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Safety-aware Adversarial Inverse Reinforcement Learning (S-AIRL) for Highway Autonomous Driving 公路自动驾驶安全感知对抗逆强化学习(S-AIRL)
Pub Date : 2022-01-07 DOI: 10.1115/1.4053427
Fangjian Li, J. Wagner, Yue Wang
Inverse reinforcement learning (IRL) has been successfully applied in many robotics and autonomous driving studies without the need for hand-tuning a reward function. However, it suffers from safety issues. Compared to the reinforcement learning (RL) algorithms, IRL is even more vulnerable to unsafe situations as it can only infer the importance of safety based on expert demonstrations. In this paper, we propose a safety-aware adversarial inverse reinforcement learning algorithm (S-AIRL). First, the control barrier function (CBF) is used to guide the training of a safety critic, which leverages the knowledge of system dynamics in the sampling process without training an additional guiding policy. The trained safety critic is then integrated into the discriminator to help discern the generated data and expert demonstrations from the standpoint of safety. Finally, to further improve the safety awareness, a regulator is introduced in the loss function of the discriminator training to prevent the recovered reward function from assigning high rewards to the risky behaviors. We tested our S-AIRL in the highway autonomous driving scenario. Comparing to the original AIRL algorithm, with the same level of imitation learning (IL) performance, the proposed S-AIRL can reduce the collision rate by 32.6%.
逆强化学习(IRL)已经成功地应用于许多机器人和自动驾驶研究中,而无需手动调整奖励函数。然而,它存在安全问题。与强化学习(RL)算法相比,IRL算法更容易受到不安全情况的影响,因为它只能根据专家论证来推断安全的重要性。在本文中,我们提出了一种安全感知对抗逆强化学习算法(S-AIRL)。首先,使用控制障碍函数(CBF)来指导安全评论家的培训,该方法利用采样过程中的系统动力学知识,而无需训练额外的指导策略。然后,训练有素的安全评论家被整合到鉴别器中,以帮助从安全的角度辨别生成的数据和专家演示。最后,为了进一步提高安全意识,在鉴别器训练的损失函数中引入了一个调节器,以防止恢复后的奖励函数给风险行为分配高奖励。我们在高速公路自动驾驶场景中测试了S-AIRL。与原始的AIRL算法相比,在相同的模仿学习性能下,本文提出的S-AIRL算法可以将碰撞率降低32.6%。
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引用次数: 1
A Multi-Objective Optimization Approach for Multi-Vehicle Path Planning Problems considering Human-Robot Interactions 考虑人机交互的多车辆路径规划问题的多目标优化方法
Pub Date : 2022-01-07 DOI: 10.1115/1.4053426
Venkata Sirimuvva Chirala, Saravanan Venkatachalam, J. Smereka, Sam Kassoumeh
There has been unprecedented development in the field of unmanned ground vehicles (UGVs) over the past few years. UGVs have been used in many fields including civilian and military with applications such as military reconnaissance, transportation, and search and research missions. This is due to their increasing capabilities in terms of performance, power, and tackling risky missions. The level of autonomy given to these UGVs is a critical factor to consider. In many applications of multi-robotic systems like “search-and-rescue” missions, teamwork between human and robots is essential. In this paper, given a team of manned ground vehicles (MGVs) and unmanned ground vehicles (UGVs), the objective is to develop a model which can minimize the number of teams and total distance traveled while considering human-robot interaction (HRI) studies. The human costs of managing a team of UGVs by a manned ground vehicle (MGV) are based on human-robot interaction (HRI) studies. In this research, we introduce a combinatorial, multi objective ground vehicle path planning problem which takes human-robot interactions into consideration. The objective of the problem is to find: ideal number of teams of MGVs-UGVs that follow a leader-follower framework where a set of UGVs follow an MGV; and path for each team such that the missions are completed efficiently.
