Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents

In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

The suitability of coconut shell concrete as a replacements in term of mechanical and thermal properties – a review

The most critical issue in environment protection and natural resource conservation is waste management [1]. Changes in environment and an increase in population are the main causes of the many processes of deterioration which have altered the ecosystem of our planet, including the generation of municipal solid waste (MFS) [2]. Therefore, there is a need to reuse waste to create a greener and healthier place on earth. The usage of agricultural waste will be emphasized in this research. Being renewable, low-cost, lightweight, having high specific strength and stiffness have made agricultural waste ideal for use as construction materials [3]. Coconut shell, oil palm shell, oil palm clinker, corncob ash, and rice husk ash are all agricultural by-products. Although some of these materials can be used as animal feed or fuel in biomass power plants or boilers of various industrial sectors to produce steam, a lot of these materials are still disposed off into landfills or burnt. This leads to serious environmental problems..

Visual analytics of flight trajectories for uncovering decision making strategies

In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences

Effect of transient event frequency content and scale on the human detection of road surface type

This paper describes two laboratory-based experiments which evaluate the effect of transient event frequency content and scale on the human detection of road surface type by means of steering wheel vibration. This study used steering wheel tangential direction acceleration time histories which had been measured in a mid-sized European automobile that was driven over two different types of road surface. The steering acceleration stimuli were manipulated by means of the mildly non-stationary mission synthesis (MNMS) algorithm in order to produce test stimuli which were selectively modified in terms of the number, and size, of transient vibration events they contained. Fifteen test participants were exposed to both unmanipulated and manipulated steering wheel rotational stimuli by means of a steering wheel vibration simulator. For each road surface type a total of 45 vibration test stimuli were presented to each participant. Each participant was asked to state, by means of a simple "yes" or "no" answer, whether each individual stimuli was from a road surface which was being presented in front of the simulator as a picture on a large board. Using Signal Detection Theory as the analytical framework the results were summarized by means of the detectability index d' and by means of receiver operating curve (ROC) points. Improvements of up to 20 percentage points in the rate of correct detection were achieved by means of selective manipulation of the steering vibration stimuli. The results suggested that no single setting of the MNMS algorithm proved optimal for both two road surface types that were investigated