Amardeep Sathyanarayana

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In this investigation, driver behavior signals are modeled using Hidden Markov Models (HMM) in two different and complementary approaches. The first approach considers isolated maneuver recognition with model concatenation to construct a generic route (bottom-to-top), whereas the second approach models the entire route as a 'phrase' and refines the HMM to(More)
This paper describes our first step for advances in humanmachine interactive systems for in-vehicle environments of the UTDrive project. UTDrive is part of an on-going international collaboration to collect and research rich multi-modal data recorded for modeling behavior while the driver is interacting with speech-activated systems or performing other(More)
Increasing stress levels in drivers, along with their ability to multi task with infotainment systems cause the drivers to deviate their attention from the primary task of driving. With the rapid advancements in technology, along with the development of infotainment systems, much emphasis is being given to occupant safety. Modern vehicles are equipped with(More)
With the proliferation of smart portable devices, more people have started using them within the vehicular environment while driving. Although these smart devices provide a variety of useful information, using them while driving significantly affects the driver's attention towards the road. This can in turn cause driver distraction and lead to increased(More)
In this study we propose a system for overlapped-speech detection. Spectral harmonicity and envelope features are extracted to represent overlapped and single-speaker speech using Gaussian mixture models (GMM). The system is shown to effectively discriminate the single and overlapped speech classes. We further increase the discrimination by proposing a(More)
Although there is currently significant development in active vehicle safety (AVS) systems, the number of accidents, injury severity levels and fatalities has not reduced. In fact, human error, low performance, drowsiness and distraction may account for a majority in all the accident causation. Active safety systems are unaware of the context and driver(More)
The last decade has witnessed the introduction of several driver assistance and active safety systems in modern vehicles. Considering only systems that depend on computer vision, several independent applications have emerged such as lane tracking, road/traffic sign recognition, and pedestrian/vehicle detection. Although these methods can assist the driver(More)
Cars have become a part of almost everyone’s life taking people from one place to another. In such a fast paced mode of transport, there are a variety of ways in which drivers can get distracted while driving. Getting stuck in a traffic jam, doing other tasks simultaneously while drivingfor example drinking, reading, talking over the mobile phone are(More)
Novice young drivers are more frequently involved in traffic accidents, and studies have shown that effective supervised driver training is the key in reducing young drivers' risks. Using our previously developed Mobile-UTDrive in-vehicle data acquisition platform, two 16-age novice drivers participated in naturalistic drive training data collection. This(More)
Modern safety systems are transforming vehicles from human-controlled passive devices into human-centric intelligent/ active systems. There is a wide range of systems from fully autonomous vehicles to humanaugmented control devices which have emerged in this field. In current trends, co-operative active systems have the driver in the decision and control(More)