Learning-powered Prediction and Decision-making for Autonomous Driving (LPAD)

2023 IEEE International Conference on Intelligent Transportation Systems (ITSC) 

Bilbao, Spain, September 24 - 28, 2023

About the Special Session


🎉🎉We are thrilled to announce that the special session has received a remarkable total of 17 paper submissions! What's even more fantastic is that each and every submission has been accepted! Congratulations to all the talented authors for their outstanding contributions!

🎉🎉 We extend our heartfelt gratitude to all authors for submitting such high-quality papers. This promises to be an enriching event! 

🎉🎉 The special session will be held in Room 0B, Euskalduna Conference Centre, starting at 11:00 on September 25th. Looking forward to seeing you there!

Motivation and Scope

One of the key challenges for autonomous driving is to enable vehicles to operate safely and socially alongside human traffic participants. Central to this task is to effectively predict the intents of other road users, forecast their trajectories within a scene, and utilize these predictions to make informed decisions. The prediction task is incredibly challenging since the future motion of road users is affected by various factors, such as dynamics, road conditions, and surrounding agents, and can often exhibit multi-modal behavior. Moreover, how to leverage the prediction models to enable real-time and interaction-aware decision-making is also a challenging task to assure safety, adhere to traffic rules, interact with diverse traffic participants, and meet the needs of passengers. Machine learning-based methods show promise in addressing these challenges and can greatly enhance the performance, scalability, and intelligence of the system, allowing autonomous vehicles to operate in complex driving environments. The use of machine learning-based methods for prediction and decision-making in autonomous driving is a rapidly growing area of research, with a scope that extends from learning decision-making policies to predicting the multi-modal movements of heterogeneous road users for better decision-making. 

This special session aims to present the latest research and invite submissions for new works on cutting-edge learning-based methods for prediction and decision-making problems. We are soliciting original contributions that are not published or currently under consideration by any other journals/conferences.

Topics of interest (not limited to)

 Call For Papers

Authors are invited to submit full-length papers of up to 6 pages for technical content including figures and references. A maximum of 2 additional pages is allowed, but at an extra cost per page. The maximum number of pages is 6 + 2 (with additional cost) = 8. Each paper will undergo a peer-reviewing process by at least two independent reviewers. All the accepted papers, if they are presented at ITSC 2023, will be published in IEEE Xplore and eligible for the journal special issues.


Important Dates



Nanyang Technological University

Nanyang Technological University

Nanyang Technological University

NVIDIA Research

Shanghai AI Lab

Prof. Chen Lyu

Nanyang Technological University