SPRING: SENSING AND PERCEPTION FOR INTELLIGENT SYSTEMS
SPRING: SENSING AND PERCEPTION FOR INTELLIGENT SYSTEMS
Advancing Sensing Technology
Our research group is dedicated to pushing the boundaries of sensing technology. We explore innovative robust solutions to real-world challenges in intelligent systems, e.g. autonomous driving and infrastructure inspection, harnessing the power of sensing and perception.
We develop innovative sensing technologies for autonomous driving and infrastructure inspection, tackling challenges in perception and sensor integration.
Advance sensor technology
We strive to develop more accurate, reliable, and robust sensor systems for a wide range of applications.
Develop intelligent algorithms
We focus on creating intelligent algorithms that can analyze sensor data and provide valuable insights.
Explore real-world applications
We translate our research into practical applications in areas such as autonomous driving, infrastructure inspection, and healthcare.
Collaborate with industry partners
We work closely with industry partners to ensure our research addresses real-world needs and translates into impactful solutions.
Projects
We work together with academia and industry to conceptualise and tackle challenges to sensing technologies to enable more robust and resilient autonomy systems, building on the strengths of all collaboration partners.
The ROADVIEW project is a multi-national collaborative project funded by EU Horizon (€9.7 million) to tackle automated driving under harsh weather, aiming to go from "autonomous to snowtonomous".
Sensors can create a lot of digital data. The strive for functionality and redundancy has resulted in an overwhelming amount of data to be transmitted across a system or to be stored. This research topic aims to understand smart and efficient methods to reduce the data without compromising on perception quality.
Sim4CAMSens comprise of a large UK consortium funded by CCAV (the Centre for Connected and Autonomous Vehicles) to enable an accurate representation of perception sensors in simulation and enable trusted virtual testing and validation.
A unique collaboration with NPL and Prof. Donzella to investigate different effects degrading LiDAR data, from designing experiments to data analysis.
Learn more here
Sensor Noise Framework
Lots of factors can affect the quality of sensor data for perception. The group have developed a framework to identify and breakdown these sources, and proposed a list of noise factors for automotive camera, LiDAR and 4D RADAR
Publications
We publish our research in leading journals and conferences. Explore our latest papers and presentations on sensing technology.
