This project aims at developing fleet management optimization algorithms for driverless shuttles able to connect to a manned head vehicle to operate on regular roads.
Autonomous shuttles will be part of the renewal of the urban landscape by allowing the widespread use of on-demand mobility. Additionally, their electrical functioning makes them a sustainable and environmentally friendly solution, helping to increase public transport network coverage through last mile offerings.
This on-demand accessible transport project aims at coupling driverless shuttles with a “mother shuttle” that would be operated with a human driver. These driverless shuttles (with a capacity of 4-6 passengers) would therefore form a platoon and would be electronically connected with a manned head vehicle on regular roads. Depending on passengers’ origin-destination routes, shuttles could then separate from a given platoon to stop at a station, to be operated in an unmanned way along a short branch around the station, and to connect to another head vehicle/platoon that is operated towards another route/destination.
To successfully manage the diversity of situations autonomous vehicles will have to face, complex fleet management optimization algorithms have to be developed including flexibility in routing, scheduling and transferring while keeping high reliability for the services provided to the customers. These algorithms will be developed by the Urban Transport Systems Laboratory (LUTS) who has a renowned expertise in modeling, monitoring and control of urban transport systems.
This nine months’ project is conducted by the LUTS, led by Professor Nikolas Geroliminis. It is sponsored by Toyota.
| Principal investigator | Prof. Nicolas Geroliminis |
| Sponsor | Toyota |
| Period | 2016-2017 |
| Laboratory | LUTS |
| Collaboration | TRACE |