Automotive as the IT industry is one of the most rapidly developing sectors, which attracts a significant number of investments. 


Archer Software acquired the basis for the development of prototype software - electric Nissan Leaf (also known now among employees as Archer's E-car) to deepen the expertise in the Automotive field.


First of all, it is a sample of new technologies and best practices on the basis of examination of the electric Nissan Leaf. Archer Software provides the car to our developers "try your wings" (or rather, try a code) and launched few prototypes programs based on ideas brainstormed and selected via a bunch of meetings within the company and external community of interested engineers.


The Technologies are constantly evolving. New languages, frameworks, wearable devices and hardware solutions appear every month. It is not always possible to try all of them out, to run the live projects using the freshest and brightest. But for these prototypes - the more interesting, sexy technology (including hardware) we will use - the better. 


Our team has embedded developers, which extends a lot our capabilities of interaction with the vehicle. Like proprietary sensors in addition to typical OBD device. 

This way we consider Archer eCar as a tests and development platform. 

Extended with motion, lighting, temperature, pressure sensors and other hardware, with navigation and GPS data and the data that we can get from the car itself (like battery status, charge consumption based on current driver behavior, expected distance, voltage, battery temperature, angels of helm and acceleration / deceleration force) we can develop a lot of great apps and try mostly any ideas our team will come up to.


Let’s talk a bit about the prototypes we started to work on:


As the first app to go, we baked a simple iOS application to track the driver’s behavior, quality of his driving - simple implementation of what every UBI (Usage based insurance) related project needs. The next step for it is more precise integration with data that OBD and vehicle gives out, adding more sensors to detect events like aggressive lanes change and dangerous passage of the road turn. The following plans should cover the ideas of integration of this functionality with live map and GPS analysis which can help come to the main goal of the prototype - driver awareness of him getting asleep and road map based accidents forecasting!


The second big track for us is the usage of deep learning approaches to analyse and try determining the patterns in driving behaviour. While such a research is an fantastic process process by itself, the works in this direction can come a basis for such functionality like car owner identification by driving behaviour template, matching people with the same driving skills and many others.


While moving this exciting path, we’ll keep updates and news : )