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This paper presents the design of an Eco-Driving Assistant System (EDAS) in which the main goal is to minimize the energy use of battery electric vehicles, in particular, vehicles utilized for public transportation. The system optimizes the speed profile of a real route schedule while satisfying the constraints imposed on speed and time. It includes a driver feedback and a driver scoring GUI which allows the driver improving his/her driving skills and comparing him/herself to a “theoretical perfect driver”. The system also includes a backward simulator that generates information related to the vehicle operation under the particular route to be optimized. The output information from the simulator is used as an input to the optimization algorithm. The simulator was validated using real data from a battery electric vehicle. The EDAS system was tested for three different driving profiles and energy consumption reductions of up to 30.33% were achieved.

In the next 20 years fully autonomous vehicles are expected to be in the market. The advance on their development is creating paradigm shifts on different automotive related research areas. Vehicle interiors design and human vehicle interaction are evolving to enable interaction flexibility inside the cars. However, most of today’s vehicle manufacturers’ autonomous car concepts maintain the steering wheel as a control element. While this approach allows the driver to take over the vehicle route if needed, it causes a constraint in the previously mentioned interaction flexibility. Other approaches, such as the one proposed by Google, enable interaction flexibility by removing the steering wheel and accelerator and brake pedals. However, this prevents the users to take control over the vehicle route if needed, not allowing them to make on-route spontaneous decisions, such as stopping at a specific point of interest.