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The coordinated regulation for α-Al 2 O 3 and mullite structures in the alumina-mullite duplex fiber poses a challenging yet crucial task in achieving a fiber with exceptional high-temperature properties. In this study, Fe element was added into the alumina-mullite fibers in the form of iron sol and ferric nitrate solution, respectively, with the aim of obtaining a fiber consisting of fine mullite grains embedded with nano α-Al 2 O 3 grains. The results revealed that the addition of Fe element significantly enhances the crystallization process of mullite in alumina-mullite fibers. Specifically, adding iron sol resulted in the reduce of α-Al 2 O 3 formation temperature to 1300 °C, while introducing Ferric nitrate solution promoted the formation of mullite phase. Simultaneous introduction of 1.3 wt% iron sol and 1.3 wt% Ferric nitrate solution contributed to achieve alumina-mullite fiber with tensile strength of 2.06 GPa. In addition, the influence mechanism of Fe source on the phase transformation and microstructures of alumina-mullite fiber were discussed. [ABSTRACT FROM AUTHOR]

Controlling coordinately heterogeneous grain growth is challenging for preparing multiphase structural materials, especially for high-performance alumina-based fibers. In this study, we achieved refining the mullite grains by controlling the composition of the silicon source, resulting in the successful preparation of fine-grained alumina-mullite diphasic fibers with high thermal stability. The alumina-mullite diphasic fibers consisted of lamellar α-Al 2 O 3 and mosaic-like mullite grains with a biphasic grain size below 500 nm. The Weibull strength of diphasic fibers could reach 1.70 GPa. The strength retention rate exceeded 85% even after thermal exposure at 1100 ℃ for 50 h and at 1200 ℃ for 5 h. Moreover, our investigation revealed the presence of an ordered complexion on the (0001) plane of α-Al 2 O 3 and discussed a possible situation of its two-dimensional atomic arrangement distribution, providing great enlightenment for understanding grain boundary segregation theory. [ABSTRACT FROM AUTHOR]

• We quantify the cognitive load under varying non-driving-related tasks (NDRTs) in automated driving by physiological state of eye movement. • Dempster-Shafer (D-S) Evidence Theory is used to associate the workload of different typical scenarios, NDRTs and the safety of takeover. • Takeover safety is predicted by Convolutional Neural Networks (CNN) based on cognitive load. • The effect of window size of the input data for takeover safety prediction is especially considered. Automated vehicles alleviate the need for driver attention and control. However, a takeover request (TOR) to the driver remains essential for emergencies and safety–critical scenarios beyond automation's capability. Thus, accessing drivers' safety performance in various TOR scenarios is crucial for conditionally automated driving (SAE L3). However, TOR safety performance is seldom examined concerning drivers' cognitive load, despite its presumed relevance to TOR scenarios and human–machine interaction. Moreover, the adequacy of the time window preceding a TOR, critical for TOR safety prediction, remains inadequately explored. This study aims to assess safety performance across diverse TOR scenarios in Level 3 conditional automation, incorporating drivers' cognitive load, and predict TOR safety by considering the time window's impact. A driving simulator experiment gathered eye movement and driving behavior data from 37 recruited participants. Participants were instructed to take control of the vehicle from automated driving within a time budget (TB) of 3 s or 7 s in obstacle avoidance (OA) or lane keeping (LK) scenarios while engaging in non-driving-related tasks (NDRTs). Participants' subjective cognitive load in the different TOR scenarios was scaled using NASA-TLX. Furthermore, safe TOR performance was predicted utilizing Convolutional Neural Networks (CNNs) across different time window sizes preceding a TOR. The results indicate that: 1) cognitive loads in takeover scenarios ranked from highest to lowest as TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; and the cognitive loads of the NDRTs ranked from highest to lowest as mistake finding, texting, chatting, monitoring; 2) the takeover safety performance in the four scenarios from lowest to highest was TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; likewise, the takeover safety performance during the four NDRTs ranked from lowest to highest as mistake finding, monitoring, texting, chatting; 3) the time window size before the TORs significantly affected the prediction performance of the model. A 30-second window was recommended as optimal for predicting takeover safety using the CNN model, achieving an average F1 score of 0.8120 and 81.98 % accuracy. This study's findings enhance our comprehension of driving behavior characteristics during TOR and offer valuable insights for detecting driver states in conditionally automated driving contexts. [ABSTRACT FROM AUTHOR]

Understanding the mechanism of the oriented growth in multi-component alumina-based fibers and inhibiting its progression is crucial for enhancing their thermal stability. In this study, a novel diphasic fiber composed of lamellar α-Al 2 O 3 and mosaic-like mullite grains was prepared. We discussed the oriented growth behavior of lamellar α-Al 2 O 3 grains and the diffusion phenomenon of the Al element in SiO 2 substrate under the combined addition of iron sol and La 2 O 3 , proposing a new mechanism for oriented growth in diphasic fibers. The diffusion of the Si element to the tips of alumina grains contributed to a significant increase in the mobility of non-basal planes, resulting in (0001) surface growth of α-Al 2 O 3 grains. Furthermore, the La segregation at grain boundaries significantly impeded the oriented growth of α-Al 2 O 3 grains, which was critical for improving the thermal stability of multiphase alumina-based fibers. [ABSTRACT FROM AUTHOR]

Improved thermal stability of fibers is increasingly demanded for high-temperature applications. In this study, alumina-mullite diphasic fibers with 0–5 wt % La 2 O 3 addition were synthesized via the sol-gel method. The precipitation of LaAl 11 O 18 occurred when the content of added La 2 O 3 exceeded a critical range. Subsequently, the effects of 1 wt % La 2 O 3 on alumina-mullite diphasic fibers were systematically discussed in terms of pyrolysis removal, phase transition pathways, microstructure and thermal stability. The results indicated that alumina-mullite diphasic fibers consisted of γ, θ-Al 2 O 3 and mullite phases. The spherical γ, θ-Al 2 O 3 grains, ranging in size from 20 to 100 nm, were dispersed within the mullite matrix with an irregular shape. Additionally, the La element was segregated into the γ, θ-Al 2 O 3 grain boundaries during the crystallization process. This resulted in stabilized γ, θ-Al 2 O 3 and mullite grains and an improved strength retention rate of 84 % after thermal exposure at 1200 ℃ for 5 h. [ABSTRACT FROM AUTHOR]

