9 posts tagged with "Autonomous Driving"

After my previous experiments with camera-based detection and FLOAM-based LiDAR odometry in CARLA, I wanted to push things further — this time focusing on robustness under adversarial conditions.

After my previous CARLA experiment with camera-based object detection, I wanted to push further — this time focusing on LiDAR and localization.

I recently started experimenting with the CARLA simulator and decided to test whether a pre-trained perception model — with zero exposure to simulated data — could hold up against synthetic imagery.

After completing my SCNN lane detection project, I reviewed the RESA paper (2020), which extends SCNN's message-passing mechanism with a Recurrent Feature Shift Aggregator (RESA) and a Bilateral Up-Sampling Decoder (BUSD). The paper claimed a 9% F1-score improvement over SCNN. My experiments told a different story.

I recently worked on a lane marking detection project. After reviewing the literature, I decided to use the SCNN (Spatial CNN) model — not because it's state of the art, but because it falls squarely into my "if you can't implement it, you don't really understand it" category.

I recently deployed three deep learning models covering three core perception tasks — all running simultaneously in real-time on a single GPU.

I've been experimenting with YOLOv11 recently, and I was genuinely surprised by how easy it is to use — it delivers accurate results with minimal effort.

When people think about YOLO model versions, the assumption is usually straightforward: newer is better. But that isn't always the case — especially when you're using pre-trained weights without any fine-tuning.

I am pleased to share that my paper titled "Road Geometry Estimation Using Vehicle Trails: A Linear Mixed Model Approach" has been published in the Journal of Intelligent Transportation Systems: Technology, Planning, and Operations.