Shoaib Azam — accepted AI-conference papers

2025

Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving

Kalle Kujanpää, Daulet Baimukashev, Farzeen Munir, Shoaib Azam, Tomasz Piotr Kucner, Joni Pajarinen +1

ICRA 2025

Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversity and variance. We address this challenge by proposing a novel approach that leverages contrastive learning to extract a

Cited by 1 Source ↗Scholar ↗

2024

Multi-Task Adaptive Gating Network for Trajectory Distilled Control Prediction

Shoaib Azam, Ville Kyrki

RA-L 2024

End-to-end autonomous driving is often categorized based on output into trajectory prediction or control prediction. Each type of approach provides benefits in different contexts, resulting in recent studies on how to combine them. However, the current proposals are based on heuristic choices that o

Cited by 4 Source ↗Scholar ↗

2021

SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving

Farzeen Munir, Shoaib Azam, Moongu Jeon

IROS 2021poster

The perception of the environment plays a decisive role in the safe and secure operation of autonomous vehicles. The perception of the surrounding is way similar to human vision. The human’s brain perceives the environment by utilizing different sensory channels and develop a view-invariant represen…

Cited by 50 Source ↗Scholar ↗