Sol-gel method, as one of the most effective methods, has been widely used in producing high-property ceramics. In this method, the pyrolysis of precursor significantly influences the following microstructure evolution and properties of ceramic materials. In this work, the effect of residual carbon, influenced by the pyrolysis atmosphere, on the phase transition and microstructure evolution of the alumina-mullite fibers was investigated. It was revealed that the phase transition path of the preheated fibers with 0.53 wt% residual carbon was amorphous phase → aluminosilicate → tetragonal mullite → orthorhombic mullite + θ-Al 2 O 3. The phase transition path of the preheated fibers with 10.51 wt% residual carbon became amorphous phase → γ-Al 2 O 3 → orthorhombic mullite + α-Al 2 O 3 + θ-Al 2 O 3. In addition, the residual carbon contributes to improve the densification of alumina-mullite fibers and refine the fiber grain size. The effect of residual carbon on the formation of mullite coarse grains was further discussed. [ABSTRACT FROM AUTHOR]

Maintaining high mechanical properties and thermal stability of alumina fibers is a contradictory problem. In order to solve this problem, the effects of MgO addition on the microstructure evolution and properties of alumina fibers prepared by sol-gel were studied. The addition of MgO inhibited the formation of α-Al 2 O 3 and the 2 wt% MgO addition was conducive to grain refinement, reducing the grain size from 227 nm to 162 nm and increasing the Weibull strength by 66%, up to 2.03 GPa with a peak value of 2.60 GPa. The grain growth exponent increased from 4 to 7.2 during the long-term holding at 1100 °C, indicating the better thermal stability of alumina fibers with 2 wt% MgO as well. The mechanism of MgO addition to the phase transition and grain growth of alumina fibers was further discussed. [ABSTRACT FROM AUTHOR]

• QN-ACTR could model effect of limited sight distance on car-following performance. • Cognition modeling provides support for adverse fog traffic safety assessments. • Frequent eye movement led to increasing of cognitive time when driving in fog. • Driver's perception, decision-making and control behavior were more complex in fog. • Reduction of cognitive capacity led to an impairment of the overall driver behavior. This study explored human error risk factors in car-following behaviors, cognitive resource bottlenecks and performance impairment mechanisms in the context of cognitive capacity while driving on foggy roads. Firstly, a hierarchical driving behavior assessment was used to observe risky driving behavior in a foggy area. A driver's dynamic speed adjustment analog experiment was carried out in foggy conditions. Unique parameters were substituted into the traditional QN-ACTR driver behavior model. The visual far-time of the traditional model and the latitude-longitude control sensitivity parameters were estimated. Foggy conditions were modeled based on the QN-ACTR cognitive architecture to observe the effect of limited sight distance through fog on car-following performance. Jsim in the Eclipse environment and TORCS were applied in co-simulation and verified. The results show that (1) the proposed cognitive modeling approach effectively simulates a foggy driving environment and allows researchers to study affected and related driver behavior; and (2) the car-following performance in low visibility is significantly worse than in high visibility. These findings provide theoretical support and a scientific basis for the study of speed, safe distance, standard of limited speed and design methods for engineering safety mechanisms to counter the negative effects of driving on foggy roads. This study can inform actions to increase road safety during fog. [ABSTRACT FROM AUTHOR]

Highlights • A novel piloting decision recognition model for fuzziness and uncertainty problems. • Automatic acquisition and representation of the pilot's decision-making knowledge. • A flexible method that can mine the key factors which affect piloting decisions. • The standardization principle of piloting decision-making factors is proposed. • A feasibility basis for the realization of automatic smart ship piloting systems. Abstract With the further development of marine and information technologies, ship intelligence, green policies and automation will become mainstream with global cargo ships. Ship labor costs increase every year, so for the foreseeable future, the number of experienced crew members will be greatly reduced as smart ship emergence accelerates. At present, there is no mature research system for the human-like piloting of smart ships. In this paper, we use an improved decision tree, which could address problems of fuzziness and uncertainty. This will allow us to study the decision mechanisms of different piloting behaviors in order to realize the automatic acquisition and representation of the pilot's decision-making knowledge in inbound ship analysis as well as the simulated reproduction of the pilot's behavior. The simulation results show that the piloting decision recognition model, based on the fuzzy Iterative Dichotomiser 3 (ID3) decision tree, possesses a high reasoning speed and can accurately identify current piloting behavior. This provides theoretical guidance and a feasibility basis for research into human-like piloting behavior and the realization of automatic smart ship piloting systems. [ABSTRACT FROM AUTHOR]

Highlights • Firstly tested Traffic Climate Scale (TCS) and Positive Driver Behaviours Scale among 887 drivers in China. • The Chinese TCS has a highly reliable and stable three-factor structure. • Measures of convergent validity support validity of the Chinese TCS. • Showed the links between traffic safety climate, driver behaviours and traffic accidents involvement. Abstract Traffic Climate Scale (TCS) and Positive Driver Behaviours Scale (PDBS) are new measurement tools. The study aims to translate the TCS and PDBS into Chinese and to assess their factor structures in a large sample of licensed motor vehicle drivers in China. A further aim is to investigate the effects of TCS factors on drivers' behaviours and traffic accidents involvement. Data were collected using an online survey. Participants were 887 fully licensed motor vehicle drivers, including 531 males and 356 females who completed a Chinese translated questionnaire including the TCS, PDBS, Driver Behaviour Questionnaire (DBQ), items related to drivers' driving records and demographic characteristics. The result of the exploratory factor analysis revealed clear three-factor solution ('Functionality', 'External affective demand' and 'Internal requirement') of TCS with high item loadings and acceptable internal consistency coefficients. The convergent validity of the Chinese TCS was supported by its relationship with driver behaviour factors (violations, errors, lapses and positive behaviours), the traffic accidents involvement and demographic characteristics. The results further show that the external affective demand consistently and positively relate to aberrant behaviours and negatively relate to positive behaviours with indirect positive significant effects on accidents involvement. Functionality is concurrently and negatively related to aberrant behaviours and positively related to positive behaviours with no effects on accidents involvement. The internal requirement is negatively related to aberrant behaviours but, positively related to positive behaviours with positive significant direct effects on accidents involvement. [ABSTRACT FROM AUTHOR]

The preparation of high-performance Al 2 O 3 -ZrO 2 fibers is a significant and challenging task. In this study, continuous Al 2 O 3 -ZrO 2 fibers were synthesized via the sol-gel method using aluminum powder and zirconium acetate as raw materials. It was observed that the amorphous state of Al 2 O 3 transformed into γ-Al 2 O 3 and then to α-Al 2 O 3 , while the amorphous ZrO 2 directly transformed into t-ZrO 2. The formation temperatures of t-ZrO 2 and α-Al 2 O 3 were both approximately 1100 °C. The optimal sintering temperature during the single-step sintering process for Al 2 O 3 -ZrO 2 fibers was 1400 °C. Based on the experiment results, fully dense Al 2 O 3 -ZrO 2 fibers with fine grains were successfully synthesized via a two-step sintering process. The average sizes of Al 2 O 3 and ZrO 2 grains were 153 nm and 62 nm, respectively. The Weibull strength of the Al 2 O 3 -ZrO 2 fibers reached 2.2 GPa with a peak value of 2.6 GPa, which exhibited an increase of approximately 30% compared to that formed by the single-step sintering process. Furthermore, the nucleation, growth, and densification behaviors of Al 2 O 3 -ZrO 2 fibers during the two-step sintering process were discussed. [ABSTRACT FROM AUTHOR]

Liquefied Natural Gas (LNG) is a clean, efficient and economic energy, which is mainly transported by LNG carriers in the marine industry. Possible spilling of cryogenic LNG puts safety of crew in danger due to fire and explosion hazards. Taking into account various uncertainties, a modified fault tree model for LNG spill accident during LNG carriers’ handling operations is constructed in this study. Human factor analysis is introduced with a goal to predict human errors in LNG carriers’ handling operation. Finally, the results of the Fault Tree Analysis (FTA) and Human Reliability Analysis (HRA) are combined, so that risks can be assessed using Monte Carlo Simulation (MCS). Comparing the results of risk assessment with the traditional FTA and corresponding criterion set, an important reference for LNG carrier management is provided for administration. [ABSTRACT FROM AUTHOR]

Existing fatigued driving analysis methods mainly focus on lateral driving performance by using the measurements related to the steering wheel or lane position. There is a lack of research on longitudinal car following behavior. In this study, 40 professional drivers are invited to participate in field expressway driving experiment, lasting at least for 6 h. During the test, their performance is measured in terms of their self-reported fatigued driving level according to the Karolinska Sleepiness Scale (KSS), the PERcentage of eye CLOSures (PERCLOS) and the Time Headway (THW). Then the effects of the fatigued driving level on car following behavior are evaluated. The results indicate that the fatigue level (for both KSS and PERCLOS) has significantly impact on THW parameters, including the mean, standard deviation and minimum THW. An increase in KSS and PERCLOS leads to a lower mean and minimum THW. Meanwhile, the standard deviation of THW increases with the increase of KSS and PERCLOS. In conclusion, this study found that a higher fatigue level leads to the driver keeping a smaller THW when following another vehicle and choosing shorter THW to make lane change. More deviation of car following performance was also found with the increase of fatigue level. Therefore, the findings of this study can be used to explain fatigue as one of the major reasons for rear-end collisions. Also, the research findings demonstrate the impact of fatigue on driving behavior in terms of car following performance, which can be used as a measurement for monitoring fatigued drivers. [ABSTRACT FROM AUTHOR]

Achieving road safety depends on driver attitudes and behaviours in handling the vehicle on roads. The availability of good road, improvement of vehicle designs and drivers experience lead to reduction in crashes but not prevention of crashes. The study aims to predict the drivers’ intentions towards speeding and overtaking violations when under the influence of motivational factors using belief measure of TPB and DBQ variables. To achieve this, questionnaires were randomly administered to a sample of Ghanaian drivers ( N = 354) who held valid driving licenses. This study applied regression techniques. The result shows that the components of TPB and DBQ variables were able to predict drivers’ intentions towards speeding and overtaking violations. The study further shows that components of TPB made larger contributions to the prediction of divers’ intentions to speeding and overtaking than the DBQ. Further analysis revealed that, in the prediction of drivers’ intentions, speeding attitude was the most frequent violations compared to overtaking. The drivers tend to involved in overtaking violations when they perceived the driving motivations would enhance the performance of the behaviour. Additionally, control belief has been the strongest predictor of drivers’ intentions under the influence of motivations to speeding and overtaking violations. It appeared that the drivers who intended to involve in speeding and overtaking violations had strong beliefs in the factors and are more likely to violate based on their beliefs. The practical implications of the findings for the development of interventions to promote road safety and positive changes are also discussed. [ABSTRACT FROM AUTHOR]

With the rapid development of Intelligent Transport Systems (ITS), emerging technologies such as Advanced Vehicle Driving Assist Systems, and Connected Vehicles, Intelligent Vehicles introduce new opportunities and challenges into our traffic systems and raise questions regarding the interaction of drivers' cognitive and driving behaviors with these technologies. Meanwhile, development of these technologies makes innovative vehicle data collection, Vehicle to Vehicle communication, and Vehicle to Infrastructure communication possible. [4] introduced a driver behavior influence factor associated with drivers' driving styles comparing to a theoretical curve speed model that only considers the vehicle-road interaction. Driverless vehicles promise to remove the human operator in favor of safer driving, but drivers still need to participate driving tasks in the lower tiers of vehicle automation. [Extracted from the article]

Pedestrian crossing safety has attracted increased attention in recent years. However, little research has been conducted for examining the interaction between vehicles and pedestrians at uncontrolled mid-block crosswalks. In this paper, both a decision model and a motion model are developed for simulating this interaction process. Cumulative prospect theory is embedded in the evolutionary game framework for modeling the decision behaviors of drivers and pedestrians during the interactions, which can capture the phenomenon of disagreement among a pedestrian crossing group. Cellular automata-based moving rules are used to depict the motion of vehicles with consideration of the three-second rule, and a modified bidirectional pedestrian model is developed in order to consider the right-moving preference and resolve the deadlock among mixed flows. Results of calibration and validation of the proposed model are also presented. An application is designed for the purpose of illustrating the model’s capabilities. The results demonstrate that the proposed model can well replicate actual observed traffic. [ABSTRACT FROM AUTHOR]

