Integrating big data analytics in autonomous driving: An unsupervised hierarchical reinforcement learning approach
In the realm of autonomous vehicular systems, there has been a notable increase in end-to-
end algorithms designed for complete self-navigation. Researchers are increasingly …
end algorithms designed for complete self-navigation. Researchers are increasingly …
Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles
Vehicle control in autonomous traffic flow is often handled using the best decision-making
reinforcement learning methods. However, unexpected critical situations make the collisions …
reinforcement learning methods. However, unexpected critical situations make the collisions …
Potential game-based decision-making for autonomous driving
Decision-making for autonomous driving is challenging, considering the complex
interactions among multiple traffic agents (including autonomous vehicles (AVs), human …
interactions among multiple traffic agents (including autonomous vehicles (AVs), human …
Explainable AI-based federated deep reinforcement learning for trusted autonomous driving
Recently, the concept of autonomous driving became prevalent in the domain of intelligent
transportation due to the promises of increased safety, traffic efficiency, fuel economy and …
transportation due to the promises of increased safety, traffic efficiency, fuel economy and …
Vision-based uncertainty-aware lane keeping strategy using deep reinforcement learning
Recent deep learning techniques promise high hopes for self-driving cars while there are
still many issues to be addressed such as uncertainties (eg, extreme weather conditions) in …
still many issues to be addressed such as uncertainties (eg, extreme weather conditions) in …
Simulation of vehicle interaction behavior in merging scenarios: A deep maximum entropy-inverse reinforcement learning method combined with game theory
W Li, F Qiu, L Li, Y Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Simulation testing based on virtual scenarios can improve the efficiency of safety testing for
high-level autonomous vehicles (AVs). In most traffic scenarios, such as merging scenarios …
high-level autonomous vehicles (AVs). In most traffic scenarios, such as merging scenarios …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Safe model-based off-policy reinforcement learning for eco-driving in connected and automated hybrid electric vehicles
Deep Reinforcement Learning (DRL) has recently been applied to eco-driving to intelligently
reduce fuel consumption and travel time. While previous studies synthesize simulators and …
reduce fuel consumption and travel time. While previous studies synthesize simulators and …
Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems
A hierarchical game theoretic decision making framework is exploited to model driver
decisions and interactions in traffic. In this paper, we apply this framework to develop a …
decisions and interactions in traffic. In this paper, we apply this framework to develop a …
Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections
C Spatharis, K Blekas - Journal of Intelligent Transportation …, 2024 - Taylor & Francis
In this work we present a multiagent deep reinforcement learning approach for autonomous
driving vehicles that is able to operate in traffic networks with unsignalized intersections. The …
driving vehicles that is able to operate in traffic networks with unsignalized intersections. The …