Exploring applications of deep reinforcement learning for real-world autonomous driving systems
By Victor Talpaert, Ibrahim Sobh, Ravi Kiran, Patrick Mannion, Senthil Kumar Yogamani, Ahmad El-Sallab, Patrick Pérez. 2019
81 citations recorded by the source corpus.
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.
Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
- Ciarán Hughes
- Ibrahim Sobh
- B. R. Kiran
- Patrick Pérez
- Stefan Milz
- Senthil Kumar Yogamani
- Ahmad A. Al Sallab
- Patrick Mannion
In Control and Systems Engineering
- Ibrahim Sobh
- B. R. Kiran
- Patrick Pérez
- Stefan Milz
- Isabelle Leang
- Senthil Kumar Yogamani
- Ahmad A. Al Sallab
- Sumanth Chennupati
Citations in the corpus
- Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, background): “We also focus our review on the different real world deployments of RL in the domain of autonomous driving expanding our conference paper [2] since their deployment has not been reviewed in an academic setting.”
- NeurAll: Towards a Unified Model for Visual Perception in Automated Driving (2019, background): “However neural networks are beginning to be applied in other areas of automated driving [2].”
- NeurAll: Towards a Unified Visual Perception Model for Automated Driving (2019, background): “However neural networks are beginning to be applied in other areas of automated driving [2].”