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 …
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 …
Highway lane change decision-making via attention-based deep reinforcement learning
Deep reinforcement learning (DRL), combining the perception capability of deep learning
(DL) and the decision-making capability of reinforcement learning (RL)[1], has been widely …
(DL) and the decision-making capability of reinforcement learning (RL)[1], has been widely …
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 …
Lane change decision-making through deep reinforcement learning with rule-based constraints
Autonomous driving decision-making is a great challenge due to the complexity and
uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q …
uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q …
Combining decision making and trajectory planning for lane changing using deep reinforcement learning
S Li, C Wei, Y Wang - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
In the context of Automated Vehicles, the Automated Lane Change system, is fundamentally
based upon the separate constructs of Perception, Decision making, Trajectory Planning …
based upon the separate constructs of Perception, Decision making, Trajectory Planning …
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 …
A reinforcement learning based approach for automated lane change maneuvers
P Wang, CY Chan… - 2018 IEEE Intelligent …, 2018 - ieeexplore.ieee.org
Lane change is a crucial vehicle maneuver which needs coordination with surrounding
vehicles. Automated lane changing functions built on rule-based models may perform well …
vehicles. Automated lane changing functions built on rule-based models may perform well …
Online prediction of lane change with a hierarchical learning-based approach
In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …