Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles

Learning-based approaches to autonomous vehicle planners have the potential to scale to many complicated real-world driving scenarios by leveraging huge amounts of driver demonstrations. However, prior work only learns to estimate a single planning trajectory, while there may be multiple acceptable plans in real-world scenarios. To solve the problem, we propose an interpretable neural planner to regress a heatmap, which effectively represents multiple potential goals in the bird's-eye view of an autonomous vehicle. The planner employs an adaptive Gaussian kernel and relaxed hourglass loss to better capture the uncertainty of planning problems. We also use a negative Gaussian kernel to add supervision to the heatmap regression, enabling the model to learn collision avoidance effectively. Our systematic evaluation on the Lyft Open Dataset across a diverse range of real-world driving scenarios shows that our model achieves a safer and more flexible driving performance than prior works

A Pairwise Dataset for GUI Conversion and Retrieval between Android Phones and Tablets

With the popularity of smartphones and tablets, users have become accustomed to using different devices for different tasks, such as using their phones to play games and tablets to watch movies. To conquer the market, one app is often available on both smartphones and tablets. However, although one app has similar graphic user interfaces (GUIs) and functionalities on phone and tablet, current app developers typically start from scratch when developing a tablet-compatible version of their app, which drives up development costs and wastes existing design resources. Researchers are attempting to employ deep learning in automated GUIs development to enhance developers' productivity. Deep learning models rely heavily on high-quality datasets. There are currently several publicly accessible GUI page datasets for phones, but none for pairwise GUIs between phones and tablets. This poses a significant barrier to the employment of deep learning in automated GUI development. In this paper, we collect and make public the Papt dataset, which is a pairwise dataset for GUI conversion and retrieval between Android phones and tablets. The dataset contains 10,035 phone-tablet GUI page pairs from 5,593 phone-tablet app pairs. We illustrate the approaches of collecting pairwise data and statistical analysis of this dataset. We also illustrate the advantages of our dataset compared to other current datasets. Through preliminary experiments on this dataset, we analyse the present challenges of utilising deep learning in automated GUI development and find that our dataset can assist the application of some deep learning models to tasks involving automatic GUI development.Comment: 10 pages, 9 figure

Deep Learning Approaches in Pavement Distress Identification: A Review

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society

Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing

Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles. Traditional optimization techniques suffer from the curse of dimensionality and limit the search space to fixed parameter spaces. To address these challenges, we propose a deep reinforcement learning approach that generates scenarios by sequential editing, such as adding new agents or modifying the trajectories of the existing agents. Our framework employs a reward function consisting of both risk and plausibility objectives. The plausibility objective leverages generative models, such as a variational autoencoder, to learn the likelihood of the generated parameters from the training datasets; It penalizes the generation of unlikely scenarios. Our approach overcomes the dimensionality challenge and explores a wide range of safety-critical scenarios. Our evaluation demonstrates that the proposed method generates safety-critical scenarios of higher quality compared with previous approaches

Raman on-chip : current status and future tracks

On-chip Raman sensing enabled by large-scale photonic integration is a promising technology for biological and healthcare applications. In this contribution we give a review the current status of on-chip Raman sensing with a particular focus on the ultimate performances. We discuss the limitations in terms of detection limit and the different paths currently followed to get around them

Ultra-sensitive silicon nitride waveguide-enhanced Raman spectroscopy for aqueous solutions of organic compounds

We demonstrate a waveguide-enhanced Raman sensor functionalized with mesoporous silica coating for organic compounds in aqueous solutions. The detection limit of cyclohexanone in water is improved by at least 100 times compared to bare waveguides. (C) 2020 The Author(s