Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294452
Tim Puphal, Benedict Flade, Daan de Geus, J. Eggert
We consider the problem of intelligently navigating through complex traffic. Urban situations are defined by the underlying map structure and special regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles (cars, bicycles, etc.) move forward, while trying to keep accident risks low.Especially at intersections, the combination and interaction of traffic elements is diverse and human drivers need to focus on specific elements which are critical for their behavior. To support the analysis, we present in this paper the so-called Risk Navigation System (RNS). RNS leverages a graph-based local dynamic map with Time-To-X indicators for extracting upcoming sharp curves, intersection zones and possible vehicle-to-object collision points.In real car recordings, recommended velocity profiles to avoid risks are visualized within a 2D environment. By focusing on communicating not only the positional but also the temporal relation, RNS potentially helps to enhance awareness and prediction capabilities of the user.
{"title":"Proactive Risk Navigation System for Real-World Urban Intersections","authors":"Tim Puphal, Benedict Flade, Daan de Geus, J. Eggert","doi":"10.1109/ITSC45102.2020.9294452","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294452","url":null,"abstract":"We consider the problem of intelligently navigating through complex traffic. Urban situations are defined by the underlying map structure and special regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles (cars, bicycles, etc.) move forward, while trying to keep accident risks low.Especially at intersections, the combination and interaction of traffic elements is diverse and human drivers need to focus on specific elements which are critical for their behavior. To support the analysis, we present in this paper the so-called Risk Navigation System (RNS). RNS leverages a graph-based local dynamic map with Time-To-X indicators for extracting upcoming sharp curves, intersection zones and possible vehicle-to-object collision points.In real car recordings, recommended velocity profiles to avoid risks are visualized within a 2D environment. By focusing on communicating not only the positional but also the temporal relation, RNS potentially helps to enhance awareness and prediction capabilities of the user.","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":"130063129","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/ITSC45102.2020.9294276
Sokratis Mamarikas, Nikolaos E. Aletras, S. Doulgeris, Z. Samaras, L. Ntziachristos
New electrified powertrains are increasingly entering the vehicular fleet and therefore their energy response to traffic management measures that have been designed for conventional vehicles is under consideration. The present paper examines the effect of traffic signals’ adjustment on the energy consumption and CO2 emissions of various types of modern powertrains, such as hybrids and electric vehicles, estimating the effect of signal settings on vehicular energy consumption. This examination follows a modeling approach, where vehicular speed profiles for various signal setting scenarios were evaluated in energy terms, with the use of detailed instantaneous powertrain models of hybrid and electric vehicles. The evaluation reveals the formed trends on the energy performance of modern vehicles when an adjustment of traffic signal settings is applied to traffic. The recognition of these trends is essential as traffic streams will be increasingly penetrated by new electrified powertrains.
{"title":"Energy behavior analysis of electric and hybrid vehicles over traffic signals’ adjustment scenarios","authors":"Sokratis Mamarikas, Nikolaos E. Aletras, S. Doulgeris, Z. Samaras, L. Ntziachristos","doi":"10.1109/ITSC45102.2020.9294276","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294276","url":null,"abstract":"New electrified powertrains are increasingly entering the vehicular fleet and therefore their energy response to traffic management measures that have been designed for conventional vehicles is under consideration. The present paper examines the effect of traffic signals’ adjustment on the energy consumption and CO2 emissions of various types of modern powertrains, such as hybrids and electric vehicles, estimating the effect of signal settings on vehicular energy consumption. This examination follows a modeling approach, where vehicular speed profiles for various signal setting scenarios were evaluated in energy terms, with the use of detailed instantaneous powertrain models of hybrid and electric vehicles. The evaluation reveals the formed trends on the energy performance of modern vehicles when an adjustment of traffic signal settings is applied to traffic. The recognition of these trends is essential as traffic streams will be increasingly penetrated by new electrified powertrains.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"124 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":"130165713","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}
Driving confidence psychology can guide drivers in calm driving operation when dealing with traffic issues, which is of substantial significance for reducing the accident rate and improving the road traffic efficiency. This study mainly analyzes the differences in driving confidence psychology in the face of an emergency braking event of a front vehicle with warning as opposed to the same situation without warning information. First, an emergency braking event of a front vehicle in a connected vehicle environment was designed based on driving simulation technology, which can provide warning information from the emergency-braking vehicle by using an onboard human-machine interface (HMI). Second, the features of lateral lane position changing and the average angle of the gas pedal were used to analyze the differences in driving confidence with versus without warning information. Finally, the entropy weight method was used to obtain the driving confidence degree of each driver in both scenarios. The results demonstrate that the driving confidence level is higher when warning information is provided, and the average driving confidence degree is 2.11% higher than the average driving confidence degree without warning information.
