Artificial Intelligence is redefining how commercial vehicles perceive and react to their surroundings. With the new EU General Safety Regulation (GSR 2024), systems such as the Blind Spot Information System (BSIS), Moving Off Information System (MOIS), and Passive Lane Change Assist (PLCA) have become essential for improving road safety. This talk explores how sensor-based perception, sensor fusion, and deep learning enable these intelligent systems to assist drivers in real time. It also highlights how Generative AI, simulation, and vehicle platooning are paving the way toward adaptive and autonomous transportation.

1. November 2025
AI on the Road: Smart Systems for Safer Commercial Vehicles
From Perception to Intelligent Assistance
Filip Tirnanić – R&D Team Lead, Aumovio Serbia

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The Shift in Mobility: From Manual to Intelligent
Driving
› Mobility has always been evolving
› 1950s: seat belts and basic safety
› 1990s: ABS and ESC
› 2010s: ADAS (Blind Spot, Emergency
Braking)
› 2020s: AI-driven assistance, connected
vehicles
› Entering from Level 0-2 (Assistance) to Level 3-
4 (Partial/High Automation)
› Future Level 5 – autonomous trucks
› Without AI, the vehicle is just a collection of
sensors
› AI as the essential enabler - turns data into
driving intelligence
Autonomou
s
Truck
Assistance and
High
Automation
Truck
No
Automation
Features
Truck

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Why Safety Systems Matter: GSR 2024 & Real-World
Need
› Every year, human error causes ~90% of
traffic accidents.
› Vulnerable road users (cyclists, pedestrians)
are at greatest risk near large vehicles.
› Trucks make up a small share of vehicles but
~15% of EU road deaths.
› EU General Safety Regulation (GSR 2024)
makes key driver-assistance systems
mandatory in new trucks and buses.
› Vision Zero (EU’s goal to eliminate road
fatalities)
› Safety systems like BSIS, MOIS, and ISA are
now the foundation for next-gen AI-assisted
driving.
EU road fatalities by road user
(2021)

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System Zones
Smart Safety Systems: The Intelligence Behind the
Driver
BSIS System
MOIS System
GSR CV Systems

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How AI Powers Intelligent Driving – From Perception
to Action
›All smart systems share the same intelligence
structure
›Three-layer structure is used across all
safety and automation systems
›Perception: sensors + AI detect and classify
objects
›Decision: algorithms evaluate context and
predict motion
›Action: system warns or assists the driver in
real time
›Designed for on the edge computation –
milliseconds from sensing to response

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Perception Layer – Seeing Through Sensors
FRight 3D Radar
RRight 3D Radar
Camera
Fusion
CNN
NN
Output Fused Information
Environment Estimation
• Real-time detection of moving and stationary
objects
• Continuous scene awareness in complex
urban traffic
• Robust in rain, fog, and darkness
cyclist

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Decision and Action Layer - From Awareness to
Assistance
Decision Layer:
1. Object Tracking &
Prediction Motion
estimation with uncertainty
2. Context Awareness
Understanding road
situations
3. Decision Logic
Evaluate risks, determine
alert level
cyclis
t
truck
Action Layer:
Driver Interaction – visual/ acoustic/ vibration
feedback for safe reaction
Rear mirror
visual interaction
HMI interaction
Steering wheel
vibration interaction

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From Safety Systems to Learning Fleets: AI Driving the Next Era
of Mobility
Autonomous Trucking
Evolution
› Collaboration: Aumovio &
Aurora – building L4
autonomous truck platforms
› Focus on reducing driver
fatigue, increasing logistics
efficiency, and expanding 24/7
freight operations
› AI stack integrates radar, lidar,
camera fusion with robust
decision & control
Smart Platooning &
Connected Fleets
› Trucks communicate and drive
in coordinated platoons,
reducing drag and energy use
› V2V and V2X enable collective
intelligence on the road
› Enhances safety and overall fleet
optimization
Generative AI for Training
and Simulation
› Use of GenAI to synthesize
training data for rare and
complex scenarios
› Scenario generation for
perception and decision-making
models
› Bridges the data gap and
reduces expensive real-world
testing
Continuous Learning and
Edge Intelligence
› Vehicles learn collaboratively
via federated learning –
sharing model updates, not raw
data
› Enables on-the-road model
refinement and safer
adaptation to new conditions
› Foundation for self-improving
fleets of the future

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Thank You for
your
attention!
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