Neural Architecture Search and Deep Reinforcement Learning for
Recently, autonomous driving has received considerable attention from many companies and research institutions. Autonomous driving cars are expected to play a key role in the future of urban transportation systems, asthey can increase driving safety, ease traffic congestion, reduce energy consumption, set the driver free and so on. In order to deal with complex urban scenarios, Deep Neural Network (DNN) and Deep Reinforcement Learning (DRL) are developed to improve the intelligence of self-driving cars. However, the constrained resource of self-driving cars hinders DNN and DRL applications. Neural Architecture Search (NAS) can provide the light and well-performed models for the resource-constrained cars. Recently, the approaches based on NAS and DRL are used to address the perception/prediction/navigation/planning/ control tasks instead of the traditional methods for autonomous driving in complex or rare scenarios. This special session aims to discuss the recent development and existed problems of NAS and DRL in autonomous driving.
Scope and Topics
The aim of this special session will be to provide an account of state-of-the-art in this fast moving and cross-disciplinary field of neural architecture search and deep reinforcement learning in autonomous driving. It is expected to bring together the researchers in relevant areas to discuss latest progress, propose new research problems for future research. All the original papers related to NAS, DRL and autonomous driving are welcome.
The topics of the special session include, but are not limited to:
- Neural architecture search algorithms
- Neural architecture search for resource-constrained environment
- Neural architecture search for autonomous driving
- Deep reinforcement learning algorithms
- Inverse reinforcement learning algorithms
- Multi-agent reinforcement learning algorithms
- Adaptive dynamic programming algorithms
- Navigation/Perception/Planning/Control schemes for autonomous driving
- Reinforcement learning for autonomous driving
- Deep reinforcement learning for autonomous driving
- Transfer learning for autonomous driving
- Dataset, Hardware implementation and algorithms acceleration for autonomous driving
- Paper Submission: January 15, 2020
- Notification of Acceptance: March 15, 2020
- Camera Ready Deadline: April 15, 2020
- Conference Dates: July 19-24, 2020
This special session will be held in 2020 International Joint Conference on Neural Networks (IJCNN) (wcci2020.org/ijcnn-sessions/), part of 2020 IEEE World Congress on Computational Intelligence (https://wcci2020.org/ ) (Glasgow, Scotland, United Kingdom, July 19-24, 2020).
All papers should be prepared according to the IJCNN 2020 policy and should be submitted electronically using the conference website (https://wcci2020.org/submissions/)
Dr. Yaran Chen, Assistant Professor, Institute of Automation, Chinese Academy of Sciences, China.
Dr. Qichao Zhang, Associate Professor, Institute of Automation, Chinese Academy of Sciences, China.
Pro. Dongbin Zhao, Professor, Institute of Automation, Chinese Academy of Sciences, China.