Journal of Autonomous Vehicles and Systems最新文献_第4页

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)

Journal of Autonomous Vehicles and Systems Pub Date : 2022-01-07 DOI: 10.1115/1.4053427

Fangjian Li, J. Wagner, Yue Wang

{"title":"Safety-aware Adversarial Inverse Reinforcement Learning (S-AIRL) for Highway Autonomous Driving","authors":"Fangjian Li, J. Wagner, Yue Wang","doi":"10.1115/1.4053427","DOIUrl":"https://doi.org/10.1115/1.4053427","url":null,"abstract":"\u0000 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%.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134276457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Autonomous Vehicles and Systems Pub Date : 2022-01-07 DOI: 10.1115/1.4053426

Venkata Sirimuvva Chirala, Saravanan Venkatachalam, J. Smereka, Sam Kassoumeh

{"title":"A Multi-Objective Optimization Approach for Multi-Vehicle Path Planning Problems considering Human-Robot Interactions","authors":"Venkata Sirimuvva Chirala, Saravanan Venkatachalam, J. Smereka, Sam Kassoumeh","doi":"10.1115/1.4053426","DOIUrl":"https://doi.org/10.1115/1.4053426","url":null,"abstract":"\u0000 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.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124731644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Autonomous Vehicles and Systems Pub Date : 2021-11-12 DOI: 10.1115/1.4052996

Yuan Liu, J. Zhang

{"title":"A Repeated Commuting Driving Cycle Dataset with Application to Short-term Vehicle Velocity Forecasting","authors":"Yuan Liu, J. Zhang","doi":"10.1115/1.4052996","DOIUrl":"https://doi.org/10.1115/1.4052996","url":null,"abstract":"\u0000 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.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Autonomous Vehicles and Systems Pub Date : 2021-11-11 DOI: 10.1115/1.4052991

Rodrigo Ayala, Tauheed Khan Mohd

{"title":"Sensors in Autonomous Vehicles: A Survey","authors":"Rodrigo Ayala, Tauheed Khan Mohd","doi":"10.1115/1.4052991","DOIUrl":"https://doi.org/10.1115/1.4052991","url":null,"abstract":"\u0000 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.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114376068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Autonomous Vehicles and Systems Pub Date : 2021-11-11 DOI: 10.1115/1.4052992

F. Fish, B. Bras

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

Journal of Autonomous Vehicles and Systems Pub Date : 2021-09-29 DOI: 10.1115/1.4052604

Punarjay Chakravarty, Tom Roussel, Gaurav Pandey, T. Tuytelaars

{"title":"Can we Localize an AV from a Single Image? Deep-Geometric 6 DoF Localization in Topo-metric Maps","authors":"Punarjay Chakravarty, Tom Roussel, Gaurav Pandey, T. Tuytelaars","doi":"10.1115/1.4052604","DOIUrl":"https://doi.org/10.1115/1.4052604","url":null,"abstract":"\u0000 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.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132387873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Autonomous Vehicles and Systems Pub Date : 2021-09-27 DOI: 10.1115/1.4052556

Youssef Damak, Y. Leroy, Guillaume Trehard, M. Jankovic

{"title":"Operational Context Change Propagation Prediction on Autonomous Vehicles Architectures","authors":"Youssef Damak, Y. Leroy, Guillaume Trehard, M. Jankovic","doi":"10.1115/1.4052556","DOIUrl":"https://doi.org/10.1115/1.4052556","url":null,"abstract":"\u0000 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.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125825798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Autonomous Vehicles and Systems Pub Date : 2021-04-01 DOI: 10.1115/1.4052529

V. John, S. Mita, Annam Lakshmanan, Ali Boyali, Simon Thompson

{"title":"Deep Visible and Thermal Camera-based Optimal Semantic Segmentation using Semantic Forecasting","authors":"V. John, S. Mita, Annam Lakshmanan, Ali Boyali, Simon Thompson","doi":"10.1115/1.4052529","DOIUrl":"https://doi.org/10.1115/1.4052529","url":null,"abstract":"\u0000 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.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128459969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Autonomous Vehicles and Systems Pub Date : 2021-01-22 DOI: 10.1115/1.4053244

Kaiwen Liu, Nan I. Li, I. Kolmanovsky, Denise M. Rizzo, A. Girard

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

Journal of Autonomous Vehicles and Systems Pub Date : 2020-10-14 DOI: 10.1115/1.4048376

T. Yeh, Tzu-hsiang Weng

{"title":"Analysis and Control of an In-Pipe Wheeled Robot With Spiral Moving Capability","authors":"T. Yeh, Tzu-hsiang Weng","doi":"10.1115/1.4048376","DOIUrl":"https://doi.org/10.1115/1.4048376","url":null,"abstract":"\u0000 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.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124259916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6