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The use of smart portable devices in vehicles creates the possibility to record useful data and helps develop a better understanding of driving behavior. In the past few years the UTDrive mobile App (a.k.a MobileUTDrive) has been developed with the goal of improving driver/passenger safety, while simultaneously maintaining the ability to establish monitoring techniques that can be used on mobile devices on various vehicles. In this study, we extend the ability of MobileUTDrive to understand the impact on driver performance on public roads in the presence of distraction from speech/voice based tasks versus tactile/hands-on tasks. Drivers are asked to interact with the device in both voice-based and hands-on modalities and their reaction time and comfort level are logged. To evaluate the driving patterns while handling the device by speech/hand, the signals from device inertial sensors are retrieved and used to construct Gaussian Mixture Models (GMM).

In-vehicle signal processing plays an increasingly important role in driving behavior and traffic modeling. Maneuvers, influenced by the driver's choice and traffic/road conditions, are useful in understanding variations in driving performance and to help rebuild the intended route. Since different maneuvers are executed in varied lengths of time, having a fixed time window for analysis could either miss part of maneuver or include consecutive maneuvers in it evaluation. This results in reduced accuracies in maneuver analysis. Therefore, with access to continuous real-time in-vehicles signals, a suitable framing strategy should be adopted for maneuver recognition. In this paper, a non-uniform time window analysis is presented.

Towards developing of advanced driver specific active/passive safety systems it is important to be able to continuously evaluate driving performance variations. These variations are best captured when evaluated against similar driving patterns or maneuvers. Hence, accurate maneuver recognition in the preliminary stage is vital for the evaluation of driving performance. Rather than using simulated or fixed test track data, it is important to collect and analyze on-road real-traffic naturalistic driving data to account for all possible driving variations in different maneuvers. Towards this, massive free style naturalistic driving data corpora are being collected. Human transcription of these massive corpora is not only a tedious task, but also subjective and hence prone to errors/inconsistencies which can be due to multiple transcribers as well as lack of enough training/instructions.

Growing congestion in terms of competing technology within, and traffic outside the vehicle has motivated the evolution of advanced safety systems to be context and situation aware by processing multi-sensor information effectively providing timely decisions to assist the driver in driving safely. Towards vehicular and occupant safety, it is important to understand how drivers drive and to identify any variations in their driving performance. One approach to accomplish this is to analyze driving maneuvers. These maneuvers are influenced by the driver's choice and traffic/road conditions, so analyzing these gives an indication of the driving performance. Various framing strategies have been adopted to analyze these continuous temporal information in manageable lengths of data to obtain analysis results as quickly and accurately as possible. Either fixed time window frames or event based frames are amongst the most widely used.

The use of portable devices for in-vehicle environments has become a major cause for driver distraction which can be a contributing factor in crashes of varying intensity. Despite this fact, the number of drivers choosing to use using these devices while driving is increasing rapidly. On the positive side, smart portable devices are equipped with a variety of useful sensors such as cameras, microphones, accelerometer, gyroscope, etc. which could be leveraged to help reduce driver distraction. Careful utilization and delivery of information extracted from these sensors could potentially prove more useful to drivers rather than distracting them. As a proof of concept, using the sensor information available from an off-the-shelf smart portable device, an automatic system is proposed here for driving maneuver recognition and analysis. Driving maneuvers form the basic building blocks of the driver's intent in completing a route.

There has been rapid growth in the mobile-phone industry in terms of technology and growing number of users with migration into the car environment. There is also a significant demand for smart phones capable of accessing email, listening to music, organizing daily activities, linking to social networking sites, while the user is on the move. The automotive industry has been significantly impacted by such mobile-phone usage. Driving a car is a complicated and skillful task requiring attention and focus. However many people perceive driving to be easy - second-to-habit or an extension of their natural skills. This complacency encourages drivers to multitask while driving. While many drivers manage this multitasking comfortably, it becomes a distraction and contributes to increased risk while driving for some. Since the effect of multitasking is variable on different drivers, it is important to understand its impact on individual drivers.