An Online Data-driven Intelligent Automation Platform for Drivers Considering the Psychological Condition Instability and Behaviours for a Sustainable and Safe Transportation System
Human factors are always a key contributor to road traffic accidents. A research team aims to predict risk-taking behaviours that they proposed an online data-driven intelligent automation system that can help reduce risk-taking behaviours among drivers by taking into account psychological condition instability.
In the proposed system, cutting-edge neuro-ergonomics technologies were used to collect multi-modal data from drivers in real-time to identify driving mental fatigue and risk-taking patterns using machine learning and deep reinforcement learning techniques. The system is expected to provide relevant correction actions with drivers and attendants in due course to restore vigilance and safe-driving behaviour. The research outputs of this project would be a systematic framework for online data-driven risk-taking driving behaviour prediction to improve overall driving safety in Hong Kong. The development of commercial technical driving aids would be promoted based on a real-time intervention system as well in the near future.
The project has received support from the Smart Traffic Fund.
(Smart Traffic Fund is funding initiated by the Transport Department to support local organisations or enterprises for conducting research and application of innovation and technology with the objectives of enhancing commuting convenience, enhancing the efficiency of the road network or road space, and improving driving safety)
