Advanced Driver-Assistance Systems (ADAS) are now standard in most modern vehicles: automatic emergency braking, lane keeping, adaptive cruise control, blind spot detection… All of these systems rely on accurate perception and rapid reaction. But their true performance is also shaped by a sometimes overlooked factor: vehicle dynamics.
Understanding, modeling, and validating vehicle dynamics is not just a technical step — it is a core requirement for safety and functional reliability. This is true both in virtual simulations and in Hardware-in-the-Loop (HIL) testing environments.
What do we mean by vehicle dynamics?
Vehicle dynamics refers to the physical behavior of a vehicle in motion: acceleration, braking, cornering, weight transfer, tire grip, suspension response… It is determined by a range of parameters including mass, drivetrain, wheelbase, tire characteristics, center of gravity, and more.
In simulation, these dynamics are represented by mathematical models of varying complexity — from simple 3-DOF (Degrees of Freedom) kinematic models to high-fidelity multibody systems.
In SCANeR™, AVSimulation offers a wide library of vehicle dynamics models, including advanced multibody configurations, tailored to the needs of ADAS validation and real-time simulation.
Why vehicle dynamics directly impacts ADAS performance
Even the most advanced ADAS systems ultimately control a physical vehicle. That vehicle doesn’t react instantly or identically in all conditions. It has inertia, friction limits, steering behavior, and nonlinear responses under load.
Take a common example: automatic emergency braking (AEB). If the simulated vehicle uses an oversimplified model that ignores braking distances, tire conditions or mass load, the system might appear to work perfectly — but in reality, the vehicle may not stop in time.
This issue extends to other key ADAS functions:
- Automated lane change, affected by yaw rate and lateral dynamics
- Automated parking, dependent on chassis geometry and steering constraints
- Adaptive cruise control, relying on realistic acceleration and deceleration curves
In each case, a realistic vehicle dynamics model ensures that the ADAS system is not only logically correct — it is physically valid and safe.
Essential for both simulation and HIL testing
Many AVSimulation customers use our models in Hardware-in-the-Loop (HIL) environments, where real ECUs are connected to a simulated vehicle.
In these setups, real-time feedback on speed, yaw, lateral acceleration, brake pressure, etc. is provided directly from the vehicle dynamics model running in SCANeR™. If that model is too basic, the tests lose credibility and fail to represent real-world risks.
To address this, we’ve developed a comprehensive library of dynamics models adapted for ADAS validation. These allow engineers to:
- Run Euro NCAP or ISO 21448/SOTIF test scenarios with real physical behavior
- Simulate edge cases such as loss of grip, oversteer, emergency maneuvers
- Calibrate dynamics based on OEM-specific vehicle parameters
Case study: simplified vs high-fidelity modeling
A leading OEM compared the same AEB scenario under two different configurations:
🔹 A basic 3-DOF kinematic model
🔹 A full 10-DOF vehicle dynamics model with detailed tire and suspension behavior
In the first case, the AEB system triggered and stopped the car in time.
But in the high-fidelity simulation, the vehicle did not stop in time due to tire slip and reduced grip on a wet surface.
This highlights a critical reality: ADAS performance is not just a function of logic and perception — it depends heavily on how the vehicle actually behaves.
Simulation: accelerating safe ADAS validation
By integrating realistic vehicle dynamics, simulation allows teams to:
- Validate control strategies in critical scenarios
- Reduce physical testing by prioritizing high-risk edge cases
- Run repeatable, low-cost, zero-risk tests
- Prepare early for formal certification campaigns like Euro NCAP
SCANeR™ is used by OEMs and Tier 1 suppliers to speed up the development of safe, reliable ADAS functions in complex environments.
Conclusion: the hidden but essential pillar of ADAS validation
ADAS systems are often judged by their sensors or algorithms — but in the end, they must control a physical vehicle with real-world limitations.
This is why any serious ADAS validation, whether virtual (SIL) or real-time (HIL), must include accurate, dynamic modeling of the vehicle. Without it, we risk validating systems that fail when physics take over.
