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学者姓名：李振龙
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Abstract ：
In the evolving autonomous driving field, diverse car-following models and lack of regulations pose challenges. This study establishes human-like car-following rules for different driving styles and conditions across countries. Utilizing Chinese TJRD TS and German High D datasets, Bisecting K-means clustering and ROC curves identified distinct car-following rules. Research showed significant differences in speed and Distance Headway (DHW) among trajectories. Notably, even with the same driving style, speed and instability varied across countries. The study suggests specific Time Headway (THW) settings: in Germany, 1.45 s for conservative and 1.01 s for aggressive driving; in China, 2.25 s for conservative and 1.60 s for aggressive. This research provides insights for tailoring autonomous driving rules to regional characteristics, contributing to the field's development and enhancing understanding of autonomous driving from a human perspective.
Keyword ：
ROC curves ROC curves driving car-following rules driving car-following rules bisecting K-means clustering algorithm bisecting K-means clustering algorithm Autonomous vehicle Autonomous vehicle
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| GB/T 7714 | Li, Zhanghe , Li, Zhenlong , Zhao, Xiaohua et al. Development of human-like automated driving following rules: a comparison between China and Germany [J]. | TRANSPORTATION PLANNING AND TECHNOLOGY , 2024 , 48 (2) : 366-386 . |
| MLA | Li, Zhanghe et al. "Development of human-like automated driving following rules: a comparison between China and Germany" . | TRANSPORTATION PLANNING AND TECHNOLOGY 48 . 2 (2024) : 366-386 . |
| APA | Li, Zhanghe , Li, Zhenlong , Zhao, Xiaohua , Fu, Qiang , Ren, Wenhao . Development of human-like automated driving following rules: a comparison between China and Germany . | TRANSPORTATION PLANNING AND TECHNOLOGY , 2024 , 48 (2) , 366-386 . |
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Abstract ：
The driver's perception level and takeover performance are two major factors that result in accidents in autonomous vehicles. This study's goal is to analyze the change in drivers' perception level and its influence on takeover performance during autonomous driving. A takeover behavior test platform is implemented based on a high-fidelity driving simulator. The fog zone is selected as the takeover scenario. Thus, a 2 (takeover request time: 5 s, 10 s) by 2 (non-driving-related task: work task, entertainment task) takeover experiment was conducted. A generalized linear mixed model is developed to explore the influence of the perception level on takeover performance. The study finds out that, after the takeover request is triggered, the driver's gaze duration is shortened and the pupil area is enlarged, which is helpful for the driver to extract and understand the road information faster. Male drivers have greater perception levels than female drivers, and they prioritize leisure tasks more than professional ones. The drivers' perception level decreases when age increases. The shorter the gaze duration is, and the larger the pupil area is, the shorter the takeover response time will be. In addition, drivers' perception level has a positive effect on takeover performance. Finally, this study provides a reference for revealing the changing rules of drivers' perception level in autonomous driving, and the study can provide support for the diagnosis of takeover risks of autonomous vehicles from the perspective of human factors.
Keyword ：
perception level perception level takeover performance takeover performance autonomous vehicles autonomous vehicles driving simulator driving simulator generalized linear mixed model generalized linear mixed model
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| GB/T 7714 | Wang, Qiuhong , Chen, Haolin , Gong, Jianguo et al. Studying Driver's Perception Arousal and Takeover Performance in Autonomous Driving [J]. | SUSTAINABILITY , 2023 , 15 (1) . |
| MLA | Wang, Qiuhong et al. "Studying Driver's Perception Arousal and Takeover Performance in Autonomous Driving" . | SUSTAINABILITY 15 . 1 (2023) . |
| APA | Wang, Qiuhong , Chen, Haolin , Gong, Jianguo , Zhao, Xiaohua , Li, Zhenlong . Studying Driver's Perception Arousal and Takeover Performance in Autonomous Driving . | SUSTAINABILITY , 2023 , 15 (1) . |
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Abstract ：
To alleviate the traffic congestion at road intersections, based on the theory of coordinated control of trunk signals, the timing scheme of arterial traffic signals is optimized. First, we take Gao Feng Road in Bei Chen District of Tianjin as the research object and investigate the current situation of each intersection. Then, based on the Webster calculation signal period model, the phase and period of the signal are optimized by using synchro. Finally, taking vehicle travel time, total stops, delay, and exhaust emissions as performance evaluation indicators, through simulation and comparison with the current situation, it can be concluded that after optimization, the travel time is reduced by 27.78%, the total stops are reduced by 20.21%, and the delay is reduced by 50.00%. Finally, it can be proved that the optimized signal timing can alleviate traffic congestion and reduce pollution and exhaust emissions to a certain extent.
