due to the rise of shared vehicle services, more land could be
available for green spaces, housing, or other community-
enhancing projects.
4.Resource Efficiency: Autonomous vehicles could
lead to more efficient use of vehicles through shared services,
reducing the number of cars needed and decreasing the
material footprint of the automotive industry.
5.Recycling and Sustainability Challenges:
The shift towards more electronics and batteries in vehicles
presents challenges for recycling and sustainability. The
industry will need to address the environmental impact of
battery production and disposal.

31. Market Disruptions
• Automotive Industry Transformation: The shift towards
autonomous vehicles will disrupt traditional automotive manufacturing,
favoring new entrants skilled in AI, software, and electronics.
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• Insurance Models: Autonomous vehicles promise to reduce the
number of accidents caused by human error, potentially lowering the
demand for auto insurance as we know it today.
• Public and Private Transport: The distinction between
public transport and private vehicle ownership could blur as autonomous
mobility services offer flexible, efficient alternatives to both.
• Urban Planning and Real Estate: With fewer cars needing
parking spaces due to increased sharing and efficiency, cities might see
vast areas of land currently used for parking repurposed for commercial or
residential use.
• Job Markets: There will be a significant shift in employment,
especially in professions involving driving, such as trucking and taxis.

32. Future Trends and Innovations
• Level 4 and 5 Autonomy: We are likely to see
significant strides in achieving Level 4 (high automation) and
possibly Level 5 (full automation) autonomy, where vehicles can
operate without human oversight under specific conditions or
universally.
• Improved Sensor Technology: Advances in
sensor technology, including LiDAR, radar, and cameras, will
improve the vehicles' ability to perceive and interact with their
environment more accurately and reliably.
• Enhanced AI and Machine Learning:
Algorithms will become more sophisticated, allowing vehicles to
make safer and more effective decisions in complex, dynamic
environments.

33. Conclusion
1.Mobility as a Service (MaaS): AV enable new business models such
as MaaS, where customers use vehicles on-demand, transforming how people
access transport services and potentially reducing the need for car ownership.
2.Increased Safety and Efficiency: Reduced human error in driving
can lead to safer roads and more efficient travel. Fewer accidents and optimized
driving patterns.
3.Environmental Benefits: AV are often electric or hybrid, reducing fossil
fuels pollution. Efficient routing and reduced traffic congestion.
4.Accessibility: AV could greatly enhance mobility for the elderly, disabled,
and others who are currently unable to drive, providing them with greater
independence and improving their quality of life.
5.Data-Driven Insights: The data collected by AV can be used to improve
traffic management, urban planning, and vehicle maintenance.
6.Global Supply Chains: In logistics and shipping, autonomous driving
technology can revolutionize supply chains by making freight transport safer,
faster, and less costly, impacting everything from warehousing to retail.

34. Questions?
Thanks for attention!
dr Velibor Ilić, velibor.ilic@ivi.ac.rs
Senior AI/ML Research Scientist
http://www.linkedin.com/in/velibor
http://www.researchgate.net/profile/Velibor_Ilic/
Autonomous Driving - Transforming the Automotive Industry Through AI