Safe driving with mobile devices and wearables
Creators: name: Karatas, Cagdas
Publisher: name: No Publisher Supplied
Year: 2018

description: Driver errors due to distracted driving and inadequate awareness of surroundings  is an increasing concern which has led to national attention. With increasing amount  of information available through the ecosystem of in-vehicle devices like smartphones,  wearables, and mobile OS for cars, opportunities to prevent accidents with safety services  are now more available than ever before. The safety services are expected to be aware of  the driver’s actions and car’s context, and prevent unsafe driving. Mobile and wearable  safety apps differ from conventional built-in automotive safety systems in that they  promise low-cost designs that reach a much larger population. On the other hand,  detecting the driver’s actions and the car’s context from mobile sensors is a challenging  task due to the dynamic nature of the car and the sensors.  To assist in safer driving, we designed and evaluated fundamental solutions that  can be used to detect and prevent driver errors. First, we propose methods to monitor  the vehicle, driver’s steering, and attention to detect driver errors. The preventive  methods are auxiliary services which decrease the need for distracting interactions with  the vehicle and phone.  In order to detect driver errors, mobile devices and wearables such as wrist-worn  devices and head-mounted devices can be used. The mobile device can be used to  monitor the vehicle’s movements while the wearables can be used to monitor the driver’s  head and arm movements. We are particularly interested in the driver’s hand and head  ii  movements since these type of movements shows the user’s attention and the driving  interactions with the vehicle, particularly with the steering wheel. Additionally, the  detection techniques proposed in this thesis can be also be further utilized to prevent  errors by warning the drivers about dangerous conditions.  Preventive techniques propose a mechanism for convenient interaction between the  user and the environment. Examples of preventive techniques include easy-to use  customizable input interfaces as well as management of notifications to appropriate  communication channels and scheduling them to the most convenient times, e.g. when  the vehicle stops at a red traffic light.  Through various real-driving scenarios, we show that our approach can detect the  wrist-worn device user as driver correctly 98.9% of times and achieve hand on steering  wheel detection with a true positive rate around 99% and provide warning of unsafe  driving when a driver’s hand is off the steering wheel with a true negative rate above  80%. Additionally, the system can achieve accurate steering angle estimation with  errors less than 3.4 degrees to facilitate applications such as curve speed warning and  understeer/oversteer detection. In the second part of our system, we introduced a  novel method to estimate sensor orientation and the vehicle’s heading from only a  single inertial sensor in a moving vehicle. The method was able to estimate sensor  orientation with a mean error of 5.61o  for yaw angle and a 3.73o  for pitch angle while  the vehicle is driven in controlled environment and without restrictions. We believe  this method can be especially suitable for head tracking applications where the sensor’s  translational motion is limited. Finally, we proposed a framework that enables users  to create customizable printable paper button interfaces that might be used to create  shortcuts and reduce mobile-device usage related distractions. Our experiments indicate  that the sensor achieves touch detection accuracy over 99% with up to ten different  touch points and over 90% with 15 different touch points.