A New Behavior-Assisted Semantic Recognition Method for Smart Home

In the recent Smart Home(SH)research work,intelligent service recommendation technique based on behavior recognition,it has been extensively preferred by researchers.However,most current research uses the Semantic recognition to construct the user’s basic behavior model.This method is usually restricted by environmental factors,the way these models are built makes it impossible for them to dynamically match the services that might be provided in the user environment.To solve this problem,this paper proposes a Semantic behavior assistance(Semantic behavior assistance,SBA).By joining the semantic model on the intelligent gateway,building an SA model,in this way,a logical Internet networks for smart home is established.At the same time,a behavior assistant method based on SBA model is proposed,among them,the user environment-related entities,sensors,devices,and user-related knowledge models exist in the logical interconnection network of the SH system through the semantic model.In this paper,the data simulation experiment is carried out on the method.The experimental results show that the SBA model is better than the knowledge-based pre-defined model.

Behavioral adaptation of drivers when driving among automated vehicles

Purpose–This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles(AVs)compared to driving among manually driven vehicles(MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations.Here,mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.Design/methodology/approach–A driving simulator study is designed to explore whether such behavioral adaptations exist.Two different driving scenarios were explored on a three-lane highway:driving on the main highway and merging from an on-ramp.For this study,18 research participants were recruited.Findings–Behavioral adaptation can be observed in terms of car-following speed,car-following time gap,number of lane change and overall driving speed.The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs.Although significant differences in behavior were found in more than 90%of the research participants,they adapted their behavior differently,and thus,magnitude of the behavioral adaptation remains unclear.Originality/value–The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles.This finding differs from previous studies,which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles.Furthermore,the surrounding vehicles in this study are more“free flow’”compared to previous studies with a fixed formation such as platoons.Nevertheless,long-term observations are required to further support this claim.

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Purpose–An individual’s driving style significantly affects overall traffic safety.However,driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data.As such,the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies,improving traffic safety and reducing fuel consumption.This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions(DOCs)using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system(ADAS).Design/methodology/approach–Specifically,a driving style recognition framework based on longitudinal DOCs was established.To train the model,a real-world driving experiment was conducted.First,the driving styles of 44 drivers were preliminarily identified through natural driving data and video data;drivers were categorized through a subjective evaluation as conservative,moderate or aggressive.Then,based on the ADAS driving data,a criterion for extracting longitudinal DOCs was developed.Third,taking the ADAS data from 47 Kms of the two test expressways as the research object,six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed.Finally,four machine learning classification(MLC)models were used to classify and predict driving style based on the natural driving data.Findings–The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion.Cautious drivers undertook the largest proportion of the free cruise condition(FCC),while aggressive drivers primarily undertook the FCC,following steady condition and relative approximation condition.Compared with cautious and moderate drivers,aggressive drivers adopted a smaller time headway(THW)and distance headway(DHW).THW,time-to-collision(TTC)and DHW showed highly significant differences in driving style identification,while longitudinal acceleration(LA)showed no significant difference in driving style identification.Speed and TTC showed no significant difference between moderate and aggressive drivers.In consideration of the cross-validation results and model prediction results,the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting>multi-layer perceptron>logistic regression>support vector machine.Originality/value–The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models.This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment,such as ADAS.

Performance evaluation framework of Wyoming connected vehicle pilot deployment program: summary of Phase 2 pre-deployment efforts and lessons learned

Purpose–This paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle(CV)Pilot Deployment Program(WYDOT Pilot).Design/methodology/approach–This paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot.An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data.Findings–This paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications.Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects.Analyses were conducted on data collected during the baseline period,and pre-deployment conditions were established for 1 performance measures.Additionally,microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system,particularly when evaluating system performance under various CV penetration rates and/or CV strategies.Practical implications–The proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities.Originality/value–To the best of the authors’knowledge,this is thefirst research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions.This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.

Analysis of drivers’characteristic driving operations based on combined features

Purpose–Analysis of characteristic driving operations can help develop supports for drivers with different driving skills.However,the existing knowledge on analysis of driving skills only focuses on single driving operation and cannot reflect the differences on proficiency of coordination of driving operations.Thus,the purpose of this paper is to analyze driving skills from driving coordinating operations.There are two main contributions:the first involves a method for feature extraction based on AdaBoost,which selects features critical for coordinating operations of experienced drivers and inexperienced drivers,and the second involves a generating method for candidate features,called the combined features method,through which two or more different driving operations at the same location are combined into a candidate combined feature.A series of experiments based on driving simulator and specific course with several different curves were carried out,and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.Design/methodology/approach–AdaBoost was used to extract features and the combined features method was used to combine two or more different driving operations at the same location.Findings–A series of experiments based on driving simulator and specific course with several different curves were carried out,and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method.Originality/value–There are two main contributions:the first involves a method for feature extraction based on AdaBoost,which selects features critical for coordinating operations of experienced drivers and inexperienced drivers,and the second involves a generating method for candidate features,called the combined features method,through which two or more different driving operations at the same location are combined into a candidate combined feature.

Influence of automated driving on driver’s own localization:a driving simulator study

Purpose–Level 3 automated driving,which has been defined by the Society of Automotive Engineers,may cause driver drowsiness or lack of situation awareness,which can make it difficult for the driver to recognize where he/she is.Therefore,the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving.Design/methodology/approach–Seventeen drivers were divided into the automated operation group and manual operation group.Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations.The automated operation group was forced to select a course after receiving a Request to Intervene(RtI)from an automated driving system.Findings–A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI.Originality/value–This is a fundamental research that examined how the automated driving operation affects the driver's own localization.The experimental results suggest that it is not enough to simply issue an RtI,and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI.This conclusion can be taken into consideration for engineers who design automatic driving vehicles.

Safety effects of work zone advisory systems under the intelligent connected vehicle environment:a microsimulation approach

Purpose–This study aims to investigate the safety effects of work zone advisory systems.The traditional system includes a dynamic message sign(DMS),whereas the advanced system includes an in-vehicle work zone warning application under the connected vehicle(CV)environment.Design/methodology/approach–A comparative analysis was conducted based on the microsimulation experiments.Findings–The results indicate that the CV-based warning system outperforms the DMS.From this study,the optimal distances of placing a DMS varies according to different traffic conditions.Nevertheless,negative influence of excessive distance DMS placed from the work zone would be more obvious when there is heavier traffic volume.Thus,it is recommended that the optimal distance DMS placed from the work zone should be shortened if there is a traffic congestion.It was also revealed that higher market penetration rate of CVs will lead to safer network under good traffic conditions.Research limitations/implications–Because this study used only microsimulation,the results do not reflect the real-world drivers’reactions to DMS and CV warning messages.A series of driving simulator experiments need to be conducted to capture the real driving behaviors so as to investigate the unresolved-related issues.Human machine interface needs be used to simulate the process of in-vehicle warning information delivery.The validation of the simulation model was not conducted because of the data limitation.Practical implications–It suggests for the optimal DMS placement for improving the overall efficiency and safety under the CV environment.Originality/value–A traffic network evaluation method considering both efficiency and safety is proposed by applying traffic simulation.