1 research outputs found
Evaluation of the Driving Performance and User Acceptance of a Predictive Eco-Driving Assistance System for Electric Vehicles
In this work, a predictive eco-driving assistance system (pEDAS) with the
goal to assist drivers in improving their driving style and thereby reducing
the energy consumption in battery electric vehicles while enhancing the driving
safety and comfort is introduced and evaluated. pEDAS in this work is equipped
with two model predictive controllers (MPCs), namely reference-tracking MPC and
car-following MPC, that use the information from onboard sensors, signal phase
and timing (SPaT) messages from traffic light infrastructure, and geographical
information of the driving route to compute an energy-optimal driving speed. An
optimal speed suggestion and informative advice are indicated to the driver
using a visual feedback. pEDAS provides continuous feedback and encourages the
drivers to perform energy-efficient car-following while tracking a preceding
vehicle, travel at safe speeds at turns and curved roads, drive at
energy-optimal speed determined using dynamic programming in freeway scenarios,
and travel with a green-wave optimal speed to cross the signalized
intersections at a green phase whenever possible. Furthermore, to evaluate the
efficacy of the proposed pEDAS, user studies were conducted with 41
participants on a dynamic driving simulator. The objective analysis revealed
that the drivers achieved mean energy savings up to 10%, reduced the speed
limit violations, and avoided unnecessary stops at signalized intersections by
using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated
using the Technology Acceptance Model (TAM) and Theory of Planned Behavior
(TPB). The results showed an overall positive attitude of users and that the
perceived usefulness and perceived behavioral control were found to be the
significant factors in influencing the behavioral intention to use pEDAS.Comment: Submitted to Transportation Research Part C: Emerging Technologies
Journa