Quick learner automated vehicle adapting its roadmanship to varying traffic cultures with meta reinforcement learning
It is essential for an automated vehicle in the field to perform discretionary lane changes with
appropriate roadmanship-driving safely and efficiently without annoying or endangering …
appropriate roadmanship-driving safely and efficiently without annoying or endangering …
Meta reinforcement learning-based lane change strategy for autonomous vehicles
The field of autonomous driving has seen increasing proposed use of machine learning
methodologies. However, there are still challenges in applying such methods since …
methodologies. However, there are still challenges in applying such methods since …
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 …
Discretionary Lane-Change Decision and Control via Parameterized Soft Actor–Critic for Hybrid Action Space
Y Lin, X Liu, Z Zheng - Machines, 2024 - mdpi.com
This study focuses on a crucial task in the field of autonomous driving, autonomous lane
change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating …
change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating …
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 …
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 …
Human Knowledge Enhanced Reinforcement Learning for Mandatory Lane-Change of Autonomous Vehicles in Congested Traffic
Y Huang, Y Gu, K Yuan, S Yang, T Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Mandatory lane-change scenarios are often challenging for autonomous vehicles in
complex environments. In this paper, a human-knowledge-enhanced reinforcement learning …
complex environments. In this paper, a human-knowledge-enhanced reinforcement learning …
Addressing inherent uncertainty: Risk-sensitive behavior generation for automated driving using distributional reinforcement learning
J Bernhard, S Pollok, A Knoll - 2019 IEEE Intelligent Vehicles …, 2019 - ieeexplore.ieee.org
For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …
reliably consider the inherent uncertainties of the traffic environment, eg arising from 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 …
A Real-World Reinforcement Learning Framework for Safe and Human-Like Tactical Decision-Making
Lane-change decision-making for vehicles is a challenging task for many reasons, including
traffic rules, safety, and the stochastic nature of driving. Because of its success in solving …
traffic rules, safety, and the stochastic nature of driving. Because of its success in solving …
相关搜索
- traffic cultures reinforcement learning
- automated driving reinforcement learning
- meta reinforcement autonomous vehicles
- highway environment reinforcement learning
- congested traffic autonomous vehicles
- inherent uncertainty reinforcement learning
- behavior generation reinforcement learning
- autonomous driving reinforcement learning