On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving

The pursuit of autonomous driving technology hinges on the sophisticated integration of perception, decision-making, and control systems. Traditional approaches, both data-driven and rule-based, have been hindered by their inability to grasp the nuance of complex driving environments and the intentions of other road users. This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving. The advent of Visual Language Models (VLM) represents a novel frontier in realizing fully autonomous vehicle driving. This report provides an exhaustive evaluation of the latest state-of-the-art VLM, GPT-4V(ision), and its application in autonomous driving scenarios. We explore the model's abilities to understand and reason about driving scenes, make decisions, and ultimately act in the capacity of a driver. Our comprehensive tests span from basic scene recognition to complex causal reasoning and real-time decision-making under varying conditions. Our findings reveal that GPT-4V demonstrates superior performance in scene understanding and causal reasoning compared to existing autonomous systems. It showcases the potential to handle out-of-distribution scenarios, recognize intentions, and make informed decisions in real driving contexts. However, challenges remain, particularly in direction discernment, traffic light recognition, vision grounding, and spatial reasoning tasks. These limitations underscore the need for further research and development. Project is now available on GitHub for interested parties to access and utilize: \url{https://github.com/PJLab-ADG/GPT4V-AD-Exploration

Study of unsteady aerodynamic performance of a high-speed train entering a double-track tunnel under crosswind conditions

Significant variation exists in the aerodynamic performances of high-speed trains (HSTs) traveling in different infrastructure scenarios. When high-speed trains travel from one infrastructure scenario to another, the aerodynamic loads acting on the trains change significantly. To investigate the safety as a HST enters a tunnel under crosswind conditions, the unsteady aerodynamic performance of the HST and the flow structures around the train were numerically studied. The results demonstrated that the flow field and the pressure field were symmetrically distributed under conditions with no crosswind, while the distribution of the flow field and the pressure field were clearly asymmetric when crosswinds were present at the tunnel entrance. Furthermore, the flow structures near the train and the pressure distributed on the train surfaces outside the tunnel were most severely affected by the presence of crosswinds. Vortex structures appeared on the windward side surface of the train inside the tunnel and on the leeward side surface of the train outside the tunnel during the entrance of the train into the tunnel. Due to the sudden changes in the flow and the pressure as the train entered the tunnel, the aerodynamic loads changed drastically, and the variations of each vehicle were different, resulting in complex dynamic responses, including lateral vibrations and pitching movement. In particular, the aerodynamic performance of the rear vehicle was the worst as the train entered the tunnel when no crosswind conditions existed, while the head vehicle was the most negatively affected for safe operation of the HST when strong crosswinds were present at the tunnel entrance. (c) 2023 Elsevier Ltd. All rights reserved

Drug in Therapeutic Polymer: Sinomenine-Loaded Oxidation-Responsive Polymeric Nanoparticles for Rheumatoid Arthritis Treatment

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease that frequently involves cartilage damage and the destruction of the bone structure, ultimately resulting in disability and long-term pain. It is clear that overexpression of reactive oxygen species (ROS) and the complex inflammatory microenvironment are the main causes of RA pathogenesis; thereby, the efficacy of any single-drug treatment is limited. Herein, we formulated a therapeutic hyaluronic acid derivative (PAM-HA) with adsorption capacity to the subchondral bone, a long retention time within inflamed joints, and ROS-scavenging capacity, which was used as a drug carrier for realizing the controlled release of sinomenine (Sin) within arthritic joints. This “drug in therapeutic polymer” design strategy was aimed at realizing antioxidant and anti-inflammatory combination therapy for RA. In vivo experiments suggest that PAM-HA@Sin NPs can be retained in the inflamed joints of rats for a long time compared with commercially available free Sin injections. As expected, therapeutic PAM-HA polymeric carriers can increase joint lubrication and reduce oxidative stress, while the released Sin induces downregulation of proinflammatory factors (TNF-α and IL-1β) and upregulation of anti-inflammatory factors (Arg-1 and IL-10) via the NF-κB pathway. In summary, a ROS-scavenging hyaluronic acid (HA) derivative was developed as the nanocarrier for Sin delivery to simultaneously remodel the oxidative/inflammatory microenvironment in RA, which opens up new horizons for the development of therapeutic polymers and the combined therapeutic strategies

Piglets cloned from induced pluripotent stem cells

Embryonic stem (ES) cells are powerful tools for generating genetically modified animals that can assist in advancing our knowledge of mammalian physiology and disease. Pigs provide outstanding models of human genetic diseases due to the striking similarities to human anatomy, physiology and genetics, but progress with porcine genetic engineering has been hampered by the lack of germline-competent pig ES cells. To overcome this limitation, genetically modified pigs have been produced using genetically modified somatic cells and nuclear transfer (NT). Yet, somatic cells exhibit limited proliferative capacity and have an extremely low frequency of homologous recombination compared to ES cells. Hence, only a few knockout pig models have been reported thus far using standard gene-targeting approaches