•90 documents were identified and reviewed using the Task-Capability Interface model.•There is a need to empirically assess dwell time for changeable roadside advertising signs.•Research needs to ...consider other road users (i.e., motorcycle riders and pedestrians).•Roadside advertising technology is constantly changing.
Driver inattention and distraction are recognised as two of the most critical factors for road safety worldwide. While roadside advertising is often identified as a potential source of distraction, it has received less attention compared to other types of distractions such as texting or calling while driving. Therefore, this study focused on the impact of roadside advertising signs on driver behaviour and road safety. To examine this, a theory-driven systematic literature review was undertaken. In total, 90 unique documents were identified and reviewed using the Task-Capability Interface (TCI) Model to explain the potential safety impact of roadside advertising. The findings confirmed that the TCI model is a useful tool for describing the relationship between roadside advertising and driver behaviour. From this perspective, roadside advertising signs can be considered environmental clutter, which adds additional demands to the driving task. In particular, roadside advertising signs impaired eye movement patterns of drivers. Additionally, it was demonstrated that the impact of roadside advertising on driving behaviour is greatly moderated by individual differences among drivers. Of great importance was that young drivers invest more attentional resources in interacting with roadside advertising, which suggests a lower capacity to discriminate between relevant and irrelevant driving information. Based on the available evidence, however, it is not possible to definitively conclude that there is a direct relationship between the driving behaviour changes attributed to roadside advertising and road crashes. Nonetheless, while most studies remain inconclusive, there is an emerging trend in the literature suggesting that roadside advertising can increase crash risk, particularly for those signs that have the capacity to frequently change (often referred to as digital billboards). Lastly, it is important to mention that most of the empirical studies undertaken to date feature strong methodological limitations. Consequently, there is an urgent need for more research in this area, given that roadside technology and the transport system are changing rapidly.
Efficient construction and optimization of mapping correlation of organic Rankine cycle (ORC) system under driving environment is the key to obtain the actual waste heat recovery limit. Under ...external and internal disturbances, the ORC operating characteristics have obvious uncertainty and nonlinearity. Based on driving conditions and ORC operation characteristics, this paper proposes an ensemble approach of self-organizing adaptive maps and dynamic multi-objective optimization for ORC under driving environment from the perspectives of coupling ORC integration system, variable data selection, parameter coupling correlation, adaptive structure design and multi-objective optimization. This approach can achieve efficient capture, construction and optimization of ORC dynamic characteristics in complex driving environment. Compared with direct modeling, input variables decreased by at least 69.23%. RMSE decreased by at least 66.06%. Approach can adjust operating parameters in real time according to the fluctuation of actual driving environment, break through the trade-off effect between thermal efficiency and emissions of CO2 equivalent (ECE), to keep the optimal state of thermal efficiency and ECE continuously. The approach proposed in this paper can provide a new idea for efficient construction and rapid optimization of ORC dynamic mapping association in driving environment.
•Proposed self-organizing adaptive dynamic modeling for organic Rankine cycle (ORC).•An ensemble approach for multi-objective optimization of ORC in driving environment.•Approach proved to be effective and adaptive.•The driving environment data confirmed the robustness of our approach.
•The number and mean duration of glances away from the non-driving related tasks (NDRTs) increased significantly when the standard deviation (SD) of speed was high.•The mean speed had a significant ...effect on the mean glance duration, with longer glances away from NDRTs when mean speed was low, compared to that in high speed.•There was a significant effect of age on NDRT engagement, with older drivers less likely to engage in NDRTs, while female drivers were more engaged in NDRTs than males.
