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Autonomous driving requires robust and highly accurate perception technologies. Various deep learning algorithms based on only image processing satisfy this requirement, but few such algorithms are based on LiDAR. However, images are only one part of the perceptible sensors in an autonomous driving vehicle; LiDAR is also essential for the recognition of driving environments. The main reason why there exist few deep learning algorithms based on LiDAR is a lack of data. Recent translation technology using generative adversarial networks (GANs) has been proposed to deal with this problem. However, these technologies focus on only image-to-image translation, although a lack of data occurs more often with LiDAR than with images. LiDAR translation technology is required not only for data augmentation, but also for driving simulation, which allows algorithms to practice driving as if they were commanding a real vehicle, before doing so in the real world. In other words, driving simulation is a key technology for evaluating and verifying algorithms which are practically applied to vehicles. In this paper, we propose a GAN-based LiDAR translation algorithm for autonomous driving and driving simulation. It is the first LiDAR translation approach that can deal with various types of weather that are based on an empirical approach. We tested the proposed method on the JARI data set, which was collected under various adverse weather scenarios with diverse precipitation and visible distance settings. The proposed method was also applied to the real-world Spain data set. Our experimental results demonstrate that the proposed method can generate realistic LiDAR data under adverse weather conditions.

Fully autonomous driving has been widely studied and is becoming increasingly feasible. However, such autonomous driving has yet to be achieved on public roads, because of various uncertainties due to surrounding human drivers and pedestrians. In this paper, we present an end-to-end learningbased autonomous driving system named SuperDriver AI, where Deep Neural Networks (DNNs) learn the driving actions and policies from the experienced human drivers and determine the driving maneuvers to take while guaranteeing road safety. In addition, to improve robustness and interpretability, we present a slit model and a visual attention module. We build a datacollection system and emulator with real-world hardware, and we also test the SuperDriver AI system with real-world driving scenarios. Finally, we have collected 150 runs for one driving scenario in Tokyo, Japan, and have shown the demonstration of SuperDriver AI with the real-world vehicle.

Various methods have been proposed for utilizing Large Language Models (LLMs) in autonomous driving. One strategy of using LLMs for autonomous driving involves inputting surrounding objects as text prompts to the LLMs, along with their coordinate and velocity information, and then outputting the subsequent movements of the vehicle. When using LLMs for such purposes, capabilities such as spatial recognition and planning are essential. In particular, two foundational capabilities are required: (1) spatial-aware decision making, which is the ability to recognize space from coordinate information and make decisions to avoid collisions, and (2) the ability to adhere to traffic rules. However, quantitative research has not been conducted on how accurately different types of LLMs can handle these problems. In this study, we quantitatively evaluated these two abilities of LLMs in the context of autonomous driving. Furthermore, to conduct a Proof of Concept (POC) for the feasibility of implementing these abilities in actual vehicles, we developed a system that uses LLMs to drive a vehicle.

Novel ruthenium complexes Ru(L)(bpy) 2 (PF 6 ) 2 and platinum organometallic complexes Pt(L)(−≡−C 6 H 5 CH 3 ) 2 with bis-(pyridinyl)ethynyl-phenanthrolines (L = 3,8-bis[2-(3-pyridinyl)ethynyl]-1,10-phenanthroline or 3,8-bis[2-(4-pyridinyl)ethynyl]-1,10-phenanthroline) that function as metalloligands by extra pyridyl units have been prepared using respective synthetic methods. These complexes have broad absorption bands assignable to the MLCT band as the main contributing factor in the 400 to 550 nm wavelength region. Furthermore, these complexes show phosphorescence centered around 680 nm upon excitation at 425 nm. These emissions were assigned to a triplet MLCT-based luminescence for the ruthenium complexes, while a triplet MLCT as the main element, including the interligand charge transfer as the minor element, was assigned for the platinum organometallic complexes. The quantum yields of the emission of the present ruthenium complexes were relatively high, and these complexes are exactly phosphorescent dyes, although the emission intensities of the platinum complexes are poor. These two types of complexes are capable of selective photophysical detection of some metal ions and can serve as metalloligands in the construction of supramolecular metallocycles.

Background The optimal timing of intervention to obtain significant effects with regard to reducing the consumption of antimicrobial agents or antimicrobial-resistant bacteria in facilities that lack the manpower to maintain an antimicrobial stewardship team (AST) is not well-known. Methods An observational retrospective study was performed at Fukuoka University Hospital between April 1, 2013 and March 31, 2016 to evaluate the optimal timing of intervention on appropriate antimicrobial therapy for specific antimicrobial agents, including broad-spectrum antimicrobial agents (piperacillin–tazobactam, carbapenems, fluoroquinolones) and anti-MRSA (vancomycin, teicoplanin, daptomycin, and linezolid) agents. In period 1, interventions were performed for patients using specific antimicrobial agents for >14 days. In period 2, interventions were performed for patients using anti-MRSA agents, and in period 3, interventions were performed for patients using any specific antimicrobial agents, regardless of the days of use, on a weekly basis. The effects on antimicrobial use, the antimicrobial-resistant bacteria, and the clinical outcomes among the three periods were compared. Results The AUDs of piperacillin–tazobactam and carbapenems decreased significantly (10.8 → 9.2 and 15.7 → 14.2; period 2 vs. period 3, P < 0.05). The rates of piperacillin–tazobactam, meropenem and levofloxacin resistance in Pseudomonas aeruginosa isolates decreased from 13.8%, 16.2%, 11.9% in period 1 to 10.4%, 8.7%, 6.5% in period 3, respectively. The annual costs of these antimicrobials decreased according to the period: period 1, US$ 1,080,000; period 2, US$ 944,000; and period 3, US$ 763,000 (period 3 vs. period 1, P <0.01). No recurrence was observed within 7 days after intervention and the mortality rate and length of stay did not change to a statistically significant extent in any of the study periods. Conclusion When interventions were performed once a week by an ASP team, accelerating the timing of intervention from patients with >14 days of use to all patients treated with the specific antimicrobial agents was significantly more effective for reducing the consumption of antimicrobials leading to reduction of the related costs and antimicrobial-resistant P. aeruginosa without compromising the patient outcomes. Disclosures T. Takata, Taisho Toyama Pharmaceutical Co. Ltd.: Speaker’s Bureau, Speaker honorarium