Drowsy/distracted driving has become one of the leading causes of traffic crash. Only certain particular drowsy/distracted driving behaviors have been studied by previous studies, which are mainly based on dedicated sensor devices such as bio and visual sensors. The objective of this study is to extract the common features for identifying drowsy/distracted driving through a set of common vehicle motion parameters. An intelligent vehicle was used to collect vehicle motion parameters. Fifty licensed drivers (37 males and 13 females, M = 32.5 years, SD = 6.2) were recruited to carry out road experiments in Wuhan, China and collecting vehicle motion data under four driving scenarios including talking, watching roadside, drinking and under the influence of drowsiness. For the first scenario, the drivers were exposed to a set of questions and asked to repeat a few sentences that had been proved valid in inducing driving distraction. Watching roadside, drinking and driving under drowsiness were assessed by an observer and self-reporting from the drivers. The common features of vehicle motions under four types of drowsy/distracted driving were analyzed using descriptive statistics and then Wilcoxon rank sum test. The results indicated that there was a significant difference of lateral acceleration rates and yaw rate acceleration between “normal driving” and drowsy/distracted driving. Study results also shown that, under drowsy/distracted driving, the lateral acceleration rates and yaw rate acceleration were significantly larger from the normal driving. The lateral acceleration rates were shown to suddenly increase or decrease by more than 2.0 m/s 3 and the yaw rate acceleration by more than 2.5°/s 2 . The standard deviation of acceleration rate (SDA) and standard deviation of yaw rate acceleration (SDY) were identified to as the common features of vehicle motion for distinguishing the drowsy/distracted driving from the normal driving. In order to identify a time window for effectively extracting the two common features, a double-window method was used and the optimized “Parent Window” and “Child Window” were found to be 55 s and 6 s, respectively. The study results can be used to develop a driving assistant system, which can warn drivers when any one of the four types of drowsy/distracted driving is detected. [ABSTRACT FROM AUTHOR]

Vehicular cyber physical system (VCPS) can comprehensively acquire road traffic safety related information, and provide drivers with early warning or driving assistance in emergency, in order to assist them avoid vehicle crash in the driving process. Literature review shows that previous studies mainly rely on observed vehicle motion/location data for assessing vehicle collision risk, where predicted vehicle motion/location, driver behavior and road geometry (e.g., curvature) are rarely considered. In this study, based on the simulated VCPS, a collision avoidance system that can explicitly consider the above issues is designed and presented in detail. Within the proposed collision avoidance system, an assessment method, which can predict collision risk by comprehensively considering vehicles motion/location, driver behavior and road geometry information from the VCPS, is developed. Firstly, the short-term motion of the objective vehicle and surrounding vehicles are predicted based on the Kalman Filter (KF) algorithm and the vehicle motion model. Furthermore, the proposed method that can explicitly take driver behavior and road curvature into account is used to predict vehicle location and calculate the traveled distance among vehicles in real-time. Then, the predicted vehicle gaps are compared with a safe distance threshold and the vehicle collision risk is predicted. Finally, the accuracy of the proposed collision risk assessment method is examined with a receiver operating characteristic (ROC) curve analysis over a section of curved road. Simulation results show that the proposed method is effective for detecting collision risk and providing accurate warnings in a timely fashion. [ABSTRACT FROM AUTHOR]

• The influence of urgency levels and TORlts on takeover performance was discussed. • The validity of takeover time (TOT) as an indicator of takeover safety was discussed. • The TORlt had a significant influence on takeover performance and safety. • The TOT correlated with TORlt and was not significantly related to other factors. • The takeover reaction time was divided into two parts and discussed respectively. • Effective evaluation indicators for takeover performance and safety were proposed. During conditionally automated driving, drivers are sometimes required to take over control of the vehicle if a so-called takeover request (TOR) is issued. TORs are generally issued due to system limitations. This study investigated the effect of different urgency scenarios and takeover-request lead times (TORlts) on takeover performance and safety. The experiment was conducted in a real vehicle-based driving simulator. Manual driving, 7-second TORlt and 5-second TORlt were each tested. Participants experienced three progressively urgent driving scenarios: one cut-in scenario and two obstacle-avoidance scenarios. The results indicate that the TORlt significantly affected takeover performance and safety. Within a certain range, the longer the TORlt, the safer the takeover. However, while takeover reaction time depended mainly on the length of the TORlt and was not significantly related to other factors, such as workload, greater workloads that were caused by the TORlt were associated with shorter reaction times and decreased safety. This is evidence that the reaction time should not be used as the preferred indicator to evaluate takeover performance and safety. Indicators, such as workload, minimum TTC, feature point distribution position and slope of the obstacle avoidance trajectory, can better measure and evaluate takeover performance and safety. This study can provide data support for takeover safety evaluation of conditionally automated driving. [ABSTRACT FROM AUTHOR]

Studying the rear-end early warning methods of connected automated vehicles (CAVs) is useful for issuing early warnings and reducing traffic accidents. Establishing a corresponding driving model according to CAV characteristics is necessary when designing intelligent decision and control systems, especially for the safety speed threshold. However, since traffic systems are stochastic, there are random factors that influence car-following behavior. Therefore, this study proposes a rear-end collision warning method for CAVs based on a stochastic local multivehicle optimal speed (SLMOV) car-following model. First, the SLMOV model is proposed to characterize the car-following behavior of CAVs. Simultaneously, a stability analysis and parameter estimation method are discussed. Second, the safety distance between the CAVs changes with time because the speed of the rear vehicles satisfies the SLMOV model, which is used to calculate the safety probability of rear-end CAV collisions through an analysis of the driving process. The speed threshold is assessed by controlling the rear-end collision probability. Third, next-generation simulation (NGSIM) data are used in an empirical analysis of a rear-end collision warning method on the basis of a parameter estimation of the SLMOV model. The results present the speed thresholds of vehicles under different braking deceleration levels. Finally, the merits and demerits of fixed-speed and variable-speed adjustment time intervals are compared by considering driving safety and comfort as evaluation indexes. A reasonable CAV adjustment time interval of 0.4 s is determined. This result can be used to help develop a vehicle loading rear-end collision warning system. [ABSTRACT FROM AUTHOR]

• Three ICV environment HMI groups: control, warning, and guidance, were designed to study their impact on driver's performance. • Warning group exhibited cautious and smooth acceleration before merging, while the Guidance group demonstrated better speed control after merging. • Mainline vehicle gaps had no significant impact on merging positions across HMI groups. • Guidance group merged closest to taper, Control group farthest. • Abundant gaps led to smaller initial merging gaps and shorter adjustment times for Guidance group. Intelligent Connected Vehicle (ICV) is considered one of the most promising active safety technologies to address current transportation challenges. Human-Machine Interface (HMI) plays a vital role in enhancing user driving experience with ICV technology. However, in an ICV environment, drivers may exhibit excessive reliance on HMI, resulting in diminished proactive observation and analysis of the road environment, and subsequently leading to a potential decrease in drivers' situational awareness. This reduced situational awareness may consequently lead to a decline in their overall engagement in driving tasks. Therefore, to comprehensively investigate the impact of HMI on driver performance in various ICV environments, this study incorporates three distinct HMI systems: Control group, Warning group, and Guidance group. The Control group provides basic information, the Warning group adds front vehicle icon and real-time headway information, while the Guidance group further includes speed and voice guidance features. Additionally, the study considers three types of mainline vehicle gaps, namely, 30 m, 20 m, and 15 m. Through our self-developed ICV testing platform, we conducted driving simulation experiments on 43 participants in a freeway interchange merging area. The findings reveal that, drivers in the Guidance group exhibited explicit acceleration while driving on the ramp. Drivers in the Guidance and Warning groups demonstrated smoother speed change trends and lower mean longitudinal acceleration upon entering the acceleration lane compared to the Control group, indicating a preference for more cautious driving strategies. During the pre-merging section, drivers in the Warning group demonstrated a more cautious and smooth longitudinal acceleration. The Guidance group's HMI system assisted drivers in better speed control during the post-merging section. Differences in mainline vehicle gaps did not significantly impact the merging positions of participants across the three HMI groups. Drivers in the Guidance group merged closest to the left side of the taper section, while the Control group merged farthest. The research findings offer valuable insights for developing dynamic human–machine interfaces tailored to specific driving scenarios in the environment of ICVs. Future research should investigate the effects of various HMIs on driver safety, workload, energy efficiency, and overall driving experience. Conducting real-world tests will further validate the findings obtained from driving simulators. [ABSTRACT FROM AUTHOR]

