Deep Reinforcement Learning Reward Function Design for Autonomous Driving in Lane-Free Traffic
Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the notion of lanes, and consider the whole lateral space within the road boundaries. This constitutes an entirely different problem domain for autonomous driving compared to lane-based traffic, as there is no leader...
Main Authors: | Athanasia Karalakou, Dimitrios Troullinos, Georgios Chalkiadakis, Markos Papageorgiou |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Systems |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-8954/11/3/134 |
Similar Items
-
Effect of Number of Lanes on Traffic Characteristics of Reinforcement Learning Based Autonomous Driving
by: Esther Aboyeji, et al.
Published: (2023-01-01) -
A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning
by: Kexuan Lv, et al.
Published: (2022-05-01) -
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
by: Wei Zhou, et al.
Published: (2022-03-01) -
A Hierarchical Framework for Multi-Lane Autonomous Driving Based on Reinforcement Learning
by: Xiaohui Zhang, et al.
Published: (2023-01-01) -
Emergency Vehicle Aware Lane Change Decision Model for Autonomous Vehicles Using Deep Reinforcement Learning
by: Ahmed Alzubaidi, et al.
Published: (2023-01-01)