Autonomous vehicles are becoming a reality and most of the major automakers have plans to commercially release an autonomous vehicle (nearly or fully self-driving, i.e. SAE levels L4 or L5 vehicles, respectively) by 2020-2024. However, the human factor will remain essential for the safety and performance of road transport in the forthcoming decades, mainly for two reasons:
Central to the human role in the Connected Automated Driving (CAD) is the transition from automated to manual driving mode. This might be system-initiated or user-initiated.
Evidently, in such a dynamic driver-vehicle interaction scheme, several challenges arise: to evaluate the driver’s availability to intervene; the transition must be supported by an appropriate and comprehensible Human-Machine Interfaces; a proper driver training; legal and ethics perspective.
Trustonomy will investigate, setup, test and comparatively assess, in terms of performance, ethics and acceptability, different relevant technologies and approaches in a variety of autonomous driving and RtI scenarios, covering different types of users (in terms of age, gender, driving experience, etc.), road transport modes (private cars, trucks, buses), levels of automation (L3 – L5), driving conditions, etc.
The specific objective of Trustonomy are:
Develop a Methodological Framework for the operational assessment of different Driver State Monitoring (DSM) systems
Develop a Methodological Framework for the operational assessment of various HMI Designs
Develop an ethical automated-decision-support framework, covering liability concerns and risk assessment
Develop novel Driver Training Curricula for human drivers of ADS
Define a Driver Intervention Performance Assessment (DIPA) Framework
Measure performance, trust and acceptance (simulations and field trials) of human drivers of ADS
Organise Communication and Exploitation Actions, Policy Recommendations and Contributions to Standards