Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294700
Yuxin He, Yang Zhao, Hao Wang, K. Tsui
Accurate passenger flow forecasting is vital for passenger flow management and planning. However, it is a challenging task in practice as passenger flow of a certain transportation network is affected by complex factors including the unstructured spatial dependencies constrained by the transportation network topological structure, intra-location correlations (inflow relates to outflow), temporal dependencies, and exogenous factors. To cope with the aforementioned challenges, this paper proposes a novel deep learning-based spatiotemporal passenger flow forecasting model, named Graph Convolutional-Long Short Term Memory (GC-LSTM). The designed architecture of GC-LSTM extends convolution with Graph Convolutional Network (GCN) to handle graph-based spatial dependencies, while LSTM in the architecture is employed to capture the long-term temporal dependencies as well as nonlinear traffic dynamics. The proposed method also enables collectively forecasting of inflow and outflow at the location of interest within transportation network by capturing the intra-location correlations in parallel views. Then the proposed method is validated by the real-world passenger flow data of China High-Speed Rail (HSR) network, and the experimental results show that GC-LSTM can well capture the graph-based spatial and temporal dependencies and outperform state-of-art baselines in terms of forecasting accuracy.
{"title":"GC-LSTM: A Deep Spatiotemporal Model for Passenger Flow Forecasting of High-Speed Rail Network","authors":"Yuxin He, Yang Zhao, Hao Wang, K. Tsui","doi":"10.1109/ITSC45102.2020.9294700","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294700","url":null,"abstract":"Accurate passenger flow forecasting is vital for passenger flow management and planning. However, it is a challenging task in practice as passenger flow of a certain transportation network is affected by complex factors including the unstructured spatial dependencies constrained by the transportation network topological structure, intra-location correlations (inflow relates to outflow), temporal dependencies, and exogenous factors. To cope with the aforementioned challenges, this paper proposes a novel deep learning-based spatiotemporal passenger flow forecasting model, named Graph Convolutional-Long Short Term Memory (GC-LSTM). The designed architecture of GC-LSTM extends convolution with Graph Convolutional Network (GCN) to handle graph-based spatial dependencies, while LSTM in the architecture is employed to capture the long-term temporal dependencies as well as nonlinear traffic dynamics. The proposed method also enables collectively forecasting of inflow and outflow at the location of interest within transportation network by capturing the intra-location correlations in parallel views. Then the proposed method is validated by the real-world passenger flow data of China High-Speed Rail (HSR) network, and the experimental results show that GC-LSTM can well capture the graph-based spatial and temporal dependencies and outperform state-of-art baselines in terms of forecasting accuracy.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"21 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":"125223907","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294567
Jing Li, Jingqiu Guo, Min Qiu
Compared to normal incidents, secondary incidents are more likely to result in severe injuries and fatalities. However, limited efforts have been made to unveil the factors affecting the severity of secondary incidents. Incidents that occurred on the Interstate-5 in California within five years were collected. Detailed real-time traffic flow conditions, geometric characteristics, and weather conditions were obtained. First, a Random Forest-based (RF) feature selection approach was adopted. Then, Support Vector Machine (SVM) models were developed to investigate the effects of contributing factors. For comparison, RF and Ordered Logistic (OL) models were also built based on the same dataset. It was found that the SVM model has a high capacity for solving classification problems with limited data availability. Further, sensitivity analysis assessed the impacts of explanatory variables on the injury severity level. Explanatory variables, including occupancy, duration, frequency of lanes changes, and number of lanes, were found to contribute to injury severity of secondary incidents. Smoothing these traffic conditions after an incident occurs and responding fast in incident handling and clearance have the potential to reduce road trauma caused by secondary incidents.
