2018 21st International Conference on Intelligent Transportation Systems (ITSC)最新文献_第8页

Attack-Resilient Sensor Fusion for Cooperative Adaptive Cruise Control 协同自适应巡航控制的攻击弹性传感器融合

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569578

Pengyuan Lu, Limin Zhang, B. Park, Lu Feng

引用次数: 6

A New Feature Pyramid Network For Road Scene Segmentation 一种新的道路场景分割特征金字塔网络

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569247

Wujing Zhan, Jiaxing Chen, Lei Fan, X. Ou, Long Chen

引用次数: 0

Spatiotemporally Consistent Smooth Speed Profiles for Autonomous Driving 自动驾驶的时空一致性平稳速度分布

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569465

Benjamin C. Heinrich, Alexander Frericks, Hans-Joachim Wünsche

引用次数: 1

Short-Term Traffic Speed Prediction for Freeways During Hurricane Evacuation: A Deep Learning Approach 飓风疏散期间高速公路短期交通速度预测:一种深度学习方法

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569443

Rezaur Rahman, Samiul Hasan

{"title":"Short-Term Traffic Speed Prediction for Freeways During Hurricane Evacuation: A Deep Learning Approach","authors":"Rezaur Rahman, Samiul Hasan","doi":"10.1109/ITSC.2018.8569443","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569443","url":null,"abstract":"Hurricane evacuation plays a critical role for effective disaster preparations. Giving accurate traffic prediction to evacuees enables a safe and smooth evacuation. Moreover, reliable traffic state prediction allows emergency managers to proactively respond to changes in traffic conditions. In this paper, we present a deep learning model to predict traffic speeds in freeways under extreme traffic demand, such as a hurricane evacuation. For prediction, we adopt a Long Short-Term Memory Neural Network (LSTM-NN) model. The approach is tested using real-world traffic data collected during hurricane Irma's evacuation for the interstate 75 (I-75), a major evacuation route in Florida. Using LSTM-NN, we perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as KNN, ANN, ARIMA. We find that LSTM-NN performs better than these parametric and non-parametric models. The proposed method can be integrated with evacuation traffic management systems for a better evacuation operation.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594430","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: 21

A Game Theoretic Model for Aggregate Bypassing Behavior of Vehicles at Traffic Diverges 交通分流处车辆总体绕开行为的博弈论模型

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569463

Negar Mehr, Ruolin Li, R. Horowitz

{"title":"A Game Theoretic Model for Aggregate Bypassing Behavior of Vehicles at Traffic Diverges","authors":"Negar Mehr, Ruolin Li, R. Horowitz","doi":"10.1109/ITSC.2018.8569463","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569463","url":null,"abstract":"Vehicle bypassing is known to negatively affect delays at traffic diverges. However, due to the complexities of this phenomenon, accurate and yet simple models of such lane change maneuvers are hard to develop. In this work, we present a macroscopic model for predicting the number of vehicles that bypass at a traffic diverge. We take into account the selfishness of vehicles in selecting their lanes; every vehicle selects lanes such that its own cost is minimized. We discuss how we model the costs that are experienced by vehicles. Then, taking into account the selfish behavior of vehicles, we model the lane choice of vehicles at a traffic diverge as a Wardrop equilibrium. We state and prove the properties of Wardrop equilibrium in our model. We show that there always exists an equilibrium for our model. Moreover, unlike most nonlinear asymmetrical routing games, we prove that the equilibrium is unique under mild assumptions. We discuss how our model can be easily calibrated by running a simple optimization problem. Using our calibrated model, we validate it through simulation studies and demonstrate that our model successfully predicts the aggregate lane change maneuvers that are performed by vehicles for bypassing at a traffic diverge. We further discuss how our model can be employed to obtain the optimal lane choice behavior of vehicles, where the social or total cost of vehicles is minimized.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122305736","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

Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles 基于传感器的智能汽车前馈神经网络人类驾驶决策预测

