Kikuo Fujimura
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View article: Response of the Ruminal Microbial Environment to a High‐Dose Supplementation of Blended Essential Oils
Response of the Ruminal Microbial Environment to a High‐Dose Supplementation of Blended Essential Oils Open
We conducted two experiments to investigate the effects of a high‐dose blend of essential oils (BEO) on rumen microbial community structure and methane (CH 4 ) production. The first was an in vitro study that measured gas production, metha…
View article: Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation Open
Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural networks as function approximators is typically considered a bla…
View article: Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Robust Driving Policy Learning with Guided Meta Reinforcement Learning Open
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior policy to control social vehicles in the training environmen…
View article: Supplementary Material for: In utero exposure to valproic acid throughout pregnancy causes phenotypes of autism in offspring mice
Supplementary Material for: In utero exposure to valproic acid throughout pregnancy causes phenotypes of autism in offspring mice Open
Valproic acid (VPA) is an antiepileptic drug that inhibits the epileptic activity of neurons mainly by inhibiting sodium channels and GABA transaminase. VPA is also known to inhibit histone deacetylases, which epigenetically modify the cel…
View article: Lane-Change in Dense Traffic With Model Predictive Control and Neural Networks
Lane-Change in Dense Traffic With Model Predictive Control and Neural Networks Open
This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We pro…
View article: Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning Open
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requiring complex interactions, existing algorithms can suffer …
View article: Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning Open
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requiring complex interactions, existing algorithms can suffer …
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
others to the state of the art development and pr vehicles.
View article: Risk-Aware Lane Selection on Highway with Dynamic Obstacles
Risk-Aware Lane Selection on Highway with Dynamic Obstacles Open
This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic flow. When lane-changing is discretionary, it is advised no…
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
The IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report signifi cant theoretical fi ndings and application case studies, and raise awar…
View article: Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships Open
Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate drastically different outcomes remains an open problem with …
View article: Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic Open
Maneuvering in dense traffic is a challenging task for autonomous vehicles because it requires reasoning about the stochastic behaviors of many other participants. In addition, the agent must achieve the maneuver within a limited time and …
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
The IEEE Transactions on Intelligent Vehicles (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report significant theoretical findings and application case studies, and raise awaren…
View article: Reinforcement Learning with Iterative Reasoning for Merging in Dense\n Traffic
Reinforcement Learning with Iterative Reasoning for Merging in Dense\n Traffic Open
Maneuvering in dense traffic is a challenging task for autonomous vehicles\nbecause it requires reasoning about the stochastic behaviors of many other\nparticipants. In addition, the agent must achieve the maneuver within a limited\ntime a…
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
The IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report signifi cant theoretical fi ndings and application case studies, and raise awar…
View article: Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic Open
Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the mane…
View article: Safe Reinforcement Learning on Autonomous Vehicles
Safe Reinforcement Learning on Autonomous Vehicles Open
There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safety critical applications. Recent work in safe reinforcement…
View article: Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals
Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals Open
In a multi-agent setting, the optimal policy of a single agent is largely dependent on the behavior of other agents. We investigate the problem of multi-agent reinforcement learning, focusing on decentralized learning in non-stationary dom…
View article: Cooperation-Aware Lane Change Control in Dense Traffic
Cooperation-Aware Lane Change Control in Dense Traffic Open
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of human drivers. This paper especially focuses on heavy traffic where vehicles cannot change lane w…
View article: Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network Open
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on heavy traffic where vehicles cannot change lanes without coo…
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
The IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report signifi cant theoretical fi ndings and application case studies, and raise awar…
View article: Cluster Analysis using Spherical SOM
Cluster Analysis using Spherical SOM Open
A cluster analysis method is proposed in this paper. As benchmark data, the Fisher's iris and the Wine recognition data sets are used. As a result of the numerical experiment, a clustering method using the dendrogram yielded 97 % in accura…
View article: Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments Open
Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants. We propose a modular decision making algorithm to auto…
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
The IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report signifi cant theoretical fi ndings and application case studies, and raise awar…
View article: Uncertainty-Aware Data Aggregation for Deep Imitation Learning
Uncertainty-Aware Data Aggregation for Deep Imitation Learning Open
Estimating statistical uncertainties allows autonomous agents to communicate their confidence during task execution and is important for applications in safety-critical domains such as autonomous driving. In this work, we present the uncer…
View article: Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving Open
Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain environments, but it does not provide any guarantee on the…
View article: Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios Open
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when …
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
The IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report signifi cant theoretical fi ndings and application case studies, and raise awar…
View article: IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Open
The IEEE TRANSACTIONS ON INTELLIGENT VEHICLES (T-IV) publishes peer-reviewed articles that provide innovative research concepts and application results, report signifi cant theoretical fi ndings and application case studies, and raise awar…