Integrated Cooperative Automated Vehicles
Duration: 1/2017 – 12/2020 Funding: NWO – TTW
In i-CAVE, a Cooperative Dual Mode Automated Transport (C-DMAT) system is researched and designed, consisting of dual-mode vehicles which can be driven automatically and manually to allow maximum flexibility. The program integrates technological roadmaps for automated and cooperative driving, accelerating the development of novel transportation systems addressing today’s and future mobility demands. Besides these enabling technologies, the focus is put on fault tolerance and fail-safety, wireless communications, human factors and others addressing transition of control between manual and automated driving and the response of other road users. i-CAVE tackles the main challenges of automated driving, i.e., achieving high levels of safety and reliability through rigorous technological design, combined with seamless integration between automated and manual driving to obtain maximum flexibility and user acceptance. A living-lab will be used for the integration and evaluation of accurate vision-based mapping and localization techniques, distributed cooperative vehicle control algorithms and fleet management methods. In addition, it allows for a close-to-market transport system, which can be commercialized by the transport industry, specifically leading automotive tiers in the Netherlands, by applying the results in their roadmaps.
Almende mainly contributes to the sub-project of dynamic fleet management, which is concerned with developing methods that take into account the uncertainty in demands and dynamic situations. Almende utilizes and extends its DEAL platform to facilitate information flows between agents in the system (drivers and end-users) and enable continuous planning.
The project provides a practical use case for the DEAL platform, which encompasses Almende’s vision on eliminating planning and optimizing information flows in the logistics sector. By applying this logic to (partly) autonomous vehicles, new insights can be gained on the application of DEAL to future logistics networks.