{"title":"Injury Severity Analysis of Secondary Incidents","authors":"Jing Li, Jingqiu Guo, Min Qiu","doi":"10.1109/ITSC45102.2020.9294567","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294567","url":null,"abstract":"Compared to normal incidents, secondary incidents are more likely to result in severe injuries and fatalities. However, limited efforts have been made to unveil the factors affecting the severity of secondary incidents. Incidents that occurred on the Interstate-5 in California within five years were collected. Detailed real-time traffic flow conditions, geometric characteristics, and weather conditions were obtained. First, a Random Forest-based (RF) feature selection approach was adopted. Then, Support Vector Machine (SVM) models were developed to investigate the effects of contributing factors. For comparison, RF and Ordered Logistic (OL) models were also built based on the same dataset. It was found that the SVM model has a high capacity for solving classification problems with limited data availability. Further, sensitivity analysis assessed the impacts of explanatory variables on the injury severity level. Explanatory variables, including occupancy, duration, frequency of lanes changes, and number of lanes, were found to contribute to injury severity of secondary incidents. Smoothing these traffic conditions after an incident occurs and responding fast in incident handling and clearance have the potential to reduce road trauma caused by secondary incidents.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"58 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":"132499157","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294323
Marc Ehret, Mathias Böhm, G. Malzacher, A. Popa
To achieve the climate protection targets despite the increasing transport demand, the shift from carbon-intensive to more environmentally friendly modes, such as rail, is indispensable in the field of freight transport. The Next Generation Train CARGO concept is intended to improve the competitiveness of rail freight, especially for low-density high value goods. However, the corresponding transhipment infrastructure has not yet been analysed in detail. In this work, we introduce a Model-Based Systems Engineering approach for the closer analysis and specification of an intermodal freight terminal for this high-speed freight train concept. This includes the elaboration of the system idea and context, the most important stakeholders and their requirements as well as the identification of the essential system functions. The systematic approach reveals a broad diversity of stakeholders and points out the complexity of the procedures taking place at the terminal. The chosen approach applied in this work has proven to be promising for the holistic system analysis of an intermodal transport node.
{"title":"System analysis of a high-speed freight train terminal*","authors":"Marc Ehret, Mathias Böhm, G. Malzacher, A. Popa","doi":"10.1109/ITSC45102.2020.9294323","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294323","url":null,"abstract":"To achieve the climate protection targets despite the increasing transport demand, the shift from carbon-intensive to more environmentally friendly modes, such as rail, is indispensable in the field of freight transport. The Next Generation Train CARGO concept is intended to improve the competitiveness of rail freight, especially for low-density high value goods. However, the corresponding transhipment infrastructure has not yet been analysed in detail. In this work, we introduce a Model-Based Systems Engineering approach for the closer analysis and specification of an intermodal freight terminal for this high-speed freight train concept. This includes the elaboration of the system idea and context, the most important stakeholders and their requirements as well as the identification of the essential system functions. The systematic approach reveals a broad diversity of stakeholders and points out the complexity of the procedures taking place at the terminal. The chosen approach applied in this work has proven to be promising for the holistic system analysis of an intermodal transport node.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"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":"134556406","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294593
L. Ye, Yuqing Chen, Qingwen Han, Lingqiu Zeng, Sheng Cheng, Lei Xiao, Xujing Ding
The vehicle density determines the frequency of information congestion and collision in VANETs, and affects the quality of communication. Cluster management of vehicle nodes can effectively improve communication efficiency of the network. A clustering framework based on changes in vehicle density and an improved k-means clustering algorithm based on vehicle movement characteristics are proposed in this paper. According to the change of vehicle density, nodes are dynamically clustered, in some cases MSCNs(Mobile Secondary Computing Node) are selected and virtual computing areas are divided. The simulation on NS3 shows that the proposed improved k-means-based adaptive clustering algorithm has strong stability and high communication efficiency.
{"title":"Vehicle Message Distribution Mechanism Based on Improved K-means Adaptive Clustering Algorithm","authors":"L. Ye, Yuqing Chen, Qingwen Han, Lingqiu Zeng, Sheng Cheng, Lei Xiao, Xujing Ding","doi":"10.1109/ITSC45102.2020.9294593","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294593","url":null,"abstract":"The vehicle density determines the frequency of information congestion and collision in VANETs, and affects the quality of communication. Cluster management of vehicle nodes can effectively improve communication efficiency of the network. A clustering framework based on changes in vehicle density and an improved k-means clustering algorithm based on vehicle movement characteristics are proposed in this paper. According to the change of vehicle density, nodes are dynamically clustered, in some cases MSCNs(Mobile Secondary Computing Node) are selected and virtual computing areas are divided. The simulation on NS3 shows that the proposed improved k-means-based adaptive clustering algorithm has strong stability and high communication efficiency.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"51 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":"134252385","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}
With the wide spread of artificial intelligence (AI) technologies, many applications using AI are increasingly deployed in many fields. Specially anomaly detection is one of the key applications of AI. Among several targets, detecting anomaly behavior of drivers or vehicles has been attracting due to the growing demand of safety driving. It is crucial to study and evaluate techniques for anomaly driving detection with AI technologies. The Online Sequential Extreme Learning Machine (OS-ELM) is a recently attracting neural network model that has high memory efficiency and can perform highspeed sequential learning with streaming data. Though OSELM is known to be effective for anomaly detection, it has not yet been verified for non-stationary time series data such as driving sensor data. In this paper, we study the effectiveness of OS-ELM based anomaly driving behavior detector using sensor data of vehicles and compared the performance of it with a Hidden Markov Model (HMM) based and traditional Long Short-Term Memory (LSTM) based methods. Since the existing driving behavior benchmark data is not enough for evaluating anomaly driving, we also create a new dataset with a powered wheelchair. Throughout the evaluation, we show that the OS-ELM based anomaly driving detector has almost the same or even better accuracy in anomaly driving detection with much faster sequential learning speed compared with the HMM or LSTM based detector.
