The Predictive Autonomy Lab (PAL) provides a simulation environment to explore how humans interact with automated driving systems – focusing on the relation to safety risks identified through systematic safety engineering.
Empirical driver research experiments driven by safety engineering
The Predictive Autonomy Lab (PAL) provides a simulation environment to explore how humans interact with automated driving systems – focusing on the relation to safety risks identified through systematic safety engineering.
In our driving simulator laboratory, we conduct empirical research on driver behavior from a safety perspective. Using safety engineering methods, we investigate safety‑critical driving situations to build and apply actionable safety knowledge. The simulator enables more accurate modeling of driver behavior by accounting for the external environment and interactions with other road users. These causal insights into human decision‑making improve the performance of driving automation systems. We also design and evaluate warning and intervention strategies that explicitly consider environmental influences on risk and safety, making them more timely, precise, and effective.
At the Predictive Autonomy Lab, Fraunhofer IESE offers safety-motivated research that ensures compliance with current standards and enables cost-efficient safety argumentation through validated driver behavior models.
There is a lack of accurate and safe driver monitoring systems that meet the driver’s expectations. Drivers who monitor DCAS are prone to distractions. However, today's DMSs remain limited: they assume worst-case driving situations, rely solely on data from inside the vehicle, and use simplified warning and intervention logic. This leads to frequent false alarms that frustrate and confuse drivers and undermine market acceptance, while rapidly evolving safety regulations make continuous compliance increasingly difficult.
Our situation-sensitive and risk-aware driver monitoring approach can increase customer acceptance and satisfaction. It helps you to design better DMS solutions embedded into the safety engineering processes while benefiting from the relation between identified safety risks and the behavior of the driver in its supervision task.