This paper proposes a driver assistance system with a collaborative control framework between the human driver and a Collision Avoidance System (CAS) for vehicles on the road while considering the driver drowsiness status. Driver drowsiness detection is performed with two inputs: driver's facial data from a camera mounted in front of the driver and steering wheel data from the car controller system. We use the driver's drowsiness state as an input to the collaborative control framework in which the CAS algorithm runs in parallel with the human control and only intervenes under certain situations to assist the human driver. Experiments were performed on a simulated vehicle driving testbed to evaluate our drowsiness detection system and demonstrate the effectiveness of the proposed collaborative control framework.

Drowsiness is one of the main causes of severe traffic accidents occurring in our daily life. In order to reduce the number of drowsiness-induced accidents, various researches have been conducted with the aim of finding practical and non-invasive drowsiness detection systems by using behavioral measuring techniques. Many of the previous works on behavioral measuring techniques have mainly focused on the analysis of eye closure and blinking of the driver. It is recently that more attention started to shift to inclusion of other facial expressions and only few, among those researches, have been done on the analysis of temporal dynamics of facial expressions for drowsiness detection. In this paper we propose a new method of analyzing the facial expression of the driver through Hidden Markov Model (HMM) based dynamic modeling to detect drowsiness. We have implemented the algorithm using a simulated driving setup. Experimental results verified the effectiveness of the proposed method.

In this paper, we propose and develop a framework for automatic switching of manual driving and autonomous driving based on driver drowsiness detection. We first present the scale-down intelligent transportation system (ITS) testbed. This testbed has four main parts: an arena; an indoor localization system; automated radio controlled (RC) cars; and roadside monitoring facilities. Second, we present the drowsiness detection algorithm which integrates facial expression and racing wheel motion to recognize driver drowsiness. Third, a manual and autonomous driving switching mechanism is developed, which is triggered by the detection of drowsiness. Finally, experiments were performed on the ITS testbed to demonstrate the effectiveness of the proposed framework.