Wejo Neural Edge™ uses machine learning to address data overload and deliver faster, more cost effective, and sustainable vehicle communication insights.
Delivered in partnership with Microsoft, this breakthrough platform will enable intelligent handling of data from vehicles, at scale. Designed to produce incredible insights that protect privacy, this ground-breaking development will act as the catalyst for automotive innovation and a driver in the growth of autonomous mobility.
Wejo Neural Edge™ filters and analyzes vast amounts of autonomous, electric and connected vehicle data before transmitting only the essential information to the cloud. This is made possible by utilizing in-car edge processing that Wejo is developing to filter only useful and valuable vehicle data before it is transmitted to the cloud.
This process not only reduces data overload and maximizes data insights but will reduce costs for automotive manufacturers and improve manufacturing of the vehicle to provide a better driving experience.
Leveraging embedded software within the vehicle chipset, Wejo Neural Edge™ is designed to intelligently choose and prioritize the data to be sent from the vehicle to the cloud.
Wejo Neural Edge™ can take 20% of the data from autonomous, electric, and other connected vehicles and reconstruct it to represent 100% of the data, without any loss in data fidelity or integrity. The positive environmental impact is significant, as less data requires less storage which in turn reduces power consumption.
Wejo Neural Edge™ enables the standardisation and centralisation of the data that comes from autonomous, electric and connected vehicles. Not only does this provide a key building block for communication in near real time, but it also supports communication with infrastructure services such as road signs, traffic lights and parking lots, so vehicles can easily anticipate the road ahead and optimise mobility experiences.
In a simulation environment, a digital twin of the US can be constructed to simulate how vehicles in different cities need to respond and navigate without having to outlay massive infrastructure costs of physical hardware or vehicles to be able to relearn how a vehicle should behave as an AV or EV, in the Smart City, etc.