预测道路用户的未来行为是自主驾驶中最具挑战性和最重要的问题之一。应用深度学习对此问题需要以丰富的感知信号和地图信息的形式融合异构世界状态,并在可能的期货上推断出高度多模态分布。在本文中,我们呈现MultiPath ++,这是一个未来的预测模型,实现了在流行的基准上实现最先进的性能。 MultiPath ++通过重新访问许多设计选择来改善多径架构。第一关键设计差异是偏离基于图像的基于输入世界状态的偏离,有利于异构场景元素的稀疏编码:多径++消耗紧凑且有效的折线,直接描述道路特征和原始代理状态信息(例如,位置,速度,加速)。我们提出了一种背景感知这些元素的融合,并开发可重用的多上下文选通融合组件。其次,我们重新考虑了预定义,静态锚点的选择,并开发了一种学习模型端到端的潜在锚嵌入的方法。最后,我们在其他ML域中探索合奏和输出聚合技术 - 常见的常见域 - 并为我们的概率多模式输出表示找到有效的变体。我们对这些设计选择进行了广泛的消融,并表明我们所提出的模型在协会运动预测竞争和Waymo开放数据集运动预测挑战上实现了最先进的性能。
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自动驾驶的运动预测是一项艰巨的任务,因为复杂的驾驶场景导致静态和动态输入的异质组合。这是一个开放的问题,如何最好地表示和融合有关道路几何,车道连接,时变的交通信号状态以及动态代理的历史及其相互作用的历史。为了模拟这一不同的输入功能集,许多提出的方法旨在设计具有多种模态模块的同样复杂系统。这导致难以按严格的方式进行扩展,扩展或调整的系统以进行质量和效率。在本文中,我们介绍了Wayformer,这是一个基于注意力的运动架构,用于运动预测,简单而均匀。 Wayformer提供了一个紧凑的模型描述,该描述由基于注意力的场景编码器和解码器组成。在场景编码器中,我们研究了输入方式的早期,晚和等级融合的选择。对于每种融合类型,我们通过分解的注意力或潜在的查询关注来探索策略来折衷效率和质量。我们表明,尽管早期融合的结构简单,但不仅是情感不可知论,而且还取得了最先进的结果。
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近年来,行为预测模型已经激增,尤其是在自动驾驶的流行现实机器人技术应用中,代表移动代理可能未来的分布对于安全舒适的运动计划至关重要。在这些模型中,选择代表输入和输出的坐标框架的选择具有至关重要的交易折扣,这些折扣通常属于两个类别之一。以代理为中心的模型转换输入并在以代理为中心的坐标中执行推断。这些模型在场景元素之间的翻译和旋转上本质上不变,在公共排行榜上表现最好,但与代理和场景元素的数量相互缩小。以场景为中心的模型使用固定的坐标系来处理所有代理。这为他们提供了在所有代理之间共享表示形式的优势,并提供有效的摊销推理计算,该计算与代理数量线性缩放。但是,这些模型必须学习场景元素之间的翻译和旋转的不变性,并且通常以表现为中心的模型。在这项工作中,我们在概率运动预测模型之间开发知识蒸馏技术,并应用这些技术来缩小以代理为中心和以场景为中心的模型之间的性能差距。这将以场景为中心的模型性能提高了13.2%,在公共Argoverse基准中,Waymo Open Datatet的7.8%,在大型内部数据集中最多可达9.4%。这些以场景为中心的改进的模型在公共排行榜中排名很高,在繁忙场景中以代理商为中心的教师的效率高15倍。
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The task of motion forecasting is critical for self-driving vehicles (SDVs) to be able to plan a safe maneuver. Towards this goal, modern approaches reason about the map, the agents' past trajectories and their interactions in order to produce accurate forecasts. The predominant approach has been to encode the map and other agents in the reference frame of each target agent. However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent. To tackle the scaling challenge, the solution thus far has been to encode all agents and the map in a shared coordinate frame (e.g., the SDV frame). However, this is sample inefficient and vulnerable to domain shift (e.g., when the SDV visits uncommon states). In contrast, in this paper, we propose an efficient shared encoding for all agents and the map without sacrificing accuracy or generalization. Towards this goal, we leverage pair-wise relative positional encodings to represent geometric relationships between the agents and the map elements in a heterogeneous spatial graph. This parameterization allows us to be invariant to scene viewpoint, and save online computation by re-using map embeddings computed offline. Our decoder is also viewpoint agnostic, predicting agent goals on the lane graph to enable diverse and context-aware multimodal prediction. We demonstrate the effectiveness of our approach on the urban Argoverse 2 benchmark as well as a novel highway dataset.
