Wejo Group Ltd., a smart mobility cloud and software analytics company for connected, electric and autonomous vehicle (AV) data, has launched its Wejo Autonomous Vehicle Operating System (AV-OS) smart platform. The AV-OS platform will leverage billions of daily data points from millions of connected vehicles, including AV data, to generate AV outcomes.
Interpreted and analyzed by Wejo’s Smart Mobility intelligence offerings, the platform will unlock insights for any AV developers supporting the rapid acceleration of AV testing, carried out safely in a virtual simulation environment. Democratizing access to data, the AV-OS platform will remove the silos of traditional development processes that AV OEMs must go through, enabling rapid innovation.
Turning the raw connected vehicle data into meaningful and actionable insights, Wejo helps developers better understand vehicles, journeys, roads and locations to create safer and more environmentally friendly driving experiences.
“The widespread adoption of autonomous driving is integral to safer roads, lower emissions and a significant reduction of traffic accidents,” says Richard Barlow, Wejo’s founder and CEO. “Auto accidents and emissions combine for a staggering 9.3+ million deaths per year. But without cross-industry collaboration, innovation will be slow. By democratizing access to connected vehicle data, our AV-OS platform will remove barriers to widespread AV adoption in ways never before possible, rapidly reducing development time.”
Simulation and modeling capabilities include using connected vehicle data to inform AV model development; providing real-world data and intelligence about automated and non-automated (human) driving; removing silos and supporting the development, testing and operating AVs through live visualizations, without the need to have a physical AV on the road; and supporting the development, testing and operation of AVs.
It will help provide AV OEMs and developers with intelligence about longer-range conditions across traffic, roadways and environments in a live 3D environment, instead of learning from yesterday’s data. It also grows connected vehicle and autonomous vehicle data (e.g., lidar and camera outputs) as well as edge and distributed infrastructure to reduce latency barriers.
In addition, it creates a common language for standardization, developing vehicle and road network digital twins that represent a common language for AVs, including vehicle status and dynamics, plus intended movements (lane changes/exits). It also ensure safer roads by understanding the pattern of traffic and driving behaviors to allow for better planning and a smarter, faster AV reality.
