Defining an Evaluation Method for External Human-Machine Interfaces
Authors: Jose Gonzalez-Belmonte, Jaerock Kwon
Organizations: Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, 48128, Michigan, United States of America. · Math and Computer Science, Lawrence Technological University, 21000 W Ten Mile Rd, Southfield, 48076, Michigan, United States of America.
As the number of fatalities involving Autonomous Vehicles increase, the need for a universal method of communicating between vehicles and other agents on the road has also increased. Over the past decade, numerous proposals of external Human-Machine Interfaces (eHMIs) have been brought forward with the purpose of bridging this communication gap, with none yet to be determined as the ideal one. This work proposes a universal evaluation method conformed of 223 questions to objectively evaluate and compare different proposals and arrive at a conclusion. The questionnaire is divided into 7 categories that evaluate different aspects of any given proposal that uses eHMIs: ease of standardization, cost effectiveness, accessibility, ease of understanding, multifacetedness in communication, positioning, and readability. In order to test the method it was used on four existing proposals, plus a baseline using only kinematic motions, in order to both exemplify the application of the evaluation method and offer a baseline score for future comparison. The result of this testing suggests that the ideal method of machine-human communication is a combination of intentionally-designed vehicle kinematics and distributed well-placed text-based displays, but it also reveals knowledge gaps in the readability of eHMIs and the speed at which different observers may learn their meaning. This paper proposes future work related to these uncertainties, along with future testing with the proposed method.
Figures & tables
Figure 1: Diagram illustrating the configurations used to determine visibility.
Figure 2: Diagram illustrating the external parts of the SUV from the simulation software, as defined by this section of the questionnaire.
Figure 3: Results showing how many times a Grid Point was recorded, across all scenarios where the vehicle was an SUV driving North, on the left side of the virtual environment, and the observer was looking North, towards the front of the vehicle.
Vehicle Part
Configurations where any Grid Point on the vehicle part was recorded in at least 50% of its permutations
Back Bumper.
II,X
Back Central Light
II
Back Fenders
II,IV,VII,VIII
Back Low Reflector
X
Back Plate
II,X
Back Window Rails
II,IV,VII,VIII
Table 1: Visibility of different external vehicle elements. Each row indicates the configurations from Fig. 1 in which any Grid Point on the given vehicle part was captured in at least 50% of its permutations.
Figure 4: The baseline example with no eHMIs.
Figure 5: Version of frontal braking lights being evaluated.
Figure 6: Example of the Knight Rider display mid-animation.
Figure 7: Example of the smiling display, taken from a promotional video of the Smiling Car concept [ 55 ] .
Figure 8: Example of a text display on the front bumper of a vehicle.
Autonomous driving develops vehicles that perceive road scenes, predict interactions, and plan and control safe movement with limited or no human input.
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