Driver Response Research Articles

Analysis of Driver's Physiological Responses and Task Load in Curved and Spiral Tunnels: A Naturalistic Driving Experiment

Analysis of Driver's Physiological Responses and Task Load in Curved and Spiral Tunnels: A Naturalistic Driving Experiment

Cooperative control of mixed vehicle platoon based on pinning consensus of heterogeneous multi‐agent system

AbstractThis paper proposes a longitudinal controller for the mixed vehicle platoon in the presence of driver response time‐delay and communication time‐delay based on the pinning consensus of heterogeneous multi‐agent system. Firstly, an adaptive pinning consensus control protocol and derive sufficient conditions for the heterogeneous non‐linear multi‐agent system are designed to achieve pinning consistency. Then, the heterogeneous characteristics of the vehicle are described based on the car‐following model, then the mixed vehicle platoon system model is constructed and a mixed vehicle platoon controller based on heterogeneous multi‐agent pinning consensus is proposed. Besides, the driver response time‐delay and communication time‐delay are introduced into the model, and a controller based on time‐delay heterogeneous multi‐agent pinning consensus is designed. Finally, the effectiveness of the proposed controller is verified by numerical simulations, and the effect of number of connected autonomous vehicle, different types of vehicle order, driver response time‐delay, communication time‐delay and the parameter of the controller on stability of mixed vehicle platoon is also quantitatively demonstrated.

Comparing Static and Dynamic Vibration Cues in Wristband Haptic Feedback for Enhanced Driver Response in Automated Vehicles

Higher levels of driving automation make effective takeover requests critical. The wrist’s sensitivity to vibration makes wristband devices a potential carrier for sending these requests. However, the impacts of conveying takeover requests through directional vibrotactile patterns such as dynamic patterns (sequential stimuli occurring at different locations on the wrist) and static patterns (fixed stimuli at the same locations on the wrist) are unclear. Therefore, this study examined the effects of directional vibrotactile patterns on takeover performance among younger and older adults. Participants responded to four patterns (two dynamic, one static, and one baseline) in a simulated SAE Level 3 automated vehicle. Takeover performance was evaluated using reaction time and takeover time. The results show that the static and baseline patterns had shorter reaction and takeover times compared to the dynamic patterns. In addition, younger adults react faster to takeover requests compared to older adults. Findings provide important insights for the future design of human-machine interfaces via wristband devices for automated vehicles.

Evaluating Driver Response to Bridge Deck Warning Systems During Winter Weather Conditions

Warning signs are often deployed at bridge overpasses during winter to warn motorists of potentially icy surface conditions on the bridge, although the effectiveness of these signs is questionable. One potential improvement to the standard signage methods is the bridge deck warning system (BDWS), which activates a flashing warning sign border or beacon when conditions warrant based on real-time weather and bridge surface data. However, BDWSs have not been broadly implemented in the United States, and consequently, their effectiveness as a safety countermeasure is not well established. To address this knowledge gap, a series of winter field evaluations were performed along three freeway bridge overpasses in Michigan to assess the effectiveness of various BDWS strategies as a speed reduction countermeasure for motorists approaching a potentially icy bridge. The results showed that a BDWS with a flashing light emitting diode (LED) border reduced motorist speeds when encountering a bridge during winter weather conditions compared with the standard “Bridge Ices Before Road” warning sign. The speed reduction effect was consistent between passenger cars and heavy trucks, but was somewhat stronger at night compared with daytime. Greater speed reductions were observed when the BDWS sign was combined with a dynamic speed feedback sign (DSFS) that displayed a “Slow Down” or “Icy Road” message to approaching motorists, with the strongest effects observed when the DSFS message was pulsed between high and low brightness intensities. BDWSs that included only a flashing overhead beacon did not have a significant effect on speeds compared with the standard warning sign.

