Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294310
Nils Katzorke, Matthias Moosmann, Reiner Imdahl, H. Lasi
Automotive proving grounds currently face an increasing complexity in testing requirements, especially in the field of automated driving. Thus, the variety of necessary test infrastructure grows. This challenges proving ground operators to constantly satisfy the demand. Requirements are a quick adoption of existing test tracks including its facilities and enhancements of the test infrastructure portfolio. Commercial proving ground operators usually strive to provide a broad set of test tracks so their customers can conduct most tests at one location. Customer loyalty is a key success factor for proving ground operators. A sufficient variety of test tracks and test infrastructure is a lever for customer loyalty. This paper provides a method to measure the quotient of testing demand satisfaction for automated vehicles. This allows benchmarking with other proving grounds. Furthermore, this method can be used to identify gaps between the current portfolio and the demand. Afterwards, action plans can be generated in order to close these gaps.
{"title":"A Method to Assess and Compare Proving Grounds in the Context of Automated Driving Systems","authors":"Nils Katzorke, Matthias Moosmann, Reiner Imdahl, H. Lasi","doi":"10.1109/ITSC45102.2020.9294310","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294310","url":null,"abstract":"Automotive proving grounds currently face an increasing complexity in testing requirements, especially in the field of automated driving. Thus, the variety of necessary test infrastructure grows. This challenges proving ground operators to constantly satisfy the demand. Requirements are a quick adoption of existing test tracks including its facilities and enhancements of the test infrastructure portfolio. Commercial proving ground operators usually strive to provide a broad set of test tracks so their customers can conduct most tests at one location. Customer loyalty is a key success factor for proving ground operators. A sufficient variety of test tracks and test infrastructure is a lever for customer loyalty. This paper provides a method to measure the quotient of testing demand satisfaction for automated vehicles. This allows benchmarking with other proving grounds. Furthermore, this method can be used to identify gaps between the current portfolio and the demand. Afterwards, action plans can be generated in order to close these gaps.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"32 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":"129469023","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/ITSC45102.2020.9294672
Simone Magistri, Francesco Sambo, Fabio Schoen, Douglas Coimbra de Andrade, Matteo Simoncini, Stefano Caprasecca, Luca Kubin, L. Bravi, L. Taccari
Vehicle viewpoint estimation from vehicle cameras is a crucial component of road scene understanding.In this paper, we propose a deep lightweight method to predict vehicle viewpoint from a single RGB dashcam image. To this aim, we customize and adapt state-of-the-art deep learning techniques for general object viewpoint estimation to the vehicle viewpoint estimation task. Furthermore, we define a novel objective function that takes into account errors at different granularity to improve neural network training. To keep the model lightweight and fast, we rely upon MobileNetV2 as backbone.Tested both on benchmark viewpoint estimation data (Pascal3D+) and on actual vehicle camera data (nuScenes), our method is shown to outperform the state of the art in vehicle viewpoint estimation, in terms of both accuracy and memory footprint.
{"title":"A Lightweight Deep Learning Model for Vehicle Viewpoint Estimation from Dashcam Images","authors":"Simone Magistri, Francesco Sambo, Fabio Schoen, Douglas Coimbra de Andrade, Matteo Simoncini, Stefano Caprasecca, Luca Kubin, L. Bravi, L. Taccari","doi":"10.1109/ITSC45102.2020.9294672","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294672","url":null,"abstract":"Vehicle viewpoint estimation from vehicle cameras is a crucial component of road scene understanding.In this paper, we propose a deep lightweight method to predict vehicle viewpoint from a single RGB dashcam image. To this aim, we customize and adapt state-of-the-art deep learning techniques for general object viewpoint estimation to the vehicle viewpoint estimation task. Furthermore, we define a novel objective function that takes into account errors at different granularity to improve neural network training. To keep the model lightweight and fast, we rely upon MobileNetV2 as backbone.Tested both on benchmark viewpoint estimation data (Pascal3D+) and on actual vehicle camera data (nuScenes), our method is shown to outperform the state of the art in vehicle viewpoint estimation, in terms of both accuracy and memory footprint.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"23 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":"129892639","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/ITSC45102.2020.9294630
Haohao Hu, Aoran Wang, Marc Sons, M. Lauer
In this work, we present a visual pose regression network: ViPNet. It is robust and real-time capable on mobile platforms such as self-driving vehicles. We train a convolutional neural network to estimate the six degrees of freedom camera pose from a single monocular image in an end-to-end manner. In order to estimate camera poses with uncertainty, we use a Bayesian version of the ResNet-50 as our basic network. SEBlocks are applied in residual units to increase our model’s sensitivity to informative features. Our ViPNet is trained using a geometric loss function with trainable parameters, which can simplify the fine-tuning process significantly. We evaluate our ViPNet on the Cambridge Landmarks dataset and also on our Karl-Wilhelm-Plaza dataset, which is recorded with an experimental vehicle. As evaluation results, our ViPNet outperforms other end-to-end monocular camera pose estimation methods. Our ViPNet requires only 9-15ms to predict one camera pose, which allows us to run it with a very high frequency.