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Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction and planning. As sensors and hardware get improved, there is trending popularity to devise a system that can perform a wide diversity of tasks to fulfill higher-level intelligence. Contemporary approaches resort to either deploying standalone models for individual tasks, or designing a multi-task paradigm with separate heads. These might suffer from accumulative error or negative transfer effect. Instead, we argue that a favorable algorithm framework should be devised and optimized in pursuit of the ultimate goal, i.e. planning of the self-driving-car. Oriented at this goal, we revisit the key components within perception and prediction. We analyze each module and prioritize the tasks hierarchically, such that all these tasks contribute to planning (the goal). To this end, we introduce Unified Autonomous Driving (UniAD), the first comprehensive framework up-to-date that incorporates full-stack driving tasks in one network. It is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective. Tasks are communicated with unified query design to facilitate each other toward planning. We instantiate UniAD on the challenging nuScenes benchmark. With extensive ablations, the effectiveness of using such a philosophy is proven to surpass previous state-of-the-arts by a large margin in all aspects. The full suite of codebase and models would be available to facilitate future research in the community.
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预测交通参与者的多模式未来行为对于机器人车辆做出安全决策至关重要。现有作品探索以直接根据潜在特征预测未来的轨迹,或利用密集的目标候选者来识别代理商的目的地,在这种情况下,由于所有运动模式均来自相同的功能,而后者的策略具有效率问题,因此前者策略的收敛缓慢,因为其性能高度依赖关于候选目标的密度。在本文中,我们提出了运动变压器(MTR)框架,该框架将运动预测模拟为全球意图定位和局部运动改进的联合优化。 MTR不使用目标候选者,而是通过采用一系列可学习的运动查询对来结合空间意图。每个运动查询对负责特定运动模式的轨迹预测和完善,这可以稳定训练过程并促进更好的多模式预测。实验表明,MTR在边际和联合运动预测挑战上都达到了最新的性能,在Waymo Open Motion DataSet排行榜上排名第一。代码将在https://github.com/sshaoshuai/mtr上找到。
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相应地预测周围交通参与者的未来状态,并计划安全,平稳且符合社会的轨迹对于自动驾驶汽车至关重要。当前的自主驾驶系统有两个主要问题:预测模块通常与计划模块解耦,并且计划的成本功能很难指定和调整。为了解决这些问题,我们提出了一个端到端的可区分框架,该框架集成了预测和计划模块,并能够从数据中学习成本函数。具体而言,我们采用可区分的非线性优化器作为运动计划者,该运动计划将神经网络给出的周围剂的预测轨迹作为输入,并优化了自动驾驶汽车的轨迹,从而使框架中的所有操作都可以在框架中具有可观的成本,包括成本功能权重。提出的框架经过大规模的现实驾驶数据集进行了训练,以模仿整个驾驶场景中的人类驾驶轨迹,并在开环和闭环界面中进行了验证。开环测试结果表明,所提出的方法的表现优于各种指标的基线方法,并提供以计划为中心的预测结果,从而使计划模块能够输出接近人类的轨迹。在闭环测试中,提出的方法表明能够处理复杂的城市驾驶场景和鲁棒性,以抵抗模仿学习方法所遭受的分配转移。重要的是,我们发现计划和预测模块的联合培训比在开环和闭环测试中使用单独的训练有素的预测模块进行计划要比计划更好。此外,消融研究表明,框架中的可学习组件对于确保计划稳定性和性能至关重要。
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在这项工作中,我们提出了一种新的多模态多代理轨迹预测架构,专注于使用图形表示的地图和交互建模。出于地图建模的目的,我们将丰富的拓扑结构捕获到基于向量的星形图中,使代理能够直接参加用于代表地图的折线上的相关区域。我们表示此架构Starnet,并将其集成在单次代理预测设置中。作为主要结果,我们将此架构扩展到联合场景级预测,同时产生多个代理的预测。联合赛斯网的关键思想在自己的参考框中将一个代理的意识与其他代理人的观点察觉到。我们通过蒙面的自我关注实现这一目标。两个提出的架构都建立在我们以前的工作中介绍的动作空间预测框架之上,这确保了运动学上可行的轨迹预测。我们评估了富含互动的IND和交互数据集的方法,其中STARNET和联合星网实现了最先进的技术。
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Motion prediction systems aim to capture the future behavior of traffic scenarios enabling autonomous vehicles to perform safe and efficient planning. The evolution of these scenarios is highly uncertain and depends on the interactions of agents with static and dynamic objects in the scene. GNN-based approaches have recently gained attention as they are well suited to naturally model these interactions. However, one of the main challenges that remains unexplored is how to address the complexity and opacity of these models in order to deal with the transparency