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569441

Shriram C. Jugade, A. Victorino, V. Berge-Cherfaoui, S. Kanarachos

{"title":"Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles","authors":"Shriram C. Jugade, A. Victorino, V. Berge-Cherfaoui, S. Kanarachos","doi":"10.1109/ITSC.2018.8569441","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569441","url":null,"abstract":"Prediction of human driving decisions is an important aspect of modeling human behavior for the application to Advanced Driver Assistance Systems (ADAS) in the intelligent vehicles. This paper presents a sensor based receding horizon model for the prediction of human driving commands. Human driving decisions are expressed in terms of the vehicle speed and steering wheel angle profiles. Environmental state and human intention are the two major factors influencing the human driving decisions. The environment around the vehicle is perceived using LIDAR sensor. Feature extractor computes the occupancy grid map from the sensor data which is filtered and processed to provide precise and relevant information to the feed-forward neural network. Human intentions can be identified from the past driving decisions and represented in the form of time series data for the neural network. Supervised machine learning is used to train the neural network. Data collection and model validation is performed in the driving simulator using the SCANeR studio software. Simulation results are presented alone with the analysis.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121087914","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: 7

Non-Guided Depth Completion with Adversarial Networks 对抗网络的非制导深度补全

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569389

Yuki Tsuji, Hiroyuki Chishiro, S. Kato

{"title":"Non-Guided Depth Completion with Adversarial Networks","authors":"Yuki Tsuji, Hiroyuki Chishiro, S. Kato","doi":"10.1109/ITSC.2018.8569389","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569389","url":null,"abstract":"Depth completion, which interpolates dense depth maps based on sparse inputs acquired from 3D LiDAR sensors, enhances perception capabilities of autonomous driving using object detection and 3D mapping. Recent studies on depth completion have leveraged deep learning approaches applying traditional convolutional neural networks to prediction of invisible information in sparse and irregular inputs. Due to the lack of local and global structures such as object boundary cues, however, the predicted information results in unstructured and noisy depth maps. This paper presents a supervised depth completion method using an adversarial network based only on sparse inputs. In the presented method, a fully convolutional depth completion network, along with the adversarial network, is designed to find and correct inconsistencies between ground truth distributions and the resulting depth maps interpolated by the depth completion network. This leads to more realistic and structured depth images without compromising runtime performance of inference. Experimental results based on the KITTI depth completion benchmark show that the presented adversarial network method achieves about 60% improvements for the accuracy of inference and increases the rate of convergence during training.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121193322","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

Driver Assistance after Handover of Control from Automation 自动控制移交后的驾驶员辅助

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569499

Mishel Johns, G. Strack, Wendy Ju

引用次数: 5

High-level Decision Making for Safe and Reasonable Autonomous Lane Changing using Reinforcement Learning 基于强化学习的安全合理自动变道高层决策

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569448

Branka Mirchevska, Christian Pek, M. Werling, M. Althoff, J. Boedecker

引用次数: 116

Estimation of Driver's Insight for Safe Passing based on Pedestrian Attributes 基于行人属性的驾驶员安全通行洞察力估计

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569955

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, I. Ide, H. Murase, H. Fujiyoshi

{"title":"Estimation of Driver's Insight for Safe Passing based on Pedestrian Attributes","authors":"Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Takatsugu Hirayama, I. Ide, H. Murase, H. Fujiyoshi","doi":"10.1109/ITSC.2018.8569955","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569955","url":null,"abstract":"In order to reduce traffic accidents between a vehicle and a pedestrian, recognition of a pedestrian who has a possibility of collision with a vehicle should be helpful. However, since a pedestrian may suddenly change his/her direction and cross the road, it is difficult to predict his/her behavior directly. Here, we focus on the fact that experienced drivers usually pass by a pedestrian while preparing to step on the brake at any moment when they feel danger. If driver assistant systems can estimate such experienced driver's decisions, they could early detect the pedestrian in danger of collision. Therefore, we classify the driver's decisions into three types by referring to the accelerator operation of drivers, and propose a method to estimate the type of the driver's decision. The drivers are considered to decide their actions focusing on various behaviors and states of a pedestrian, namely pedestrian's attributes. Since the driver's decisions change along the timeline, the use of a temporal context is considered to be effective. Thus, in this paper, we propose an estimation method using a recurrent neural network architecture with the pedestrian's attributes as input. We constructed a dataset collected by experienced drivers in control of the vehicle and evaluated the performance, and then confirmed the effectiveness of the use of pedestrian's attributes.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114288189","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: 5