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| GB/T 7714 | Cao, Shuyu , Li, Zhenlong , Chen, Liang et al. Coordinated Control of Arterial Road Signals Based on Synchro [J]. | CICTP 2023: INNOVATION-EMPOWERED TECHNOLOGY FOR SUSTAINABLE, INTELLIGENT, DECARBONIZED, AND CONNECTED TRANSPORTATION , 2023 : 2624-2632 . |
| MLA | Cao, Shuyu et al. "Coordinated Control of Arterial Road Signals Based on Synchro" . | CICTP 2023: INNOVATION-EMPOWERED TECHNOLOGY FOR SUSTAINABLE, INTELLIGENT, DECARBONIZED, AND CONNECTED TRANSPORTATION (2023) : 2624-2632 . |
| APA | Cao, Shuyu , Li, Zhenlong , Chen, Liang , Huo, Jiaqi . Coordinated Control of Arterial Road Signals Based on Synchro . | CICTP 2023: INNOVATION-EMPOWERED TECHNOLOGY FOR SUSTAINABLE, INTELLIGENT, DECARBONIZED, AND CONNECTED TRANSPORTATION , 2023 , 2624-2632 . |
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Abstract ：
To clarify the effect of the cooperative vehicle infrastructure system (CVIS) application on drivers' visual performance, a total of 37 drivers were recruited to drive the simulated roadway in a freeway work zone under baseline and cooperative vehicle environments. Drivers' attention and concentration on the forward roadway, attention distraction, and attention distribution in both scenarios were analyzed. The results indicated that the CVIS application changed drivers' information-processing mode in the forward roadway as manifested by higher glance frequency and shorter average dwell time. In addition, more off-road distractions were observed in the range of 500 m in front of the work zone, but focusing on human-machine interfaces (HMIs) was not the main cause. In conclusion, the change in the driver's attention allocation and the diversion was clarified with the proposed visual link diagram. This paper provides a comprehensive approach to visual assessment of CVIS and contributes to the customized design and optimization of future CVIS-HMI.
Keyword ：
driving simulator driving simulator cooperative vehicle infrastructure system cooperative vehicle infrastructure system work zone work zone driver attention driver attention
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| GB/T 7714 | Li, Xuewei , Rong, Jian , Li, Zhenlong et al. Effects of cooperative vehicle infrastructure system on driver's attention--A simulator study on work zone [J]. | JOURNAL OF TRANSPORTATION SAFETY & SECURITY , 2022 , 15 (6) : 541-562 . |
| MLA | Li, Xuewei et al. "Effects of cooperative vehicle infrastructure system on driver's attention--A simulator study on work zone" . | JOURNAL OF TRANSPORTATION SAFETY & SECURITY 15 . 6 (2022) : 541-562 . |
| APA | Li, Xuewei , Rong, Jian , Li, Zhenlong , Zhao, Xiaohua , Ma, Jianming , Yang, Jiaxia . Effects of cooperative vehicle infrastructure system on driver's attention--A simulator study on work zone . | JOURNAL OF TRANSPORTATION SAFETY & SECURITY , 2022 , 15 (6) , 541-562 . |
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Abstract ：
Lane change identification based on long short-term memory (LSTM) neural networks has received increasing attention. The most commonly known disadvantage of the approach is the "black box " nature. This study, to improve the interpretability, proposed a novel method for identifying lane changes. The philosophy was to measure the similarity of the glance behavior during the maneuver. The glance behavior in the same maneuver is more similar, while that in different maneuvers is different. First, a driving simulator was used to collect driving behavior data. The eye gaze time series were captured by the eye tracker. The median of the eye gaze sequence of 3 s before the initial moment of lane change was obtained. With the median as the center, the driver's visual field plane was divided into several grids with the side length of 600 pixels. A sliding space-time cuboid algorithm was proposed to extract the scanning path. Second, four-dimensional dynamic time warping (4DDTW) distance was used to measure the similarity of the scanning paths. Third, the K nearest neighbor (KNN) algorithm was used to classify the driving maneuver into the lane-keeping (LK), the right lane change (RLC), and the left lane change (LLC) based on the 4DDTW distance of the scan paths. LSTM was also used to classify the driving maneuver into LK, RLC, and LLC. The performances of 4DDTW-KNN and LSTM were compared. The accuracies of 4DDTW-KNN and LSTM were 86.50% and 86.33%, respectively. LSTM is not better than 4DDTW-KNN in lane change identification based on eye gaze data with a comprehensive consideration of the time efficiency, the accuracy and the interpretability.