{"title":"Fast Semi-Supervised Anomaly Detection of Drivers’ Behavior using Online Sequential Extreme Learning Machine","authors":"Hiroki Oikawa, Tomoya Nishida, Ryuichi Sakamoto, Hiroki Matsutani, Masaaki Kondo","doi":"10.1109/ITSC45102.2020.9294659","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294659","url":null,"abstract":"With the wide spread of artificial intelligence (AI) technologies, many applications using AI are increasingly deployed in many fields. Specially anomaly detection is one of the key applications of AI. Among several targets, detecting anomaly behavior of drivers or vehicles has been attracting due to the growing demand of safety driving. It is crucial to study and evaluate techniques for anomaly driving detection with AI technologies. The Online Sequential Extreme Learning Machine (OS-ELM) is a recently attracting neural network model that has high memory efficiency and can perform highspeed sequential learning with streaming data. Though OSELM is known to be effective for anomaly detection, it has not yet been verified for non-stationary time series data such as driving sensor data. In this paper, we study the effectiveness of OS-ELM based anomaly driving behavior detector using sensor data of vehicles and compared the performance of it with a Hidden Markov Model (HMM) based and traditional Long Short-Term Memory (LSTM) based methods. Since the existing driving behavior benchmark data is not enough for evaluating anomaly driving, we also create a new dataset with a powered wheelchair. Throughout the evaluation, we show that the OS-ELM based anomaly driving detector has almost the same or even better accuracy in anomaly driving detection with much faster sequential learning speed compared with the HMM or LSTM based detector.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"37 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":"134438329","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294579
Milad Leyli-Abadi, Abderrahmane Boubezoul, L. Oukhellou
The automatic recognition of different riding patterns in the context of naturalistic riding studies (NRSs) facilitates the behavioral analysis of powered two-wheelers (PTW), which is a challenging problem. In the NRS context, various multivariate time series data are provided using an inertial measurement unit (IMU). Modeling the temporal dependency between riding patterns using state-of-the-art machine learning methods is not a straightforward task and requires the extraction of relevant features. In this article, we suggest the use of recurrent neural networks (RNNs) for modeling the temporal dependence between successive patterns without requiring manual feature engineering. Experiments are carried out using a real-world dataset of instrumented motorbikes. The analysis of the network activations and estimated weights allows us to describe the complex riding patterns. Furthermore, comparisons with state-of-the-art machine learning methods show the effectiveness of RNNs in the identification of riding patterns.
{"title":"Riding Pattern Recognition for Powered Two-Wheelers Using a Long Short-Term Memory Network","authors":"Milad Leyli-Abadi, Abderrahmane Boubezoul, L. Oukhellou","doi":"10.1109/ITSC45102.2020.9294579","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294579","url":null,"abstract":"The automatic recognition of different riding patterns in the context of naturalistic riding studies (NRSs) facilitates the behavioral analysis of powered two-wheelers (PTW), which is a challenging problem. In the NRS context, various multivariate time series data are provided using an inertial measurement unit (IMU). Modeling the temporal dependency between riding patterns using state-of-the-art machine learning methods is not a straightforward task and requires the extraction of relevant features. In this article, we suggest the use of recurrent neural networks (RNNs) for modeling the temporal dependence between successive patterns without requiring manual feature engineering. Experiments are carried out using a real-world dataset of instrumented motorbikes. The analysis of the network activations and estimated weights allows us to describe the complex riding patterns. Furthermore, comparisons with state-of-the-art machine learning methods show the effectiveness of RNNs in the identification of riding patterns.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"9 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":"132426754","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294266
Bryce Hallmark, Jing Dong
Many past efforts have been exerted towards describing and quantifying the effects of winter maintenance operations on traffic conditions and safety. As highly granular data on snowplow activity become available, many agencies are becoming interested in incorporating these data in their decision-making processes. However, due to its sheer volume, the processing of snowplow automatic vehicle location (AVL) data has been challenging. In addition, adverse weather conditions are usually accompanied by higher crash rates and also correlate with an increase in maintenance operations. Thus, improper model and variable selection can produce misleading results that indicate maintenance operations lead to a higher crash rate. This paper presents simple visualization tools and analysis methods that examine the effects of winter road maintenance operations on traffic safety by combining various data sources including weather, traffic, snowplow AVL, and crash data. Such intuitive tools and results can help agencies better understand the relationship between winter road maintenance activities and traffic safety.
