Mobiliya System Development

As buyers yearn for increasingly sophisticated and well-rounded connected car experience, developing the most seamless infotainment products, features and systems remains one of the biggest challenges and an ultimate goal. In order to deliver the most innovative and futuristic automotive connectivity platforms and infotainment systems, it is critical that automobile manufacturers use multiple technologies at the same time efficiently and effectively. Mobiliya offers system development services for automotive manufacturers and Tier-1s to implement robust software system across multiple chipset platform.

Mobiliya System Development Services

There are multiple operating systems on which IVI (In-Vehicle Infotainment) devices function. The popular ones are Automotive Grade Linux, TIZEN, GENIVI IVI platform, and recently Android is gaining significant consideration as OS for IVI devices. Mobiliya extends its platform engineering capabilities, across these operating systems, to its automotive customers to develop and maintain their software stack. This includes:

Offerings

Operating System platform

Operating System platform bring-up on target hardware

Rear view camera

Rear view camera bring-up within 2 seconds

Bluetooth

Bluetooth (BT) stack integration

Wi-Fi

Wi-Fi Direct (WFD) integration

User Interface Back Channel

User Interface Back Channel (UIBC) implementation

Audio policies configuration

Audio policies configuration (for phone connectivity)

Dual panel implementation

Dual panel implementation

 Digital cluster implementation

Digital cluster implementation

Mobiliya has experience in developing automotive software system on top of semiconductor platforms such as

Renesas RH850

Renesas RH850 – AUTOSAR Integration

2 NXP Halo

NXP Halo – Mobiliya is an exclusive HALO System Partner. Developed Digital Cluster, AR based navigation for Head Unit Display (HUD)

Texas Instrument

Texas Instrument – ADAS application & AUTOSAR OS development

nVIDIA

nVIDIA – ADAS application development using Deep Learning