Keyword ：
Space-time cuboid Space-time cuboid KNN KNN LSTM LSTM 4DDTW 4DDTW Lane change identification Lane change identification
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| GB/T 7714 | Long, Yan , Huang, Jianling , Zhao, Xiaohua et al. Does LSTM outperform 4DDTW-KNN in lane change identification based on eye gaze data? [J]. | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES , 2022 , 137 . |
| MLA | Long, Yan et al. "Does LSTM outperform 4DDTW-KNN in lane change identification based on eye gaze data?" . | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 137 (2022) . |
| APA | Long, Yan , Huang, Jianling , Zhao, Xiaohua , Li, Zhenlong . Does LSTM outperform 4DDTW-KNN in lane change identification based on eye gaze data? . | TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES , 2022 , 137 . |
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Abstract ：
Investigating the acceptance of augmented reality head-up displays (AR-HUD) in connected environments is an important research issue as there is great potential to enhance driving safety for both manual and autonomous driving. This study investigated drivers' acceptance of AR-HUD with the technology acceptance model (TAM) with extended variables, including trust, information service quality, display service quality, perceived distraction, and driving skills. The answers to a survey completed by 388 drivers were analyzed with structural equation modeling (SEM) to verify the reliability and validity of the measurement model and explore the re-lationships between the observed variables. The results show that all the hypotheses of foundational TAM are supported. Further analysis has indicated that: (1) drivers' trust has a positive effect on perceived usefulness, attitude, and intention to use; (2) display service quality affects perceived ease of use positively, while infor-mation service quality has a positive influence on trust and intention to use; (3) perceived distraction has a significant negative impact on perceived ease of use and trust, while the negative effect on perceived usefulness is not significant; and (4) drivers' subjective driving skills have a positive influence on perceived ease of use and a negative impact on perceived usefulness. Further analysis attempts to explain the causes of the influence rela-tionship between variables, and suggestions are proposed to improve the drivers' perceived usefulness and perceived ease of use of AR-HUD. The study provides valuable insights into the factors that significantly affect drivers' intention to use AR-HUD systems in theory and provides suggestions to AR-HUD designers on system design.
Keyword ：
Driving skills Driving skills Augmented reality head up display Augmented reality head up display Technology acceptance model Technology acceptance model Trust Trust Perceived distraction Perceived distraction Service quality Service quality
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| GB/T 7714 | Li, Xuewei , Rong, Jian , Li, Zhenlong et al. Modeling drivers? acceptance of augmented reality head-up display in connected environment [J]. | DISPLAYS , 2022 , 75 . |
| MLA | Li, Xuewei et al. "Modeling drivers? acceptance of augmented reality head-up display in connected environment" . | DISPLAYS 75 (2022) . |
| APA | Li, Xuewei , Rong, Jian , Li, Zhenlong , Zhao, Xiaohua , Zhang, Yu . Modeling drivers? acceptance of augmented reality head-up display in connected environment . | DISPLAYS , 2022 , 75 . |
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Abstract ：
This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I-most severe, II-police-reportable, III-minor crash, and IV-low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes.
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| GB/T 7714 | Qu, Yansong , Li, Zhenlong , Liu, Qin et al. Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining [J]. | JOURNAL OF ADVANCED TRANSPORTATION , 2022 , 2022 . |
| MLA | Qu, Yansong et al. "Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining" . | JOURNAL OF ADVANCED TRANSPORTATION 2022 (2022) . |
| APA | Qu, Yansong , Li, Zhenlong , Liu, Qin , Pan, Mengniu , Zhang, Zihao . Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining . | JOURNAL OF ADVANCED TRANSPORTATION , 2022 , 2022 . |
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Abstract ：
This study explores the influence of cooperative vehicle infrastructure system (CVIS) on the driver's visual and driving performance. Taking the work zone as an example, a driving simulation experiment involving 37 drivers has been conducted to collect driver's eye movement and vehicle running data in the same scenario with and without CVIS information respectively. Next, the nonparametric test and grey relational analysis (GRA) have been used to compare the specific performance and to analyze the correlations among the multi-stage of driver's information process. The results present that CVIS information has the potential to alleviate the driver's tension and the difficulty of information perception. Meanwhile, the driver tends to search visual information more actively and complete the lane changing behavior earlier. Further analysis indicates that CVIS information can reduce the influence of mental workload on lane-changing decisions, and enhance the correlation between visual information processing and lane-changing decisions, as well as the effect of decision-making timing on the running state. Therefore, drivers' speed control ability was improved and the traffic flow was smoother. The findings give an insight into the influence mechanism of the cooperative information on driving performance and provide a direction for a comprehensive assessment of CVIS based on human factors.
