When, Where and How Does it Fail? A Spatial-Temporal Visual Analytics Approach for Interpretable Object Detection in Autonomous Driving.

Arguably the most representative application of artificial intelligence, autonomous driving systems usually rely on computer vision techniques to detect the situations of the external environment. Object detection underpins the ability of scene understanding in such systems. However, existing object detection algorithms often behave as a black box, so when a model fails, no information is available on When, Where and How the failure happened.

Visual Evaluation for Autonomous Driving.

Autonomous driving technologies often use state-of-the-art artificial intelligence algorithms to understand the relationship between the vehicle and the external environment, to predict the changes of the environment, and then to plan and control the behaviors of the vehicle accordingly. The complexity of such technologies makes it challenging to evaluate the performance of autonomous driving systems and to find ways to improve them. The current approaches to evaluating such autonomous driving systems largely use a single score to indicate the overall performance of a system, but domain experts have difficulties in understanding how individual components or algorithms in an autonomous driving system may contribute to the score.

Gradient structured micro/nanofibrous sponges with superior compressibility and stretchability for broadband sound absorption.

Ultrafine fibrous porous materials obtained by electrospinning technology have broad application prospects in the field of noise reduction. However, the two-dimensional fibrous membranes faced low thickness and dense structure, resulting in a single internal structure and narrow sound absorption band. Here, we report a simple and robust strategy to prepare gradient structured fiber sponges with superelasticity and stretchability by combining humidity-assisted multi-step electrospinning and a unique physical/chemical dual cross-linking method.

Exploiting Kalman Filtering Non-linear Exponential Fitting to Promote the Energy Resolution of 137Cs and 60Co Gamma Ray Spectra.

For Cs and Co gamma ray spectra, gamma ray energy is proportional to the amplitude of the pulse signal, and energy resolution can be improved by pulse signal processing with mathematical algorithms. Influenced by system measurement noise and baseline fluctuation, the pulse amplitude is difficult to calculate accurately. A method that combines the Kalman filter baseline estimation with the non-linear exponential fitting has been used.