Pub Date : 2015-08-27DOI: 10.1109/IVS.2015.7225678
Kuo-Yun Liang, Q. Deng, J. Mårtensson, Xiaoliang Ma, K. Johansson
Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20 % compared to the ideal case with no traffic.
{"title":"The influence of traffic on heavy-duty vehicle platoon formation","authors":"Kuo-Yun Liang, Q. Deng, J. Mårtensson, Xiaoliang Ma, K. Johansson","doi":"10.1109/IVS.2015.7225678","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225678","url":null,"abstract":"Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20 % compared to the ideal case with no traffic.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132160829","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225767
Zhiwei Song, Weiwei Huang, Ning Wu, Xiaojun Wu, Chern Yuen Anthony Wong, V. B. Saputra, Benjamin Chia Hon Quan, Chen Jian Simon, Qun Zhang, Susu Yao, Boon Siew Han
This paper proposes a map free lane following solution based on low-cost 2D laser scanners for Autonomous Service Vehicle to fill the gap between future driverless car and the lane keeping assistant. The applications of autonomous service vehicle include feeder bus in a local residential area, shuttle bus in a park or playground, sprinkler car, sweeper car, and transporter in airport or container terminal. As autonomous service vehicle is running only in a limited area and its speed is slow compared to normal vehicles, we can further simplify the problem regardless of the issues of road infrastructure detection/communication and V2I maps which prevent the popularization of driverless car, and to propose a unique map free solution. The features of our approach include: 1) an innovative configuration for two 2D laser scanners to detect the lane with sharp curve; 2) a fast and accurate lane detection algorithm based on 2D laser's raw date directly; 3) a reliable and smooth path planning based on local lane fitting and prediction; and 4) a self-built unique drive-by-wire system for electronic car. We successfully tested our vehicle with autonomous driving in the testing field. The experiments show that the vehicle's trajectory matched the planned path accurately.
{"title":"Map free lane following based on low-cost laser scanner for near future autonomous service vehicle","authors":"Zhiwei Song, Weiwei Huang, Ning Wu, Xiaojun Wu, Chern Yuen Anthony Wong, V. B. Saputra, Benjamin Chia Hon Quan, Chen Jian Simon, Qun Zhang, Susu Yao, Boon Siew Han","doi":"10.1109/IVS.2015.7225767","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225767","url":null,"abstract":"This paper proposes a map free lane following solution based on low-cost 2D laser scanners for Autonomous Service Vehicle to fill the gap between future driverless car and the lane keeping assistant. The applications of autonomous service vehicle include feeder bus in a local residential area, shuttle bus in a park or playground, sprinkler car, sweeper car, and transporter in airport or container terminal. As autonomous service vehicle is running only in a limited area and its speed is slow compared to normal vehicles, we can further simplify the problem regardless of the issues of road infrastructure detection/communication and V2I maps which prevent the popularization of driverless car, and to propose a unique map free solution. The features of our approach include: 1) an innovative configuration for two 2D laser scanners to detect the lane with sharp curve; 2) a fast and accurate lane detection algorithm based on 2D laser's raw date directly; 3) a reliable and smooth path planning based on local lane fitting and prediction; and 4) a self-built unique drive-by-wire system for electronic car. We successfully tested our vehicle with autonomous driving in the testing field. The experiments show that the vehicle's trajectory matched the planned path accurately.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114263720","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225671
Jun Liu, T. Shi, Ping Li, X. Ren, Hongbin Ma
By the cooperative and competitive PSO algorithm, the goal of this study is to provide the cooperative trajectories of multiple UAVs in the three dimensional space. To effectively reduce the dimension of this problem, the optimization process is mainly divided into two substages to reduce the difficulty of selecting the weights of objectives and constraints in the considered objective function. Considering several objectives and constraints, the cooperative trajectories in the first substage are given by the cooperative and competitive PSO algorithm in the two dimensional space. In the second substage, the altitude of cooperative trajectories is adjusted according to the considered objectives and constraints. In the complicated scenarios, simulation results demonstrate the effectiveness and the robustness of the cooperative and competitive PSO algorithm, which possibly provides one guideline for optimal cooperative planning trajectories of multiple UAVs in the three-dimensional space.
{"title":"Trajectories planning for multiple UAVs by the cooperative and competitive PSO algorithm","authors":"Jun Liu, T. Shi, Ping Li, X. Ren, Hongbin Ma","doi":"10.1109/IVS.2015.7225671","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225671","url":null,"abstract":"By the cooperative and competitive PSO algorithm, the goal of this study is to provide the cooperative trajectories of multiple UAVs in the three dimensional space. To effectively reduce the dimension of this problem, the optimization process is mainly divided into two substages to reduce the difficulty of selecting the weights of objectives and constraints in the considered objective function. Considering several objectives and constraints, the cooperative trajectories in the first substage are given by the cooperative and competitive PSO algorithm in the two dimensional space. In the second substage, the altitude of cooperative trajectories is adjusted according to the considered objectives and constraints. In the complicated scenarios, simulation results demonstrate the effectiveness and the robustness of the cooperative and competitive PSO algorithm, which possibly provides one guideline for optimal cooperative planning trajectories of multiple UAVs in the three-dimensional space.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125161583","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225873
Dong Woon Ryu, Hyeon Bin Jeong, Sang Hun Lee, Woon-Sung Lee, J. H. Yang
In this study, we develop and evaluate an estimation algorithm of abnormal driving states (drowsiness, distraction, and workload) based on a Hybrid Bayesian Network (HBN) using multimodal information. The HBN algorithm is expected to increase transportation safety by combining merits of both the Bayesian Network and clustering algorithm. In addition, multimodal data efficacy analysis through human-in-the-loop experiments is used to enhance the performance of the driver-state estimation algorithm. Performance results obtained the lowest false alarm rate and fastest calculation speed. The false alarm rate decreased from 18.2 to 15.5%, whereas the calculation speed decreased by 4.35%.
{"title":"Development of driver-state estimation algorithm based on Hybrid Bayesian Network","authors":"Dong Woon Ryu, Hyeon Bin Jeong, Sang Hun Lee, Woon-Sung Lee, J. H. Yang","doi":"10.1109/IVS.2015.7225873","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225873","url":null,"abstract":"In this study, we develop and evaluate an estimation algorithm of abnormal driving states (drowsiness, distraction, and workload) based on a Hybrid Bayesian Network (HBN) using multimodal information. The HBN algorithm is expected to increase transportation safety by combining merits of both the Bayesian Network and clustering algorithm. In addition, multimodal data efficacy analysis through human-in-the-loop experiments is used to enhance the performance of the driver-state estimation algorithm. Performance results obtained the lowest false alarm rate and fastest calculation speed. The false alarm rate decreased from 18.2 to 15.5%, whereas the calculation speed decreased by 4.35%.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125240844","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225817
Ning Wu, Weiwei Huang, Zhiwei Song, Xiaojun Wu, Qun Zhang, Susu Yao
For autonomous navigation system of intelligent vehicle, robust and stable control with accurate tracking ability is one of the key requirements. In this paper, we present a systematic controller design approach for autonomous vehicle navigation system. The proposed controller integrates dynamic vehicle model and online updated path model by quadratic programming (QP) cost function, which considers both tracking error and stability. A novel path planner based on the differential dynamic programming (DDP) with consideration of the kinematic feasibility is used. The path tracking accuracy has been improved by utilizing the proposed dynamic preview controller. Promising experimental results showed that the overall navigation system is robust and stable.
{"title":"Adaptive dynamic preview control for autonomous vehicle trajectory following with DDP based path planner","authors":"Ning Wu, Weiwei Huang, Zhiwei Song, Xiaojun Wu, Qun Zhang, Susu Yao","doi":"10.1109/IVS.2015.7225817","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225817","url":null,"abstract":"For autonomous navigation system of intelligent vehicle, robust and stable control with accurate tracking ability is one of the key requirements. In this paper, we present a systematic controller design approach for autonomous vehicle navigation system. The proposed controller integrates dynamic vehicle model and online updated path model by quadratic programming (QP) cost function, which considers both tracking error and stability. A novel path planner based on the differential dynamic programming (DDP) with consideration of the kinematic feasibility is used. The path tracking accuracy has been improved by utilizing the proposed dynamic preview controller. Promising experimental results showed that the overall navigation system is robust and stable.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939764","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225717
Yongbon Koo, Jinwoo Kim, Wooyong Han
Many researchers have reported that a decline in driving concentration caused by drowsiness or inattentiveness is one of the primary sources of serious car accidents. One of the most well-known methods to measure a driver's concentration is called driver state monitoring, where the driver is warned when he or she is falling asleep based on visual information of the face. On the other hand, autonomous driving systems have garnered attention in recent years as an alternative plan to reduce human-caused accidents. This system shows the possibility of realizing a vehicle with no steering wheel or pedals. However, lack of technical maturity, human acceptance problems, and individual desire to drive highlight the demand to keep human drivers in the loop. For these reasons, it is necessary to decide who will be responsible for driving the vehicle and adjusting the vehicle control system. This is known as the driving control authority. In this paper, we present a system that can suggest transitions in various driving control authority modes by sensing a decline of the human driver's performance caused by drowsiness or inattentiveness. In more detail, we identify the problems of the legacy driving control authority transition made only with vision-based driver state recognition. To address the shortcomings of this method, we propose a new recommendation method that combines the vision-based driver state recognition results and path suggestion of an autonomous system. Experiment results of simulated drowsy and inattentive drivers on an actual autonomous vehicle prototype show that our method has better transition accuracy with fewer false-positive errors compared with the legacy transition method that only uses vision-based driver state recognition.
{"title":"A method for driving control authority transition for cooperative autonomous vehicle","authors":"Yongbon Koo, Jinwoo Kim, Wooyong Han","doi":"10.1109/IVS.2015.7225717","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225717","url":null,"abstract":"Many researchers have reported that a decline in driving concentration caused by drowsiness or inattentiveness is one of the primary sources of serious car accidents. One of the most well-known methods to measure a driver's concentration is called driver state monitoring, where the driver is warned when he or she is falling asleep based on visual information of the face. On the other hand, autonomous driving systems have garnered attention in recent years as an alternative plan to reduce human-caused accidents. This system shows the possibility of realizing a vehicle with no steering wheel or pedals. However, lack of technical maturity, human acceptance problems, and individual desire to drive highlight the demand to keep human drivers in the loop. For these reasons, it is necessary to decide who will be responsible for driving the vehicle and adjusting the vehicle control system. This is known as the driving control authority. In this paper, we present a system that can suggest transitions in various driving control authority modes by sensing a decline of the human driver's performance caused by drowsiness or inattentiveness. In more detail, we identify the problems of the legacy driving control authority transition made only with vision-based driver state recognition. To address the shortcomings of this method, we propose a new recommendation method that combines the vision-based driver state recognition results and path suggestion of an autonomous system. Experiment results of simulated drowsy and inattentive drivers on an actual autonomous vehicle prototype show that our method has better transition accuracy with fewer false-positive errors compared with the legacy transition method that only uses vision-based driver state recognition.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127716452","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225656
G. Markkula, Johan Eklov, L. Laine, Erik Wikenhed, Niklas Frojd
An experiment was carried out on a low friction test track, where seven truck drivers repeatedly performed collision avoidance and stabilization with a 4×2 tractor. A previous finding from a simulator study was confirmed: In severe yaw instability, drivers engaged in a yaw rate nulling type of steering behavior, in conflict with the assumptions of conventional electronic stability control (ESC), and the experiment provided indications of conventional ESC behaving suboptimally in these situations. Promising results were obtained for modified versions of the ESC, based on the yaw rate nulling model of steering, but further development work is needed.
{"title":"Improving yaw stability control in severe instabilities by means of a validated model of driver steering","authors":"G. Markkula, Johan Eklov, L. Laine, Erik Wikenhed, Niklas Frojd","doi":"10.1109/IVS.2015.7225656","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225656","url":null,"abstract":"An experiment was carried out on a low friction test track, where seven truck drivers repeatedly performed collision avoidance and stabilization with a 4×2 tractor. A previous finding from a simulator study was confirmed: In severe yaw instability, drivers engaged in a yaw rate nulling type of steering behavior, in conflict with the assumptions of conventional electronic stability control (ESC), and the experiment provided indications of conventional ESC behaving suboptimally in these situations. Promising results were obtained for modified versions of the ESC, based on the yaw rate nulling model of steering, but further development work is needed.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126704855","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225780
A. Cabani, R. Khemmar, J. Ertaud, J. Mouzna
The aim of this work is to design and build by 2015 an electric four-seater equipped with an autonomous extension device. The project was born from two observations: in a context of necessary diversification of energy sources and the development of electric vehicles, the main problem remains the battery life and availability of charging stations. The issue of our work lies both in the optimization of energy consumption and improving the electric vehicle. Our team was tasked to develop and implement an Energy Management System of Electric Vehicle. The objective of the mission is to create a program that calculates the set speed to minimize the cost of energy consumption and maximize battery life. This calculation is done by taking into account prevention parameters are: vehicle speed, real-time parameters from Maps (elevations in the path, wind speed, etc.), the forces applied to the vehicle.
{"title":"Intelligent navigation system-based optimization of the energy consumption","authors":"A. Cabani, R. Khemmar, J. Ertaud, J. Mouzna","doi":"10.1109/IVS.2015.7225780","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225780","url":null,"abstract":"The aim of this work is to design and build by 2015 an electric four-seater equipped with an autonomous extension device. The project was born from two observations: in a context of necessary diversification of energy sources and the development of electric vehicles, the main problem remains the battery life and availability of charging stations. The issue of our work lies both in the optimization of energy consumption and improving the electric vehicle. Our team was tasked to develop and implement an Energy Management System of Electric Vehicle. The objective of the mission is to create a program that calculates the set speed to minimize the cost of energy consumption and maximize battery life. This calculation is done by taking into account prevention parameters are: vehicle speed, real-time parameters from Maps (elevations in the path, wind speed, etc.), the forces applied to the vehicle.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115254921","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225827
Dominik Nuss, Ting Yuan, Gunther Krehl, M. Stuebler, Stephan Reuter, K. Dietmayer
Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.
{"title":"Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter","authors":"Dominik Nuss, Ting Yuan, Gunther Krehl, M. Stuebler, Stephan Reuter, K. Dietmayer","doi":"10.1109/IVS.2015.7225827","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225827","url":null,"abstract":"Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"404 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115340990","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 : 2015-08-27DOI: 10.1109/IVS.2015.7225700
S. Li, Yang Zheng, Keqiang Li, Jianqiang Wang
The platooning of autonomous ground vehicles has potential to largely benefit the road traffic, including enhancing highway safety, improving traffic utility and reducing fuel consumption. The main goal of platoon control is to ensure all the vehicles in the same group to move at consensual speed while maintaining desired spaces between adjacent vehicles. This paper presents an overview of vehicular platoon control techniques from networked control perspective, which naturally decomposes a platoon into four interrelated components, i.e., 1) node dynamics (ND), 2) information flow topology (IFT), 3) distributed controller (DC) and, 4) geometry formation (GF). Under the four-component framework, existing literature are categorized and analyzed according to their technical features. Three main performance metrics, i.e. string stability, stability margin and coherence behavior, are also discussed.
{"title":"An overview of vehicular platoon control under the four-component framework","authors":"S. Li, Yang Zheng, Keqiang Li, Jianqiang Wang","doi":"10.1109/IVS.2015.7225700","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225700","url":null,"abstract":"The platooning of autonomous ground vehicles has potential to largely benefit the road traffic, including enhancing highway safety, improving traffic utility and reducing fuel consumption. The main goal of platoon control is to ensure all the vehicles in the same group to move at consensual speed while maintaining desired spaces between adjacent vehicles. This paper presents an overview of vehicular platoon control techniques from networked control perspective, which naturally decomposes a platoon into four interrelated components, i.e., 1) node dynamics (ND), 2) information flow topology (IFT), 3) distributed controller (DC) and, 4) geometry formation (GF). Under the four-component framework, existing literature are categorized and analyzed according to their technical features. Three main performance metrics, i.e. string stability, stability margin and coherence behavior, are also discussed.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114904085","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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