在过去的几年里,无人驾驶地面车辆(ugv)领域得到了前所未有的发展。ugv已经在包括民用和军事在内的许多领域得到了应用,如军事侦察、运输、搜索和研究任务。这是由于它们在性能、动力和处理危险任务方面的能力不断增强。赋予这些ugv的自主程度是一个需要考虑的关键因素。在许多多机器人系统的应用中,如“搜索和救援”任务,人与机器人之间的团队合作是必不可少的。在本文中,给定一个由载人地面车辆(mgv)和无人地面车辆(ugv)组成的团队,目标是在考虑人机交互(HRI)研究的同时,开发一个可以最小化团队数量和总行驶距离的模型。通过载人地面车辆(MGV)管理ugv团队的人力成本是基于人机交互(HRI)研究的。本文提出了一种考虑人机交互的组合多目标地面车辆路径规划问题。问题的目标是找到:遵循领导-追随者框架的MGV - ugv团队的理想数量,其中一组ugv遵循一个MGV;以及每个团队的路径,以便有效地完成任务。
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引用次数: 2
A Repeated Commuting Driving Cycle Dataset with Application to Short-term Vehicle Velocity Forecasting 重复通勤驾驶周期数据集及其在短期车速预测中的应用
Pub Date : 2021-11-12 DOI: 10.1115/1.4052996
Yuan Liu, J. Zhang
Vehicle velocity forecasting plays a critical role in operation scheduling of varying systems and devices for a passenger vehicle. The forecasted information serves as an indispensable prerequisite for vehicle energy management via predictive control algorithms or vehicle ecosystem control Co-design. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further energy management study. To explore the dynamic properties, these driving cycles are piecewise divided into cycle segments via intersection/stop identification. A vehicle velocity forecasting model pool is then developed for each segment, including the hidden Markov chain model, long short-term memory network, artificial neural network, support vector regression, and similarity methods. To further improve the forecasting performance, higher-level algorithms like localized model selection, ensemble approaches, and a combination of them are investigated and compared. Results show that (i) the segment-based forecast improves the forecasting accuracy by up to 20.1%, compared to the whole cycle-based forecast; and (ii) the hybrid localized model framework that combines dynamic model selection and an ensemble approach could further improve the accuracy by 9.7%. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.
车速预测在客车多系统设备的运行调度中起着至关重要的作用。通过预测控制算法或车辆生态系统控制协同设计,预测信息是车辆能量管理不可或缺的先决条件。本文首先生成了达拉斯地区固定路线上的重复城市驾驶循环数据集,旨在模拟日常通勤路线,为进一步的能量管理研究提供基础。为了研究其动态特性,通过交叉口/停车识别将这些行驶周期分段划分。在此基础上,建立了基于隐马尔可夫链模型、长短期记忆网络、人工神经网络、支持向量回归和相似度方法的车辆速度预测模型池。为了进一步提高预测性能,对局部模型选择、集成方法以及它们的组合等更高层次的算法进行了研究和比较。结果表明:(1)与全周期预测相比,分段预测的预测精度提高了20.1%;(ii)结合动态模型选择和集成方法的混合局部模型框架可以进一步提高9.7%的精度。此外,本研究还评估了利用交叉口停车位置来估计等待时间的潜力。
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引用次数: 2
Sensors in Autonomous Vehicles: A Survey 自动驾驶汽车中的传感器:一项调查
Pub Date : 2021-11-11 DOI: 10.1115/1.4052991
Rodrigo Ayala, Tauheed Khan Mohd
Research and technology in autonomous vehicles is beginning to become well recognized among computer scientists and engineers. Autonomous vehicles contain combination of GPS, LIDAR, cameras, RADAR and ultrasonic sensors (which are hardly ever included). These autonomous vehicles use no less than two sensing modalities, and usually have three or more. The goal of this research is to determine which sensor to use depending on the functionality of the autonomous vehicle and analyze the simi- larities and differences of sensor configurations (which may come from different industries too). This study summarizes sensors in four industries: personal vehicles, public transportation, smart farming, and logistics. In addition, the paper includes advantages and disadvantages of how each sensor configuration are helpful by taking into considerations the activity that has to be achieved in the autonomous vehicle. A table of results is incorporated to organize most of the sensors' availability in the market and their advantages and disadvantages. After comparing each sensor configuration, recommendations are going to be proposed for different scenarios in which some types of sensors will be more useful than others.
