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Embedded Service Case Study

SMART Dashcam with Advanced ADAS Capability

Introduction: SMART Automotive Dash Cam 

A dashcam or dashboard camera is a recording device typically attached to the interior windscreen of a vehicle and continuously records views & events.
Essentially, a dashcam should be able to capture driving footage, so that if something unexpected happens on the road, users have evidence at their fingertips. A video recording of significant events can also save users from expensive disputes. A SMART Automotive Dash Cam goes even further by leveraging the AI & machine learning technologies, and it brings in safety features typically found in Advanced Driver Assistance Systems (ADAS).

Utilization

⦁ Police & Insurance – 
⦁ Live Recording (Front/Back Camera)
⦁ Event Detection (gSensor, Theft etc.) 
⦁ Speedometer/GPS speed limit violations etc. 
⦁ ADAS
⦁ Driver Alerts  
⦁ Driver Monitoring System (DMS)
⦁ Ranking / Rewarding (Fleet Management System)

Challenge

Our client wanted a next-generation SMART Dashcam that offered ADAS services using a cloud-connected platform and advanced AI algorithms.  
 
Here are some of the unique challenges concerning this project:  

⦁  New HW platform with next-generation Neural co-processor 
⦁ Enabling advanced Connected DashCam & AI service 
⦁ Critical Time to Market pressure: Less than 90 days from design to production. 
⦁ Creating Factory Mode tools for EMS support. 
⦁ Language gap (South Korean customer) & 
⦁ Remote teams (HW, QA, EMS & PM)     

Solution

⦁ New HW platform build-up 
a. Custom HW based on HiSilicon Multicore ARM SoC with a propriety OS & Linux OS system
b. Dual Sensor 
c. LCD UI 
d. Connectivity, SD, USB, and 
e. UI Application as per specification
⦁ Enabling advanced features
a. ADAS, Advanced Night Vision 
b. Hyper Lapse (Driving Mode) 
c. Time Lapse (Parking Mode) 
d. Smart Format Free record 
e. Auto-Saver (Heat Protection) 
f. Smart Air Quality Notification (CO2 Sensor) among others

Our Role

We designed the entire end-to-end solution, including implementation, testing, and EMS (Factory) support.

Impact

We created an advanced product that was delivered successfully to our customer. We now follow up on projects in different stages of development. 

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Categories
Embedded Service Case Study

SMART Warehousing

SMART Warehousing

AI driven using NBIoT with LTE connected RFID Readers

Introduction – RFID

RFID is a widespread remote identification sensing and tracking technology used in Warehouse Management, Manufacturing and Transportation for – 

⦁ Inventory Location, Transition, and Direction
⦁ Overhead Portal – Dock-Doors, Entry/Exit Areas
⦁ Area Monitoring – Staging and Mezzanine Areas, Large Facilities, Shipping Lanes
⦁ Pallet Build and Shipping Verification
⦁ Asset Tracking – Pallets, Containers, Freight, Equipment
⦁ Industrial Automation
⦁ Real-Time Workflow

Challenge

The Client had a solution to read RFID Tags (UHF RFID reader), however, what they did not have was – 

⦁ A solution to read and access tags remotely
⦁ A solution to know the location of the tags
⦁ The ability to expand its current RFID solution to support different languages, mainly Python.
⦁ The ability to develop and support Full SDK on various platforms from scratch.
⦁ AI/ML support for multiple applications.

Solution

We designed the entire end-to-end solution from design, implementation, and test. 

⦁ We designed and implemented a solution to access RFID data remotely. We used Raven LTE modem to connect and access RFID and applications remotely.
⦁ With an LTE modem (via GNSS Port), we implemented the ability to read and embedded tag locations and even fix tag data remotely. 
⦁ We implemented Python CFFI to access and use the C library in Python.
⦁ We expanded its usage with python applications for AI/ML in the future.
⦁ We added support for older/previous versions of Java.  
⦁ Ported the solution to the latest version of Java 1.8 (on ARM) and Linux BSP. 
The platform for Java and C SDK support:

a. SDK: Eclipse Photon v4.8 supports Windows OS, Linux OS, plus Docker for both (Linux and Windows).
b. Application: Plug and play application with remote debugging capability with support for all platforms.
c. Debug Guide: A comprehensive guide to debugging the application remotely using eclipse on RFID Reader.

⦁ We implemented an energy-saving feature for RFID reading using Blob.
 

Impact

The solution was implemented in various applications and deployed across multiple industry verticals such as Logistics & Transportation, Oil and Natural Gas, Medical, Military and Supply Chain Management. 

It is also being used by The National Highways Authority for their toll-gates.
 
 

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