{"title":"ViPNet: An End-to-End 6D Visual Camera Pose Regression Network","authors":"Haohao Hu, Aoran Wang, Marc Sons, M. Lauer","doi":"10.1109/ITSC45102.2020.9294630","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294630","url":null,"abstract":"In this work, we present a visual pose regression network: ViPNet. It is robust and real-time capable on mobile platforms such as self-driving vehicles. We train a convolutional neural network to estimate the six degrees of freedom camera pose from a single monocular image in an end-to-end manner. In order to estimate camera poses with uncertainty, we use a Bayesian version of the ResNet-50 as our basic network. SEBlocks are applied in residual units to increase our model’s sensitivity to informative features. Our ViPNet is trained using a geometric loss function with trainable parameters, which can simplify the fine-tuning process significantly. We evaluate our ViPNet on the Cambridge Landmarks dataset and also on our Karl-Wilhelm-Plaza dataset, which is recorded with an experimental vehicle. As evaluation results, our ViPNet outperforms other end-to-end monocular camera pose estimation methods. Our ViPNet requires only 9-15ms to predict one camera pose, which allows us to run it with a very high frequency.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 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":"130399055","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/ITSC45102.2020.9294350
Jonas Löhdefink, Fabian Hüger, Peter Schlicht, T. Fingscheidt
Recently, learned image compression by means of neural networks has experienced a performance boost by the use of adversarial loss functions. Typically, a generative adversarial network (GAN) is designed with the generator being an autoencoder with quantizer in the bottleneck for compression and reconstruction. It is well known from rate-distortion theory that vector quantizers provide lower quantization errors than scalar quantizers at the same bitrate. Still, learned image compression approaches often use scalar quantization instead. In this work we provide insights into the image reconstruction quality of the often-employed uniform scalar quantizers, non-uniform scalar quantizers, and the rarely employed but bitrate-efficient vector quantizers, all being integrated into backpropagation and operating under the exact same bitrate. Further interesting insights are obtained by our investigation of an MSE loss and a GAN loss. We show that vector quantization is always beneficial for the compression performance both in the latent space and the reconstructed image space. However, image samples demonstrate that the GAN loss produces the more pleasing reconstructed images, while the non-adversarial MSE loss provides better quality scores of various instrumental measures both in the latent space and on the reconstructed images.
{"title":"Scalar and Vector Quantization for Learned Image Compression: A Study on the Effects of MSE and GAN Loss in Various Spaces","authors":"Jonas Löhdefink, Fabian Hüger, Peter Schlicht, T. Fingscheidt","doi":"10.1109/ITSC45102.2020.9294350","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294350","url":null,"abstract":"Recently, learned image compression by means of neural networks has experienced a performance boost by the use of adversarial loss functions. Typically, a generative adversarial network (GAN) is designed with the generator being an autoencoder with quantizer in the bottleneck for compression and reconstruction. It is well known from rate-distortion theory that vector quantizers provide lower quantization errors than scalar quantizers at the same bitrate. Still, learned image compression approaches often use scalar quantization instead. In this work we provide insights into the image reconstruction quality of the often-employed uniform scalar quantizers, non-uniform scalar quantizers, and the rarely employed but bitrate-efficient vector quantizers, all being integrated into backpropagation and operating under the exact same bitrate. Further interesting insights are obtained by our investigation of an MSE loss and a GAN loss. We show that vector quantization is always beneficial for the compression performance both in the latent space and the reconstructed image space. However, image samples demonstrate that the GAN loss produces the more pleasing reconstructed images, while the non-adversarial MSE loss provides better quality scores of various instrumental measures both in the latent space and on the reconstructed images.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"12 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":"130523302","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/ITSC45102.2020.9294644
E. Andreotti, Pinar Boyraz Baykas, Selpi Selpi
The aim of this paper is to highlight and investigate the effects of increasing presence rate of autonomous vehicles (AVs) in terms of traffic safety and traffic flow characteristics. For this purpose, using existing driver models in traffic simulator SUMO we identify and analyze those parameters that characterize and distinguish AVs’ driving from manual driving in a heterogeneous traffic context. While it is essential to identify the parameters for traffic flow characteristics of heterogeneous fleets compared to homogeneous ones comprising manually driven vehicles (MV) only (i.e. current status), the safety aspects must be also accounted for. In order to combine these two fundamental aspects of heterogeneous traffic, we used a complete description of a highway driving scenario. The scenario integrates the perceptions of different type of vehicles (i.e. AV and MV) involved and the reaction times of human drivers and decision-making units of autonomous vehicles, to explore the impact of both the rate of AV presence and the perturbation in perception capabilities in highway scenarios.
{"title":"Safety-centred analysis of transition stages to traffic with fully autonomous vehicles","authors":"E. Andreotti, Pinar Boyraz Baykas, Selpi Selpi","doi":"10.1109/ITSC45102.2020.9294644","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294644","url":null,"abstract":"The aim of this paper is to highlight and investigate the effects of increasing presence rate of autonomous vehicles (AVs) in terms of traffic safety and traffic flow characteristics. For this purpose, using existing driver models in traffic simulator SUMO we identify and analyze those parameters that characterize and distinguish AVs’ driving from manual driving in a heterogeneous traffic context. While it is essential to identify the parameters for traffic flow characteristics of heterogeneous fleets compared to homogeneous ones comprising manually driven vehicles (MV) only (i.e. current status), the safety aspects must be also accounted for. In order to combine these two fundamental aspects of heterogeneous traffic, we used a complete description of a highway driving scenario. The scenario integrates the perceptions of different type of vehicles (i.e. AV and MV) involved and the reaction times of human drivers and decision-making units of autonomous vehicles, to explore the impact of both the rate of AV presence and the perturbation in perception capabilities in highway scenarios.","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":"129297735","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/ITSC45102.2020.9294361
J. Cao, Yonghui Hu, Manolis Diamantis, Siyu Zhang, Yibing Wang, Jingqiu Guo, I. Papamichail, M. Papageorgiou, Lihui Zhang, J. Hu
Max pressure (MP) is a distributed strategy for adaptive urban traffic signal control. Real-time queue estimation for road links is indispensable for MP-based traffic control. All works conducted so far on MP traffic signal control assumed that accurate information of vehicle queues was directly available in real time. This paper studies joint queue estimation and MP control for signalized urban networks with connected vehicles. For the sake of practical significance, the cases of link queue estimation and lane-wise queue estimation were both considered as input to the MP traffic signal control. A congested 3*3 network was emulated using AIMSUN to evaluate the performance of the developed queue estimation and MP traffic signal control algorithms, with study results reported.
{"title":"A Max Pressure Approach to Urban Network Signal Control with Queue Estimation using Connected Vehicle Data","authors":"J. Cao, Yonghui Hu, Manolis Diamantis, Siyu Zhang, Yibing Wang, Jingqiu Guo, I. Papamichail, M. Papageorgiou, Lihui Zhang, J. Hu","doi":"10.1109/ITSC45102.2020.9294361","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294361","url":null,"abstract":"Max pressure (MP) is a distributed strategy for adaptive urban traffic signal control. Real-time queue estimation for road links is indispensable for MP-based traffic control. All works conducted so far on MP traffic signal control assumed that accurate information of vehicle queues was directly available in real time. This paper studies joint queue estimation and MP control for signalized urban networks with connected vehicles. For the sake of practical significance, the cases of link queue estimation and lane-wise queue estimation were both considered as input to the MP traffic signal control. A congested 3*3 network was emulated using AIMSUN to evaluate the performance of the developed queue estimation and MP traffic signal control algorithms, with study results reported.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 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":"130584067","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/ITSC45102.2020.9294520
Manuel Martin, M. Voit, R. Stiefelhagen
The drivers activities and the resulting distraction is relevant for all levels of vehicle automation. It is especially important for take-over scenarios in partially automated vehicles. To this end we investigate graph neuronal networks for pose based driver activity recognition. We focus on integrating additional input modalities like interior elements and objects and investigate how this data can be integrated in an activity recognition model. We test our approach on the Drive & Act dataset [1]. To this end we densely annotate and publish the bounding boxes of the dynamic objects contained in the dataset. Our results show that adding the additional input modalities boosts the recognition results of classes related to interior elements and objects by a large margin closing the gap to popular image based methods.
{"title":"Dynamic Interaction Graphs for Driver Activity Recognition","authors":"Manuel Martin, M. Voit, R. Stiefelhagen","doi":"10.1109/ITSC45102.2020.9294520","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294520","url":null,"abstract":"The drivers activities and the resulting distraction is relevant for all levels of vehicle automation. It is especially important for take-over scenarios in partially automated vehicles. To this end we investigate graph neuronal networks for pose based driver activity recognition. We focus on integrating additional input modalities like interior elements and objects and investigate how this data can be integrated in an activity recognition model. We test our approach on the Drive & Act dataset [1]. To this end we densely annotate and publish the bounding boxes of the dynamic objects contained in the dataset. Our results show that adding the additional input modalities boosts the recognition results of classes related to interior elements and objects by a large margin closing the gap to popular image based methods.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"11 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":"121162196","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/ITSC45102.2020.9294710
Ahmed Darwish, Momen Khalil, Karim Badawi
Public Transportation Buses are an integral part of our cities, which relies heavily on optimal planning of routes. The quality of the routes directly influences the quality of service provided to passengers, in terms of coverage, directness, and in-vehicle travel time. In addition, it affects the profitability of the transportation system, since the network structure directly influences the operational costs. We propose a system which automates the planning of bus networks based on given demand. The system implements a paradigm, Deep Reinforcement Learning, which has not been used in past literature before for solving the well-documented multi-objective Transit Network Design and Frequency Setting Problem (TNDFSP). The problem involves finding a set of routes in an urban area, each with its own bus frequency. It is considered an NP-Hard combinatorial problem with a massive search space. Compared to state-of-the-art paradigms, our system produced very competitive results, outperforming state-of-the-art solutions.
{"title":"optimising Public Bus Transit Networks Using Deep Reinforcement Learning","authors":"Ahmed Darwish, Momen Khalil, Karim Badawi","doi":"10.1109/ITSC45102.2020.9294710","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294710","url":null,"abstract":"Public Transportation Buses are an integral part of our cities, which relies heavily on optimal planning of routes. The quality of the routes directly influences the quality of service provided to passengers, in terms of coverage, directness, and in-vehicle travel time. In addition, it affects the profitability of the transportation system, since the network structure directly influences the operational costs. We propose a system which automates the planning of bus networks based on given demand. The system implements a paradigm, Deep Reinforcement Learning, which has not been used in past literature before for solving the well-documented multi-objective Transit Network Design and Frequency Setting Problem (TNDFSP). The problem involves finding a set of routes in an urban area, each with its own bus frequency. It is considered an NP-Hard combinatorial problem with a massive search space. Compared to state-of-the-art paradigms, our system produced very competitive results, outperforming state-of-the-art solutions.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"130 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":"116296644","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/ITSC45102.2020.9294603
G. Aifadopoulou, M. Konstantinidou, Neofytos Boufidis, Josep Maria Salanova Grau
Value-of-time (VOT) and willingness-to-pay (WTP) measures are valuable in a wide range of transport policies and planning applications. The purpose of the present research is to estimate these measures in Thessaloniki, where a pilot mobility scheme inspired by the concept of sharing economy is implemented. The pilot focuses on reducing the commuting trips from the eastern part of the city to the city centre by using a taxi-sharing service. A questionnaire including a stated-preference (SP) experiment has been developed and administered to a random sample of 90 people. The survey combines trip-based characteristics (mode, travel time, and travel cost), with socioeconomic characteristics, such as profession, education, and car ownership. Discrete choice models are developed within a methodological framework and the estimated coefficients have been used to estimate VOT. A second sample consisted of users of the pilot service is selected for the estimation of WTP through the development of a Price Sensitivity Model. The model results in a range of acceptable prices from 2.00 to 3.50€ for the taxi-sharing service use supporting the long-term sustainability of the service.
