城市交叉点的交通效率提高在自动交叉管理领域具有强大的研究兴趣。到目前为止，提出了大多数非学习算法（例如预订或基于优化的算法）来解决基本的多代理计划问题。同时，使用机器学习方法越来越多地实施了单个自我车辆的自动驾驶功能。在这项工作中，我们基于先前呈现的基于图的场景表示和图形神经网络，以使用强化学习来解决问题。除了车辆的现有节点功能外，通过使用边缘功能，通过使用边缘功能改进了场景表示。这会导致更高的表示网络体系结构利用的表示质量提高。本文对针对自动交叉路口管理通常使用的基线的建议方法进行了深入的评估。与传统的信号交叉路口和增强的第一届第一方案相比，在变化的交通密度下，观察到诱导延迟的显着减少。最后，通过测试训练过程中未见的交叉路口布局的策略来评估基于图的表示的概括能力。该模型实际上将较小的相交布局概括，并且在某些范围内对较大的交叉路口进行了概括。

由于静态优先规则和遮挡限制了对优先流量的观点，城市交叉口容易延迟和效率低下。改善交通流量的现有方法（广泛称为自动交叉管理系统）主要基于非学习预订方案或优化算法。基于机器学习的技术在计划单个自我车辆方面显示出令人鼓舞的结果。这项工作建议通过共同计划多辆车来利用机器学习算法来优化城市交叉点的交通流量。基于学习的行为计划提出了几个挑战，要求适合的输入和输出表示以及大量的基础数据。我们通过使用基于图形的柔性输入表示并伴随图神经网络来解决以前的问题。这允许有效地编码场景，并固有地为所有相关车辆提供单独的输出。为了学习明智的政策，而不依赖于专家示范的模仿，合作计划任务被视为强化学习问题。我们在开源模拟环境中训练并评估提出的方法，以进行自动驾驶的决策。与静态优先规则管理的第一届第一局和流量相比，学识渊博的计划者表现出显着的流速增长，同时减少了诱导停止的数量。除合成模拟外，还基于从公开可用的IND数据集中获取的现实世界流量数据进行评估。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

We address the problem of extracting key steps from unlabeled procedural videos, motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training and performance. We decompose the problem into two steps: representation learning and key steps extraction. We employ self-supervised representation learning via a training strategy that adapts off-the-shelf video features using a temporal module. Training implements self-supervised learning losses involving multiple cues such as appearance, motion and pose trajectories extracted from videos to learn generalizable representations. Our method extracts key steps via a tunable algorithm that clusters the representations extracted from procedural videos. We quantitatively evaluate our approach with key step localization and also demonstrate the effectiveness of the extracted representations on related downstream tasks like phase classification. Qualitative results demonstrate that the extracted key steps are meaningful to succinctly represent the procedural tasks.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

In training neural networks, batch normalization has many benefits, not all of them entirely understood. But it also has some drawbacks. Foremost is arguably memory consumption, as computing the batch statistics requires all instances within the batch to be processed simultaneously, whereas without batch normalization it would be possible to process them one by one while accumulating the weight gradients. Another drawback is that that distribution parameters (mean and standard deviation) are unlike all other model parameters in that they are not trained using gradient descent but require special treatment, complicating implementation. In this paper, I show a simple and straightforward way to address these issues. The idea, in short, is to add terms to the loss that, for each activation, cause the minimization of the negative log likelihood of a Gaussian distribution that is used to normalize the activation. Among other benefits, this will hopefully contribute to the democratization of AI research by means of lowering the hardware requirements for training larger models.

In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong dependency on co-modalities or additional refinement. These have been previously necessary to provide training signals for convergence. We formulate such a scheme as two sub-optimisation problems on texture learning and pose learning. We separately learn to predict realistic texture of objects from real image collections and learn pose estimation from pixel-perfect synthetic data. Combining these two capabilities allows then to synthesise photorealistic novel views to supervise the pose estimator with accurate geometry. To alleviate pose noise and segmentation imperfection present during the texture learning phase, we propose a surfel-based adversarial training loss together with texture regularisation from synthetic data. We demonstrate that the proposed approach significantly outperforms the recent state-of-the-art methods without ground-truth pose annotations and demonstrates substantial generalisation improvements towards unseen scenes. Remarkably, our scheme improves the adopted pose estimators substantially even when initialised with much inferior performance.

Prevailing methods for assessing and comparing generative AIs incentivize responses that serve a hypothetical representative individual. Evaluating models in these terms presumes homogeneous preferences across the population and engenders selection of agglomerative AIs, which fail to represent the diverse range of interests across individuals. We propose an alternative evaluation method that instead prioritizes inclusive AIs, which provably retain the requisite knowledge not only for subsequent response customization to particular segments of the population but also for utility-maximizing decisions.

We designed and constructed an A-sized base autonomous underwater vehicle (AUV), augmented with a stack of modular and extendable hardware and software, including autonomy, navigation, control and high fidelity simulation capabilities (A-size stands for the standard sonobuoy form factor, with a maximum diameter of 124 mm). Subsequently, we extended this base vehicle with a novel tuna-inspired morphing fin payload module (referred to as the Morpheus AUV), to achieve good directional stability and exceptional maneuverability; properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. The morphing fin payload allows the base AUV to dynamically change its stability-maneuverability qualities by using morphing fins, which can be deployed, deflected and retracted, as needed. The base vehicle and Morpheus AUV were both extensively field tested in-water in the Charles river, Massachusetts, USA; by conducting hundreds of hours of operations over a period of two years. The maneuvering capability of the Morpheus AUV was evaluated with and without the use of morphing fins to quantify the performance improvement. The Morpheus AUV was able to showcase an exceptional turning rate of around 25-35 deg/s. A maximum turn rate improvement of around 35% - 50% was gained through the use of morphing fins.

Imitation learning (IL) is a simple and powerful way to use high-quality human driving data, which can be collected at scale, to identify driving preferences and produce human-like behavior. However, policies based on imitation learning alone often fail to sufficiently account for safety and reliability concerns. In this paper, we show how imitation learning combined with reinforcement learning using simple rewards can substantially improve the safety and reliability of driving policies over those learned from imitation alone. In particular, we use a combination of imitation and reinforcement learning to train a policy on over 100k miles of urban driving data, and measure its effectiveness in test scenarios grouped by different levels of collision risk. To our knowledge, this is the first application of a combined imitation and reinforcement learning approach in autonomous driving that utilizes large amounts of real-world human driving data.