Previous simulator and real-world studies with SAE Level 2 automated vehicles (AVs) have shown that, when compared to manual driving, drivers are more inattentive to the driving environment when automation is engaged, as reflected by fewer glances towards the forward roadway and side/rear view mirrors, and more focus on non-driving related tasks (NDRTs). Manual driving studies also suggest that drivers are more likely to engage in NDRTs during slow-moving or stationary traffic conditions. The aim of the current study was to understand whether NDRT engagement and visual attention patterns are impacted by the driving environment while drivers experienced a ride in a real-world SAE Level 3 AV. Forty-six video clips, from 32 drivers interacting with NDRTs during L3 motorway driving were analysed for this study. Due to the absence of externally facing cameras, the mean and standard deviation (SD) of driving speed were used as a proxy for assessing the surrounding traffic volume. The number of glances, and mean glance duration away from NDRTs per minute, were used as proxy measures for NDRT engagement. A generalised linear mixed model (GLMM) was used to investigate the effect of surrounding traffic on NDRT engagement. Results showed that the number and mean duration of glances away from the NDRT increased significantly when the SD of speed was high. The mean speed had a significant effect on the mean glance duration, with longer glances away from NDRTs when mean speed was low, compared to that in high speed. There was a significant effect of age on NDRT engagement, with older drivers less likely to engage in another task, while female drivers were more engaged in NDRTs than males. Overall, the results indicate that drivers’ propensity to engage in NDRTs is impacted by the AV’s speed, which is influenced by the volume of surrounding traffic. These results are useful for understanding the implications of surrounding traffic on drivers’ self-regulated engagement in NDRTs in the real world during SAE Level 3 driving.
Driving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency ...models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction.
Crash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models.
Model estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood.
The study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.
•Real-time refined-scale data are incorporated to characterize the time-varying nature of these contributing factors•Unbalanced panel data Mixed logit models are developed to predict hourly crash likelihood•The estimation results show that a number of factors were related to crash likelihood on I-25•This study underscores the unique and significant impacts of the real-time weather, road surface and traffic conditions
To improve driving safety and avoid accidents caused by driving fatigue, drowsiness detection aims to alarm the driver before he/she falls asleep. Since breathing rate is a key indicator of the ...drowsy state, respiration monitoring in the noisy driving environment is critical for developing an effective driving fatigue detection system. In this paper, we propose, for the first time, an RFID based respiration monitoring system for driving environments. The system estimates the respiration rate of a driver based on phase values sampled from multiple RFID tags attached to the seat belt, while exploiting the tag diversity to combat the strong noise in the driving environment. Both tensor completion and tensor Canonical Polyadic Decomposition (CPD) are applied to process the phase values, to overcome the influence of frequency hopping, random sampling, vehicle vibration, and other environmental movements. The proposed system is analyzed and implemented with commodity RFID devices. Its accurate and robust performance is demonstrated with extensive experiments conducted in a real driving car.
Vigilance decrement in driving tasks has been reported to be a major factor in fatal accidents and could severely endanger public transportation safety. However, efficient approaches for estimating ...vigilance in real driving environment are still lacking. In this paper, we propose a novel approach for implementing continuous vigilance estimation using forehead electrooculograms (EOGs) acquired by wearable dry electrodes in both simulated and real driving environments. To improve the feasibility of this approach for real-world applications, a forehead EOG-based electrode placement with only four electrodes is designed. Flexible dry electrodes and an acquisition board are integrated as a wearable device for recording EOGs. Twenty and ten subjects participated in the simulated and real-world driving environment experiments, respectively. Accurate eye movement parameters from eye-tracking glasses are extracted to calculate the PERCLOS index for vigilance annotation. This is because the vigilance state is a temporally dynamic process, and a continuous conditional random field and a continuous conditional neural field are introduced to construct more accurate vigilance estimation models. To evaluate the efficiency of our system, systematic experiments are performed in real scenarios under various illumination and weather conditions following laboratory simulations as preliminary studies. The experimental results demonstrate that the wearable dry electrode prototype, which has a relatively comfortable forehead setup, can efficiently capture vigilance dynamics. The best mean correlation coefficients achieved by our proposed approach are 71.18% and 66.20% in laboratory simulations and real-world driving environments, respectively. The cross-environment experiments are performed to evaluate the simulated-to-real generalization and a best mean correlation coefficient of 53.96% is achieved.
•Proposed an objective dynamic driving environment complexity quantification method.•A quantification method based on vehicle-pair complexity as the basic analysis unit.•Safety-oriented dynamic ...interactive object identification.•Conducted case studies to demonstrate the feasibility of the quantification method.•Validated the consistency of objective model and subjective human drivers’ judgment.