Abstract A106: Disparities in the presence of driver mutations and response to tyrosine kinase inhibitors in young patients with lung cancer

Abstract Background: A concerning number of young patients (<50 years old) have been diagnosed with advanced lung cancer in recent years, likely due to driver mutations. This study examined disparities in the presence of driver mutations and response to tyrosine kinase inhibitors (TKI) in young patients with lung adenocarcinoma. Methods: This retrospective study included 98 patients (18-50 years old) with lung adenocarcinoma treated at the University of Miami Sylvester Comprehensive Cancer Center (2004-2023). Data from 81 patients with genetic information were analyzed. Patient demographics, clinical characteristics, and outcomes were collected. Chi- square and Kaplan-Meier methods were used, and p-values < 0.05 were considered significant. Results: Among the 17 patients without biomarker testing, 88% were white, 64.7% female and 59% Hispanic. The mean age at diagnosis for the 81 patients with genetic data was 41. Most were never smokers (56%), female (67%), white (83%), and Hispanic (52%) with stage IV disease (64%). At data cutoff in June 2023, 43 (53%) patients were alive, with a mOS of 27 months. Of the 81 patients, 58 (71.6%) had at least one driver mutation. The most common mutations were EGFR (31%), ALK (27%), and ROS1 (6%). No differences in mutation expression were found by gender, race, or ethnicity. Among patients with an actionable mutation, there were no significant differences in treatment with a TKI by race (p=0.337), gender (p=0.347), or ethnicity (p=0.940). However, 50% of eligible Black patients were not treated with a TKI compared to 26% of white patients. TKI as first-line treatment did not significantly vary by race (p=0.551), gender (p=0.883), or ethnicity (p=0.062), but 53% of white patients received a TKI as first-line therapy compared to 33% of Black patients. At data cutoff, there were no differences in the number of patients still taking a TKI based on gender (p=0.616), race (p=0.665), or ethnicity (p=0.465), although 46% of Hispanic patients were still on treatment compared to 35% of non-Hispanic patients. PFS on a TKI was similar for race (p=0.352) and gender (p=0.258), however Hispanic patients with an actionable mutation had a significantly longer PFS on a TKI than non-Hispanic patients (p=0.019), but not after controlling for age (p=0.116). Conclusion: This study is among the first to analyze disparities in driver mutations and response to targeted therapy in young patients with lung adenocarcinoma. No significant differences were found in TKI treatment, TKI as first-line therapy, or current TKI use by gender, race, or ethnicity. Notable trends included a greater percentage of eligible Black patients not receiving TKIs and fewer Black patients receiving TKIs as first-line therapy, although the sample of Black patients was small. Hispanic patients had a significantly longer PFS on TKIs than non-Hispanics, but not after controlling for age. Further research is needed to evaluate these trends, particularly with a larger cohort of Black patients. Citation Format: Kyle Edwards, Brandon Rose, Gilberto Lopes, Estelamari Rodriguez, Coral Olazagasti. Disparities in the presence of driver mutations and response to tyrosine kinase inhibitors in young patients with lung cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A106.

Driver Behavior in Response to Forward Collision Warnings Considering Driving Context

Forward collision warnings (FCW) are designed to warn drivers of potential collisions, but their effectiveness may vary based on driving conditions and driver responses. This study investigated driver behavior in response to real-world FCW alerts across various driving contexts with the aims of evaluating the prevalence and characteristics of FCW events, examining how often these events occur, their severity, and the contexts in which they happen. The current analysis considered driver responses to FCW events, including changes in vehicle kinematics, visual attention, and hand-on-wheel (HOW) behavior. Findings suggest that lower-severity FCWs are more common in local road contexts and associated with adjacent vehicles, while higher-severity FCWs were more frequent on highways and in the presence of lead vehicles. The study highlights the potential value of integrating additional contextual information into FCW systems to inform drivers better and reduce unnecessary alerts.

Driving in the Extreme: Sensing Drifting and the Expert Driver Response

Driving in the Extreme: Sensing Drifting and the Expert Driver Response

Examination of Driver Interpretation and Response to Snowplow Rear-End Lighting Configurations

Examination of Driver Interpretation and Response to Snowplow Rear-End Lighting Configurations

Geographically driven shifts in land use influence phytoplankton community patterns in the Inner Mongolian Plateau lakes

