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Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two‐dimensional LiDAR. Results show that reliable MCL that performs robust localization, self‐failure detection, and quick failure recovery can be realized. [ABSTRACT FROM AUTHOR]

Most of the recent automated driving systems assume the accurate functioning of localization. Unanticipated errors cause localization failures and result in failures in automated driving. An exact localization failure detection is necessary to ensure safety in automated driving; however, detection of the localization failures is challenging because sensor measurement is assumed to be independent of each other in the localization process. Owing to the assumption, the entire relation of the sensor measurement is ignored. Consequently, it is difficult to recognize the misalignment between the sensor measurement and the map when partial sensor measurement overlaps with the map. This paper proposes a method for the detection of localization failures using Markov random fields with fully connected latent variables. The full connection enables to take the entire relation into account and contributes to the exact misalignment recognition. Additionally, this paper presents localization failure probability calculation and efficient distance field representation methods. We evaluate the proposed method using two types of datasets. The first dataset is the SemanticKITTI dataset, whereby four methods are compared with the proposed method. The comparison results reveal that the proposed method achieves the most accurate failure detection. The second dataset is created based on log data acquired from the demonstrations that we conducted in Japanese public roads. The dataset includes several localization failure scenes. We apply the failure detection methods to the dataset and confirm that the proposed method achieves exact and immediate failure detection. [ABSTRACT FROM AUTHOR]

The main stream of development of autonomous driving technology (ADT) is for four‐wheel motor vehicles. ADT for motorcycles has received scant attention. The motorcycle stability tends to diminish when being driven at extremely low speed. This study addresses how a motorcycle should be stabilized under low‐speed driving. To obtain a linearized motorcycle model without skidding, we introduce a model based on SPACAR, a finite element method computation program. Moreover, velocity‐dependent gain‐scheduling linear quadratic regulator (LQR) is applied. The experimental results demonstrate stabilized driving responses at 1.5 km/h, which is slower than a person's typical walking speed. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper introduces a method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors. Deep learning techniques were used to extract latent features from the collected data. Extracted features were clustered into behaviors and used to create velocity profiles allowing an autonomous driving agent could drive in a human-like manner. By using proactive driving behaviors, the agent could limit potential sources of discomfort such as jerk and uncomfortable velocities. Additionally, we proposed a method to compare trajectories where not only the geometric similarity is considered, but also velocity, acceleration and jerk. Experimental results in a simulator implemented in ROS show that the autonomous agent built with the driving behaviors was capable of driving similarly to expert human drivers. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel localization approach that simultaneously estimates the reliability of estimation results. In the approach, a convolutional neural network (CNN) is used to make decision whether the localization process has failed or not. We train the CNN using a dataset that includes successful localization results and faults. However, the decision will contain some noise and many misdetection results may occur when the decision made by the CNN is used directly to detect faults. Therefore, we estimate both a robot's pose and reliability of the localization results based on the decision. To simultaneously estimate the robot's pose and reliability, we propose a new graphical model and implement a Rao-Blackwellized particle filter based on the model. We evaluated the proposed approach based on simulations and actual environments, which showed that the reliability estimated by the proposed approach can be used as an exact criterion for detecting localization faults. In addition, we show that the proposed approach can be applied in actual environments even when a dataset created from a simulation is used to train the CNN. [ABSTRACT FROM AUTHOR]

Quaternary ammonium compounds (QACs) found in many microbial environments, which have different biological functions. Recently, we obtained two enzymes, 4-N-trimethylaminobutyraldehyde dehydrogenase (TMABaldehyde-DH) and 4-N-trimethylamino-1-butanol dehydrogenase from Pseudomonas sp. 13CM those oxidizes QACs, respectively, 4-N-trimethylaminobutyraldehyde (TMABaldehyde) and 4-N-trimethylamino-1-butanol. TMABaldehyde-DH also mediates the biosynthesis of carnitine. In this study, the genes encoding both enzymes were sequenced and the TMABaldehyde-DH gene over-expressed in Escherichia coli. Their primary structures showed, respectively 93.95% and 87.0% positional identity with aldehyde dehydrogenase and iron containing alcohol dehydrogenase of Pseudomonas putida GB-1. Characterization of purified recombinant TMABaldehyde-DH, confirmed the enzyme had essentially the same properties as that of TMABaldehyde-DH purified from cell-free extract of Pseudomonas sp. 13CM. It is indicated that the function of aldehyde dehydrogenase and iron containing alcohol dehydrogenase of Pseudomonas putida GB-1, function as oxidization of QACs. [ABSTRACT FROM AUTHOR]