[HTML][HTML] Leveraging reinforcement learning for dynamic traffic control: A survey and challenges for field implementation
In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …
interest in reinforcement learning (RL) within the traffic and transportation community …
Towards robust decision-making for autonomous driving on highway
Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …
Adaptive on-ramp merging strategy under imperfect communication performance
X Tong, Y Shi, Q Zhang, S Chen - Vehicular Communications, 2023 - Elsevier
On-ramp merging is one of the important V2X (Vehicle-to-Everything) applications and is
critical for both driving safety and traffic efficiency. The ramp vehicle needs to get information …
critical for both driving safety and traffic efficiency. The ramp vehicle needs to get information …
Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
Robust decision making for autonomous vehicles at highway on-ramps: A constrained adversarial reinforcement learning approach
Reinforcement learning has demonstrated its potential in a series of challenging domains.
However, many real-world decision making tasks involve unpredictable environmental …
However, many real-world decision making tasks involve unpredictable environmental …
Toward personalized decision making for autonomous vehicles: a constrained multi-objective reinforcement learning technique
Reinforcement learning promises to provide a state-of-the-art solution to the decision
making problem of autonomous driving. Nonetheless, numerous real-world decision making …
making problem of autonomous driving. Nonetheless, numerous real-world decision making …
Uncertainties in onboard algorithms for autonomous vehicles: Challenges, mitigation, and perspectives
K Yang, X Tang, J Li, H Wang, G Zhong… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
future mobility and quality of life. However, safety remains a critical hurdle in the way of …
Toward trustworthy decision-making for autonomous vehicles: A robust reinforcement learning approach with safety guarantees
While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
A hybrid deep reinforcement learning and optimal control architecture for autonomous highway driving
Autonomous vehicles in highway driving scenarios are expected to become a reality in the
next few years. Decision-making and motion planning algorithms, which allow autonomous …
next few years. Decision-making and motion planning algorithms, which allow autonomous …
Multi-View Graph Convolution Network Reinforcement Learning for CAVs Cooperative Control in Highway Mixed Traffic
D Xu, P Liu, H Li, H Guo, Z Xie… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The control of connected autonomous vehicles (CAVs) for cooperative sensing and driving
in mixed traffic flows is critical for the development of intelligent transportation systems …
in mixed traffic flows is critical for the development of intelligent transportation systems …