Research Engineer — Autonomous Vehicle Perception & Prediction
Building and deploying perception systems for autonomous vehicles at Khalifa University's Autonomous Vehicle Lab (AVLab).
Perception and prediction for autonomous systems: LiDAR-camera fusion, 3D object detection, multi-object tracking, and probabilistic trajectory prediction. I build ROS2 pipelines, deploy them on real vehicles via ONNX/TensorRT, and test them in structured field environments.
| Paper | Venue |
|---|---|
| Transformer-based probabilistic trajectory prediction · first author | ICAR 2023 |
| Trajectory prediction: progress, limitations, and future directions | Info. Fusion 2026 |
| EMT: multi-task AV benchmark for Gulf-region driving · co-first author | arXiv 2025 |
| Guardian: safety stack with VLM-based reasoning | IEEE Access (review) |
Full list on Google Scholar
Perception PyTorch · YOLO · PointPillars · Camera-LiDAR Fusion · MOT · Trajectory Prediction
Robotics ROS1/ROS2 · Gazebo · RViz · Nav2 · CARLA
Deployment ONNX · TensorRT · CUDA · Docker · NVIDIA Edge
Languages Python · C++ · MATLAB
End-to-end perception pipeline on a real autonomous vehicle:
