Driving Skill Modeling Using Neural Networks for Performance-Based Haptic Assistance
Hojin Lee, Hyoungkyun Kim, Seungmoon Choi
Abstract
This article addresses a data-driven framework, modeling expert driving skills for performance-based haptic assistance using neural networks (NNs). We have built a haptic driving training simulator to collect expert driving data and to provide proper haptic feedback. We establish an expert driving skill model by training NNs with the collected data. Then, the skill model is applied to the performance-based haptic assistance to provide optimized references of the steering/pedaling movements. We evaluate the skill model and its application to the performance-based haptic assistance in two user experiments. The results of the first experiment demonstrate that our skill model has appropriately captured experts' steering/pedaling skills. The results of the second experiment show that our performance-based haptic assistance can help novice drivers perform steering as expert drivers, but cannot assist their pedaling performance.