Josh Siegel
Assistant Professor | Michigan State University
East Lansing, MI, UNITED STATES
Josh Siegel works across disciplines to develop “deep technologies” within the mobility sector.
Biography
Josh Siegel works across disciplines to develop “Deep Technologies” - technologies that were impossible yesterday, that are barely feasible today, and that tomorrow have the potential to pervasively and invisibly solve meaningful and significant problems.
In a mobility context, this includes: pervasive sensing for identifying transit mode and vehicle diagnostics, defensive self-driving, applications for vehicular data, efficient vehicle-to-vehicle connectivity, transportation cybersecurity, human-vehicle interactions, simulator design, and trajectory forecasting.
Industry Expertise (1)
Education/Learning
Areas of Expertise (7)
Vehicle Diagnostics
Cybersecurity
Autonomous Vehicles
Sensor Fusion
Pervasive Sensing
V2X Connectivity
Artificial Intelligence
Accomplishments (1)
Lemelson-MIT National Collegiate Student Prize Competition "Drive It" Winner (professional)
2015
Education (3)
Massachusetts Institute of Technology: Ph.D., Mechanical Engineering
Massachusetts Institute of Technology: S.M., Mechanical Engineering
Massachusetts Institute of Technology: S.B., Mechanical Engineering
Links (4)
News (1)
Pilot Test Begins for Tech to Connect Everyday Vehicles
IEEE Spectrum online
2020-06-19
Connected-vehicle communications can run either on a wireless technology called Dedicated Short-Range Communications (DSRC) or on a cellular-based alternative called Cellular-V2X. DSRC is scalable, offers low latency, and works well in an urban environment, says mechanical engineer Joshua Siegel from Michigan State University (MSU). But it has a limited range (roughly 1 kilometer).
Patents (2)
System And Method For Providing Predictive Software Upgrades
US9086941
2015
System and method for providing road condition and congestion monitoring using smart messages
US8566010
2013
Journal Articles (3)
Metaverse and circular economy
Waste Management & Research2023 The revolutionary concepts of the Metaverse and circular economy are poised to reshape the future of society, academia and all business sectors. The Metaverse as a digital realm promises to transform peoples’ way of life, while circular economy offers a sustainable and regenerative approach to economic growth. Together, these concepts can be combined to unlock new opportunities, drive innovation and address pressing challenges facing humanity. Within this new era of technological and environmental transformation, understanding the capabilities and potential of the Metaverse intertwined with the circular economy is crucial for individuals and nations alike.
Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization
Sensors2022 In a world dependent on road-based transportation, it is essential to understand automobiles. We propose an acoustic road vehicle characterization system as an integrated approach for using sound captured by mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel deep learning cascading architectures, which we define as conditional, multi-level networks that process raw audio to extract highly granular insights for vehicle understanding.
Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Using Electric Vehicles
Vehicles2022 Robust lane-following algorithms are one of the main challenges in developing effective automated vehicles. In this work, a team of four undergraduate students designed and evaluated several automated lane-following algorithms using computer vision as part of a Research Experience for Undergraduates program funded by the National Science Foundation. The developed algorithms use the Robot Operating System (ROS) and the OpenCV library in Python to detect lanes and implement the lane-following logic on the road. The algorithms were tested on a real-world test course using a street-legal vehicle with a high-definition camera as input and a drive-by-wire system for output.