• The moderating effect of driving skills on the traffic climate-driver behaviors relationship was investigated. • In Turkey, both perceptual motor skills and safety skills moderated the majority of the relationships. • In China, only safety skills moderated some of the relationships. • There are both cultural differences and similarities in the moderating effect of driving skills. Introduction: While road traffic accidents and fatalities are a worldwide problem, the rates of road traffic accidents and fatalities show differences among countries. Similarly, driver behaviors, traffic climate, and their relationships also show differences among countries. The aim of the current study is to investigate the moderating effect of driving skills on the relationship between traffic climate and driver behaviors by country. (Turkey and China). Method: There were 294 Turkish drivers and 292 Chinese drivers, and they completed the Traffic Climate Scale, the Driving Skills Inventory, and the Driver Behavior Questionnaire. The moderated moderation analyses were conducted with Hayes PROCESS tool on SPSS. Results: The results showed that safety skills moderated the relationship between internal requirements and violations both in Turkey and China. Safety skills also moderated the relationship between internal requirements and errors only in China and the relationship between functionality and violations in Turkey. Perceptual-motor skills moderated the relationships between external affective demands and errors, and also the relationship between internal requirements and positive driver behaviors in Turkey. It can be inferred that driving skills has different influences on traffic climate-driver behaviors relationship in different cultures and there might be cultural differences in the evaluation of drivers' own driving skills. Practical Applications: Among driving skills, safety skills have a more critical role to increase road safety by decreasing number of violations. Interventions to increase safety skills of drivers might be promising for road safety. [ABSTRACT FROM AUTHOR]

Duplex oxide ceramic fibers have excellent high temperature properties. However, controlling the microstructure of duplex oxide ceramic fibers is difficult and complexed. In this work, the effect of silica sols affecting the interaction of precursor sol-gel particles on the high-temperature phase transition and microstructure of alumina-mullite fibers was investigated. The results show that the silica sol affects the particle interactions and distribution in the mixed sol. Alumina-mullite fibers prepared from non-homogeneous precursor sols generated α-Al 2 O 3 at 1400 °C, while fibers prepared from homogeneous precursor sols required higher temperatures. In addition, mullite (3Al 2 O 3 :2SiO 2) with an orthogonal structure is more likely to be generated in the non-homogeneous Al 2 O 3 -SiO 2 precursor sol. The prepared alumina-mullite fibers have a tensile strength of up to 1.68 GPa. Finally, the mechanism of the influence of silica-sol properties on the final organization of the alumina-mullite fiber was further discussed. [ABSTRACT FROM AUTHOR]

• Driver behaviour influence factor was introduced to the curve safe speed model. • Correlation was examined between DBQ subscales and driver behaviour influence factor. • The violations subscale in DBQ was utilized to classify drivers' driving style. • Simulation study indicated the practicability of the proposed curve speed model. • The proposed model is a successful application of DBQ. Inappropriate curve speed influenced by the interactions of driver behaviours, vehicle dynamics and road environments is the dominant cause of vehicle lateral instability induced crashes, like sideslips and rollovers. The present study introduced a driver behaviour influence factor associated with drivers' driving styles comparing to a theoretical curve speed model that only considers the vehicle-road interaction. This factor is defined as the ratio of drivers' actual selected speed to the theoretical curve speed. Aiming at deriving the factor for different driving styles, it was utilized the 28-item Chinese version of Driver Behaviour Questionnaire (DBQ). A correlation analysis between DBQ subscales and the factor indicated that a driver with higher violations scores is prone to drive faster in curve negotiation. Based on this finding, 24 experienced professional drivers were classified into two types, i.e. the moderate and the aggressive, corresponding to their scores on DBQ violations scale. Through a simulation, it showed that the improved curve speed model could not only prevent the risks of rollover and sideslip, but also provided different appropriate curve safe speeds in accordance with drivers' driving styles. [ABSTRACT FROM AUTHOR]

• The relationships between traffic climate and driver behaviors were examined cross-culturally. • External affective demands and aberrant driver behaviors were positively related in Turkey and China. • External affective demands and positive driver behaviors were negatively related in Turkey and China. • Violations were negatively related to functionality in Turkey and negatively related to internal requirements in China. • The patterns of relationships might show both similarities and differences between countries. Road traffic accidents/fatalities and driver behaviors show regional differences. It is assumed that the perceived traffic climate in a given context is closely related to driver behaviors. In the current study, this assumption was tested cross-culturally for the first time. The aim was to compare the perceived traffic climate and driver behaviors between Turkey and China. Also, the relationships between traffic climate and driver behaviors in Turkey and China were investigated. In the study, there were 292 drivers aged between 21 and 64 years from China and 294 drivers aged between 19 and 61 from Turkey. The results revealed that Turkish drivers perceived their traffic climate higher in internal requirements and lower in external affective demands and functionality than Chinese drivers. Among driver behaviors, Turkish drivers reported higher numbers of violation and lower numbers of error than Chinese drivers. Perceiving traffic climate as externally demanding was positively related to aberrant driver behaviors (i.e., violations and errors) and negatively related to positive driver behaviors in both Turkey and China. Functionality was negatively related to violations in Turkey, and internal requirements were negatively related to violations in China. In both Turkey and China, external affective demands and functionality were closely related to driver behaviors and might be critical dimensions in road traffic safety. The interventions to improve road traffic safety should be planned based on the differences among cultures. Based on the differences, it might be plausible to suggest that more functional traffic is desired to increase road safety in Turkey, whereas higher internal requirements are important to increase road safety in China. [ABSTRACT FROM AUTHOR]

