Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a Driver Digital Twin (DDT) for the online prediction of personalized lane change behavior, allowing CAVs to predict surrounding vehicles' behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the lane change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network. The lane change intention can be recognized in 6 seconds on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 meters within a 4-second prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM

米菓の咀嚼振動が食感に及ぼす影響について

博士(農学)岐阜大学(Gifu University

Deep Forest-Based Monocular Visual Sign Language Recognition

Sign language recognition (SLR) is a bridge linking the hearing impaired and the general public. Some SLR methods using wearable data gloves are not portable enough to provide daily sign language translation service, while visual SLR is more flexible to work with in most scenes. This paper introduces a monocular vision-based approach to SLR. Human skeleton action recognition is proposed to express semantic information, including the representation of signs’ gestures, using the regularization of body joint features and a deep-forest-based semantic classifier with a voting strategy. We test our approach on the public American Sign Language Lexicon Video Dataset (ASLLVD) and a private testing set. It proves to achieve a promising performance and shows a high generalization capability on the testing set

Microstructure Evolution, Hardness, and Tribological Behaviors of Ti-50.8Ni SMA Alloy with Ultrasonic Surface Shot Peening Treatment

To explore a new method to improve the wear resistance of TiNi shape memory alloy (SMA), Ti-50.8Ni alloy was treated by the method of ultrasonic surface shot peening. The microstructure evolution, hardness, and tribological behaviors have been further investigated to evaluate the effect of ultrasonic surface shot peening (USSP). The surface microstructure can be refined to some extent while the basic phase composition has little change. USSP can facilitate the martensitic transformation in the surface layer, which benefits improving the surface hardness. Additionally, the hardness of Ti-50.8Ni alloy increases first and then decreases with the increase of applied load, but the USSP-treated alloy tends to be more sensitive to load. USSP treatment can improve the wear resistance and reduce the coefficient of friction (COF) in case of a low sliding wear speed of 5 mm/s. However, the tribological properties of USSP-treated alloy are reversely worse in the case of 10 mm/s. This is mainly attributed to the combined effect of stress-induced martensite transformation and degeneration resulting from the frictional heating during the dry sliding wear process

Electrochemical behavior of 2205 duplex stainless steel in simulated solution containing high concentration Cl− and saturated CO2 at different temperatures

Abstract 2205 duplex stainless steel (DSS) has good corrosion resistance due to its typical duplex organization, but the increasingly harsh CO2-containing oil and gas environment leads to different degrees of corrosion, especially pitting corrosion, which seriously threatens the safety and reliability of oil and gas development. In this paper, the effect of temperature on the corrosion behavior of 2205 DSS in a simulated solution containing 100 g/L Cl− and saturated CO2 was investigated with immersion tests and electrochemical tests and combined with characterization techniques such as laser confocal microscopy and X-ray photoelectron spectroscopy. The results show that the average critical pitting temperature of 2205 DSS was 66.9 °C. When the temperature was higher than 66.9 °C, the pitting breakdown potential, passivation interval, and self-corrosion potential decreased, while the dimensional passivation current density increased, and the pitting sensitivity was enhanced. With a further increase in temperature, the capacitive arc radius of 2205 DSS decreased, the film resistance and charge transfer resistance gradually decreased, the carrier density of the donor and acceptor in the product film layer with n + p bipolar characteristics also increased and the inner layer of the film with Cr oxide content decreased, while the outer layer with Fe oxide content increased, the dissolution of the film layer increased, the stability decreased, and the number and pore size of pits increased

Progress on Transition Metal Ions Dissolution Suppression Strategies in Prussian Blue Analogs for Aqueous Sodium-/Potassium-Ion Batteries

Highlights Comprehensive insights into Prussian blue analogs for aqueous sodium- and potassium-ion batteries. Unveiling the dissolution mechanism of transition metal ions in Prussian blue analogs. Innovative solutions to suppression transition metal ion dissolution, spanning electrolyte engineering, transition metal doping/substitution, minimize defects, and composite materials

Fast 3D Semantic Mapping in Road Scenes

Fast 3D reconstruction with semantic information in road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a fast 3D semantic mapping system based on the monocular vision by fusion of localization, mapping, and scene parsing. From visual sequences, it can estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping. Our system consists of three modules: a parallel visual Simultaneous Localization And Mapping (SLAM) and semantic segmentation module, an incrementally semantic transfer from 2D image to 3D point cloud, and a global optimization based on Conditional Random Field (CRF). It is a heuristic approach that improves the accuracy of the 3D semantic labeling in light of the spatial consistency on each step of 3D reconstruction. In our framework, there is no need to make semantic inference on each frame of sequence, since the 3D point cloud data with semantic information is corresponding to sparse reference frames. It saves on the computational cost and allows our mapping system to perform online. We evaluate the system on road scenes, e.g., KITTI, and observe a significant speed-up in the inference stage by labeling on the 3D point cloud

Research on Rotational Angle Measurement for the Smart Wheel Force Sensor

The measurement of the rotational angle of the wheel is critical for the smart wheel force sensor (SWFS) to obtain the wheel forces defined in the vehicle coordinates. To simplify the structure of the SWFS and overcome the shortcomings of the traditional angular transducer, a new method to evaluate the rotational speed of the wheel and then calculate the rotational angle is proposed in this paper. In this method, the centripetal acceleration caused by the rotation is recorded by three accelerometers and used carefully. What’s more, the possible sources of error are classified and analyzed. Simulations and stand experiment are carried out to demonstrate the effectiveness of the proposed method

Heterostructured BiS-BiO nanosheets with a built-in electric field for improved sodium storage

Constructing novel heterostructures has great potential in tuning the physical/chemical properties of functional materials for electronics, catalysis, as well as energy conversion and storage. In this work, heterostructured BiS-BiO nanosheets (BS-BO) have been prepared through an easy water-bath approach. The formation of such unique BS-BO heterostructures was achieved through a controllable thioacetamide-directed surfactant-assisted reaction process. BiO sheets and BiS sheets can be also prepared through simply modifying the synthetic recipe. When employed as the sodium-ion battery anode material, the resultant BS-BO displays a reversible capacity of ∼630 mA h g at 100 mA g. In addition, the BS-BO demonstrates improved rate capability and enhanced cycle stability compared to its BiO sheets and BiS sheets counterparts. The improved electrochemical performance can be ascribed to the built-in electric field in the BS-BO heterostructure, which effectively facilitates the charge transport. This work would shed light on the construction of novel heterostructures for high-performance sodium-ion batteries and other energy-related devices