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Full-Text Articles in Engineering
Neural Network Fusion Of Multi-Modal Sensor Data For Autonomous Surface Vessels, David J. Thompson
Neural Network Fusion Of Multi-Modal Sensor Data For Autonomous Surface Vessels, David J. Thompson
Doctoral Dissertations and Master's Theses
Autonomous surface vessels (ASV) can potentially improve the safety of vessels traditionally operated by humans. Despite advancements in autonomous on-road vehicles, many of these advancements have yet to be realized for ASVs. This is primarily due to lacking ASV sensing platforms and public datasets for ASV-based perception research. To that end, this dissertation demonstrates the design of a synchronized multi-modal sensing platform for ASVs utilizing GPS/INS, LiDAR, LWIR cameras, HDR camera, and high-resolution cameras. The sensing platform is designed to maximize the overlap of sensors for multi-modal research and provides accurate intrinsic and extrinsic calibration between each sensor. Furthermore, the …
Dsrc Versus Lte-V2x: Empirical Performance Analysis Of Direct Vehicular Communication Technologies, Ehsan Moradi-Pari, Danyang Tian, Mojtaba Bahramgiri, Samer Rajab, Sue Bai
Dsrc Versus Lte-V2x: Empirical Performance Analysis Of Direct Vehicular Communication Technologies, Ehsan Moradi-Pari, Danyang Tian, Mojtaba Bahramgiri, Samer Rajab, Sue Bai
Michigan Tech Publications
Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicle-to-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP’s Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world …