Highlights • The FOT data from 46 participants was used to analyze the effectiveness of drivers' characteristics, including gender, occupation and experience, on driving behaviors on a freeway deceleration lane. • Male drivers recognize risk more than female drivers, and also have more aggressive driving tendencies. • Professional drivers and experienced drivers made the last lane-change as early as possible to enter the deceleration lane. • The speed of the vehicles entering the exit ramp was significantly higher than the design speed. • The minimum TTC and the maximum deceleration show that the certain driving behaviors are related to high traffic risk. Abstract Deceleration lanes improve traffic flow by reducing interference, increasing capacity and enhancing safety. However, accident rates are higher on these interchange segments than on other freeway segments. It is important to attempt to reduce traffic accidents on these interchange segments by further exploring the behavior of different types of drivers on a highway deceleration lane. In this study, with field operational test (FOT) data from 89 driving instances (derived from 46 participants driving the test road twice) on a typical freeway deceleration lane, section speed profiles, vehicle trajectories, lane position and other key parameters were obtained. The lane-change characteristics and speed profiles of drivers with different genders, occupations and experiences were analyzed. The significant disparities between them reflects the risk associated with different groups of drivers. The study shows that male drivers changed to the outside lane earlier; professional drivers and experienced drivers made the last lane change as early as possible to enter the deceleration lane; and the speed of the vehicles entering the exit ramp was significantly higher than the speed limit. This research work provides ground truth data for deceleration lane design, driver ability training and off-ramp traffic safety management. [ABSTRACT FROM AUTHOR]

With the development of terrestrial networks and satellite constellations, vessel movement information can be effectively collected based on Automatic Identification System (AIS) receivers. Vessel motion pattern classification using AIS plays an important role in maritime monitoring and management. However, classifying vast amounts of vessel motion information is prohibitive workload. The aim of this study is to develop effective methods that can aid in automatic vessel motion pattern classification in inland waterways. First, the Least-squares Cubic Spline Curves Approximation (LCSCA) technique is used to represent the vessel motion trajectory. Then, a traditional classification model based on Lp-norm (0 < p < 1) sparse representation is improved to classify vessel motion patterns. And a Matching Pursuit - Fletcher Reeves (MPFR) method is developed to find the sparse solutions of the proposed model. To validate the performance of the proposed model, two AIS datasets from the Yangtze River are collected and applied in our experiment. According to the results, we can know that the proposed model can effectively classify vessel motion pattern in inland waterways. And the effectiveness of the proposed model is superior to those of other representative classification methods. [ABSTRACT FROM AUTHOR]

Hydroxyl groups are essential groups in the preparation of alumina-mullite fibers by the sol-gel process, which contributes to the formation of spinnable precursor sol. In this work, the removal of hydroxyl groups during pyrolysis and sintering, and its effect on fibers phase transition and microstructural evolution were systematically studied. Five pyrolyzed alumina-mullite fibers samples with different hydroxyl groups content were obtained by adjusting the pyrolysis temperature or introducing water vapor. It was revealed that the removal of hydroxyl groups existed throughout the sintering process, mainly concentrated before 800 °C. The mullitization temperature of the pyrolyzed fibers with more hydroxyl groups was about 1300 °C, while that of pyrolyzed fibers with fewer hydroxyl groups was 700 °C. In addition, an appropriate amount of residual hydroxyl groups (13.3 wt%) was beneficial to obtain a dense fine-grained microstructure of the pyrolyzed fibers after sintering, and the tensile strength of the fibers was increased to 1.6 ± 0.22 GPa. Finally, the influence mechanism of different hydroxyl content on the alumina-mullite fibers was discussed. [ABSTRACT FROM AUTHOR]

• 1319 questionnaires were collected from cargo TTWs groups all China. • Most attributes of Cargo TTWs groups: young men, less experience, high workload. • The three-layer risk framework was verified and extent with SEM. • Mediating mechanism, "risk factors - violations - errors", have been discovered. • Anxiety has a negligible risk effect and Workload has a direct weak risk effect. Cargo two- or three-wheeled vehicles (TTWs), as a new form of micro-mobility, have become a popular mode of urban cargo transportation in China. Cargo TTW riders' psychological factors and risky behaviors lead to a number of accidents. A questionnaire is designed by comprehensively considering these factors and behaviors of cargo TTW riders that includes eleven risk factors to quantitatively analyze the risky behaviors based on structural equation modeling (SEM). One thousand three hundred nineteen participants reported using cargo TTWs on a questionnaire distributed across the country. The characteristics of riding behavior data are analyzed to verify the three-layer risk theoretical framework of "Psychological factors (Personality traits/specific factors) - Psychological acceptability of risks (confidence/perception/attitude) - Risky behaviors". The results show that anger has a strong direct effect on riding violations, while normlessness and altruism have a direct effect on riding errors. Workload has a weak but direct effect on risky behaviors, and riding feedback has a weak and mixed effect. In addition, high-risk groups are identified by analysis of variance (ANOVA) with rider population attributes. These quantitative analyses can help guide safety countermeasures to mitigate accidents involving cargo TTWs. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. • A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed. • A three-layer decision-making framework is established for the OWT selection. • Comprehensively consider each influencing factor's qualitative and quantitative characteristics. • Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation. [ABSTRACT FROM AUTHOR]

• A novel improved GRA and fuzzy theories based prioritizing model is proposed. • Comprehensive consideration for varying weight of each influencing factor and expert. • Rational fuzzy numbers of domain experts are utilized to the newly proposed technique. • A new framework for influencing factors analysis system of autonomous ship maneuvering. • A feasibility basis for autonomous ship maneuvering decision-making analysis system. Ship maneuvering decisions are influenced by several factors, and it is essential to prioritize the main influencing factors for efficient selection of the corresponding maneuvering decisions. Meanwhile, the autonomous ship maneuvering decision-making influencing factors constitute a typical grey system, which is suitable for research by grey relational analysis. Furthermore, in the fuzzy approach, linguistic assessment of factors is evaluated to obtain priorities numbers. Therefore, this study mainly focuses on the concept of human-like maneuvering for autonomous ships. Based on experimental data of experienced seafarers and using a simulation platform under the scenario of the Shanghai Waigaoqiao wharf, an inference model utilizing grey and fuzzy theories is proposed. The proposed model combined with expert linguistic terms in order to select the ship maneuvering decision-making main influencing factors from multi-source influencing factors (in overall and separated categories of natural environment, ship motion, force parameters, draft, and position), and to study the decision-making prioritization for maritime traffic safety for specific ship maneuvering scenarios. This method can prioritize the main factors which affect maneuvering decisions as well as guide an autonomous ship-assisted or automatic maneuvering evaluation system for the research of human-like maneuvering behavior. This study provides a new perspective on the identification of main ship maneuvering decision-making influencing factors in theory and in practice. It can be utilized for better decision-making concerning maritime traffic safety of autonomous ship maneuvering, which in turn makes shipping safer and promote the application and spreading of autonomous ships. [ABSTRACT FROM AUTHOR]

