Siba Haidar
Papers
1
Total Citations
3
H-Index
1
About
Siba Haidar is a researcher at the forefront of autonomous driving perception, specializing in real-time 3D LiDAR semantic segmentation. Her work tackles the critical challenge of enabling mobile robotic systems to understand their environment by directly interpreting sparse point cloud data. In her highly cited 2024 paper, "Are We Ready for Real-Time LiDAR Semantic Segmentation in Autonomous Driving?" (3 citations), Haidar evaluates the readiness of current deep learning architectures for deployment in latency-sensitive, safety-critical applications. She systematically benchmarks state-of-the-art models against the dual demands of accuracy and inference speed, identifying key bottlenecks in existing frameworks. Her contributions provide a clear roadmap for bridging the gap between academic research and industrial deployment, emphasizing the need for efficient, lightweight networks that can operate on embedded hardware. By highlighting the trade-offs between segmentation fidelity and computational cost, Haidar’s work directly informs the next generation of autonomous vehicle perception stacks. Her research is essential reading for engineers and scientists working to make real-time scene understanding a practical reality in self-driving cars and autonomous robots.
Research Focus
Key Achievements
Top Papers
- 1