Understanding the stability of volatile species and their compounds under various surface and subsurface conditions is of great importance in gaining insights into the formation and evolution of planetary and satellite bodies. We report the experimental results of the temperature‐ and pressure‐dependent structural transformation of methane hydrates in salt environments using in situ synchrotron X‐ray powder diffraction, solid‐state nuclear magnetic resonance, and Raman spectroscopy. We find that under pressurized and concentrated brine solutions methane hydrate forms a mixture of type I clathrate hydrate, ice, and hydrated salts. Under a low‐pressure condition, however, the methane hydrates are decomposed through a rapid sublimation of water molecules from the surface of hydrate crystals, while NaCl · 2H₂O undergoes a phase transition into a crystal growth of NaCl via the migration of salt ions. In ambient pressure conditions, the methane hydrate is fully decomposed in brine solutions at temperatures above 252 K, the eutectic point of NaCl · 2H₂O.

This paper introduces a novel methodology for evaluating human driving behavior influenced by shoe type and its impact on collision risk. While human factors, such as footwear, are recognized to affect driving safety, studies quantitively assessing the effects of shoe types on safety has been limited. To address this, we utilize an autoencoder and human-centered risk assessment algorithms to investigate human driving behavior and collision risk. Experiments were conducted in various real-world driving scenarios, involving two distinct types of shoes. The autoencoder extracts features from the driving data and enables us to analyze the effects of shoe type on driving behavior. Additionally, collision risk analysis is used to verify the validity and impact of the feature extraction results on safe driving. This study contributes to enhancing our understanding of how footwear influences driver behavior and safety. Furthermore, this methodology establishes a groundwork for future research on applying quantitative evaluations to other human factors that influence driving behavior.

This study focuses on the tire–road friction estimation for the predictive control strategy of electronically limited slip differential (ELSD) to improve the handling and acceleration performance of front-wheel drive cars, which typically suffer from excessive understeer and inner drive wheel spin during acceleration while turning due to reduced vertical load on the wheel. To mitigate this, we propose a control logic for ELSD that enhances course followability and acceleration by pre-transferring the driving torque from the inside to the outside wheel, considering the estimated traction potential for rapid response. It is essential to improve the control accuracy of wheel spin prediction by predicting the friction coefficient of the road surface. Furthermore, this study extends to the analysis of vehicle dynamics during lane-change maneuvers on low-friction surfaces, emphasizing the role of accurate tire–road friction estimation in vehicle safety. A CarSim 2023-based simulation study was conducted to investigate the vehicle response on snowy roads with low friction coefficients (μ = 0.2) and low temperatures (−5 °C). The results demonstrated that even minimal steering input could result in significant side-slip angles, highlighting the nonlinear vehicle behavior and the critical need for robust traction estimation in such challenging conditions of general racing tracks. The proposed friction-estimation method was evaluated through vehicle testing and has been substantiated by patents for its originality in control and friction-estimation approaches. The outcomes of these combined methodologies underline the critical importance of tire–road friction coefficient estimation in both the effectiveness of the ELSD system and the broader context of active safety systems.

This paper presents a data-driven automated driving control algorithm designed to enhance human acceptance in autonomous vehicles. To achieve this, we utilize an LSTM autoencoder to extract latent driving features from collected data, which are then clustered into lateral and longitudinal behaviors. These clustered behaviors serve as the foundation for generating steering and acceleration profiles, enabling the automated driving system to replicate individualized human driving styles. By incorporating three distinct driving behaviors, the proposed approach effectively mitigates potential sources of discomfort, such as excessive jerk and abrupt accelerations, ensuring a smoother ride experience. Additionally, we introduce a comprehensive ride quality evaluation metric that considers both trajectory similarity (trajectory score) and passenger comfort (comfort energy expression). The effectiveness of the proposed algorithm is validated through real-world vehicle tests, focusing on driving scenarios known to cause ride discomfort. Experimental results demonstrate that the automated driving control framework successfully enhances ride quality while adapting to individual passenger driving preferences, thus improving overall human acceptance of autonomous vehicles.

Learning Model Predictive Control (LMPC) is a data-driven approach to MPC that enhances closed-loop performance by leveraging data from successive task iterations to approximate the solution of optimal control problems. The value function in LMPC is pivotal for performance enhancement, but its discrete nature—where each point corresponds to a data point—renders the LMPC problem computationally intensive due to its mixed-integer nature. This letter introduces a novel method to construct the LMPC value function. The proposed value function is a piecewise affine approximation that interpolates the discrete data points of the original value function, resulting in a nonlinear relaxation of the mixed-integer LMPC problem. By connecting the discrete data points with piecewise affine segments, the essential characteristics of the original value function are preserved. The proposed algorithm’s effectiveness is demonstrated through numerical simulations in autonomous racing.