requirements for autonomous driving systems, which includes aspects such as interpretability and explainability. In this work, we aim to improve the explainability of motion prediction systems by using different approaches. First, we propose a new Explainable Heterogeneous Graph-based Policy (XHGP) model based on an heterograph representation of the traffic scene and lane-graph traversals, which learns interaction behaviors using object-level and type-level attention. This learned attention provides information about the most important agents and interactions in the scene. Second, we explore this same idea with the explanations provided by GNNExplainer. Third, we apply counterfactual reasoning to provide explanations of selected individual scenarios by exploring the sensitivity of the trained model to changes made to the input data, i.e., masking some elements of the scene, modifying trajectories, and adding or removing dynamic agents. The explainability analysis provided in this paper is a first step towards more transparent and reliable motion prediction systems, important from the perspective of the user, developers and regulatory agencies. The code to reproduce this work is publicly available at https://github.com/sancarlim/Explainable-MP/tree/v1.1.
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仿真是对机器人系统(例如自动驾驶汽车)进行扩展验证和验证的关键。尽管高保真物理和传感器模拟取得了进步,但在模拟道路使用者的现实行为方面仍然存在一个危险的差距。这是因为,与模拟物理和图形不同,设计人类行为的第一个原理模型通常是不可行的。在这项工作中,我们采用了一种数据驱动的方法,并提出了一种可以学会从现实世界驱动日志中产生流量行为的方法。该方法通过将交通仿真问题分解为高级意图推理和低级驾驶行为模仿,通过利用驾驶行为的双层层次结构来实现高样本效率和行为多样性。该方法还结合了一个计划模块,以获得稳定的长马行为。我们从经验上验证了我们的方法,即交通模拟(位)的双层模仿,并具有来自两个大规模驾驶数据集的场景,并表明位表明,在现实主义,多样性和长途稳定性方面可以达到平衡的交通模拟性能。我们还探索了评估行为现实主义的方法,并引入了一套评估指标以进行交通模拟。最后,作为我们的核心贡献的一部分,我们开发和开源一个软件工具,该工具将跨不同驱动数据集的数据格式统一,并将现有数据集将场景转换为交互式仿真环境。有关其他信息和视频,请参见https://sites.google.com/view/nvr-bits2022/home
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Making safe and human-like decisions is an essential capability of autonomous driving systems and learning-based behavior planning is a promising pathway toward this objective. Distinguished from existing learning-based methods that directly output decisions, this work introduces a predictive behavior planning framework that learns to predict and evaluate from human driving data. Concretely, a behavior generation module first produces a diverse set of candidate behaviors in the form of trajectory proposals. Then the proposed conditional motion prediction network is employed to forecast other agents' future trajectories conditioned on each trajectory proposal. Given the candidate plans and associated prediction results, we learn a scoring module to evaluate the plans using maximum entropy inverse reinforcement learning (IRL). We conduct comprehensive experiments to validate the proposed framework on a large-scale real-world urban driving dataset. The results reveal that the conditional prediction model is able to forecast multiple possible future trajectories given a candidate behavior and the prediction results are reactive to different plans. Moreover, the IRL-based scoring module can properly evaluate the trajectory proposals and select close-to-human ones. The proposed framework outperforms other baseline methods in terms of similarity to human driving trajectories. Moreover, we find that the conditional prediction model can improve both prediction and planning performance compared to the non-conditional model, and learning the scoring module is critical to correctly evaluating the candidate plans to align with human drivers.
