The vision of i4Driving is to lay the foundation for a new industry-standard methodology to establish a credible and realistic human road safety baseline for virtual assessment of CCAM systems. The two central ideas we propose are (1) a multi-level, modular and extendable simulation library that combines existing and new models for human driving behavior; in combination with (2) an innovative cross-disciplinary methodology to account for the huge uncertainty in both human behaviors and use case circumstances.
To establish a new safety assessment standard for CCAM relative to human drivers, we argue that we need to address 3 major scientific challenges (CH), which we translate to 9 specific objectives (SO) in this project, all realistically achievable, measurable, and verifiable through defined KPIs. All challenges are highly interdisciplinary, and 9 key innovations (INNO) were envisage to address them, bringing us (far) beyond the state-of-the-art (in research, products and services already available in the market).
1st challenge: A human driver model that captures the relevant behavioural mechanisms for safety assessment. Two specific objectives to address this challenge:
• SO1: Identify causal relationships between external, and human factors, and safety-critical driver behaviors from naturalistic driving studies (NDS) and driving simulation experiments (DSE) data, at the level of specific driving situations (WP1).
• SO2: Make the most of unveiled patterns and existing behavioural and psychological theories to augment existing models with a perception-cognitive layer (WP2).
2nd challenge: Modelling the heterogeneity of human driving behaviours. Three specific objectives to address this challenge:
• SO3: Define a methodology for generating use-cases and simulation scenarios which continuously challenge human drivers in DSE (WP1, WP3).
• SO4: Map heterogeneity of human and external factors into driving performances by means of DSE, in specific driving situations (WP3).
• SO5: Encode driver heterogeneity into probabilistic human behavioural models (WP4).
3rd challenge: Credibility of model-based inferences. Four specific objectives:
• SO6: Set up a “Modelling of the Modelling Process” (WP2, WP4).
• SO7: Define a methodology to validate, or better corroborate human driver models at multiple scales (WP3, WP4, WP5).
• SO8: Evaluate models in target applications (WP6).
• SO9: Ensure transparency, reproducibility and effective communication (WP7 and WP8).
