[HTML][HTML] Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …
offers many potential benefits, including releasing drivers from exhausting driving and …
Multi-agent DRL-based lane change with right-of-way collaboration awareness
Lane change is a common-yet-challenging driving behavior for automated vehicles. To
improve the safety and efficiency of automated vehicles, researchers have proposed various …
improve the safety and efficiency of automated vehicles, researchers have proposed various …
[HTML][HTML] Multi-agent reinforcement learning for autonomous vehicles: A survey
In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …
Robustness and adaptability of reinforcement learning-based cooperative autonomous driving in mixed-autonomy traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
Harmonious lane changing via deep reinforcement learning
In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …
Distributed multiagent coordinated learning for autonomous driving in highways based on dynamic coordination graphs
C Yu, X Wang, X Xu, M Zhang, H Ge… - Ieee transactions on …, 2019 - ieeexplore.ieee.org
Autonomous driving is one of the most important AI applications and has attracted extensive
interest in recent years. A large number of studies have successfully applied reinforcement …
interest in recent years. A large number of studies have successfully applied reinforcement …
Integrated eco-driving automation of intelligent vehicles in multi-lane scenario via model-accelerated reinforcement learning
The development of intelligent driving technologies is expected to have the potential in
energy economics. Some reported studies mainly focused on the economical driving …
energy economics. Some reported studies mainly focused on the economical driving …
Attention-based hierarchical deep reinforcement learning for lane change behaviors in autonomous driving
Y Chen, C Dong, P Palanisamy… - Proceedings of the …, 2019 - openaccess.thecvf.com
Performing safe and efficient lane changes is a crucial feature for creating fully autonomous
vehicles. Recent advances have demonstrated successful lane following behavior using …
vehicles. Recent advances have demonstrated successful lane following behavior using …
Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
Automated lane change decision making using deep reinforcement learning in dynamic and uncertain highway environment
Autonomous lane changing is a critical feature for advanced autonomous driving systems,
that involves several challenges such as uncertainty in other driver's behaviors and the trade …
that involves several challenges such as uncertainty in other driver's behaviors and the trade …