Developing a merge lane change decision policy for autonomous vehicles by deep reinforcement learning
B Fan, Y Zhou, H Mahmassani - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
With autonomous vehicles (AVs) being actively developed, it becomes possible to optimize
vehicle control policies and traffic management tools in a mixed vehicular environment. For …
vehicle control policies and traffic management tools in a mixed vehicular environment. For …
Deep merging: Vehicle merging controller based on deep reinforcement learning with embedding network
Vehicles at highway merging sections must make lane changes to join the highway. This
lane change can generate congestion. To reduce congestion, vehicles should merge so as …
lane change can generate congestion. To reduce congestion, vehicles should merge so as …
Intersection crossing for autonomous vehicles based on deep reinforcement learning
WL Chen, KH Lee, PA Hsiung - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Future intersection crossings for autonomous vehicles will not be controlled by traffic signals,
rather a controller will be used for communication among vehicles that need to cross an …
rather a controller will be used for communication among vehicles that need to cross an …
Mandatory Lane-Changing Decision-Making in Dense Traffic for Autonomous Vehicles based on Deep Reinforcement Learning
Y Gu, K Yuan, S Yang, M Ning… - 2022 6th CAA …, 2022 - ieeexplore.ieee.org
Mandatory lane changing in complex and crowded traffic environment is a great challenge
for autonomous vehicles. This paper proposes a decision-making model based on …
for autonomous vehicles. This paper proposes a decision-making model based on …
Dampen the stop-and-go traffic with connected and automated vehicles–a deep reinforcement learning approach
Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic
operations, and its impacts and working mechanism have attracted much attention. Recent …
operations, and its impacts and working mechanism have attracted much attention. Recent …
Novel decision-making strategy for connected and autonomous vehicles in highway on-ramp merging
Z el abidine Kherroubi, S Aknine… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
High-speed highway on-ramp merging is a significant challenge toward realizing fully
automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine …
automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine …
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 …
Decentralized cooperative lane changing at freeway weaving areas using multi-agent deep reinforcement learning
Frequent lane changes during congestion at freeway bottlenecks such as merge and
weaving areas further reduce roadway capacity. The emergence of deep reinforcement …
weaving areas further reduce roadway capacity. The emergence of deep reinforcement …
Anti-jerk on-ramp merging using deep reinforcement learning
Deep Reinforcement Learning (DRL) is used here for decentralized decision-making and
longitudinal control for high-speed on-ramp merging. The DRL environment state includes …
longitudinal control for high-speed on-ramp merging. The DRL environment state includes …