Multimodal Polynomial Fusion for Detecting Driver Distraction

Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone. Although there has been a considerable amount of research on modeling the distracted behavior of drivers under various conditions, accurate automatic detection using multiple modalities and especially the contribution of using the speech modality to improve accuracy has received little attention. This paper introduces a new multimodal dataset for distracted driving behavior and discusses automatic distraction detection using features from three modalities: facial expression, speech and car signals. Detailed multimodal feature analysis shows that adding more modalities monotonically increases the predictive accuracy of the model. Finally, a simple and effective multimodal fusion technique using a polynomial fusion layer shows superior distraction detection results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Integrated Automotive Safety System

Most new cars today come with a host of advanced safety features including automated systems that assist the drive in maintaining control of the car and warning the driver of possible dangers. The problem, however, is that while these kinds of features greatly increase the safety of a car, they are prohibitively expensive and only available in new, high-end cars. The purpose of this MQP is to create a device that would integrate a series of safety features that could be installed in any car on the road at minimal cost to the consumer

Drivers' perceptions of smartphone applications for real-time route planning and distracted driving prevention

Given the increasing importance and availability of traffic-related smartphone applications, understanding their potential use is vital, especially in developing countries. This research explores motorist perceptions of the installation and use of two smartphone applications - a distraction-prevention application and a real-time traffic information and navigation application - in Qatar, a rapidly developing country in the Arabian Gulf region. This study represents the first attempt to investigate the potential market for these types of applications in a region with a unique social and cultural environment. A questionnaire-based survey was conducted to examine the drivers' interest in using both applications, their willingness to buy the applications, and their data privacy concerns. The results indicated that the potential market for these types of smartphone applications in Qatar is high. The potential for the real-time route planning application was found to be much higher than that of the antidistraction application, especially among female drivers. A high percentage of the drivers, especially younger and local drivers, were less enthusiastic about installing and using the distracted driving prevention application. Most of the participants willing to use both smartphone applications did not have data privacy concerns, but in return for allowing the applications to access their data, they expected some reduction in travel time and a safer trip. These findings provide a direction for the development of future policies and smart solutions in this region.This publication wasmade possiblebya UREP Award [UREP 22-062-2-022] from the Qatar Research Fund (a member of Qatar Foundation).Scopu

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Artificial Intelligence in Modern Society

Artificial intelligence is progressing rapidly into diverse areas in modern society. AI can be used in several areas such as research in the medical field or creating innovative technology, for instance, autonomous vehicles. Artificial intelligence is used in the medical field to improve the accuracy of programs used for detecting health conditions. AI technology is also used in programs such as Netflix or Spotify. This type of AI will monitor a user’s habits and make recommendations based on their recent activity. Banks use AI systems to monitor activity on members’ accounts to check for identity theft, approve loans and maintain online security. Systems like these can even be found in call centers. These programs analyze a caller’s voice in real time to provide information to the call center which helps them build a faster rapport with the caller. The purpose of this research paper is to explain how artificial intelligence is creating advanced technologies in various fields of study which will create a more efficient society