自动驾驶汽车的研究和技术开始得到计算机科学家和工程师的广泛认可。自动驾驶汽车包含GPS、激光雷达、摄像头、雷达和超声波传感器的组合(这些几乎不包括在内)。这些自动驾驶汽车使用不少于两种传感模式,通常有三种或更多。本研究的目标是根据自动驾驶汽车的功能确定使用哪种传感器,并分析传感器配置的异同(可能来自不同的行业)。这项研究总结了四个行业的传感器:个人车辆、公共交通、智能农业和物流。此外,考虑到自动驾驶汽车必须完成的活动,本文还介绍了每种传感器配置的优点和缺点。结果表被纳入组织大多数传感器在市场上的可用性和他们的优缺点。在比较了每个传感器配置之后,将针对不同的场景提出建议,在这些场景中,某些类型的传感器将比其他传感器更有用。
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引用次数: 3
A Brief of the Economics of ADAS for Full Size Light Duty Pickup Trucks: Relating Effectiveness to Cost 全尺寸轻型皮卡ADAS的经济性简论:效能与成本的关系
Pub Date : 2021-11-11 DOI: 10.1115/1.4052992
F. Fish, B. Bras
Advanced Driver Assistance Systems (ADAS) have become increasingly common in vehicles in the last decade. The majority of studies have focused on smaller vehicles with gross vehicle weight rating (GVWR) under 5,000lbs, predominantly sedans, for their ADAS evaluations. While it is sensible to use this style of vehicle because it is ubiquitous worldwide for a typical vehicle body style, these studies neglect full-size light-duty pickup trucks, GVWR 5,000 – 10,000lbs, which are abundant on the roads in the United States. The increase in mass, higher center of gravity, and utilitarianism of the vehicles allows for unique conditions for studying the effects of ADAS. This work evaluates the effectiveness of ADAS in full-size light-duty pickup trucks across brands, representing 18% of registered vehicles in the US, at reducing severity of injury for occupants during accidents involving fatalities relative to expense of the ADAS technology. This work will illustrate the cost benefit of ADAS at reducing the severity of injuries for occupants of full-size light-duty pickup trucks for multiple different brands.
在过去的十年中,高级驾驶辅助系统(ADAS)在汽车中变得越来越普遍。大多数研究都集中在车辆总重量等级(GVWR)低于5000磅的小型车辆(主要是轿车)上进行ADAS评估。虽然使用这种类型的车辆是明智的,因为它是世界范围内普遍存在的典型车身样式,但这些研究忽略了全尺寸轻型皮卡,GVWR为5,000 - 10,000磅,这在美国的道路上很丰富。车辆质量的增加、重心的提高和实用性为研究ADAS的效果提供了独特的条件。本研究评估了ADAS在不同品牌的全尺寸轻型皮卡(占美国注册车辆的18%)上的有效性,相对于ADAS技术的费用,在涉及死亡的事故中降低了乘员受伤的严重程度。这项工作将说明ADAS在降低多个不同品牌的全尺寸轻型皮卡乘客受伤严重程度方面的成本效益。
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引用次数: 1
Can we Localize an AV from a Single Image? Deep-Geometric 6 DoF Localization in Topo-metric Maps 我们能从单个图像中定位AV吗?地形图中的深度几何6自由度定位
Pub Date : 2021-09-29 DOI: 10.1115/1.4052604
Punarjay Chakravarty, Tom Roussel, Gaurav Pandey, T. Tuytelaars
We describe a Deep-Geometric Localizer that is able to estimate the full six degrees-of-freedom (DoF) global pose of the camera from a single image in a previously mapped environment. Our map is a topo-metric one, with discrete topological nodes whose 6DOF poses are known. Each topo-node in our map also comprises of a set of points, whose 2D features and 3D locations are stored as part of the mapping process. For the mapping phase, we utilise a stereo camera and a regular stereo visual SLAM pipeline. During the localization phase, we take a single camera image, localize it to a topological node using Deep Learning, and use a geometric algorithm (PnP) on the matched 2D features (and their 3D positions in the topo map) to determine the full 6DOF globally consistent pose of the camera. Our method divorces the mapping and the localization algorithms and sensors (stereo and mono), and allows accurate 6DOF pose estimation in a previously mapped environment using a single camera. With results in simulated and real environments, our hybrid algorithm is particularly useful for autonomous vehicles (AVs) and shuttles that might repeatedly traverse the same route.
我们描述了一种深度几何定位器,它能够从先前映射环境中的单个图像中估计相机的全部六个自由度(DoF)全局姿态。我们的地图是一个拓扑度量图,具有已知的6DOF姿态的离散拓扑节点。我们地图中的每个拓扑节点也由一组点组成,这些点的2D特征和3D位置被存储为映射过程的一部分。在映射阶段,我们使用立体摄像机和常规立体视觉SLAM管道。在定位阶段,我们采用单个相机图像,使用深度学习将其定位到拓扑节点,并在匹配的2D特征(及其在地形图中的3D位置)上使用几何算法(PnP)来确定相机的完整6DOF全局一致姿态。我们的方法分离了映射和定位算法以及传感器(立体声和单声道),并允许使用单个相机在先前映射的环境中进行精确的6DOF姿态估计。通过模拟和真实环境的结果,我们的混合算法对可能反复穿越同一路线的自动驾驶汽车(AVs)和班车特别有用。
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引用次数: 0
Operational Context Change Propagation Prediction on Autonomous Vehicles Architectures 基于自动驾驶汽车架构的操作上下文变化传播预测
Pub Date : 2021-09-27 DOI: 10.1115/1.4052556
Youssef Damak, Y. Leroy, Guillaume Trehard, M. Jankovic
Autonomous Vehicles (AV) are designed to operate in a specific Operational Context (OC), and the adaptability of the vehicle's architecture to its OC is considered a significant success criterion of the design. AV design projects are rarely started from scratch and are often based on reference architectures. As such, the reference architecture must be modified and adapted to the OC. The current literature on engineering change propagation does not provide a method to identify and anticipate the impact of OC changes on the AV reference architecture. This paper proposes a two-step method for OC change propagation: (1) Analyzing the direct impact of OC change and (2) evaluate the probabilities of indirect change propagation. The direct impact is assessed following a propagation path based upon a model mapping between an OC Ontology, operational situations, and Functional Chains. The effects of Functional Chain changes on the AV components are analyzed and evaluated by domain experts with Types of Changes and associated probabilities. A Bayesian Network is proposed to calculate the probabilities of indirect change propagation between component Types of Changes. The method's applicability and efficiency are validated on a real case design of AV architecture where the probabilities of the system components undergoing Types of Changes are evaluated.
自动驾驶汽车(AV)被设计为在特定的操作环境(OC)中运行,车辆架构对其OC的适应性被认为是设计成功的重要标准。AV设计项目很少从头开始,通常基于参考架构。因此,必须对参考体系结构进行修改并使其适应OC。目前关于工程变更传播的文献没有提供一种方法来识别和预测OC变更对AV参考体系结构的影响。本文提出了一种有机碳变化传播的两步方法:(1)分析有机碳变化的直接影响,(2)评估间接变化传播的概率。根据基于OC本体、操作情况和功能链之间的模型映射的传播路径评估直接影响。由领域专家根据变化类型和相关概率对功能链变化对AV组件的影响进行分析和评估。提出了一种贝叶斯网络来计算变化类型之间间接变化传播的概率。通过一个AV架构的实际设计案例验证了该方法的适用性和有效性,并对系统组件发生类型变化的概率进行了评估。
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引用次数: 1
Deep Visible and Thermal Camera-based Optimal Semantic Segmentation using Semantic Forecasting 基于语义预测的深度可见热像仪最优语义分割
Pub Date : 2021-04-01 DOI: 10.1115/1.4052529
V. John, S. Mita, Annam Lakshmanan, Ali Boyali, Simon Thompson
Visible camera-based semantic segmentation and semantic forecasting are important perception tasks in autonomous driving. In semantic segmentation, the current frame's pixel level labels are estimated using the current visible frame. In semantic forecasting, the future frame's pixel-level labels are predicted using the current and the past visible frames and pixel-level labels. While reporting state-of-the-art accuracy, both of these tasks are limited by the visible camera's susceptibility to varying illumination, adverse weather conditions, sunlight and headlight glare etc. In this work, we propose to address these limitations using the deep sensor fusion of the visible and the thermal camera. The proposed sensor fusion framework performs both semantic forecasting as well as an optimal semantic segmentation within a multi-step iterative framework. In the first or forecasting step, the framework predicts the semantic map for the next frame. The predicted semantic map is updated in the second step, when the next visible and thermal frame is observed. The updated semantic map is considered as the optimal semantic map for the given visible-thermal frame. The semantic map forecasting and updating are iteratively performed over time. The estimated semantic maps contain the pedestrian behavior, the free space and the pedestrian crossing labels. The pedestrian behavior is categorized based on their spatial, motion and dynamic orientation information. The proposed framework is validated using the public KAIST dataset. A detailed comparative analysis and ablation study is performed using pixel-level classification and IOU error metrics. The results show that the proposed framework can not only accurately forecast the semantic segmentation map but also accurately update them.
