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Researchers at the University of California, Davis have developed an approach to enhance autonomous vehicle path prediction through efficient information sharing and distributed learning among AI agents.

HydraFusion is a modular, selective sensor fusion framework designed to enhance performance and efficiency in multi-sensory computing systems across diverse contexts.

EnergyShield is a pioneering framework designed to optimize energy consumption through safe, intelligent offloading of deep neural network computations for autonomous vehicles.

A revolutionary approach to enhancing the safety and efficiency of autonomous vehicles through advanced scene-graph embeddings.

Vehicle platooning technology is an evolving segment within the broader movement towards more intelligent transportation, specifically relating to autonomous vehicles. Some early concepts dates back to the 1970s with projects like Electronic Route Guidance System developed by the U.S. Federal Highway Administration, which used a destination-oriented approach with roadside units to decode vehicle inputs and provide routing instructions. Subsequent initiatives such as the California Partners for Advanced Transportation Technology program demonstrated vehicles traveling in close formation guided by magnets embedded in roadways. The landscape has since evolved from individual vehicle automation concepts to more sophisticated vehicle-to-vehicle (V2V) communication schemes to enable coordinated movements. More recent industry developments have been driven by advancements in 5G technology, V2V communication protocols, and enhanced safety requirements. Current systems face control stability challenges, particularly as platoon size increases, with research showing that system stabilizability degrades and can lose stability entirely in infinite vehicle formations. Moreover, issues with V2V communication reliability persist, including frequent intermittent connectivity problems and wireless interference, limiting wider adoption. Additional challenges include the fundamental trade-off between fuel efficiency and safety margins, where shorter inter-vehicle distances improve aerodynamic benefits but increase collision risk.

A groundbreaking system that enables navigation in GPS-denied environments by using intelligent systems to mimic biological systems that recognize locations through visual cues and perform contextually appropriate actions.

This technology represents a significant leap in radar systems, offering millimeter-scale range resolution and high angular resolution.

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This technology enhances indoor pedestrian localization accuracy using LTE signals by mitigating multipath errors through synthetic aperture navigation.

An innovative approach to indoor localization using LTE signals and IMU data, enhancing accuracy and reliability for navigation.

An innovative approach to vehicle localization and mapping using lidar and cellular LTE data, enhancing accuracy without relying on GNSS signals.

This technology offers a novel approach to navigation by using LTE signals, providing a viable alternative to traditional GPS systems.

ROMANUS is a cutting-edge methodology designed to enhance the performance and robustness of latency-critical, real-time intelligent systems through dynamic neural architectures.

This technology offers a novel mechanical arrangement for lossless, adjustable operation of springs or inerters.

Researchers at the University of California Davis have developed a system designed to predict and prevent ATV rollovers, enhancing rider safety.

Researchers from UC San Diego have developed RadSenNet, a new approach of sequential fusing of information from radars and cameras. The key idea of sequential fusion is to fundamentally shift the center of focus in radar-camera fusion systems from cameras to radars. This shift enables their invention (RadSegNet) to achieve all-weather perception benefits of radar sensing. Keeping radars as the primary modality ensures reliability in all situations including occlusions, longrange and bad weather.

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A technology utilizing additive manufacturing (3D-Printing) processes and systems for efficient deposition of standardized aluminum 5xxx series, mitigating defects such as cracks and pores.