California team developing autonomous vehicle navigation system that is not GPS-reliant


A team of researchers at the University of California at Riverside (UCR) has developed a new navigation system that does not rely on GPS, and will support autonomous vehicle development.

UCR’s new highly reliable and accurate navigation system exploits existing environmental signals, such as cellular and wi-fi, rather than GPS (global positioning system). The technology can be used as a standalone alternative to GPS, or complement current GPS-based systems to enable highly reliable, consistent, and tamper-proof navigation. The technology could be used to develop navigation systems that meet the stringent requirements of fully autonomous vehicles, such as driverless cars and unmanned drones.

Led by assistant professor Zak Kassas (right), the team from UCR’s Autonomous Systems Perception, Intelligence and Navigation (ASPIN) Laboratory presented its research at the 2016 Institute of Navigation Global Navigation Satellite System Conference (ION GNSS+).

Most navigation systems in cars use the space-based Global Navigation Satellite System (GNSS), which includes the USA’s GPS, Russian GLONASS, European Galileo, and Chinese Beidou systems. Aerospace and missile navigation systems typically combine GPS with a high-quality onboard Inertial Navigation System (INS), which delivers a high level of short-term accuracy, but eventually drifts when it loses touch with external signals. Despite advances in this technology, current GPS/INS systems will not meet the demands of future autonomous vehicles for several reasons: GPS signals alone are weak and unusable in natural or urban canyons; they are susceptible to intentional and unintentional jamming and interference; and they are unencrypted and unauthenticated, making them hackable. Current trends in autonomous vehicle navigation systems therefore rely not only on GPS/INS, but a suite of other sensor-based technologies, such as cameras, lasers, and sonar.

UCR’s ASPIN team has been developing autonomous vehicles that could tap into the hundreds of signals that are around at any point in time, such as cellular, radio, television, wi-fi, and other satellite signals. In the research presented at the ION GNSS+ Conference, the team showcased ongoing research that exploits these existing communications signals, called ‘signals of opportunity’ (SOP), for navigation.

The system can be used by itself, or, more likely, to supplement INS data in the event that GPS fails. The team’s end-to-end research approach includes: theoretical analysis of SOPs in the environment; building specialized software-defined radios (SDRs) that will extract relevant timing and positioning information from SOPs; developing practical navigation algorithms; and testing the system on ground vehicles and unmanned drones.

“By adding more and more sensors, researchers are throwing ‘everything but the kitchen sink’ to prepare autonomous vehicle navigation systems for the inevitable scenario that GPS signals become unavailable. We took a different approach, which is to exploit signals that are already out there in the environment,” explained Kassas.

“Autonomous vehicles will inevitably result in a socio-cultural revolution. My team is addressing the challenges associated with realizing practical, cost-effective, and trustworthy autonomous vehicles. Our overarching goal is to get these vehicles to operate with no human-in-the loop for prolonged periods of time, performing missions such as search, rescue, surveillance, mapping, farming, firefighting, package delivery, and transportation.”

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Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).