Keyword ：
Driving simulator Driving simulator Cooperative vehicle infrastructure system Cooperative vehicle infrastructure system Lane-changing decision Lane-changing decision Visual performance Visual performance Driving performance Driving performance Work zone Work zone
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| GB/T 7714 | Li, Xuewei , Li, Zhenlong , Zhao, Xiaohua et al. Effects of Cooperative Vehicle Infrastructure System on Driver's Visual and Driving Performance Based on Cognition Process [J]. | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY , 2022 , 23 (5) : 1213-1227 . |
| MLA | Li, Xuewei et al. "Effects of Cooperative Vehicle Infrastructure System on Driver's Visual and Driving Performance Based on Cognition Process" . | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 23 . 5 (2022) : 1213-1227 . |
| APA | Li, Xuewei , Li, Zhenlong , Zhao, Xiaohua , Rong, Jian , Zhang, Yunlong . Effects of Cooperative Vehicle Infrastructure System on Driver's Visual and Driving Performance Based on Cognition Process . | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY , 2022 , 23 (5) , 1213-1227 . |
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Abstract ：
The aim of this paper is to explore the influence of the cooperative vehicle-infrastructure system in foggy highway on the driver's visual information processing mode. Firstly, based on the driving simulation platform, a cooperative vehicle-infrastructure system in foggy highway (CVIS-HF) was designed to obtain the driver's visual behavior parameters. Next, the road ahead was defined as area of interest, and the driver's fixation, saccade and other explicit visual behavior indexes at the global level and area of interest were extracted and analyzed. Finally, three common factors, information extraction factor, perceptual density factor and information search, were obtained by using factor analysis method to characterize the driver's visual information processing mode under the effect of CVIS-HF. The results show that the application of CVIS-HF significantly affects the driver's scanning behavior and the allocation of visual resources for the road ahead. The original information distribution is changed by the intervention of cooperative vehicle-infrastructure system information, which improves the efficiency of information extraction and information search, but reduces the perceptual information density. The research results can provide theoretical reference and technical support for the design and safety application of human machine interface (HMI) in cooperative vehicle-infrastructure system. © 2021, Editorial Department, Journal of South China University of Technology. All right reserved.
Keyword ：
Information retrieval Information retrieval Behavioral research Behavioral research Road vehicles Road vehicles Search engines Search engines Roads and streets Roads and streets
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| GB/T 7714 | Li, Xuewei , Zhao, Xiaohua , Li, Zhenlong et al. Driver's Visual Information Processing Mode in Foggy Highway Cooperative Vehicle-Infrastructure System Environment Based on Simulated Driving [J]. | Journal of South China University of Technology (Natural Science) , 2021 , 49 (3) : 131-138 and 148 . |
| MLA | Li, Xuewei et al. "Driver's Visual Information Processing Mode in Foggy Highway Cooperative Vehicle-Infrastructure System Environment Based on Simulated Driving" . | Journal of South China University of Technology (Natural Science) 49 . 3 (2021) : 131-138 and 148 . |
| APA | Li, Xuewei , Zhao, Xiaohua , Li, Zhenlong , Yang, Jiaxia , Rong, Jian . Driver's Visual Information Processing Mode in Foggy Highway Cooperative Vehicle-Infrastructure System Environment Based on Simulated Driving . | Journal of South China University of Technology (Natural Science) , 2021 , 49 (3) , 131-138 and 148 . |
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Abstract ：
A method for drunk driving detection using Feature Selection based on the Random Forest was proposed. First, driving behavior data were collected using a driving simulator at Beijing University of Technology. Second, the features were selected according to the Feature Importance in the random forest. Third, a dummy variable was introduced to encode the geometric characteristics of different roads so that drunk driving under different road conditions can be detected with the same classifier based on the random forest. Finally, the linear discriminant analysis, support vector machine, and AdaBoost classifiers were used and compared with the random forest. The accuracy, F1 score, receiver operating characteristic curve, and area under the curve value were used to evaluate the performance of the classifiers. The results show that Accelerator Depth, Speed, Distance to the Center of the Lane, Acceleration, Engine Revolution, Brake Depth, and Steering Angle have important influences on identifying the drivers' states and can be used to detect drunk driving. Specifically, the classifiers with Accelerator Depth outperformed the other classifiers without Accelerator Depth. This means that Accelerator Depth is an important feature. Both the AdaBoost and random forest classifiers have an accuracy of 81.48%, which verified the effectiveness of the proposed method.
Keyword ：
traffic safety traffic safety feature selection feature selection driving behavior driving behavior Drunk driving detection Drunk driving detection random forest random forest
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| GB/T 7714 | Li, ZhenLong , Wang, HaoXin , Zhang, YaoWei et al. Random forest-based feature selection and detection method for drunk driving recognition [J]. | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS , 2020 , 16 (2) . |
| MLA | Li, ZhenLong et al. "Random forest-based feature selection and detection method for drunk driving recognition" . | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 16 . 2 (2020) . |
| APA | Li, ZhenLong , Wang, HaoXin , Zhang, YaoWei , Zhao, XiaoHua . Random forest-based feature selection and detection method for drunk driving recognition . | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS , 2020 , 16 (2) . |
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