• The effectiveness of ADAS on braking behavior is significant. • Drivers tend to increase braking times and reducing relative speed in the ADAS. • In any road conditions, when exposed in ADAS, the time headway has increased. • The impact of road categories and experience on driving behaviors is significant. • Drivers' acceptance on FCW function is higher than that of LDW. • Drivers' acceptance on expressway and freeway is higher than that on urban road. Various advanced driver assistance systems (ADAS) have been developed to improve drivers' behavior and perceptual ability; however, whether these ADAS have any measurable effect on driving performance needs to be verified by field operational tests. The purpose of this study was to evaluate the effectiveness of ADAS on Chinese drivers as well as any possible influences of roadway type, gender and experience on driving performance, which can be measured by several variables, including longitudinal, lateral and braking behavior. The ADAS used in this study was a Mobileye M630 with forward collision warning (FCW) and lane departure warning (LDW) functions. Thirty-two participants were recruited to drive a vehicle equipped with Mobileye M630. Participants drove the same test route twice. The route consisted of a 12 km urban road, 34 km urban expressway and 45 km freeway as well as a 14 km adaption road. Vehicle dynamics, environmental information and driving operational data was recorded by CAN (Controller Area Network) bus and video cameras. The results show that ADAS significantly affects braking behavior. Braking time increased and relative speed decreased when drivers were exposed to ADAS. The ADAS also significantly affects several longitudinal behaviors, including the longitudinal deceleration and time headway (THW). The occurrence of critically low THW decreased in the experiment. However, there was no significant effect on lateral behavior. Furthermore, driver acceptance of the FCW function was much higher than the LDW function, and acceptance on the expressway and freeway was much higher than on the urban road. The results also reveal the significant influence of road type and experience on driving behaviors. These findings support policy development and technology improvements for future development of ADAS. [ABSTRACT FROM AUTHOR]

• Effect of surrounding vehicles on target driver's behavior was described by force. • Vehicle cluster situation data was collected and analyzed. • Double variation process of cluster situation and driver's propensity was modeled. • The proposed model is verified by simulation, real and virtual vehicle experiment. The vehicle cluster situation is a kind of dynamic arrangement of a target vehicle and the surrounding vehicles during driving. Revealing the transfer mechanism of vehicle clustering in complex environments is of great significant for studying automated driving systems and driver assist systems. Taking three-lane scenario as an example, vehicle cluster situations changing with driver's evolving propensity were studied. To this end, the data of vehicle cluster situations were collected and analyzed through driving experiments in different environments. In addition, the dual random variations of the vehicle cluster situation and driver's propensity were modeled to explore the transfer mechanism. The verification results show that the predicted outcomes of vehicle cluster situations using the evolution rule of driver's propensity are consistent with the real-time recognition. Therefore, the transfer mechanism of vehicle cluster situations was found to be effective and reasonable. It is important for the research of intelligent driving command system of Internet of Things (IOT). [ABSTRACT FROM AUTHOR]

Highlights • A rollover speed model is established considering human, vehicle and road factors. • The model built into an smartphone can calculate safe speeds for heavy vehicles. • The smartphone based warning system is an human-machine interface for drivers. • The system is effective due to considering individual vehicles' characteristics. Abstract Inappropriate speed selection on a curved road is a main cause of rollover accidents for heavy vehicles due to their relative higher centers of gravity, comparing with those of passenger cars. Traditional driving safety improvement methods on curves include static/dynamic roadside speed limit signs that lack individual vehicle's characteristics, and the high-cost anti-rollover stability control systems that cannot take road geometric parameters like superelevation of a vehicle's upcoming curve into consideration. In this paper,a new rollover speed prediction model based on the derivation of three-degree-of-freedom vehicle dynamics and lateral load transfer ratio (LTR) index is presented. Through numerical experiments, the results show that this model could guarantee the vehicle roll stability with the calculated speed for entering a curve whose road radius is even 50 m, in which the vehicle's LTR never exceeds 0.72 and lateral acceleration is always less than 0.63 g. Moreover, the proposed model built in a mobile smartphone app can calculate curve radius at first, then provide an early alarming to the driver with an appropriate speed if rollover accident is imminent on the curve. The field tests on freeway off-ramps show that this smartphone-based rollover speed warning system can calculate the curve radii, and alert the driver with appropriate curve speeds that are partially equivalent to professional skilled drivers' speed choices. [ABSTRACT FROM AUTHOR]

• A hybrid traffic flow model of micro and macro is proposed. • The Maximum Likelihood Estimation of stop and go coefficients is deduced. • The mechanism of traffic flow oscillation is explored with Brownian noise. To investigate the stop-and-go phenomenon triggered by car-following, a hybrid model of micro and macro is proposed. Firstly, with the stochastic nature of driving behavior, a Brownian noise is added into the velocity difference to give a modification to the car-following model. Then, to combine the macroscopic traffic flow characteristics, the traffic stream model is introduced, and the two models are fused in the context of high traffic flow. Next, the theoretical validity of the fusion model is verified by the existence and uniqueness of the solution. The coefficients of the hybrid model are extracted as the stop coefficient and go coefficient which reflect the micro driving behavior. The Maximum Likelihood Estimations of the two coefficients are also given, as well as the boundedness of traffic oscillation in stop-and-go. Finally, numerical simulations show that the model has good fitting and prediction ability for traffic flow in high flow period. Moreover, the greater the stop coefficient, the stronger the traffic flow oscillation intensity, the larger the upper boundedness of the oscillation, the more severe the stop-and-go. [ABSTRACT FROM AUTHOR]

