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 …
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 …
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 …
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 …
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 …
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 …
Risk-aware high-level decisions for automated driving at occluded intersections with reinforcement learning
Reinforcement learning is nowadays a popular framework for solving different decision
making problems in automated driving. However, there are still some remaining crucial …
making problems in automated driving. However, there are still some remaining crucial …
Autonomous driving using safe reinforcement learning by incorporating a regret-based human lane-changing decision model
It is expected that human-driven vehicles and autonomous vehicles (AVs) will coexist in a
mixed traffic for a long time. To enable AVs to safely and efficiently maneuver in this mixed …
mixed traffic for a long time. To enable AVs to safely and efficiently maneuver in this mixed …
Deep reinforcement learning with enhanced safety for autonomous highway driving
In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …
The proposed framework leverages merits of both rule-based and learning-based …
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 …