Robust lane change decision making for autonomous vehicles: An observation adversarial reinforcement learning approach
Reinforcementlearning holds the promise of allowing autonomous vehicles to learn complex
decision making behaviors through interacting with other traffic participants. However, many …
decision making behaviors through interacting with other traffic participants. However, many …
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 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 …
Adversarial evaluation of autonomous vehicles in lane-change scenarios
Autonomous vehicles must be comprehensively evaluated before deployed in cities and
highways. However, most existing evaluation approaches for autonomous vehicles are static …
highways. However, most existing evaluation approaches for autonomous vehicles are static …
Improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning
X Ma, K Driggs-Campbell… - 2018 IEEE Intelligent …, 2018 - ieeexplore.ieee.org
To improve efficiency and reduce failures in autonomous vehicles, research has focused on
developing robust and safe learning methods that take into account disturbances in the …
developing robust and safe learning methods that take into account disturbances in the …
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
Driving safety is the most important element that needs to be considered for autonomous
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
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 …
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 …
Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic
Autonomous driving has attracted significant research interests in the past two decades as it
offers many potential benefits, including releasing drivers from exhausting driving and …
offers many potential benefits, including releasing drivers from exhausting driving and …
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 …
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