Imagine handing someone the keys to a self-driving car — they have no idea when to pay attention, when to take over, or what the car will do next. This project figured out how people actually behave in automated vehicles across cars, trucks, buses, trains, and even drones, then built VR training tools and better dashboard interfaces so the handover between human and machine goes smoothly. They tested everything with over 1,000 real drivers and passengers in 12 pilots across Europe, from simulators to real roads.
Companies adopting automated vehicles — from trucking fleets to public transit operators — face a dangerous gap: their drivers don't know how to safely interact with automation. When should a driver take over? What happens during a handover? How do you train thousands of operators for vehicles that behave differently from anything they've driven before? Getting this wrong means accidents, liability, and public backlash that can stall entire AV deployment programs.
The project built VR-based training scenarios (3D for VR goggles), web training applications, optimized human-machine interfaces for driver-vehicle handovers, and behavioral models predicting how drivers react to automation across cars, trucks, buses, rail, and drones. All tools were validated through 12 European pilots with over 1,000 drivers and revised based on real feedback.
If you are a fleet operator rolling out trucks or buses with Level 2-4 automation — this project developed VR-based training tools and behavioral models tested with over 200 AV operators that show exactly how drivers react to automation handovers. This means fewer accidents during the transition period and a structured training program ready to deploy across your fleet.
If you are an automotive company designing cockpit interfaces for semi-automated vehicles — this project built and tested optimized HMI designs for driver-vehicle handovers across 12 European pilots with over 1,000 drivers. Their behavioral models quantify the gap between what drivers expect automation to do and what it actually does, letting you design interfaces that close that gap.
If you are a training provider preparing professional or consumer drivers for automated vehicles — this project created 3D VR-goggle training scenarios, web applications, and social media training content covering cars, trucks, buses, and rail. These tools were validated with participants ranging from a few hours to 6 months of training exposure across multiple transport modes.
The project data does not include pricing or licensing terms. As an RIA (Research and Innovation Action), results are typically available under open or negotiable license terms. Contact the coordinator at CERTH (Greece) to discuss commercial licensing for the VR training tools and HMI designs.
The project validated its tools with over 1,000 AV drivers/passengers and 200 AV operators across 12 pilots in multiple countries. The training tools include web applications and VR scenarios designed for scalable deployment, though moving from pilot to enterprise-wide rollout would require integration work.
IP is shared among 36 consortium partners across 16 countries under Horizon 2020 rules. Key IP likely sits with CERTH (coordinator, Greece) and the 7 industry partners. Specific licensing arrangements would need to be negotiated with the relevant partners.
The project researched legal, ethical, and operational issues around automated driving and produced policy recommendations and a Roadmap to Automation. FIA (international automobile federation), UITP, and IRU were involved, giving the guidelines industry backing. However, these are recommendations, not certified regulatory standards.
The project covers automated cars, powered two-wheelers, trucks, buses, mini-buses, rail, workboats, and drones. This breadth means the behavioral models and training approaches can transfer across modes — useful if your fleet spans multiple vehicle types.
Validation happened through 12 pilots across Europe using driving simulators, VR/AR simulation toolkits, test tracks, and real-world environments. Over 20,000 citizens were also involved, and KPIs included user acceptance, awareness of actual vs. expected automation performance, and cost effectiveness.
The project closed in October 2022. Based on available project data, there is no information about a direct follow-up. However, the consortium's size (36 partners) and industry involvement (7 companies, 6 SMEs) suggest continued development is possible through individual partners.
The consortium of 36 partners from 16 countries is large and well-balanced for transport research: 10 research organizations, 9 universities, 7 industry players, and 10 other entities including major associations like FIA, UITP, and IRU. The 7 industry partners and 6 SMEs (19% industry ratio) ensure real-world grounding, though this is a research-heavy consortium. The coordinator CERTH is a leading Greek research center with strong transport credentials. The geographic spread across 16 countries — including key automotive markets (DE, FR, IT, SE, NL) — means the pilot results reflect diverse European driving cultures and regulatory environments. For a business buyer, the involvement of FIA and transport unions means the guidelines carry industry weight beyond pure academic research.
CERTH (Centre for Research and Technology Hellas), Greece — reach out via their transport research division
Want to access the VR training tools or behavioral models from this project? SciTransfer can connect you directly with the right consortium partner for your use case.
