Department of Electrical Engineering and Automation

Autonomous Driving

We develop computational models and use Virtual Reality to simulate and understand human behavior and interactions with autonomous vehicles, aiming to create socially-aware AVs that can predict human trajectories and intentions.
project pipeline

Technological advancements have enabled autonomous vehicles (AVs) to operate effectively in controlled environments. However, deploying these vehicles in urban settings remains a significant challenge due to the complexities of interacting and communicating with pedestrians. Addressing this limitation in autonomous driving technology is crucial for enhancing the understanding and interaction with human road users. Our research tackles this issue by developing computational models that accurately simulate human behavior and their interactions with autonomous vehicles. Our work in this area is done in collaboration with Finnish Center for Artificial Intelligence (FCAI).

What we do:

  • Jointly learning trajectory and intent prediction for modeling human behavior
  • Virtual Reality-based scenario generation to model human behavior and interactions with AVs
  • Incorporating foundation models to understand the AV-human interactions
  • Building courteous AVs, that understand and communicate with humans and are socially-aware about environments

Keywords:

Large and Visual Language Models; Human-Machine Interaction; Perception for Autonomous Driving; Autonomous Driving 

 

pedestrain crossing

People Involved:

  • Farzeen Munir 
  • Tomasz Piotr Kucner

Publications

Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

Shoaib Azam, Farzeen Munir, Ville Kyrki, Tomasz Piotr Kucner, Moongu Jeon, Witold Pedrycz 2024 Engineering Applications of Artificial Intelligence

Exploring Large Language Models for Trajectory Prediction: A Technical Perspective

Farzeen Munir, Tsvetomila Mihaylova, Shoaib Azam, Tomasz Piotr Kucner, Ville Kyrki 2024 HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks

Farzeen Munir, Shoaib Azam, Tomasz Kucner, Ville Kyrki, Moongu Jeon 2023 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
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