2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)最新文献_第10页

Predictive Motion Planning of Vehicles at Intersection Using a New GPR and RRT 基于新型GPR和RRT的交叉口车辆预测运动规划

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294239

Wu Xihui, A. Eskandarian

{"title":"Predictive Motion Planning of Vehicles at Intersection Using a New GPR and RRT","authors":"Wu Xihui, A. Eskandarian","doi":"10.1109/ITSC45102.2020.9294239","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294239","url":null,"abstract":"This paper addresses the challenge of safe path planning for autonomous vehicles at intersections. Rapidly exploring Random Tree (RRT) as an effective local motion planning methodology has the ability to determine a feasible path. As the number of sampled positions increases, the probability of finding an optimal path increases. However, RRT is usually applied to the static environment due to its delay or lack of efficiency in planning a path to the goal area. In dynamic environments, redundant sampling positions near dynamic obstacles are not effective. Therefore, we proposed a methodology, pRRT, that combines Gaussian Processes Regression (GPR) and RRT to generate a local path to guide the vehicle through the intersection. The procedure includes two phases: prediction and planning. Under prediction, GPR predicts the vehicle’s future trajectory points. The prediction results are combined with destination position (intersection exit) to generate a probability map for sampling such that position sample quality is increased by avoiding redundant samples. The optimal strategy is deployed to guarantee the trajectory is collision-free in both current and future time instances. A combination of both proposed improvements can thus result in a path that is collision-free under the dynamic intersection area. The proposed method also increased the speed of path generation compared to the RRT algorithm.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115180299","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

Environment Perception and Object Tracking for Autonomous Vehicles in a Harbor Scenario 港口场景下自动驾驶汽车的环境感知和目标跟踪

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294618

Jiaying Lin, Lucas Koch, M. Kurowski, Jan-Jöran Gehrt, D. Abel, R. Zweigel

{"title":"Environment Perception and Object Tracking for Autonomous Vehicles in a Harbor Scenario","authors":"Jiaying Lin, Lucas Koch, M. Kurowski, Jan-Jöran Gehrt, D. Abel, R. Zweigel","doi":"10.1109/ITSC45102.2020.9294618","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294618","url":null,"abstract":"Environmental perception is one of the critical aspects of autonomous driving for maritime applications, especially in fields of self-navigation and maneuver planning. For near-field recognition, this paper proposes a novel framework for data fusion, which can determine the occupied static space and track dynamic objects simultaneously. An unmanned surface vessel (USV) is equipped with LiDAR sensors, a GNSS receiver, and an Inertial Navigation System (INS). In the framework, the point cloud from LiDAR sensors is firstly clustered into various objects, then associated with known objects. After dynamic segmentation, the static objects are represented using an optimized occupancy grid map, and the dynamic objects are tracked and matched to the corresponding Automatic Identification System (AIS) messages. The proposed algorithms are validated with data collected from real-world tests, which are conducted in Rostock Harbor, Germany. After applying the proposed algorithm, the perceived test area can be represented with a 3D occupancy grid map with a 10 cm resolution. At the same time, dynamic objects in the view are detected and tracked successfully with an error of less than 10%. The plausibility of the results is qualitatively evaluated by comparing with Google Maps© and the corresponding AIS messages.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115703363","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: 9

Longitudinal Control Algorithm for Cooperative Autonomous Vehicles to Avoid Accident with Vulnerable Road Users 协同自动驾驶车辆避免弱势道路使用者事故的纵向控制算法

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294180

P. Ghorai, A. Eskandarian

{"title":"Longitudinal Control Algorithm for Cooperative Autonomous Vehicles to Avoid Accident with Vulnerable Road Users","authors":"P. Ghorai, A. Eskandarian","doi":"10.1109/ITSC45102.2020.9294180","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294180","url":null,"abstract":"The cooperative perception among connected autonomous vehicles extends the field-of-view of the individual cars and adds significantly to their sensing and collision avoidance capabilities. This feature is particularly useful and essential in avoiding collisions with pedestrians, vulnerable road users, and other objects or cars which are obscured in the typical field-of-view of an ego vehicle. This paper proposes a simple to implement but effective longitudinal control algorithm to avoid collisions in a dynamic environment for cooperative autonomous vehicles. The algorithm is applied to ego and lead vehicles to control longitudinal dynamics with appropriate braking based on safety distance modeling. Simulations using dynamic models for both vehicles and pedestrians on a hazardous traffic scenario are presented to illustrate the effectiveness of the proposed control algorithm. The proposed method is also capable of warning and avoiding collisions for several other critical situations that may appear in autonomous driving. The results demonstrate a promising solution for cooperative collision avoidance, which can be further expanded to more complex scenarios.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114528564","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

Multimodal Cooperative ITS Safety System at Level-Crossings* 平道口多模式合作ITS安全系统*

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294261

Josep Maria Salanova Grau, Neofytos Boufidis, G. Aifadopoulou, Panagiotis Tzenos, Thanasis Tolikas

引用次数: 0

Scenario Definition for Prototyping Cooperative Advanced Driver Assistance Systems 协同高级驾驶辅助系统原型设计的场景定义