{"title":"Ex Post Estimation of Value-of-Time and Willingness to Pay for Shared Transport Services in Thessaloniki","authors":"G. Aifadopoulou, M. Konstantinidou, Neofytos Boufidis, Josep Maria Salanova Grau","doi":"10.1109/ITSC45102.2020.9294603","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294603","url":null,"abstract":"Value-of-time (VOT) and willingness-to-pay (WTP) measures are valuable in a wide range of transport policies and planning applications. The purpose of the present research is to estimate these measures in Thessaloniki, where a pilot mobility scheme inspired by the concept of sharing economy is implemented. The pilot focuses on reducing the commuting trips from the eastern part of the city to the city centre by using a taxi-sharing service. A questionnaire including a stated-preference (SP) experiment has been developed and administered to a random sample of 90 people. The survey combines trip-based characteristics (mode, travel time, and travel cost), with socioeconomic characteristics, such as profession, education, and car ownership. Discrete choice models are developed within a methodological framework and the estimated coefficients have been used to estimate VOT. A second sample consisted of users of the pilot service is selected for the estimation of WTP through the development of a Price Sensitivity Model. The model results in a range of acceptable prices from 2.00 to 3.50€ for the taxi-sharing service use supporting the long-term sustainability of the service.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 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":"121575090","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/ITSC45102.2020.9294633
Mingliang Chen, J. Xun, Yafei Liu
Virtual Coupling has attracted significant attention from both industry and academia, which could increase the flexibility and capacity of rail transport. At the same time, the risk of trains collision is greatly increased especially when leader train implements emergency braking. This study proposes a coordinated collision mitigation approach for Virtual Coupling trains by using model predictive control (MPC). In the proposed approach, the problem is modeled with the objective of minimizing the total relative kinetic energy for a virtuallycoupled train formation. The typical scenarios are considered in this paper: 1. Emergency braking for homogeneous fleet; 2. Emergency braking for heterogeneous fleet; 3. Emergency braking for homogeneous fleet with one train losing part of braking deceleration. The performance of the MPC based approach was compared with other two control strategies, basic adaptive cruise control (ACC) and directly maximum braking control (DBC), and the simulation results show that MPC strategy has the best performance among these three strategies in reducing the total relative kinetic energy of virtually-coupled train formation, the DBC control strategy is the second, and the basic ACC control strategy needs to be improved. The proposed MPC based control strategy has the potential to avoid the collision among virtually-coupled train formation especially when the trains have different deceleration abilities.
{"title":"A Coordinated Collision Mitigation Approach for Virtual Coupling Trains by Using Model Predictive Control*","authors":"Mingliang Chen, J. Xun, Yafei Liu","doi":"10.1109/ITSC45102.2020.9294633","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294633","url":null,"abstract":"Virtual Coupling has attracted significant attention from both industry and academia, which could increase the flexibility and capacity of rail transport. At the same time, the risk of trains collision is greatly increased especially when leader train implements emergency braking. This study proposes a coordinated collision mitigation approach for Virtual Coupling trains by using model predictive control (MPC). In the proposed approach, the problem is modeled with the objective of minimizing the total relative kinetic energy for a virtuallycoupled train formation. The typical scenarios are considered in this paper: 1. Emergency braking for homogeneous fleet; 2. Emergency braking for heterogeneous fleet; 3. Emergency braking for homogeneous fleet with one train losing part of braking deceleration. The performance of the MPC based approach was compared with other two control strategies, basic adaptive cruise control (ACC) and directly maximum braking control (DBC), and the simulation results show that MPC strategy has the best performance among these three strategies in reducing the total relative kinetic energy of virtually-coupled train formation, the DBC control strategy is the second, and the basic ACC control strategy needs to be improved. The proposed MPC based control strategy has the potential to avoid the collision among virtually-coupled train formation especially when the trains have different deceleration abilities.","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":"114813565","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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