{"title":"Examining the Effects of Winter Road Maintenance Operations on Traffic Safety through Visual Analytics","authors":"Bryce Hallmark, Jing Dong","doi":"10.1109/ITSC45102.2020.9294266","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294266","url":null,"abstract":"Many past efforts have been exerted towards describing and quantifying the effects of winter maintenance operations on traffic conditions and safety. As highly granular data on snowplow activity become available, many agencies are becoming interested in incorporating these data in their decision-making processes. However, due to its sheer volume, the processing of snowplow automatic vehicle location (AVL) data has been challenging. In addition, adverse weather conditions are usually accompanied by higher crash rates and also correlate with an increase in maintenance operations. Thus, improper model and variable selection can produce misleading results that indicate maintenance operations lead to a higher crash rate. This paper presents simple visualization tools and analysis methods that examine the effects of winter road maintenance operations on traffic safety by combining various data sources including weather, traffic, snowplow AVL, and crash data. Such intuitive tools and results can help agencies better understand the relationship between winter road maintenance activities and traffic safety.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"5 6 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":"133423972","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294409
Junyi Li, Fangce Guo, Yibing Wang, Lihui Zhang, X. Na, Simon Hu
A key problem in short-term traffic prediction is the prevailing data missing scenarios across the entire traffic network. To address this challenge, a transfer learning framework is currently used in the literature, which could improve the prediction accuracy on the target link that suffers severe data missing problems by using information from source links with sufficient historical data. However, one of the limitations in these transfer-learning based models is their high dependency on the consistency between datasets and the complex data selection process, which brings heavy computation burden and human efforts. In this paper, we propose an adaptive transfer learning method in short-term traffic flow prediction model to alleviate the complex data selection process. Specifically, a self-adaptive neural network with a novel domain adaptation loss is developed. The domain adaptation loss is able to calculate the distance between the source data and the corresponding target data in each training batch, which can help the network to adaptively filter inconsistent source data and learn target link related information in each training batch. The Maximum Mean Discrepancy (MMD) measurement, which has been fully validated and applied in transfer learning research, is used in combination with the Gaussian kernel to measure the distance between datasets in each training batch. A series of experiments are designed and conducted using 15-minute interval traffic flow data from the Highways England, UK. The results have demonstrated that the proposed adaptive transfer learning method is less affected by the inconsistency between datasets and provides more accurate short-term traffic flow prediction.
{"title":"Short-term Traffic Prediction with Deep Neural Networks and Adaptive Transfer Learning","authors":"Junyi Li, Fangce Guo, Yibing Wang, Lihui Zhang, X. Na, Simon Hu","doi":"10.1109/ITSC45102.2020.9294409","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294409","url":null,"abstract":"A key problem in short-term traffic prediction is the prevailing data missing scenarios across the entire traffic network. To address this challenge, a transfer learning framework is currently used in the literature, which could improve the prediction accuracy on the target link that suffers severe data missing problems by using information from source links with sufficient historical data. However, one of the limitations in these transfer-learning based models is their high dependency on the consistency between datasets and the complex data selection process, which brings heavy computation burden and human efforts. In this paper, we propose an adaptive transfer learning method in short-term traffic flow prediction model to alleviate the complex data selection process. Specifically, a self-adaptive neural network with a novel domain adaptation loss is developed. The domain adaptation loss is able to calculate the distance between the source data and the corresponding target data in each training batch, which can help the network to adaptively filter inconsistent source data and learn target link related information in each training batch. The Maximum Mean Discrepancy (MMD) measurement, which has been fully validated and applied in transfer learning research, is used in combination with the Gaussian kernel to measure the distance between datasets in each training batch. A series of experiments are designed and conducted using 15-minute interval traffic flow data from the Highways England, UK. The results have demonstrated that the proposed adaptive transfer learning method is less affected by the inconsistency between datasets and provides more accurate short-term traffic flow prediction.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"47 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":"132008008","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294595
Mihai Kocsis, J. Winckler, Nico Sußmann, R. Zöllner
Automated solutions for executing services in urban areas have become a trend in the past years. Examples of such services are: package delivery, transportation, street cleaning, waste disposal or vegetation care. They are also part of new concepts of smart cities. The vision is to have a vehicle fleet that provides these services at demand of inhabitants or authorities in urban areas. These vehicles have the capability to drive autonomously and interact with other traffic participants in order to accomplish a specific task. An important aspect is the mission planning of the vehicles. We present a concept of an interactive planning and management of a vehicle fleet that executes requested service demands in urban areas and the interaction between the involved stakeholders. The service requester gets immediate response regarding their request and can track, change or cancel it with immediate adaption of the plan. The concept was implemented and the system was used a few months for delivery services during a real world laboratory in a new built district in Heilbronn (Germany), with about 800 inhabitants.