{"title":"Driving Confidence in a Connected Vehicle Environment: A Case Study of Emergency Braking Events of Front Vehicles","authors":"Haijian Li, Guoqiang Zhao, Jianyu Qi, Yang Bian, Hanimaiti Aizeke, Jian-cheng Weng","doi":"10.1109/ITSC45102.2020.9294339","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294339","url":null,"abstract":"Driving confidence psychology can guide drivers in calm driving operation when dealing with traffic issues, which is of substantial significance for reducing the accident rate and improving the road traffic efficiency. This study mainly analyzes the differences in driving confidence psychology in the face of an emergency braking event of a front vehicle with warning as opposed to the same situation without warning information. First, an emergency braking event of a front vehicle in a connected vehicle environment was designed based on driving simulation technology, which can provide warning information from the emergency-braking vehicle by using an onboard human-machine interface (HMI). Second, the features of lateral lane position changing and the average angle of the gas pedal were used to analyze the differences in driving confidence with versus without warning information. Finally, the entropy weight method was used to obtain the driving confidence degree of each driver in both scenarios. The results demonstrate that the driving confidence level is higher when warning information is provided, and the average driving confidence degree is 2.11% higher than the average driving confidence degree without warning information.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"167 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":"125661448","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/ITSC45102.2020.9294614
R. Doorley, Luis Alonso, A. Grignard, Núria Macià, K. Larson
Transportation models allow for prediction of travel demands and design of interventions to improve the network performance. An essential component of such models is the origin-destination matrix, which is traditionally generated using roadside and/or household surveys. These surveys are expensive, time consuming and do not capture temporal variation in travel demand. Anonymised location data from cell phones present an alternative source of mobility information which is passively collected, widely available and naturally captures temporal trends. However, these data contain other biases which must be corrected for using more reliable data. In this study, data from the Radio Network Controller of the Andorran telecom company is combined with limited traffic count data in order to develop a calibrated urban transportation model. An initial trip matrix is generated from the telecom data and a parameterized correction model is used to modify the trip matrix before predicting traffic. The parameters of the correction model are optimized by solving a Mathematical Program with Equilibrium Constraints. Outof-sample predictions from the calibrated model are shown to agree well with actual traffic volumes. This approach can reduce or eliminate the need for travel surveys while improving understanding of travel demands and traffic.
{"title":"Travel Demand and Traffic Prediction with Cell Phone Data: Calibration by Mathematical Program with Equilibrium Constraints","authors":"R. Doorley, Luis Alonso, A. Grignard, Núria Macià, K. Larson","doi":"10.1109/ITSC45102.2020.9294614","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294614","url":null,"abstract":"Transportation models allow for prediction of travel demands and design of interventions to improve the network performance. An essential component of such models is the origin-destination matrix, which is traditionally generated using roadside and/or household surveys. These surveys are expensive, time consuming and do not capture temporal variation in travel demand. Anonymised location data from cell phones present an alternative source of mobility information which is passively collected, widely available and naturally captures temporal trends. However, these data contain other biases which must be corrected for using more reliable data. In this study, data from the Radio Network Controller of the Andorran telecom company is combined with limited traffic count data in order to develop a calibrated urban transportation model. An initial trip matrix is generated from the telecom data and a parameterized correction model is used to modify the trip matrix before predicting traffic. The parameters of the correction model are optimized by solving a Mathematical Program with Equilibrium Constraints. Outof-sample predictions from the calibrated model are shown to agree well with actual traffic volumes. This approach can reduce or eliminate the need for travel surveys while improving understanding of travel demands and traffic.","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":"130571501","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/ITSC45102.2020.9294436
Junnan Zhang, Mingda Zhu, Lei Peng
Parking data is vulnerably affected by spatiotemporal characteristics and surrounding societal events, causing the latent features of the parking data are hard to learned by GANs if solely given the time-series parking data. Hence it is impossible to generate the desired data with high quality. In this paper, we propose a multi-conditional GAN, named MCGAN to refine the generating process and optimize the generating quality via introducing external customized extendable conditions related to the parking data samples. These conditions, in forms of condition tensors in MCGAN, can help the network learn the features introduced by each defined condition and will reproduce, even combine them in the later generating process, achieving the better result. The experiments show the working process of MCGAN is not different very much from GANs, but the generating quality get improved greatly if given the output expectation more specifically.
{"title":"Customized Parking Data Generation based on Multi-conditional GAN","authors":"Junnan Zhang, Mingda Zhu, Lei Peng","doi":"10.1109/ITSC45102.2020.9294436","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294436","url":null,"abstract":"Parking data is vulnerably affected by spatiotemporal characteristics and surrounding societal events, causing the latent features of the parking data are hard to learned by GANs if solely given the time-series parking data. Hence it is impossible to generate the desired data with high quality. In this paper, we propose a multi-conditional GAN, named MCGAN to refine the generating process and optimize the generating quality via introducing external customized extendable conditions related to the parking data samples. These conditions, in forms of condition tensors in MCGAN, can help the network learn the features introduced by each defined condition and will reproduce, even combine them in the later generating process, achieving the better result. The experiments show the working process of MCGAN is not different very much from GANs, but the generating quality get improved greatly if given the output expectation more specifically.","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":"132500612","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/ITSC45102.2020.9294248
Greta Koelln, M. Klicker, S. Schmidt
Safety is a decisive factor during the development of automotive systems. Modern vehicles are becoming more software-intensive, electronic components are increasingly replacing mechanical units. This is accompanied by a further increase in the complexity of the systems. Mobility concepts could be subject to fundamental changes in the future. There is a broad consensus among safety and security experts that traditional methods alone can no longer guarantee adequate safeguarding of software-intensive systems. Faced with the problems that the fundamental changes in today’s designed systems require a need for new hazard analyses, Leveson developed the System Theoretic Process Analysis (STPA) in 2004. This paper shows how the STPA analysis can be used as a valuable tool to identify potential hazards. In this paper partial results of STPA for a vehicle SAE2 level five are presented and compared with the results of STPA, carried out for a vehicle SAE level four by the same authors. This paper has not yet been published but a draft version is available.2SAE as a definition of the automation levels as defined in the SAE J3016 standard.
{"title":"Comparison of the Results of the System Theoretic Process Analysis for a Vehicle SAE Level four and five","authors":"Greta Koelln, M. Klicker, S. Schmidt","doi":"10.1109/ITSC45102.2020.9294248","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294248","url":null,"abstract":"Safety is a decisive factor during the development of automotive systems. Modern vehicles are becoming more software-intensive, electronic components are increasingly replacing mechanical units. This is accompanied by a further increase in the complexity of the systems. Mobility concepts could be subject to fundamental changes in the future. There is a broad consensus among safety and security experts that traditional methods alone can no longer guarantee adequate safeguarding of software-intensive systems. Faced with the problems that the fundamental changes in today’s designed systems require a need for new hazard analyses, Leveson developed the System Theoretic Process Analysis (STPA) in 2004. This paper shows how the STPA analysis can be used as a valuable tool to identify potential hazards. In this paper partial results of STPA for a vehicle SAE2 level five are presented and compared with the results of STPA, carried out for a vehicle SAE level four by the same authors. This paper has not yet been published but a draft version is available.2SAE as a definition of the automation levels as defined in the SAE J3016 standard.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"18 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":"132030890","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/ITSC45102.2020.9294480
Linrunjia Liu, C. Cappelle, Y. Ruichek
Place recognition refers to the problem of finding the position of a query image based on a series of images acquired at different places. Yet the day and night place recognition problem is hard to solve due to the illumination and appearance changes. Image-to-image translation methods have been introduced to solve the place recognition problem by synthesizing daytime images from the night ones. However, these methods cannot achieve good translation performance with low-quality night-time images. In this paper, a new method is introduced to improve the quality of night-time restored images by combining image enhancement and image inpainting methods. Three kinds of enhanced night-time images are generated based on the proposed method.Our place recognition system includes a model of GoogleNet to generate deep features of input images and nearest neighbor searching for the image retrieval process. The approach is tested on the Oxford RobotCar dataset, where three low-quality night sequences are selected as query sequences, and a day sequence is selected as a reference sequence. The results obtained with the approach based on the three proposed enhanced night-time images are better than those obtained with the raw night-time images. The results of our proposed place recognition system are also compared with two state-of-art place recognition methods: ToDayGAN and densevlad.
{"title":"Day and Night Place Recognition Based on Low-quality Night-time Images","authors":"Linrunjia Liu, C. Cappelle, Y. Ruichek","doi":"10.1109/ITSC45102.2020.9294480","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294480","url":null,"abstract":"Place recognition refers to the problem of finding the position of a query image based on a series of images acquired at different places. Yet the day and night place recognition problem is hard to solve due to the illumination and appearance changes. Image-to-image translation methods have been introduced to solve the place recognition problem by synthesizing daytime images from the night ones. However, these methods cannot achieve good translation performance with low-quality night-time images. In this paper, a new method is introduced to improve the quality of night-time restored images by combining image enhancement and image inpainting methods. Three kinds of enhanced night-time images are generated based on the proposed method.Our place recognition system includes a model of GoogleNet to generate deep features of input images and nearest neighbor searching for the image retrieval process. The approach is tested on the Oxford RobotCar dataset, where three low-quality night sequences are selected as query sequences, and a day sequence is selected as a reference sequence. The results obtained with the approach based on the three proposed enhanced night-time images are better than those obtained with the raw night-time images. The results of our proposed place recognition system are also compared with two state-of-art place recognition methods: ToDayGAN and densevlad.","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":"130992635","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/ITSC45102.2020.9294315
Maytheewat Aramrattana, Tony Larsson, Cristofer Englund, J. Jansson, A. Nåbo
Cut-in situations occurs when a vehicle intentionally changes lane and ends up in front of another vehicle or in-between two vehicles. In such situations, having a method to indicate the collision risk prior to making the cut-in maneuver could potentially reduce the number of sideswipe and rear end collisions caused by the cut-in maneuvers. This paper propose a new risk indicator, namely cut-in risk indicator (CRI), as a way to indicate and potentially foresee collision risks in cut-in situations. As an example use case, we applied CRI on data from a driving simulation experiment involving a manually driven vehicle and an automated platoon in a highway merging situation. We then compared the results with time-to-collision (TTC), and suggest that CRI could correctly indicate collision risks in a more effective way. CRI can be computed on all vehicles involved in the cut-in situations, not only for the vehicle that is cutting in. Making it possible for other vehicles to estimate the collision risk, for example if a cut-in from another vehicle occurs, the surrounding vehicles could be warned and have the possibility to react in order to potentially avoid or mitigate accidents.
{"title":"A Novel Risk Indicator for Cut-In Situations","authors":"Maytheewat Aramrattana, Tony Larsson, Cristofer Englund, J. Jansson, A. Nåbo","doi":"10.1109/ITSC45102.2020.9294315","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294315","url":null,"abstract":"Cut-in situations occurs when a vehicle intentionally changes lane and ends up in front of another vehicle or in-between two vehicles. In such situations, having a method to indicate the collision risk prior to making the cut-in maneuver could potentially reduce the number of sideswipe and rear end collisions caused by the cut-in maneuvers. This paper propose a new risk indicator, namely cut-in risk indicator (CRI), as a way to indicate and potentially foresee collision risks in cut-in situations. As an example use case, we applied CRI on data from a driving simulation experiment involving a manually driven vehicle and an automated platoon in a highway merging situation. We then compared the results with time-to-collision (TTC), and suggest that CRI could correctly indicate collision risks in a more effective way. CRI can be computed on all vehicles involved in the cut-in situations, not only for the vehicle that is cutting in. Making it possible for other vehicles to estimate the collision risk, for example if a cut-in from another vehicle occurs, the surrounding vehicles could be warned and have the possibility to react in order to potentially avoid or mitigate accidents.","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":"130275084","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/ITSC45102.2020.9294697
Philippe Weingertner, Minnie Ho, A. Timofeev, S. Aubert, G. Gil
Motion planning for an autonomous vehicle is most challenging for scenarios such as large, multi-lane, and unsignalized intersections in the presence of dense traffic. In such situations, the motion planner has to deal with multiple crossing-points to reach an objective in a safe, comfortable, and efficient way. In addition, motion planning challenges include real-time computation and scalability to complex scenes with many objects and different road geometries. In this work, we propose a motion planning system addressing these challenges. We enable real-time applicability of a Monte Carlo Tree Search algorithm with a deep-learning heuristic. We learn a fast evaluation function from accurate, but non real-time models. While using Deep Reinforcement Learning techniques we maintain a clear separation between making predictions and making decisions. We reduce the complexity of the search model and benchmark the proposed agent against multiple methods: rules-based, MCTS, $A^{*}$ search, deep learning, and Model Predictive Control. We show that our agent outperforms these other agents in a variety of challenging scenarios, where we benchmark safety, comfort and efficiency metrics.
{"title":"Monte Carlo Tree Search With Reinforcement Learning for Motion Planning","authors":"Philippe Weingertner, Minnie Ho, A. Timofeev, S. Aubert, G. Gil","doi":"10.1109/ITSC45102.2020.9294697","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294697","url":null,"abstract":"Motion planning for an autonomous vehicle is most challenging for scenarios such as large, multi-lane, and unsignalized intersections in the presence of dense traffic. In such situations, the motion planner has to deal with multiple crossing-points to reach an objective in a safe, comfortable, and efficient way. In addition, motion planning challenges include real-time computation and scalability to complex scenes with many objects and different road geometries. In this work, we propose a motion planning system addressing these challenges. We enable real-time applicability of a Monte Carlo Tree Search algorithm with a deep-learning heuristic. We learn a fast evaluation function from accurate, but non real-time models. While using Deep Reinforcement Learning techniques we maintain a clear separation between making predictions and making decisions. We reduce the complexity of the search model and benchmark the proposed agent against multiple methods: rules-based, MCTS, $A^{*}$ search, deep learning, and Model Predictive Control. We show that our agent outperforms these other agents in a variety of challenging scenarios, where we benchmark safety, comfort and efficiency metrics.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"8 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":"127911883","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/ITSC45102.2020.9294408
Yougang Bian, Xiaohui Qin, Changkun Du, Biao Xu, Zeyu Yang, Manjiang Hu
Platoon control of connected vehicles (CVs) can greatly improve fuel efficiency and traffic throughput. This paper proposes a unified hierarchical framework for platoon control of CVs with two different types of control modes, i.e., desired acceleration control and desired velocity control. By separating neighboring information interaction from local dynamics control, the framework divides the task of distributed platoon control into two layers, i.e., an upper-level observing layer and a lower-level tracking control layer, to address vehicle dynamics heterogeneity. Within the proposed framework, an observer is designed for following vehicles to observe the leading vehicle’s states through vehicle-to-vehicle communication, while a tracking controller is designed to track the leading vehicle using local observation information. A necessary and sufficient condition is further derived to guarantee asymptotic stability of the platoon control system. Numerical simulation results demonstrate the effectiveness of the proposed hierarchical platoon controller.
{"title":"A Unified Hierarchical Framework for Platoon Control of Connected Vehicles with Heterogeneous Control Modes","authors":"Yougang Bian, Xiaohui Qin, Changkun Du, Biao Xu, Zeyu Yang, Manjiang Hu","doi":"10.1109/ITSC45102.2020.9294408","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294408","url":null,"abstract":"Platoon control of connected vehicles (CVs) can greatly improve fuel efficiency and traffic throughput. This paper proposes a unified hierarchical framework for platoon control of CVs with two different types of control modes, i.e., desired acceleration control and desired velocity control. By separating neighboring information interaction from local dynamics control, the framework divides the task of distributed platoon control into two layers, i.e., an upper-level observing layer and a lower-level tracking control layer, to address vehicle dynamics heterogeneity. Within the proposed framework, an observer is designed for following vehicles to observe the leading vehicle’s states through vehicle-to-vehicle communication, while a tracking controller is designed to track the leading vehicle using local observation information. A necessary and sufficient condition is further derived to guarantee asymptotic stability of the platoon control system. Numerical simulation results demonstrate the effectiveness of the proposed hierarchical platoon controller.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"2 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":"129031997","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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