Sensys Networks, the world’s leading provider of integrated wireless traffic detection and data systems for Smart Cities, is partnering with UC Berkeley and Hyundai America Technical Center, Inc. (HATCI) as part of ARPA-E's NEXTCAR Program, short for "NEXT-Generation Energy Technologies for Connected and Automated On-Road Vehicles”. The partners are developing an innovative VD&PT (Vehicle Dynamics & Power Train) control architecture based on a predictive and data-driven approach, which will optimize PHEV (Plug-in Hybrid Electric Vehicle) performance in real-world conditions, and facilitate efficient departure at intersections, predictive cruise and speed profiles, and learning-based eco-routing and tuning.
Specifically, Sensys Networks is using predictive analytics tools to combine historical data with real-time data. They are then accurately estimating the remaining time in the green or red phase and delivering it securely to autonomous vehicles.
Amine Haoui, CEO for Sensys Networks explains an example, “An autonomous vehicle approaching a signalized intersection with accurate knowledge of whether the signal will be green or red when it gets to the intersection will optimize its approach to the intersection to minimize stops or accelerations / decelerations and can thus optimize its energy consumption.” Haoui continues, “a connected vehicle stopped at a traffic light may switch its engine off if it knows that it will be 20 seconds until green but may determine that it’s more efficient not to switch off if it’s only 3 seconds to green.”
“At Berkeley we are building the intelligence beyond the next generation of connected and automated vehicles” explained Francesco Borrelli, Professor of Mechanical Engineering at UC Berkeley and the Principal Investigator (PI) for the NEXTCAR project. “This particular project with Sensys Networks demonstrates our vision to leverage vehicle connectivity with the transportation infrastructure and automation technologies to optimize vehicle controls and powertrain operation.”
The challenge in delivering accurate SPAT (Signal Phase and Timing) data is the predictive nature of the problem. The length of the green or red phase depends on real time traffic as most traffic signals would extend the green phase as more cars approach the intersection. Sensys Networks has developed a breakthrough solution that delivers Predictive SPAT data to CAVs (Connected and Autonomous Vehicles). The solution leverages the Sensys Networks FlexControl Edge Gateway and SNAPS software platform to collect and store signal phase and vehicle detection data.
Sensys Networks will be featuring its traffic data and analytics platform, SensTraffic, at ITS World Congress in Montreal, Canada.
About Sensys Networks
Sensys Networks improves the way people travel through cities. We deliver accurate and dependable detection data to drive reductions in urban traffic congestion for partners and public agencies around the globe. For more information see http://www.sensysnetworks.com .