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) …
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 …
[HTML][HTML] Connected autonomous vehicles for improving mixed traffic efficiency in unsignalized intersections with deep reinforcement learning
Human driven vehicles (HDVs) with selfish objectives cause low traffic efficiency in an un-
signalized intersection. On the other hand, autonomous vehicles can overcome this …
signalized intersection. On the other hand, autonomous vehicles can overcome this …
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 …
Automated lane change strategy using proximal policy optimization-based deep reinforcement learning
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan,
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
Driving decision and control for automated lane change behavior based on deep reinforcement learning
To fulfill high-level automation, an automated vehicle needs to learn to make decisions and
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
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 …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
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 …
Continuous control for automated lane change behavior based on deep deterministic policy gradient algorithm
Lane change is a challenging task which requires delicate actions to ensure safety and
comfort. Some recent studies have attempted to solve the lane-change control problem with …
comfort. Some recent studies have attempted to solve the lane-change control problem with …