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Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist many methods for trajectory forecasting, most do not enforce dynamic constraints and do not account for environmental information (e.g., maps). Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated with robotic planning and control frameworks; for example, it can produce predictions that are optionally conditioned on ego-agent motion plans. We demonstrate its performance on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-ofthe-art deterministic and generative methods.
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预测公路参与者的未来运动对于自动驾驶至关重要,但由于令人震惊的运动不确定性,因此极具挑战性。最近,大多数运动预测方法求助于基于目标的策略,即预测运动轨迹的终点,作为回归整个轨迹的条件,以便可以减少解决方案的搜索空间。但是,准确的目标坐标很难预测和评估。此外,目的地的点表示限制了丰富的道路环境的利用,从而导致预测不准确。目标区域,即可能的目的地区域,而不是目标坐标,可以通过涉及更多的容忍度和指导来提供更软的限制,以搜索潜在的轨迹。考虑到这一点,我们提出了一个新的基于目标区域的框架,名为“目标区域网络”(GANET)进行运动预测,该框架对目标区域进行了建模,而不是确切的目标坐标作为轨迹预测的先决条件,更加可靠,更准确地执行。具体而言,我们建议一个goicrop(目标的目标区域)操作员有效地提取目标区域中的语义巷特征,并在目标区域和模型演员的未来互动中提取语义巷,这对未来的轨迹估计很大。 Ganet在所有公共文献(直到论文提交)中排名第一个,将其源代码排在第一位。
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We propose JFP, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories. For this task, many different methods have been proposed to capture social interactions in the encoding part of the model, however, considerably less focus has been placed on representing interactions in the decoder and output stages. As a result, the predicted trajectories are not necessarily consistent with each other, and often result in unrealistic trajectory overlaps. In contrast, we propose an end-to-end trainable model that learns directly the interaction between pairs of agents in a structured, graphical model formulation in order to generate consistent future trajectories. It sets new state-of-the-art results on Waymo Open Motion Dataset (WOMD) for the interactive setting. We also investigate a more complex multi-agent setting for both WOMD and a larger internal dataset, where our approach improves significantly on the trajectory overlap metrics while obtaining on-par or better performance on single-agent trajectory metrics.
