In modern vehicles, advanced driver-assistance systems (ADAS) are built on a fundamental principle: see, understand, and react to what happens around the vehicle. This real-time perception is made possible by embedded sensors — cameras, radars, lidars — that continuously monitor the environment. But as ADAS functions become more sophisticated, these sensors are no longer just data collectors. They process. They interpret. They decide.
These are what we now call smart sensors, central to the most advanced ADAS architectures. Unlike traditional sensors, they embed their own processing logic, deliver actionable insights instantly, and can even detect malfunctions in their own operation. In systems where every millisecond counts, this level of autonomy makes all the difference.
A new role for vehicle-embedded sensors
A smart sensor is no longer just a passive observer. It also acts as a local brain. A front-facing camera doesn’t just identify a pedestrian — it analyzes their speed, posture, likely trajectory, and can trigger a warning if there’s a perceived risk. This ability to pre-process data directly at the source reduces the load on central ECUs and improves the system’s overall responsiveness.
This distributed approach aligns perfectly with Software Defined Vehicle (SDV) architectures, where intelligence is shared across embedded modules. Each smart sensor becomes an active node in the vehicle network, no longer a simple data relay.
Ensuring reliability for critical functions
When the vehicle must respond to complex, ambiguous situations — unpredictable pedestrians, dense traffic, partially obscured road signs — ADAS performance relies heavily on both data quality and its interpretation. That’s where smart sensors deliver real value.
They improve functional robustness, shorten reaction time, and introduce self-diagnostic features. If a lens is dirty or the signal degrades, the sensor can adapt its behavior or issue a system warning. These features are essential to meet SOTIF and ISO 26262 standards, and are easily integrated into rigorous validation workflows.
Case study: impact of external temperature
A typical simulation study shows that a vehicle’s range can drop by up to 30% when outside temperatures fall below 0°C. This can be explained by:
- Higher energy consumption for cabin heating
- Reduced battery performance in cold weather
- Lower energy recovery during braking phases
With simulation, these effects can be modeled, compared, and optimized right from the design phase.
Simulating to validate before physical testing
Testing a smart sensor in real-world conditions is time-consuming, expensive, and often risky in critical situations. Simulation helps overcome these limitations by modeling a wide variety of scenarios — sometimes extreme — in a safe and reproducible environment.
With SCANeR™, engineers can simulate driving scenes where sensors face a wide range of challenges: degraded weather, variable lighting, dense traffic, unpredictable human behavior. Sensor behavior can be evaluated in direct connection with the embedded processing logic, as if it were integrated in the vehicle.
Going further with real hardware: HIL testing
To expose smart sensors to realistic signals while testing their actual hardware behavior, Hardware-in-the-Loop (HIL) becomes essential. SCANeR™ natively integrates with the industry’s leading HIL platforms. This enables physical connection of the sensor or its ECU to the virtual environment, with precise signal synchronization.
With this approach, engineers can evaluate smart sensors in closed-loop conditions, over large-scale automated scenarios, including fault injection, configuration changes, or external disturbances. It’s one of the preferred methods used in our HIL applications for robust ADAS validation.
Toward safer, more autonomous vehicles
Smart sensors represent more than a hardware evolution — they signal a paradigm shift in embedded systems design. More reactive, safer, and autonomous, they enable tomorrow’s vehicles to better anticipate, decide, and adapt in real time.
To support this evolution, simulation plays a central role. It helps validate technical assumptions, secure onboard signal processing, and accelerate ADAS development while reducing risk and cost.
