2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)最新文献_第3页

Auction Based Parking Lot Assignment and Empty Cruising Limitation of Privately Owned Autonomous Vehicles in a Simple City Model 一个简单城市模型下基于拍卖的私人自动驾驶汽车停车场分配与空载巡航限制

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669251

Levente Alekszejenkó, T. Dobrowiecki

{"title":"Auction Based Parking Lot Assignment and Empty Cruising Limitation of Privately Owned Autonomous Vehicles in a Simple City Model","authors":"Levente Alekszejenkó, T. Dobrowiecki","doi":"10.1109/ivworkshops54471.2021.9669251","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669251","url":null,"abstract":"We describe an experiment of optimizing parking of privately owned connected autonomous vehicles (CAV) involving a municipal limitation of the empty cruising distances.Assuming that a considerable fraction of connected autonomous vehicles will be in private ownership, the problem of parking emerges, calling for a novel and regulated solution. To this end, we propose an auction-based parking lot assignment that optimizes the parking costs and penalizes the empty cruising.To grasp a hypothetical situation, we assess the properties of the proposed mechanism by simulations. A highly abstracted circular city model is presented as a simulation environment to the parking problem, which also considers the regulations limiting the extent of empty cruising. This hypothetical model includes two kinds of parking facilities (curb-sided parking lots and parking houses), various human activity models, pricing, and distance calculations calibrated by values measured in contemporary Budapest.Simulations indicate that using an auction mechanism does not necessarily increase parking charges significantly. The results also call attention to proper empty cruising regulations as parking lot occupancy and vehicle kilometer traveled strongly depend on them.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129992889","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

Constrained Sampling from a Kernel Density Estimator to Generate Scenarios for the Assessment of Automated Vehicles 基于核密度估计器的约束抽样生成自动驾驶车辆评估场景

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669213

E. D. Gelder, E. Cator, J. Paardekooper, O. O. D. Camp, B. Schutter

{"title":"Constrained Sampling from a Kernel Density Estimator to Generate Scenarios for the Assessment of Automated Vehicles","authors":"E. D. Gelder, E. Cator, J. Paardekooper, O. O. D. Camp, B. Schutter","doi":"10.1109/ivworkshops54471.2021.9669213","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669213","url":null,"abstract":"The safety assessment of Automated Vehicles (AVs) is an important aspect of the development cycle of AVs. A scenario-based assessment approach is accepted by many players in the field as part of the complete safety assessment. A scenario is a representation of a situation on the road to which the AV needs to respond appropriately. One way to generate the required scenario-based test descriptions is to parameterize the scenarios and to draw these parameters from a probability density function (pdf). Because the shape of the pdf is unknown beforehand, assuming a functional form of the pdf and fitting the parameters to the data may lead to inaccurate fits. As an alternative, Kernel Density Estimation (KDE) is a promising candidate for estimating the underlying pdf, because it is flexible with the underlying distribution of the parameters. Drawing random samples from a pdf estimated with KDE is possible without the need of evaluating the actual pdf, which makes it suitable for drawing random samples for, e.g., Monte Carlo methods. Sampling from a KDE while the samples satisfy a linear equality constraint, however, has not been described in the literature, as far as the authors know.In this paper, we propose a method to sample from a pdf estimated using KDE, such that the samples satisfy a linear equality constraint. We also present an algorithm of our method in pseudo-code. The method can be used to generating scenarios that have, e.g., a predetermined starting speed or to generate different types of scenarios. This paper also shows that the method for sampling scenarios can be used in case a Singular Value Decomposition (SVD) is used to reduce the dimension of the parameter vectors.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133992525","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: 4

Trajectory planner for platoon lane change* 排变道轨迹规划*

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669215

Haoran Wang, Jintao Lai, Jia Hu

引用次数: 1

Precise Control for Deep Driving using Dual Critic based DRL Approaches 基于双临界DRL方法的深度驾驶精确控制

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669204

Surbhi Gupta, Gaurav Singal, Deepak Garg

{"title":"Precise Control for Deep Driving using Dual Critic based DRL Approaches","authors":"Surbhi Gupta, Gaurav Singal, Deepak Garg","doi":"10.1109/ivworkshops54471.2021.9669204","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669204","url":null,"abstract":"Autonomous driving problems related to vehicle control using deep reinforcement learning (DRL) techniques, are still unsolved. DRL approaches have achieved notable results, its dependability on reward functions and defining the type of control actions are dominating factors of the objective, that controls its success. Several DRL approaches applied in the past consider a finite set of available actions to be controlled by the agent hence, it performs sharp actions. While real driving requires precision control capabilities that tend to apply safer and smoother actions. For incorporating such precision control capabilities, this paper considers the driving problem as a continuous control problem. For this, the gym-highway environments are used as these environments are controllable and customizable to simulate diverse driving scenarios. The simulation setup for parking is updated to resemble the complex scenario and for highway driving a novel reward function is designed to handle continuous actions. Dual critic based DRL approaches are applied as these approaches have shown remarkable performance in robotic locomotion control problems. The video results demonstrate the way different policies fulfil the objective.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134473225","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

Practical Object Detection Using Thermal Infrared Image Sensors 应用热红外图像传感器进行实际目标检测