Although trucks only account for approximately 4% of all the vehicles based on the Federal Railway Administration (FRA) database, about 25% involved truck accidents happen at highway-rail grade crossings. This study applied an ordered probit model to explore the determinants of injury severity of truck drivers at highway-rail grade crossing in the United States. Given the importance of trucking to the economics of a country and the safety concerns posed by the trucks (as a result of their large size and weight making them difficult to control, maneuver, and stop), a comprehensive research on truck accidents is critical. Based on data analysis results, the strong effects of driver-, environmental-, weather- characteristics on the injury severities in truck accidents happened at highway-rail grade crossings are found. The findings reveal that better speed control for trucks will significantly reduce driver injury severity in accidents occurring at highway-rail grade crossings. In addition, several truck driver behavior characteristics (such as driving under influence of fatigue during peak hour) were found to be statistically significant predictors of high-level injury severity. Thus, education and enforcement targeted to truck drivers could facilitate safety improvements. Moreover, environmental factor (such as area type and roadway pavement) is found to be statistically significant. Truck drivers are more likely to have severe injury in open space area with low traffic volume compared with other areas. The bad weather and visibility condition is found to increase the probability of truck drivers’ high level injury severity. [ABSTRACT FROM AUTHOR]

Abstract: This research presents a comprehensive analysis of motor vehicle–bicycle crashes using 4 years of reported crash data (2004–2007) in Beijing. The interrelationship of irregular maneuvers, crash patterns and bicyclist injury severity are investigated by controlling for a variety of risk factors related to bicyclist demographics, roadway geometric design, road environment, etc. Results show that different irregular maneuvers are correlated with a number of risk factors at different roadway locations such as the bicyclist age and gender, weather and traffic condition. Furthermore, angle collisions are the leading pattern of motor vehicle–bicycle crashes, and different irregular maneuvers may lead to some specific crash patterns such as head-on or rear-end crashes. Orthokinetic scrape is more likely to result in running over bicyclists, which may lead to more severe injury. Moreover, bicyclist injury severity level could be elevated by specific crash patterns and risk factors including head-on and angle collisions, occurrence of running over bicyclists, night without streetlight, roads without median/division, higher speed limit, heavy vehicle involvement and older bicyclists. This study suggests installation of median, division between roadway and bikeway, and improvement of illumination on road segments. Reduced speed limit is also recommended at roadway locations with high bicycle traffic volume. Furthermore, it may be necessary to develop safety campaigns aimed at male, teenage and older bicyclists. [Copyright &y& Elsevier]

Frequent water traffic accidents pose severe threats to human life and property safety, the water environment, and adverse effects on social stability. Understanding historical accidents is essential for accident prevention and risk mitigation. However, at present, research on the comprehensive statistical analysis of characteristics and causes of ship accidents that occurred in the inland water areas of the Yangtze River, especially in the fluctuating backwater area (FBA) of Three Gorges Reservoir (TGR) region, is still scanty, even less a hierarchical and systematic analysis framework. Therefore, this paper proposes a comprehensive ship accident characteristics and causes analysis framework, and summarizes and visualizes the characteristics of ship accidents through the statistical and comparative analysis of historical data in terms of categories and severity of accidents, ship types involved in accidents, spatial and temporal distribution characteristics, ship accident losses, and root causes and lessons learned from the related accidents. In order to demonstrate the value of the proposed framework, based on the official sources, ten years of ship accident data from 2009 to 2018 in the FBA of TGR region are collected and analyzed in detail. On the basis of the results, this paper summarizes accident prevention and supervision guidelines to provide decision support for maritime safety. This research is of significance to the water traffic accident precaution and risk mitigation in this region and can be useful for other similar specific scenarios worldwide. • A hierarchical and systematic investigation framework is proposed for characteristics and causes analysis of ship accidents. • Human errors, unsafe ship conditions, and harsh traffic environment conditions are the main risk categories for water traffic accidents. • Ship accidents threaten human life safety and cause substantial economic losses, and accident prevention is the core of safety management. • Comprehensive case study manifests the proposed framework is applicable and significant to ship accidents analysis and risk mitigation. [ABSTRACT FROM AUTHOR]

• A deep learning method is proposed for autonomous ship-oriented small ship detection. • A modified Generative Adversarial Network is applied for training data augmentation. • An improved YOLO v2 algorithm is used for small ship detection. • Extensive experiments are conducted to show the effectiveness of the proposed method. Small ship detection is an important topic in autonomous ship technology and plays an essential role in shipping safety. Since traditional object detection techniques based on the shipborne radar are not qualified for the task of near and small ship detection, deep learning-based image recognition methods based on video surveillance systems can be naturally utilized on autonomous vessels to effectively detect near and small ships. However, a limited number of real-world samples of small ships may fail to train a learning method that can accurately detect small ships in most cases. To address this, a novel hybrid deep learning method that combines a modified Generative Adversarial Network (GAN) and a Convolutional Neural Network (CNN)-based detection approach is proposed for small ship detection. Specifically, a Gaussian Mixture Wasserstein GAN with Gradient Penalty is utilized to first directly generate sufficient informative artificial samples of small ships based on the zero-sum game between a generator and a discriminator, and then an improved CNN-based real-time detection method is trained on both the original and the generated data for accurate small ship detection. Experimental results show that the proposed deep learning method (a) is competent to generate sufficient informative small ship samples and (b) can obtain significantly improved and robust results of small ship detection. The results also indicate that the proposed method can be effectively applied to ensuring autonomous ship safety. [ABSTRACT FROM AUTHOR]

• A novel sparse representation method for pedestrian trajectory abnormal analysis. • Utilizing Lp-regularization (0 < p < 1) to get sparser solutions. • An effective solver for the proposed method with EM algorithm and entropy. Pedestrian abnormal trajectory understanding based on video surveillance systems can improve public safety. However, manually identifying pedestrian abnormal trajectories is usually a prohibitive workload. The objective of this study is to propose an automatic method for understanding pedestrian abnormal trajectories. An improved sparse representation model, namely information entropy constrained trajectory representation method (IECTR), is developed for pedestrian trajectory classification. It aims to reduce the entropy for trajectory representation and to obtain superior analyzing results. In the proposed method, the orthogonal matching pursuit (OMP) is embedded in the expectation maximization (EM) method to iteratively obtain the selection probabilities and the sparse coefficients. In addition, the lower-bound sparser condition of L p -minimization (0 < p < 1) is applied in the proposed method to guarantee salient solutions. In order to validate the performance and effectiveness of the proposed method, classification experiments are conducted using five pedestrian trajectory datasets. The results show that the identification accuracy of the proposed method is superior to the compared methods, including naïve Bayes classifier (NBC), support vector machine (SVM), k-nearest neighbor (kNN), and typical sparse representation-based methods. [ABSTRACT FROM AUTHOR]

To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships. • Enable a ship to make decisions like a human under multiple environmental constraints. • Propose a novel intelligent ship human-like maneuvering decision recognition model. • Develop the new standardization principle of maneuvering decision-making factors. • Automatic acquisition and representation of the OOW's decision-making knowledge. • Obtain the actual crews' operational data from full-task handling simulation platform. [ABSTRACT FROM AUTHOR]