Can adas distract driver’s attention? An rgb-d camera and deep learning-based analysis

Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques

Effects of different push-to-talk solutions on driving performance

Police officers have been using the Project54 system in their vehicles for a number of years. They have also started using the handheld version of Project54 outside their vehicles recently. There is a need to connect these two instances of the system into a continuous user interface. On the other hand, research has shown that the PTT button location affects driving performance. This thesis investigates the difference between the old, fixed PTT button and a new wireless PTT glove, that could be used in and outside of the car. The thesis describes the design of the glove and the driving simulator experiment that was conducted to investigate the glove\u27s merit. The main results show that the glove allows more freedom of operation, appears to be easier and more efficient to operate and it reduces the visual distraction of the drivers

Older drivers' requirements for navigation and route guidance information

This thesis examines older drivers and navigation. Over more recent times the proportion of older people in the UK population has risen. This effect is carrying over into the driving population. Due to changing demographics, increased longevity and the increased universality of driving there will be many more older drivers than before. Older drivers find the navigation task of finding their way on unfamiliar routes very demanding due to declines in sensory and cognitive abilities. New technology such as route guidance systems could aid the older driver in this task, by reducing uncertainty when meeting decision points on the road network. However such systems can also cause distraction. This has led to concern over the safety implications of the implementation of route guidance systems into cars. If route guidance systems are to be designed safely then the characteristics and needs of the driver have to be identified. The aim of this thesis was to provide human factors knowledge in this area so that route guidance systems can be designed to meet the needs of older drivers. [Continues.

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced