Automated Lane Keep Assist System

Automated Lane Keep Assist System: A study on the formation and evolution of the mental model of the user of ALKS

Shivankar, Shantanu (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics)

Happee, R. (mentor)
Espedito, Rusciano (mentor)
Stapel, J.C.J. (graduation committee)
Farah, H. (graduation committee)

Delft University of Technology

Mechanical Engineering | Vehicle Engineering | Cognitive Robotics

2021-11-12

The mental model is a very rarely studied topic when it comes to human-machine interaction. Nearly all the studies that are done in this field are on measuring the actual performance of the human while performing the collaborative task with the automation system rather than understanding the complete picture of how the human is understanding and interpreting the automation is his actions due to just chance or does he/she have logical the reasoning for his/her actions.
This research consists of recognising the factors that affect the mental model, designing the experiment that measures the mental model as well as the factors that affect it. This work focuses on ALKS (Automated lane keep assist system) which is a subset of the SAE level 3 automation system for which the rules are laid down by UNECE(United Nation Economic Commission for Europe).
The approach of this research consists of an experimental setup in which the participants are given training and then are allowed to experience the level 3 automation in the driving simulator. The training and the simulation are designed to give a different depth in the functionalities and limitations of the system. The weak training just briefly describes the limitations of the system and how to interpret the user interface based on research done by Strand [35] with the consumer of level 2 automation system, on the other hand, the stronger training explains in detail the functionality and the limitations of the system along with the video explanations of how the automation will react in the different scenarios. Both the training are available in English and Dutch depending language preference of the participant. The participants are divided into 2 groups both of them are given different training but both of them experience similar driving conditions on the driving simulator.
Later the performance of the mental model, as well as the performance of the factors affecting the mental model are measured. Each participant receives the training once and is subjected to 3 trials of the driving simulation to understand the learning effect of the mental model and its related factors. The results of the mental model performance show that there is a significant gap of 5.5% between the weak and the strong mental model group just after the training, this trend then continues until the last trial where a significant gap of 6.5% in the mental model score.
A positive learning curve is also observed starting from the training to the last trial, the learning curve has a positive trend but the data is statistically insignificant to show the difference in the learning rate of the two different groups. There is a significant gap observed between the automation acceptance level of the two groups, although there is no significant rise in the level of acceptance of automation from just after the training to the last trials their difference in the acceptance level is present after the training and all the trials. For the trust level, the difference between the two groups is statistically insignificant, the difference is too small and the number of participants for the experiment is too less. Analysing the pragmatic significance of the trust data, a positive trend can be seen for the strong group whereas the weak group shows a negative trend, even though at the start of the experiment the weak group had a higher trust level. For situational awareness, the weak model group shows the lower situation awareness throughout the lap as compared to the strong mental model group.

Mental Models
Automated lane keep assist system
Situation Awareness
Trust
Acceptance
DAVSi
Automated Vehicles
Simulation

http://resolver.tudelft.nl/uuid:80cd2195-da49-45a0-8e20-ca0c3fc50a00

Student theses

master thesis

© 2021 Shantanu Shivankar