Scene simulation in autonomous driving has gained significant attention because of its huge potential for generating customized data. However, existing editable scene simulation approaches face limitations in terms of user interaction efficiency, multi-camera photo-realistic rendering and external digital assets integration. To address these challenges, this paper introduces ChatSim, the first system that enables editable photo-realistic 3D driving scene simulations via natural language commands with external digital assets. To enable editing with high command flexibility, ChatSim leverages a large language model (LLM) agent collaboration framework. To generate photo-realistic outcomes, ChatSim employs a novel multi-camera neural radiance field method. Furthermore, to unleash the potential of extensive high-quality digital assets, ChatSim employs a novel multi-camera lighting estimation method to achieve scene-consistent assets' rendering. Our experiments on Waymo Open Dataset demonstrate that ChatSim can handle complex language commands and generate corresponding photo-realistic scene videos. Code can be accessed at: https://github.com/yifanlu0227/chatSim.

Robustness and safety concerns of perception systems are of great importance for autonomous vehicle navigation applications. Recent researches demonstrate that the surrounding dynamic object detection results of current perception systems can be easily interfered or attacked to mislead the navigation performance of the victim vehicle. In this paper, we develop a GNN based relation learning network to detect the abnormal information in the vehicle perception results, by investigating the relations among the surrounding dynamic objects and also the overall scenario information. Our underlying logic is that the motion of each surrounding object is also affected by its neighbors as well as the whole traffic scenario information, so there should exist a certain amount of consistency among those agents. Learning their spatiotemporal relations provides critical information for detecting the abnormal perception information. Experimental results on the standard CARLA simulator demonstrate our effectiveness in various scenarios and scalability to unseen cases.

The requirements of future aeroengines challenge turbomachinery designs to be quieter, greener, and more efficient; furthermore, they must be developed at considerably reduced cycles and costs. Traditional turbomachinery design systems involve the iterative design–evaluation–improvement process, leading to high computational costs and considerable reliance on expert experience. In this paper, a knowledge-based turbomachinery aerodynamic design system consisting of a design of experiment framework and a two-stage modeling is proposed. In the first phase of the two-stage modeling, a deep neural network (DNN)-enhanced multi-output Gaussian process model is constructed. This model determines the optimal loading distribution from reference designs that are optimized for the desired performance. In the second stage, a DNN model is developed to predict the turbine profile geometry for a given loading distribution. This formulation enables the automatic determination of the optimal turbine geometry without an iterative process. Furthermore, the knowledge in the design system grows with the increasing database. The results indicate that the L2 relative errors for predicting the optimal pressure coefficient distribution and turbine profile geometry are less than 0.25% and 1.7%, respectively. A design case of a low-pressure turbine is used to validate the entire design system. The results reveal that the turbine geometry generated by the proposed design system outperforms that of the benchmark case in terms of the design and off-design aerodynamic performance. The foregoing observations are finally confirmed through experiments.