A key challenge for safe deployment of Connected, Cooperative, and Automated Mobility (CCAM) systems is the inherent uncertainty and diversity of human driving behaviour. Traditional simulation frameworks often lack the complexity needed to reflect real-world driving dynamics, especially under mixed traffic conditions involving both human drivers and automated systems.

The EC-funded i4Driving (Integrated 4D driver modelling under uncertainty) initiative is developing a set of virtual and safety assessment methodologies and tools grounded in realistic human driving behaviour.

By capturing the full spectrum of driving performance — from everyday interactions to safety-critical situations — and by integrating human factors and behavioural mechanisms, i4Driving enables more realistic representation of driver behaviour. The project approach helps to expose automated systems to a broad range of traffic conditions and behavioural patterns, including those that can trigger edge cases.

The project combines a multi-level simulation library integrating existing and new driver behaviour models with a cross-disciplinary safety assessment methodology to establish a credible and realistic human road safety baseline that supports the virtual evaluation and benchmarking of CCAM systems.

---

Source: The original article was published here