Improving a vehicle driver’s performance decreases the damage caused by, and chances of, road accidents. In recent decades, engineers and researchers have proposed several strategies to model and improve driving monitoring and assistance systems (DMAS). This work presents a comprehensive survey of the literature related to driving processes, the main reasons for road accidents, the methods of their early detection, and state-of-the-art strategies developed to assist drivers for a safe and comfortable driving experience. The studies focused on the three main elements of the driving process, viz. driver, vehicle, and driving environment are analytically reviewed in this work, and a comprehensive framework of DMAS, major research areas, and their interaction is explored. A well-designed DMAS improves the driving experience by continuously monitoring the critical parameters associated with the driver, vehicle, and surroundings by acquiring and processing the data obtained from multiple sensors. A discussion on the challenges associated with the current and future DMAS and their potential solutions is also presented.

Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

The early detection of a driver's intention prior to the initiation of actual maneuvering is to offer effective means of assisting the driver in times of safety. Conventional approaches in predicting a driver's intention include framing the motion parameters of the car and driver as well as the eye gaze sequence of the driver into the Hidden Markov Model (HMM) for analysis. However, their power of describing driver behavior is not only limited but also their performance, indicated by the recognition rate relative to the amount of preceding time in early detection, is not satisfactory and needs further improvement. This paper presents an approach for early detection of a driver's intention by modeling and analyzing driver behavior by using structural pattern recognition based on context-free and context-sensitive grammars. We specifically structured a sequence of the driver's eye fixations as well as of the vehicle speed, steering angle and signaling into a sequence of symbolic vectors to form sentences representing a specific driver behavior. It turns out that the proposed approach resulted in an average of 70.5% and 80% recognition rates at the respective 2 and 1 second preceding time to the actual initiation of maneuvering behavior. This performance can be compared to 56.8% and 69% by the state-of-the-art conventional results.