Distracted driving is one of the main contributors to traffic accidents. This paper proposes a deep learning approach to detecting multiple distracted driving behaviors. In order to obtain more accurate detection results, a synchronized image recognition system based on two cameras is designed, by which the body movements and face of the driver are monitored respectively. The images captured from driver's body and face areas are fed to two Convolutional Neural Networks (CNNs) simultaneously to ensure the performance of classification. The data collection and validation processes of the proposed distraction detection approach were conducted on a laboratory-based assisted driving testbed to provide near-realistic driving experiences. Our dataset includes distracted and safe driving images of the drivers. Furthermore, we developed a meaningful and practical application of a voice-alert system that alerts the distracted driver to focus on the driving task. We evaluated VGG-16, ResNet, and MobileNet-v2 networks for the proposed approach. Experimental results show that by using two cameras and VGG-16 networks, we can achieve a recognition accuracy of 96.7% with a computation speed of 8 fps.

With a goal to improve transportation safety, this paper proposes a collaborative driving framework based on assessments of both internal and external risks involved in vehicle driving. The internal risk analysis includes driver drowsiness detection and driver intention recognition that helps to understand the human driver's behavior. Steering wheel data and facial expression are used to detect the driver's drowsiness. Hidden Markov models are adapted to recognize the driver's intention using the vehicle's lane position, control, and state data. For the external risk analysis, a co-pilot utilizes a collision avoidance system to estimate the collision probability between the ego vehicle and other nearby vehicles. Based on the risk analyses, we design a novel collaborative driving scheme by fusing the control inputs from the human driver and the co-pilot to obtain the final control input for the ego vehicle under different circumstances. The proposed collaborative driving framework is validated in an assisted-driving testbed, which enables both autonomous and manual driving capabilities.

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.

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.