Related papers: A Machine Learning Smartphone-based Sensing for Dr…
Just like 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…
The widespread adoption of smartphones in recent years has made it possible for us to collect large amounts of traffic data. Special software installed on the phones of drivers allow us to gather GPS trajectories of their vehicles on the…
Despite advancements in vehicle security systems, over the last decade, auto-theft rates have increased, and cyber-security attacks on internet-connected and autonomous vehicles are becoming a new threat. In this paper, a deep learning…
Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records…
Human Activity Recognition (HAR) is considered a valuable research topic in the last few decades. Different types of machine learning models are used for this purpose, and this is a part of analyzing human behavior through machines. It is…
To autonomously control vehicles, driving agents use outputs from a combination of machine-learning (ML) models, controller logic, and custom modules. Although numerous prior works have shown that adversarial examples can mislead ML models…
Poor road conditions are a public nuisance, causing passenger discomfort, damage to vehicles, and accidents. In the U.S., road-related conditions are a factor in 22,000 of the 42,000 traffic fatalities each year. Although we often complain…
In this study, we propose a method for single sensor-based activity recognition, trained with data from multiple sensors. There is no doubt that the performance of complex activity recognition systems increases when we use enough sensors…
Model-based approaches have become increasingly popular in the domain of automated driving. This includes runtime algorithms, such as Model Predictive Control, as well as formal and simulative approaches for the verification of automated…
Traffic-related injuries and fatalities are major health risks in the United States. Mobile phone use while driving quadruples the risk for a motor vehicle crash. This work demonstrates the feasibility of using the mobile phone camera to…
With the fast advancements of the Autonomous Vehicle (AV) industry, detection of Vulnerable Road Users (VRUs) using smartphones is critical for safety applications of Cooperative Intelligent Transportation Systems (C-ITSs). This study…
The role of simulation in autonomous driving is becoming increasingly important due to the need for rapid prototyping and extensive testing. The use of physics-based simulation involves multiple benefits and advantages at a reasonable cost…
The paper introduces an approach to telematics devices data application in automotive insurance. We conduct a comparative analysis of different types of devices that collect information on vehicle utilization and driving style of its…
Identification of high-risk driving situations is generally approached through collision risk estimation or accident pattern recognition. In this work, we approach the problem from the perspective of subjective risk. We operationalize…
Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which…
Today, smartphone devices are owned by a large portion of the population and have become a very popular platform for accessing the Internet. Smartphones provide the user with immediate access to information and services. However, they can…
Velocity estimation is a core component of state estimation and sensor fusion pipelines in mobile robotics and autonomous ground systems, directly affecting navigation accuracy, control stability, and operational safety. In conventional…
Navigation and positioning systems dependent on both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning and the behaviour can…
An open problem in autonomous driving research is modeling human driving behavior, which is needed for the planning component of the autonomy stack, safety validation through traffic simulation, and causal inference for generating…
Autonomous vehicles require accurate and reliable short-term trajectory predictions for safe and efficient driving. While most commercial automated vehicles currently use state machine-based algorithms for trajectory forecasting, recent…