Autonomous self-adaptive services for TRansformational personalized inclUsivenesS and resilience in mobiliTy

Climate, Energy & MobilityHORIZON-RIAID: 101148123
EC Contribution
โ‚ฌ39,999
Consortium Size
16 orgs
Start Year
2024
โ–ถSummary

The AutoTRUST project aims to develop and demonstrate a novel AI-leveraged self-adaptive framework of advanced vehicle technologies and solutions which optimize usability, perception, and experience on-board, and when boarding/off-boarding, in terms of security, privacy, well-being, health and assistance. AutoTRUST provides enhanced inclusiveness and trust in the interaction between users and new automated modes of road transport and mobility services in the transition from human-driven to automated vehicles. Safety and security of vehicle occupants in all circumstances even when the vehicle is driverless by helping to prevent dangerous and inconvenient situations will be of paramount importance. Intense cooperation between users, vehicle manufacturers, suppliers, researchers, and other stakeholders to co-design vehicles with solutions that optimize the on-board experience will be adopted. Moreover, an in-depth knowledge of the benefits of new vehicle technologies and solutions in terms of on-board experience, accessibility, inclusiveness, and trust will be acquired to enable wider user acceptability and contribute to the creation of future standards.

Consortium (16)

Project Results (5)

Source: CORDIS, the EU research results database.

โ–ถPublications (5)
A Dynamic Environmental Comfort-Based Seat Selection Framework for Public Transport Vehicles
ยท 2025
Stylianidis Nearchos, Katelaris Leonidas, Hadjidemetriou Lenos, Laoudias Christos
A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm
ยท 2025DOI
Alexandros Gkillas, Christos Anagnostopoulos, Nikos Piperigkos, Dimitris Tsiktsiris, Theofilos Christodoulou, Theofanis Siamatras, Dimitrios Triantafyllou, Christos Basdekis, Theoktisti Marinopoulou,
Fast and Accurate Outlier-Aware Lidar Super-Resolution for Slam Applications
2025 IEEE International Conference on Image Processing (ICIP)ยท 2025DOI
Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
Guided Model-based LiDAR Super-Resolution for Resource-Efficient Automotive scene Segmentation
2025 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)ยท 2025DOI
Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing
2025 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)ยท 2025DOI
Maria Damanaki, Nikos Piperigkos, Alexandros Gkillas, Aris S. Lalos