AI based ADAS solutions for Indian road


Developing and testing deep learning algorithms for object detection and classification on Indian roads comes with unique challenges:

1) Live testing of the algorithm in a dense traffic situations.
2) The camera position was not fixed.
3) No camera calibration was done.
4) Presence of region-specific vehicles such as auto-rickshaws.
5) Implementation on a very simple edge device.

To address this, we extended a pre-trained model by training it with approximately 1200 auto-rickshaw images, enabling it to recognise this special class along with standard vehicles.

The results were promising:
1) The model accurately detected both regular vehicles and region-specific ones like auto-rickshaws.
2) It identified a person even when my face was partially covered with a helmet for a short duration.
3) It detected my bike correctly, even when it appeared in the camera’s view for just a brief moment.

This is one of the AI-powered features we customised as part of our ADAS (Advanced Driver Assistance Systems) solutions.

Read More

Real time object detection and velocity measurement in crash test.

AI in Crash Testing 🚗
At Aray InfoSolutions, we are showcasing how AI can be integrated into crash testing for object detection, classification, and real-time speed estimation.
Using a deep neural network with a pretrained model, we have applied object detection to identify people and vehicles during a crash test scenario. The system also calculates the real-time speed of the car.

📷 Camera calibration is a crucial step in accurate speed estimation. Factors such as camera manufacturer, FPS, focal length, and installation parameters play a big role. Changing the camera type/brand or even its position can significantly affect results — a key challenge also faced in ADAS (Advanced Driver Assistance Systems) for Level 4 autonomy.
This demonstration is just a glimpse of our capabilities in AI (ML & DL), crash analysis, and CAE. We’ll be sharing more exciting work in the coming weeks, so stay tuned!

Read More
error: