The Experts below are selected from a list of 132156 Experts worldwide ranked by ideXlab platform
Daniel Jian Sun - One of the best experts on this subject based on the ideXlab platform.
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lane Changing Behavior on urban streets an in vehicle field experiment based study
Computer-aided Civil and Infrastructure Engineering, 2012Co-Authors: Daniel Jian Sun, Lily ElefteriadouAbstract:Lane-Changing Behavior has received increas- ing attention during the recent years in traffic flow mod- eling. Researchers have developed various algorithms to model the maneuvers on both highways and ur- ban streets. However, the majority of these models was derived and validated using data such as vehicle trajecto- ries, without many considerations of driver characteris- tics. In this study, an instrumented vehicle-based experi- ment was carefully designed to observe the drivers' action under various urban lane-Changing scenarios. The per- sonal background data, and "in-vehicle" driver Behavior and trajectory data were obtained from the experiment, and used to classify 40 drivers into four general groups according to the lane-Changing maneuvers performed in an urban street environment. Additional comparisons and analysis were conducted to confirm the categoriza- tion results. The article concludes by providing recom- mendations related to the implementation of study find- ings into micro-simulators, such as using only four driver types in CORSIM instead of the existing 10 types, to bet- ter replicate driver Behavior in urban street networks.
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lane Changing Behavior on urban streets a focus group based study
Applied Ergonomics, 2011Co-Authors: Daniel Jian Sun, Lily ElefteriadouAbstract:As lane-Changing Behavior has received increasing attention during the recent years, various algorithms have been developed. However, most of these models were derived and validated using data such as vehicle trajectories, with no consideration of driver characteristics. In this research, focus group studies were conducted to obtain driver-related information so that the driver characteristics can be incorporated into lane-Changing models. Different urban lane-Changing scenarios were examined and discussed in the focus group meetings. The likelihood for initiating lane changes under each scenario was obtained. The participating drivers were categorized according to their background information and verbal responses, so that the lane-Changing Behavior can be related to driver characteristics for each group. Two types of information, quantitative and qualitative responses from participants, were used to establish this relationship. The paper concludes by providing recommendations related to the implementation of study findings into micro-simulators to better replicate driver Behavior in urban street networks.
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modeling vehicle interactions during lane Changing Behavior on arterial streets
Computer-aided Civil and Infrastructure Engineering, 2010Co-Authors: Daniel Jian Sun, Alexandra KondyliAbstract:Lane-Changing algorithms have attracted increased attention during recent years in traffic modeling. However, little has been done to address the competition and cooperation of vehicles when Changing lanes on urban streets. The main goal of this study is to quantify the vehicle interactions during a lane-Changing maneuver. Video data collected at a busy arterial street in Gainesville, Florida, was used to distinguish between free, forced, and competitive/cooperative lane changes. Models particularly for competitive/cooperative lane changes were developed, depending on whether the following vehicle cooperates with the subject vehicle or not. By referring to the "TCP/IP" protocol in computer network communications, a sequence of 'hand-shaking' negotiations were designed to handle the competition and cooperation among vehicles. The developed model was implemented and validated in the CORSIM microsimulator package, with the simulation capabilities compared against the original lane-Changing model in CORSIM. The results indicate that the new model better replicates the observed traffic under different levels of congestion.
Lily Elefteriadou - One of the best experts on this subject based on the ideXlab platform.
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lane Changing Behavior on urban streets an in vehicle field experiment based study
Computer-aided Civil and Infrastructure Engineering, 2012Co-Authors: Daniel Jian Sun, Lily ElefteriadouAbstract:Lane-Changing Behavior has received increas- ing attention during the recent years in traffic flow mod- eling. Researchers have developed various algorithms to model the maneuvers on both highways and ur- ban streets. However, the majority of these models was derived and validated using data such as vehicle trajecto- ries, without many considerations of driver characteris- tics. In this study, an instrumented vehicle-based experi- ment was carefully designed to observe the drivers' action under various urban lane-Changing scenarios. The per- sonal background data, and "in-vehicle" driver Behavior and trajectory data were obtained from the experiment, and used to classify 40 drivers into four general groups according to the lane-Changing maneuvers performed in an urban street environment. Additional comparisons and analysis were conducted to confirm the categoriza- tion results. The article concludes by providing recom- mendations related to the implementation of study find- ings into micro-simulators, such as using only four driver types in CORSIM instead of the existing 10 types, to bet- ter replicate driver Behavior in urban street networks.