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294238

Kay Massow, F. Thiele, Karl Schrab, Sebastian Bunk, I. Tschinibaew, I. Radusch

{"title":"Scenario Definition for Prototyping Cooperative Advanced Driver Assistance Systems","authors":"Kay Massow, F. Thiele, Karl Schrab, Sebastian Bunk, I. Tschinibaew, I. Radusch","doi":"10.1109/ITSC45102.2020.9294238","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294238","url":null,"abstract":"Today’s Advanced Driver Assistance Systems (ADAS) adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. In order to further enhance their benefit, ADAS need to cooperate in the future. This enables, for instance, to solve hazardous situations by coordinated maneuvers for safety intervention on multiple vehicles at the same point in time. Our prototyping environment presented in previous work addresses developing such cooperative ADAS. Its underlying approach is to either bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development, or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. In this paper, we focus on handling the application specific parameter space, and more precisely on the scenario related aspects. As a part of our iterative prototyping process, defining and tuning scenarios and application parameters are highly repetitive tasks which needs to be designed very efficiently. We, therefore, strive to create a scenario definition methodology, which provides best flexibility and a minimal expenditure of time on the developer side.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"643 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116089640","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

Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles 基于多模式行人轨迹预测的自动驾驶车辆社会感知人群导航

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294304

Kunming Li, Mao Shan, K. Narula, Stewart Worrall, E. Nebot

{"title":"Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles","authors":"Kunming Li, Mao Shan, K. Narula, Stewart Worrall, E. Nebot","doi":"10.1109/ITSC45102.2020.9294304","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294304","url":null,"abstract":"Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians’ future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124464423","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: 17

Adaptation and calibration of a social force based model to study interactions between electric scooters and pedestrians 基于社会力模型的电动滑板车与行人相互作用研究

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294608

Yeltsin Valero, A. Antonelli, Z. Christoforou, N. Farhi, Bachar Kabalan, Christos Gioldasis, Nicolas Foissaud

{"title":"Adaptation and calibration of a social force based model to study interactions between electric scooters and pedestrians","authors":"Yeltsin Valero, A. Antonelli, Z. Christoforou, N. Farhi, Bachar Kabalan, Christos Gioldasis, Nicolas Foissaud","doi":"10.1109/ITSC45102.2020.9294608","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294608","url":null,"abstract":"The Personal Mobility Vehicles (PMV) and in particular the electric scooters enjoy increasing popularity and their use has become widespread in the urban environment. The use of existing infrastructure, such as the sidewalks, by escooter drivers, poses a new challenge to policy makers trying to regulate the use of this new mode of transport so that it will be smoothly integrated in the urban networks. So far, there is limited research on the movement of electric scooters and their interaction with pedestrians, depriving the authorities of tools to draw and enforce effective policies. In this paper, we explore the applicability of the social force model for pedestrian dynamics to simulate the movement of e-scooters and the interaction between e-scooters and pedestrians. To conduct this study, we extract electric scooter and pedestrian trajectories through image analysis of videos containing pedestrian and e-scooter movement. Based on the extracted trajectories, scenarios and the respective initial conditions are generated. The social force model is used for the scenarios, and simulated trajectories of escooter and pedestrian movement are produced. The simulated trajectories are compared to the experimental trajectories with the Root Mean Squared Error (RMSE). Finally, the parameters of the social force model and the free speed of the vehicle are estimated with the Cross Entropy Method (CEM).","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124686491","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

Calibration-free traffic state estimation method using single detector and connected vehicles with Kalman filtering and RTS smoothing 基于卡尔曼滤波和RTS平滑的单检测器和联网车辆的免标定交通状态估计方法

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294229

T. Seo

{"title":"Calibration-free traffic state estimation method using single detector and connected vehicles with Kalman filtering and RTS smoothing","authors":"T. Seo","doi":"10.1109/ITSC45102.2020.9294229","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294229","url":null,"abstract":"Traffic state estimation (TSE), which reconstructs complete traffic states from partial observation data, is an essential component in intelligent transportation systems. In this study, a novel traffic state estimation method using connected vehicles and a single detector based on Kalman filtering and Rauch–Tung–Striebel (RTS) smoothing is proposed. To the author’s knowledge, while filtering is common approach for TSE, smoothing has not been employed to TSE in the literature. The important features of the proposed method are twofold. First, thanks to RTS smoothing, it can estimate accurate traffic state using a single detector, and it does not require detectors in every entries and exits of a road section. In addition, the estimation accuracy is not significantly sensitive to detector location. Second, it does not require parameter calibration thanks to the method’s data-driven nature. These features will make the method flexibly applicable for practical conditions. Estimation accuracy of the proposed method was empirically evaluated by using actual vehicle trajectories data, and the effectiveness of the above two features was confirmed.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124997052","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

Estimating the Likelihood of Reaching a Road Target Using Multiple Lane Changes for Driver Assistance 在驾驶员辅助的情况下，通过多次变道来估计到达道路目标的可能性

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294674

Goodarz Mehr, A. Eskandarian

引用次数: 2

Macroscopic Traffic Flow Control using Consensus Algorithms 基于共识算法的宏观交通流控制

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2020-09-20 DOI: 10.1109/ITSC45102.2020.9294474

S. Henning, Kevin Malena, C. Link, Sandra Gausemeier, A. Trächtler

引用次数: 0