基于可视摄像头的语义分割和语义预测是自动驾驶中重要的感知任务。在语义分割中,使用当前可见帧估计当前帧的像素级标签。在语义预测中,使用当前和过去可见的帧和像素级标签来预测未来帧的像素级标签。虽然报告了最先进的精度,但这两项任务都受到可见光摄像头对不同照明、恶劣天气条件、阳光和前照灯眩光等的敏感性的限制。在这项工作中,我们建议使用可见光和热像仪的深度传感器融合来解决这些限制。提出的传感器融合框架在多步迭代框架内进行语义预测和最优语义分割。在第一步或预测步骤中,框架预测下一帧的语义映射。在第二步中,当观察到下一个可见和热帧时,更新预测的语义映射。更新后的语义图被认为是给定可视热框架的最优语义图。随着时间的推移,迭代地执行语义映射预测和更新。估计的语义地图包含行人行为、自由空间和行人过街标记。根据行人的空间、运动和动态方向信息对其行为进行分类。使用公共KAIST数据集验证了所提出的框架。使用像素级分类和IOU误差指标进行了详细的对比分析和消融研究。结果表明,该框架既能准确预测语义分割图,又能准确更新语义分割图。
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引用次数: 6
Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidance 安全学习参考调速器:燃油车防侧翻理论与应用
Pub Date : 2021-01-22 DOI: 10.1115/1.4053244
Kaiwen Liu, Nan I. Li, I. Kolmanovsky, Denise M. Rizzo, A. Girard
This paper proposes a learning reference governor (LRG) approach to enforce state and control constraints in systems for which an accurate model is unavailable; and this approach enables the reference governor to gradually improve command tracking performance through learning while enforcing the constraints during learning and after learning is completed. The learning can be performed either on a black-box type model of the system or directly on the hardware. After introducing the LRG algorithm and outlining its theoretical properties, this paper investigates LRG application to fuel truck (tank truck) rollover avoidance. Through simulations based on a fuel truck model that accounts for liquid fuel sloshing effects, we show that the proposed LRG can effectively protect fuel trucks from rollover accidents under various operating conditions.
本文提出了一种学习参考调控器(LRG)方法,用于在无法获得精确模型的系统中执行状态和控制约束;这种方法使参考调控器在学习过程中和学习完成后,通过学习逐步提高命令跟踪性能。学习既可以在系统的黑盒模型上执行,也可以直接在硬件上执行。在介绍了LRG算法及其理论特性的基础上,研究了LRG算法在油罐车防侧翻中的应用。通过考虑液体燃料晃动效应的油罐车模型仿真,结果表明,在不同工况下,该系统能有效保护油罐车不发生侧翻事故。
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引用次数: 5
Analysis and Control of an In-Pipe Wheeled Robot With Spiral Moving Capability 具有螺旋运动能力的管内轮式机器人的分析与控制
Pub Date : 2020-10-14 DOI: 10.1115/1.4048376
T. Yeh, Tzu-hsiang Weng
This article presents analysis and control of a wheeled robot that can move spirally inside the pipeline. The wheeled robot considered is composed of two mechanical bodies, a pair of differential-drive wheels, a lifting motor, and a steering wheel. The mechatronic design allows the robot to easily press against the inner wall and spiral along pipelines of arbitrary inclination angles. Kinematic analysis shows how the lead angle of the differential-drive wheels and the steering angle should be coordinated so as to achieve stable spiraling. The steady-state force analysis further gives an analytic expression for the threshold torque needed for supporting the robot at different inclination angles. To ensure successful operation of the robot, four control systems that respectively regulate the spiraling speed, the lifting torque, the steering angle, and the lead angle are devised. Particularly for the lead angle control, it is theoretically proved that the feedback measurement can be obtained by performing algebraic operation on signals from a multi-axis gyro. A prototype robot is constructed and is controlled based on the analysis results. Experiments are conducted to verify the robot’s performance on moving spirally in pipelines of different inclination angles.
本文介绍了一种能够在管道内进行螺旋运动的轮式机器人的分析与控制。所考虑的轮式机器人由两个机械体、一对差动驱动轮、一个升降马达和一个方向盘组成。机电一体化设计使机器人可以轻松地按压内壁并沿任意倾角的管道旋转。运动学分析表明,差速驱动车轮的前导角与转向角应如何协调,才能实现稳定的螺旋传动。通过稳态力分析,给出了不同倾角下支撑机器人所需阈值力矩的解析表达式。为了保证机器人的顺利运行,设计了四种控制系统,分别调节螺旋速度、提升力矩、转向角和前置角。特别是对于超前角控制,从理论上证明了通过对多轴陀螺信号进行代数运算可以得到反馈测量结果。根据分析结果,构建了机器人样机并对其进行了控制。通过实验验证了机器人在不同倾角的管道中进行螺旋运动的性能。
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引用次数: 6
期刊
Journal of Autonomous Vehicles and Systems
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