To meet the requirements of scenario-based testing for Autonomous Vehicles (AVs), driving scenario characterization has become a critical issue. Existing studies have concluded that complexity is a necessary criticality measure for supporting critical AV testing scenario identification. However, the existing scenario complexity quantification studies mainly have two limitations, namely, subjective quantification methods highly rely on human participations and are difficult to apply to big data, and existing objective methods lack consideration of human driver characteristics and hence the performance cannot be guaranteed. To bridge these research gaps, a general objective quantification framework is proposed to quantify human drivers’ judgement on driving environment complexity, by describing the vehicle-vehicle spatial-temporal interactions from the perspectives of quantity, variety, and relations. The model mainly contains three parts. First, to describe quantity information, a dynamic influencing area was set to identify the surrounding vehicles that contribute to driving environment complexity based on Responsibility-Sensitive Safety (RSS) theory. Second, considering the various surrounding vehicles’ driving statuses, behaviors, and intentions, a basic vehicle-pair complexity quantification model was constructed based on encounter angles. Then, nonlinear relationships based upon information entropy theory were introduced to capture the heterogeneous longitudinal and lateral complexities. Third, a vehicle-pair complexity aggregation and smoothing step was conducted to reflect the characteristics of human driver's cognition. To demonstrate the abovementioned model, empirical Field Operational Test (FOT) data from Shanghai urban roadways were used to conduct case studies, and it can be concluded that this model can accurately describe the timing and extent of the complexity change, and reveal the complexity differences due to scenario type and spatial-temporal heterogeneity. Besides, Inter-Rater Reliability (IRR) index was calculated to validate the consistency of scenario complexity judgement between the proposed model and human drivers, and for performance comparison with the existing models. Finally, the applications of the proposed model and its further investigations have been discussed.
Driving fatigue is one of the main factors leading to traffic accidents. So, it is necessary to detect driver fatigue accurately and quickly.
To precisely detect driving fatigue in a real driving ...environment, this paper adopts a classification method for driving fatigue based on the wavelet scattering network (WSN). Firstly, electroencephalogram (EEG) signals of 12 subjects in the real driving environment are collected and categorized into two states: fatigue and awake. Secondly, the WSN algorithm extracts wavelet scattering coefficients of EEG signals, and these coefficients are used as input in support vector machine (SVM) as feature vectors for classification.
The results showed that the average classification accuracy of 12 subjects reached 99.33%; the average precision rate reached 99.28%; the average recall rate reached 98.27%; the average F1 score reached 98.74%; and the average classification accuracy of the public data set SEED-VIG reached 99.39%. The average precision, recall rate and F1 score reached 99.27%, 98.41% and 98.83% respectively.
In addition, the WSN algorithm is compared with traditional convolutional neural network (CNN), Sparse-deep belief networks (SDBN), Spatio-temporal convolutional neural networks (STCNN), Long short-term memory (LSTM), and other methods, and it is found that WSN has higher classification accuracy.
Furthermore, this method has good versatility, providing excellent recognition effect on small sample data sets, and fast running time, making it convenient for real-time online monitoring of driver fatigue. Therefore, the WSN algorithm is promising in efficiently detecting driving fatigue state of drivers in real environments, contributing to improved traffic safety.
The deployment of connected, automated vehicles (CAVs) provides the opportunity to enhance the safety and efficiency of transportation systems. However, despite the rapid development of this ...technology, human-driven vehicles are predicted to predominate the vehicle fleet, compelling CAVs to be able to operate in a mixed traffic environment. The key to achieving a reliable and safe human-CAV collaboration in such environments is to characterize the interactions between the actors and incorporate the underlying decision-making mechanism of human drivers into CAVs' motion planning algorithms. Towards this goal and extending a previously developed game theoretical model, the present study proposes a decision-making dynamic to achieve more realistic models of human behavior when making conflicting maneuvers at intersections. A novel field test is conducted to extract the required modeling data directly from CAVs' perception system, facilitating the incorporation of the model into CAV navigation algorithms. Model validation and sensitivity analysis provided invaluable insights into the nature of human decisions and indicated that the proposed structure is robust to environmental uncertainties and can well capture the real-world behavior of human drivers in unprotected left-turn maneuvers. The derived knowledge can be directly used in CAV motion planning algorithms to provide the vehicle with more accurate predictions of human actions when operating in mixed traffic environments.
PDF
