Robust multiagent reinforcement learning toward coordinated decision-making of automated vehicles
Automated driving is essential for developing and deploying intelligent transportation
systems. However, unavoidable sensor noises or perception errors may cause an …
systems. However, unavoidable sensor noises or perception errors may cause an …
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 …
Safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization
Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to
its potential to solve complex classification and control problems. However, existing RL …
its potential to solve complex classification and control problems. However, existing RL …
A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control …
Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a
data-driven optimization-based Model Predictive Control (MPC) modeling framework for the …
data-driven optimization-based Model Predictive Control (MPC) modeling framework for the …
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 …
Adaptive robust game-theoretic decision making strategy for autonomous vehicles in highway
In a typical traffic scenario, autonomous vehicles are required to share the road with other
road participants, eg, human driven vehicles, pedestrians, etc. To successfully navigate the …
road participants, eg, human driven vehicles, pedestrians, etc. To successfully navigate the …
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Connected and automated vehicles (CAVs) have emerged as a potential solution to the
future challenges of developing safe, efficient, and eco-friendly transportation systems …
future challenges of developing safe, efficient, and eco-friendly transportation systems …
Communication-efficient decentralized multi-agent reinforcement learning for cooperative adaptive cruise control
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with
enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs …
enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs …
Spatial-temporal-aware safe multi-agent reinforcement learning of connected autonomous vehicles in challenging scenarios
Communication technologies enable coordination among connected and autonomous
vehicles (CAVs). However, it remains unclear how to utilize shared information to improve …
vehicles (CAVs). However, it remains unclear how to utilize shared information to improve …
Efficient uncertainty-aware decision-making for automated driving using guided branching
Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due
to potentially stochastic behaviors of other traffic participants and perception uncertainties …
to potentially stochastic behaviors of other traffic participants and perception uncertainties …