Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging
… We employ this framework in a highway on-ramp merge scenario in which our algorithm
is used to actively learn the unknown behavior of other drivers in order to successfully merge …
is used to actively learn the unknown behavior of other drivers in order to successfully merge …
Reinforcement learning based safe decision making for highway autonomous driving
… To summarize the above contributions, we provide a dedicated Qlearning version for the
problem of autonomous navigation. The remainder of this paper is organized as follows: Sec. II …
problem of autonomous navigation. The remainder of this paper is organized as follows: Sec. II …
Autonomous highway driving using deep reinforcement learning
… The DRL architecture used in this work for autonomous highway driving is given in Fig. 2.
Unlike the mediated perception method that relies on complete reconstruction of scene prior […
Unlike the mediated perception method that relies on complete reconstruction of scene prior […
A deep reinforcement learning-based approach for autonomous driving in highway on-ramp merge
H Wang, S Yuan, M Guo, X Li… - Proceedings of the …, 2021 - journals.sagepub.com
… In this paper, we focus on the problem of highway merge via parallel-type on-ramp for
autonomous vehicles (AVs) in a decentralized non-cooperative way. This problem is challenging …
autonomous vehicles (AVs) in a decentralized non-cooperative way. This problem is challenging …
Towards robust decision-making for autonomous driving on highway
… This paper proposes a robust decision-making framework for autonomous highway
driving to enhance driving safety. First, a DDPG-based continuous action space RL policy is …
driving to enhance driving safety. First, a DDPG-based continuous action space RL policy is …
[HTML][HTML] How do active road users act around autonomous vehicles? An inverse reinforcement learning approach
… in this study all occurred while the AVs were operating in autonomous mode. … learning
logic is used to obtain the reward function that explains a specific action taken by the active road …
logic is used to obtain the reward function that explains a specific action taken by the active road …
Automated driving highway traffic merging using deep multi-agent reinforcement learning in continuous state-action spaces
L Schester, LE Ortiz - 2021 IEEE Intelligent Vehicles …, 2021 - ieeexplore.ieee.org
… To make fully autonomous driving ubiquitous, we must develop robust methods for highway
lane … that the societal expectation of autonomous driving seems to as near as possible to 0%. …
lane … that the societal expectation of autonomous driving seems to as near as possible to 0%. …
Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic
… Abstract— On-ramp merging is a challenging task for autonomous vehicles (AVs), especially
… -traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) …
… -traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) …
[HTML][HTML] Weakly supervised reinforcement learning for autonomous highway driving via virtual safety cages
… autonomous vehicles. In this paper, we present a reinforcement learning based approach to
autonomous … as weak supervision to the reinforcement learning agent. By guiding the agent …
autonomous … as weak supervision to the reinforcement learning agent. By guiding the agent …
Autonomous highway merging in mixed traffic using reinforcement learning and motion predictive safety controller
… Autonomous driving has been a hot issue from the past few decades … autonomous vehicles
will appear on the highway at the same time. Therefore, it is challenging for an autonomous …
will appear on the highway at the same time. Therefore, it is challenging for an autonomous …
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