Enhancing Driving Safety Via Smart Sensing Techniques PDF Download

Author: Landu Jiang
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Languages : en
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Book Description
"Drivers' "illegal maneuver" and "unsafe behavior" contribute to a large number of traffic accidents every year, which are now receiving great attention from both government regulators and car manufacturers. Indeed, many research efforts have been dedicated to understanding and recognizing dangerous driving conditions to prevent crashes and injuries. In addition to the features that are already installed in the vehicles, enhancing driving safety via mobile sensing techniques (e.g., smartphones and wearables) is becoming increasingly successful with the deep penetration of smart computing. The mobile device today is equipped with numerous sensors, which has become a very effective platform to facilitate various safety applications. In this thesis, we leverage off-the-shelf mobile sensing platforms (i.e., smartphones and wrist-worn devices) to detect and analyze dangerous driving events. Our purpose is to use real-time alerts and long-term feedbacks to increase drivers' awareness of dangerous behaviors, which could help them shape good driving habits and promote safety. Specifically, two studies are presented: 1. SafeCam - analyzing intersection-related driver behaviors using smartphone sensors, and 2. SafeDrive - monitoring distracted driving behaviors using wrist-worn devices (e.g., smartwatch). The first study focuses on the intersection safety which is a critical issue in current roadway systems. In the United States, nearly one-quarter of traffic fatalities and half of all traffic injuries are attributed to intersections. We design SafeCam that uses embedded sensors (i.e., inertial sensors and cameras) on the smartphone to track vehicle dynamics while at the same time adopts computer vision algorithms to recognize traffic control information (e.g., traffic lights and stop signs). The system is able to detect dangerous driving events not only on roads but also at intersections including speeding, lane waving, unsafe turns, running stop signs and running red lights. Our second study addresses the distracted driving problem that has been considered as a major threat to the traffic safety. It is estimated that roughly 30% of vehicle fatalities involve distracted drivers, which cause thousands of injuries and deaths every year in the United States. SafeDrive is a driving safety system that leverages the wrist-worn (i.e.,smartwatch) sensors to prevent driver distractions. By tracking driver's hand motion and utilizing machine learning algorithms, SafeDrive can detect five most common distracting activities including fiddling with the control (e.g., infotainment systems), drinking/eating, using smartphones, searching items at the passenger side and reaching back seats. In the evaluation, we conduct extensive real-road experiments using different types of vehicles (e.g., sedan, minivan, and SUV) and recruiting multiple participants (15 for SafeCam and 20 for SafeDrive). The experiment results demonstrate that both SafeCam and SafeDrive are robust to real-driving environments, which could detect critical driving events and have great potential to educate drivers on how to safely operate the vehicle." --