Producing a written report.


WP1: Gathering evidence on Human Factors from data

D1.1. Methods to harmonize data on human driving performance from different datasets
D1.2. Harmonized, annotated, and processed data in usable format
D1.3. Methods to extract statistically significant relationships between human/external factors and driver behavioral mechanisms, in uncritical and critical situations
D1.4. Open-source library of data mining techniques
D1.5. Causal relationships between human/external factors and human driving behaviors: modelling requirements & framework of testable hypotheses
D1.6. Methodology and results: relevant use cases and safety-critical scenarios

WP2: Robust 4D human driver models under uncertainty

D2.1. i4Driving framework design & modeling, and coding design principles
D2.2. Suite of unit tests for model development
D2.3. Incremental versions of i4Driving / software library
D2.4. Integrated LC with social interactions and HF models – theory and principles
D2.5. Integrated LC with social interactions and HF models – verification and calibration
D2.6. Demo integration in CommonRoad and OTS/AIMSUN/CARLA
D2.7. Sensitivity Auditing

WP3: Mapping human factors into driving performances through driving simulation

D3.1. Validated ethics assessment plans and approval from Ethics committee
D3.2. Experimental setup for the driving simulator experiments
D3.3. Software for automatically increasing the criticality of scenarios
D3.4. Map of the heterogeneity of human/external factors into driving behaviour performances
D3.5. Report on the Turing test of the probabilistic 4D library of human driver models

WP4: Encoding the heterogeneity of human and external factors into probabilistic driver behavioural models

D4.1. Critical review of state-of-the-art techniques to model drivers’ heterogeneity
D4.2. Estimation methodology to encode drivers’ heterogeneity into models
D4.3. Robust methodology to validate probabilistic human driver behavioural models, at traffic and safety scales
D4.4. Open-source library of techniques to encode drivers’ heterogeneity into models
D4.5. Open-source library of validated probabilistic human driver behavioural models

WP5: Field testing of human behaviours in urban and freeway ODDs

D5.1. Validated ethics assessment plan and approval from Ethics committee
D5.2. Evaluation criteria and detailed description of field experiments
D5.3. Experimental data relevant to model development
D5.4. Experimental data relevant to model validation

WP6: Evaluating 4D human driver models in target applications

D6.1. Implementation framework of the scenario-based evaluation workflow
D6.2. Specification of the scenario description language, and conversion tool
D6.3. Open-source GitHub evaluation software toolchain
D6.4. Demonstrator of five target applications
D6.5. Human driver models as baseline for PEARS
D6.6. Human driver models as baseline for consumer testing campaigns of ADS
D6.7. Human driver models used in a mix and constantly evolving traffic for ADS testing
D6.8. Human driver models used for safety and traffic/energy efficiency evaluation
D6.9. Human driver models used for the insurance qualification scheme

WP7: Reproducibility, dissemination and exploitation

D7.1. Dissemination and exploitation plan (including communication activities)
D7.2. Website and social networks profiles
D7.3. Report on dissemination activities, including cooperation with other projects
D7.4. Final booklet
D7.5. White paper
D7.6. Exploitation plans for each partner

WP8: Project coordination and management

D8.1. Project glossary
D8.2. Project Management Handbook
D8.3. Project Quality Handbook
D8.4. Research Data Management Plan 1
D8.5. Project glossary update
D8.6 Project Quality Handbook update
D8.7 Research Data Management Plan 2