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669227

Iljoo Baek, Wei Chen, Asish Chakrapani Gumparthi Venkat, R. Rajkumar

{"title":"Practical Object Detection Using Thermal Infrared Image Sensors","authors":"Iljoo Baek, Wei Chen, Asish Chakrapani Gumparthi Venkat, R. Rajkumar","doi":"10.1109/ivworkshops54471.2021.9669227","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669227","url":null,"abstract":"Reliable object detection is critical for autonomous vehicles (AV). An AV must be safely guided towards its destination under different illumination conditions and avoid obstacles. Thermal infrared (TIR) camera sensors can provide robust image quality under any illumination. Past object detection work using TIR sensors focused on detecting only pedestrians by filtering thermal values. Other approaches leveraged the advantages of a pre-trained RGB-based model. However, the thermal threshold-based filtering can increase false positives depending on the TIR camera capability. Moreover, a large and new TIR training dataset is needed to improve the accuracy of the RGB-based object detection networks. The time and effort to annotate new data are significantly high. In this paper, we propose efficient and practical approaches to provide robust object detection from TIR images. We first reduce the cost of training with new data by using an automated process. To increase the final object detection accuracy, we next propose fusion methods that combine results from dual TIR camera sensors. Finally, we substantiate the practical feasibility of our approach and evaluate the substantial improvement in object detection accuracy. We use various detection networks and datasets on discrete Nvidia GPUs and an Nvidia Xavier embedded platform, commonly used by automotive OEMs.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126055473","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

Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes 基于内容的复杂场景图像检索的自监督表示学习

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669246

Hariprasath Govindarajan, P. Lindskog, Dennis Lundström, Amanda Olmin, Jacob Roll, F. Lindsten

引用次数: 1

Validation of a Radar Sensor Model under non-ideal Conditions for Testing Automated Driving Systems 非理想条件下自动驾驶系统雷达传感器模型的验证

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669205

Diogo Wachtel, S. Schröder, Fabio Reway, W. Huber, M. Vossiek

{"title":"Validation of a Radar Sensor Model under non-ideal Conditions for Testing Automated Driving Systems","authors":"Diogo Wachtel, S. Schröder, Fabio Reway, W. Huber, M. Vossiek","doi":"10.1109/ivworkshops54471.2021.9669205","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669205","url":null,"abstract":"Testing of advanced driver-assistance systems (ADAS) is complex, time-consuming and expensive. Therefore, new methods for the validation of such applications are required. A common solution is the use of virtual validation via environment simulation tools, but their reliability must first be confirmed. For this purpose, a comparison of a real and a simulated radar sensor under adverse weather conditions is performed in this work.To quantify the deviation between reality and the virtual test environment, a complex scenario with multiple traffic objects is set up on the proving ground and in the simulation. The data is measured for clear weather, rain and fog and afterwards compared to validate the performance of the sensor model under those non-ideal conditions.The implemented method shows that both sensors neglect the same traffic objects. Compared to the real radar, the variation of the measured parameters according to changing weather conditions in the simulation tends to be correct, but the values are not completely realistic.Though, the validation method is implemented successfully, in further work the comparison of different sensors is recommended. Furthermore, an in-depth examination of the impact due to varying intensity of rain and fog has to be undertaken.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133377901","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

Quantitative Evaluation of Autonomous Driving in CARLA CARLA中自动驾驶的定量评价

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669240

Shang Gao, S. Paulissen, M. Coletti, R. Patton

{"title":"Quantitative Evaluation of Autonomous Driving in CARLA","authors":"Shang Gao, S. Paulissen, M. Coletti, R. Patton","doi":"10.1109/ivworkshops54471.2021.9669240","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669240","url":null,"abstract":"There have been many recent advancements in imitation and reinforcement learning for autonomous driving, but existing metrics generally lack the means to capture a wide range of driving behaviors and compare the severity of different failure cases. To address this shortcoming, we introduce Quan-titative Evaluation for Driving (QED), which assesses different aspects of driving behavior including the ability to stay in the center of the lane, avoid weaving and erratic behavior, follow the speed limit, and avoid collisions. We compare scores generated by QED against scores assigned by human evaluators on 30 different drivers and 6 different towns in the CARLA driving simulator. In \"easy\" evaluation scenarios where better drivers are easily distinguished from worse drivers, QED attains 0.96 Pearson correlation and 0.97 Spearman correlation with human evaluators, similar to the baseline inter-human-evaluator 0.96 Pearson correlation and 0.95 Spearman correlation. In \"hard\" evaluation scenarios where ranking drivers is more ambiguous, QED attains 0.84 Pearson correlation and 0.74 Spearman correlation with human evaluators, slighter higher than the baseline inter-human-evaluator 0.78 Pearson correlation and 0.7 Spearman correlation. While QED may not capture every characteristic that defines good driving, we consider it an important foundation for reproducibility and standardization in the community.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131695714","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

Use of probabilistic graphical methods for online map validation 使用概率图形方法在线地图验证

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669245

Andrea Fabris, L. Parolini, Sebastian Schneider, A. Cenedese

引用次数: 2

Predicting motorcycle riding behavior using vehicle density variation 利用车辆密度变化预测摩托车骑行行为

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669216

T. Koshizen, Fumiaki Sato, Ryoka Oishi, Kazuhiko Yamakawa

引用次数: 0