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变形金刚在NLP和计算机视觉上实现了突破,最近开始在自动驾驶汽车(AV)的轨迹预测中表现出有希望的表现。如何有效地对自我代理与其他道路和动态对象之间的交互关系建模仍然对标准注意模块仍然具有挑战性。在这项工作中,我们提出了一个类似变压器的架构模块MNM网络,该网络配备了新型掩盖的目标调节训练程序,用于AV轨迹预测。最终的模型名为高尔夫球手,取得了最先进的性能,在2022 Waymo Open DataSet Motion Predict挑战中赢得了第二名,并根据Minade排名第一。
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轨迹预测和行为决策是自动驾驶汽车的两项重要任务,他们需要对环境环境有良好的了解;通过参考轨迹预测的输出,可以更好地做出行为决策。但是,大多数当前解决方案分别执行这两个任务。因此,提出了结合多个线索的联合神经网络,并将其命名为整体变压器,以预测轨迹并同时做出行为决策。为了更好地探索线索之间的内在关系,网络使用现有知识并采用三种注意力机制:稀疏的多头类型用于减少噪声影响,特征选择稀疏类型,可最佳地使用部分先验知识,并与Sigmoid多头激活类型,用于最佳使用后验知识。与其他轨迹预测模型相比,所提出的模型具有更好的综合性能和良好的解释性。感知噪声稳健性实验表明,所提出的模型具有良好的噪声稳健性。因此,结合多个提示的同时轨迹预测和行为决策可以降低计算成本并增强场景与代理之间的语义关系。
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本文提出了一个新型的深度学习框架,用于多模式运动预测。该框架由三个部分组成:经常性神经网络,以处理目标代理的运动过程,卷积神经网络处理栅格化环境表示以及一种基于距离的注意机制,以处理不同代理之间的相互作用。我们在大规模的真实驾驶数据集,Waymo Open Motion数据集上验证了所提出的框架,并将其性能与标准测试基准上的其他方法进行比较。定性结果表明,我们的模型给出的预测轨迹是准确,多样的,并且根据道路结构。标准基准测试的定量结果表明,我们的模型在预测准确性和其他评估指标方面优于其他基线方法。拟议的框架是2021 Waymo Open DataSet运动预测挑战的第二名。
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Figure 1: We introduce datasets for 3D tracking and motion forecasting with rich maps for autonomous driving. Our 3D tracking dataset contains sequences of LiDAR measurements, 360 • RGB video, front-facing stereo (middle-right), and 6-dof localization. All sequences are aligned with maps containing lane center lines (magenta), driveable region (orange), and ground height. Sequences are annotated with 3D cuboid tracks (green). A wider map view is shown in the bottom-right.
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预测附近代理商的合理的未来轨迹是自治车辆安全的核心挑战,主要取决于两个外部线索:动态邻居代理和静态场景上下文。最近的方法在分别表征两个线索方面取得了很大进展。然而,它们忽略了两个线索之间的相关性,并且大多数很难实现地图自适应预测。在本文中,我们使用Lane作为场景数据,并提出一个分阶段网络,即共同学习代理和车道信息,用于多模式轨迹预测(JAL-MTP)。 JAL-MTP使用社交到LANE(S2L)模块来共同代表静态道和相邻代理的动态运动作为实例级车道,一种用于利用实例级车道来预测的反复出的车道注意力(RLA)机制来预测Map-Adaptive Future Trajections和两个选择器,可识别典型和合理的轨迹。在公共协议数据集上进行的实验表明JAL-MTP在定量和定性中显着优于现有模型。
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从社交机器人到自动驾驶汽车,多种代理的运动预测(MP)是任意复杂环境中的至关重要任务。当前方法使用端到端网络解决了此问题,其中输入数据通常是场景的最高视图和所有代理的过去轨迹;利用此信息是获得最佳性能的必不可少的。从这个意义上讲,可靠的自动驾驶(AD)系统必须按时产生合理的预测,但是,尽管其中许多方法使用了简单的Convnets和LSTM,但在使用两个信息源时,模型对于实时应用程序可能不够有效(地图和轨迹历史)。此外,这些模型的性能在很大程度上取决于训练数据的数量,这可能很昂贵(尤其是带注释的HD地图)。在这项工作中,我们探讨了如何使用有效的基于注意力的模型在Argoverse 1.0基准上实现竞争性能,该模型将其作为最小地图信息的过去轨迹和基于地图的功能的输入,以确保有效且可靠的MP。这些功能代表可解释的信息作为可驱动区域和合理的目标点,与基于黑框CNN的地图处理方法相反。
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