{"title":"Interactive Mission Planning System of an Autonomous Vehicle Fleet that Executes Services","authors":"Mihai Kocsis, J. Winckler, Nico Sußmann, R. Zöllner","doi":"10.1109/ITSC45102.2020.9294595","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294595","url":null,"abstract":"Automated solutions for executing services in urban areas have become a trend in the past years. Examples of such services are: package delivery, transportation, street cleaning, waste disposal or vegetation care. They are also part of new concepts of smart cities. The vision is to have a vehicle fleet that provides these services at demand of inhabitants or authorities in urban areas. These vehicles have the capability to drive autonomously and interact with other traffic participants in order to accomplish a specific task. An important aspect is the mission planning of the vehicles. We present a concept of an interactive planning and management of a vehicle fleet that executes requested service demands in urban areas and the interaction between the involved stakeholders. The service requester gets immediate response regarding their request and can track, change or cancel it with immediate adaption of the plan. The concept was implemented and the system was used a few months for delivery services during a real world laboratory in a new built district in Heilbronn (Germany), with about 800 inhabitants.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"168 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":"122785693","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}
Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294207
W. Zhou, Edmanuel Cruz, Stewart Worrall, Francisco Gomez-Donoso, M. Cazorla, E. Nebot
Predicting the condition of the road is an important task for autonomous vehicles to make driving decisions. Vehicles are expected to slow down or stop for potential road risks such as road cracks, bumps and potholes. Vision systems are widely used to provide such information given the rich colours and textures carried by images. This paper presents a weakly-supervised deep learning method to classify road images into two category sets. The first category identifies the existence of bumps or ramps in the image. The second category determines the road roughness given an input image. These two outputs are combined into a single convolutional neural network (CNN) to classify the camera image simultaneously. As a supervised learning method, deep learning algorithms normally require a large amount of training data with manually annotated labels. The annotation process is, however, very time-consuming and labour-intensive. This paper presents a method to avoid this costly process using a pipeline to automatically generate ground-truth labels by incorporating IMU and wheel encoder data. This automated pipeline does not require human effort to label images and will not be impeded by adverse environmental or illumination conditions. The experimental results presented show that after training the model using the automatically generated labels, the two-output CNN is capable to achieve good accuracy for classifying road conditions.
{"title":"Weakly-supervised Road Condition Classification Using Automatically Generated Labels","authors":"W. Zhou, Edmanuel Cruz, Stewart Worrall, Francisco Gomez-Donoso, M. Cazorla, E. Nebot","doi":"10.1109/ITSC45102.2020.9294207","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294207","url":null,"abstract":"Predicting the condition of the road is an important task for autonomous vehicles to make driving decisions. Vehicles are expected to slow down or stop for potential road risks such as road cracks, bumps and potholes. Vision systems are widely used to provide such information given the rich colours and textures carried by images. This paper presents a weakly-supervised deep learning method to classify road images into two category sets. The first category identifies the existence of bumps or ramps in the image. The second category determines the road roughness given an input image. These two outputs are combined into a single convolutional neural network (CNN) to classify the camera image simultaneously. As a supervised learning method, deep learning algorithms normally require a large amount of training data with manually annotated labels. The annotation process is, however, very time-consuming and labour-intensive. This paper presents a method to avoid this costly process using a pipeline to automatically generate ground-truth labels by incorporating IMU and wheel encoder data. This automated pipeline does not require human effort to label images and will not be impeded by adverse environmental or illumination conditions. The experimental results presented show that after training the model using the automatically generated labels, the two-output CNN is capable to achieve good accuracy for classifying road conditions.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114015299","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}
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