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lane Changing Behavior on urban streets a focus group based study
Applied Ergonomics, 2011Co-Authors: Daniel Jian Sun, Lily ElefteriadouAbstract:As lane-Changing Behavior has received increasing attention during the recent years, various algorithms have been developed. However, most of these models were derived and validated using data such as vehicle trajectories, with no consideration of driver characteristics. In this research, focus group studies were conducted to obtain driver-related information so that the driver characteristics can be incorporated into lane-Changing models. Different urban lane-Changing scenarios were examined and discussed in the focus group meetings. The likelihood for initiating lane changes under each scenario was obtained. The participating drivers were categorized according to their background information and verbal responses, so that the lane-Changing Behavior can be related to driver characteristics for each group. Two types of information, quantitative and qualitative responses from participants, were used to establish this relationship. The paper concludes by providing recommendations related to the implementation of study findings into micro-simulators to better replicate driver Behavior in urban street networks.
Jun Gao - One of the best experts on this subject based on the ideXlab platform.
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joint learning of video images and physiological signals for lane Changing Behavior prediction
Transportmetrica, 2021Co-Authors: Jun Gao, Yi Lu MurpheyAbstract:Understanding and predicting human driving Behavior play an important role in the development of intelligent vehicle systems, particularly for Advanced Driver Assistance System (ADAS) to estimate d...
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a data driven lane Changing Behavior detection system based on sequence learning
Transportmetrica B-Transport Dynamics, 2020Co-Authors: Yi Lu Murphey, Jun Gao, Honghui ZhuAbstract:Lane-Changing detection is one of the most challenging tasks in advanced driver assistance system (ADAS). However, modeling driver's lane-Changing process is challenging due to the complexity and u...
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Personalized detection of lane Changing Behavior using multisensor data fusion
Computing, 2019Co-Authors: Jun Gao, Honghui ZhuAbstract:Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. In this paper, a fusion approach is introduced that utilizes multiple differing modality data, such as video data, GPS data, wheel odometry data, potentially IMU data collected from data logging device (DL1 MK3) for detecting driver’s Behavior of lane Changing by using a novel dimensionality reduction model, collaborative representation optimized projection classifier (CROPC). The criterion of CROPC is maximizing the collaborative representation based between-class scatter and minimizing the collaborative representation based within-class scatter in the transformed space simultaneously. For lane change detection, both feature-level fusion and decision-level fusion are considered. In the feature-level fusion, features generated from multiple differing modality data are merged before classification while in the decision-level fusion, an improved Dempster–Shafer theory based on correlation coefficient, DST-CC is presented to combine the classification outcomes from two classifiers, each corresponding to one kind of the data. The results indicate that the introduced fusion approach using a CROPC performs significantly better in terms of detection accuracy, in comparison to other state-of-the-art classifiers.
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Multivariate time series prediction of lane Changing Behavior using deep neural network
Applied Intelligence, 2018Co-Authors: Jun Gao, Honghui ZhuAbstract:Many real world pattern classification problems involve the process and analysis of multiple variables in temporal domain. This type of problem is referred to as Multivariate Time Series (MTS) problem. It remains a challenging problem due to the nature of time series data: high dimensionality, large data size and updating continuously. In this paper, we use three types of physiological signals from the driver to predict lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver’s response to the driving environment. A novel Group-wise Convolutional Neural Network, MTS-GCNN model is proposed for MTS pattern classification. In our MTS-GCNN model, we present a new structure learning algorithm in training stage. The algorithm exploits the covariance structure over multiple time series to partition input volume into groups, then learns the MTS-GCNN structure explicitly by clustering input sequences with spectral clustering. Different from other feature-based classification approaches, our MTS-GCNN can select and extract the suitable internal structure to generate temporal and spatial features automatically by using convolution and down-sample operations. The experimental results showed that, in comparison to other state-of-the-art models, our MTS-GCNN performs significantly better in terms of prediction accuracy.
Hao Wang - One of the best experts on this subject based on the ideXlab platform.
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discretionary lane Changing Behavior empirical validation for one realistic rule based model
Transportmetrica, 2019Co-Authors: Victor L Knoop, Dawei Li, Lingyu Meng, Hao WangAbstract:In this paper, we discuss the mechanisms for discretionary lane-Changing Behavior in traffic flow. NGSIM video data are used to check the validity of different lane-Changing rules, and 373 lane changes at 4 locations in US-101 highway are analyzed. We find that the classical lane-Changing rules of rule-based model cannot explain many cases in the empirical dataset. Therefore, we propose one new decision rule, comparing the position after a time horizon of several seconds without a lane-change. This rule can be described as “to have a further position within 9 seconds”. The tests on NGSIM data show that this rule can explain most (76%) of the lane-Changing cases. Besides, some data when lane changes do not occur are also studied. We find that most (81%) of non-lane-Changing vehicles do not fulfill the new rule. Thus, it can be considered as one sufficient and necessary condition for discretionary lane-Changing.
Honghui Zhu - One of the best experts on this subject based on the ideXlab platform.
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a data driven lane Changing Behavior detection system based on sequence learning
Transportmetrica B-Transport Dynamics, 2020Co-Authors: Yi Lu Murphey, Jun Gao, Honghui ZhuAbstract:Lane-Changing detection is one of the most challenging tasks in advanced driver assistance system (ADAS). However, modeling driver's lane-Changing process is challenging due to the complexity and u...
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Personalized detection of lane Changing Behavior using multisensor data fusion
Computing, 2019Co-Authors: Jun Gao, Honghui ZhuAbstract:Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. In this paper, a fusion approach is introduced that utilizes multiple differing modality data, such as video data, GPS data, wheel odometry data, potentially IMU data collected from data logging device (DL1 MK3) for detecting driver’s Behavior of lane Changing by using a novel dimensionality reduction model, collaborative representation optimized projection classifier (CROPC). The criterion of CROPC is maximizing the collaborative representation based between-class scatter and minimizing the collaborative representation based within-class scatter in the transformed space simultaneously. For lane change detection, both feature-level fusion and decision-level fusion are considered. In the feature-level fusion, features generated from multiple differing modality data are merged before classification while in the decision-level fusion, an improved Dempster–Shafer theory based on correlation coefficient, DST-CC is presented to combine the classification outcomes from two classifiers, each corresponding to one kind of the data. The results indicate that the introduced fusion approach using a CROPC performs significantly better in terms of detection accuracy, in comparison to other state-of-the-art classifiers.
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Multivariate time series prediction of lane Changing Behavior using deep neural network
Applied Intelligence, 2018Co-Authors: Jun Gao, Honghui ZhuAbstract:Many real world pattern classification problems involve the process and analysis of multiple variables in temporal domain. This type of problem is referred to as Multivariate Time Series (MTS) problem. It remains a challenging problem due to the nature of time series data: high dimensionality, large data size and updating continuously. In this paper, we use three types of physiological signals from the driver to predict lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver’s response to the driving environment. A novel Group-wise Convolutional Neural Network, MTS-GCNN model is proposed for MTS pattern classification. In our MTS-GCNN model, we present a new structure learning algorithm in training stage. The algorithm exploits the covariance structure over multiple time series to partition input volume into groups, then learns the MTS-GCNN structure explicitly by clustering input sequences with spectral clustering. Different from other feature-based classification approaches, our MTS-GCNN can select and extract the suitable internal structure to generate temporal and spatial features automatically by using convolution and down-sample operations. The experimental results showed that, in comparison to other state-of-the-art models, our MTS-GCNN performs significantly better in terms of prediction accuracy.