Abstract Phytoplankton play an irreplaceable role as producers in maintaining lake ecosystems. Nevertheless, scant attention has been given to investigating the dispersion of phytoplankton communities and the factors influencing them across expansive areas. In this study, we present the results of a survey on the distribution of phytoplankton community and the effects of different driving factors in 11 lakes along Inner Mongolia in July–August 2020. Non-metric multidimensional scaling analysis and variance decomposition (VPA) were used to elucidate the distribution of phytoplankton communities and the response of drivers. A total of 169 species of phytoplankton from 8 phyla were detected. Both the abundance and diversity of phytoplankton in the Inner Mongolia lakes showed a trend of high in the east and low in the west (with Daihai Lake as the boundary). The Margalef index of phytoplankton significantly negatively correlated with salinity (r = −0.707, P < 0.05) and total dissolved solids (r = −0.720, P < 0.05), and both density and biomass highly significantly positively correlated with the suspended solids, Chlorophyll a and trophic level index. The VPA explained 38.9% of the changes in the phytoplankton community with the highest rate of explanation of land use. Therefore, preventing anthropogenic impacts, as well as reducing nutrient loads, can effectively ensure the ecological diversity of lake phytoplankton in lake populations with large geographical spans and varying levels of nutrients.

Recurrent neural networks-guided vector-valued synchronized key exchange for secure and privacy-preserving communication in Industrial Internet of Things

This paper presents an innovative approach to the problem of key exchange in the Industrial Internet of Things (IIoT) implementation. Recurrent Neural Networks (RNNs) and vector-valued neural synchronization are the key components of the proposed secure transmission method. Drive-response methods are integrated to improve speed in important applications, meeting the continuous need for effective cryptographic key exchange between IIoT devices. Conventional algorithms face drawn-out assessment procedures that jeopardize neural synchronization concealment. This work examines how random input vector generation and synchronization issues in RNNs with drive-response are affected by proportional and non-proportional delays. Furthermore, it delves into an unexplored domain, investigating the synchronization of response-based RNN systems without delays and drive-response-based RNN systems with various proportional delays. It also suggests a simplified analysis of Artificial Neural Networks (ANNs) synchronization, organizing ANNs for session key switch-over using an RNN system. To achieve polynomial and non-polynomial synchronization in the proposed driver response systems, this method offers several benefits: it introduces a polynomial synchronization theory for RNNs to generate synchronized input vectors for ANN synchronization; it uses inequality assessment techniques and Lyapunov formulas to derive relevant control inputs and time-dependent conditions; it establishes the relationship between polynomial and non-polynomial synchronization; and it provides numerical examples demonstrating its effectiveness. It also builds a neural network for generating session keys throughout the IIoT network by aligning vector-valued ANNs in a reciprocal manner. This strategy outperforms previous approaches in the literature, validated through simulations, balances resilience against attacks with minimal computational load, resulting in more effective and resilient industrial applications.

Mixed histiocytic neoplasms: A multicentre series revealing diverse somatic mutations and responses to targeted therapy.

Histiocytic neoplasms are diverse clonal haematopoietic disorders, and clinical disease is mediated by tumorous infiltration as well as uncontrolled systemic inflammation. Individual subtypes include Langerhans cell histiocytosis (LCH), Rosai-Dorfman-Destombes disease (RDD) and Erdheim-Chester disease (ECD), and these have been characterized with respect to clinical phenotypes, driver mutations and treatment paradigms. Less is known about patients with mixed histiocytic neoplasms (MXH), that is two or more coexisting disorders. This international collaboration examined patients with biopsy-proven MXH with respect to component disease subtypes, oncogenic driver mutations and responses to conventional (chemotherapeutic or immunosuppressive) versus targeted (BRAF or MEK inhibitor) therapies. Twenty-seven patients were studied with ECD/LCH (19/27), ECD/RDD (6/27), RDD/LCH (1/27) and ECD/RDD/LCH (1/27). Mutations previously undescribed in MXH were identified, including KRAS, MAP2K2, MAPK3, non-V600-BRAF, RAF1 and a BICD2-BRAF fusion. A repeated-measure generalized estimating equation demonstrated that targeted treatment was statistically significantly (1) more likely to result in a complete response (CR), partial response (PR) or stable disease (SD) (odds ratio [OR]: 17.34, 95% CI: 2.19-137.00, p = 0.007), and (2) less likely to result in progression (OR: 0.08, 95% CI: 0.03-0.23, p < 0.0001). Histiocytic neoplasms represent an entity with underappreciated clinical and molecular diversity, poor responsiveness to conventional therapy and exquisite sensitivity to targeted therapy.

Impacts of Emergency Vehicle Marking Characteristics and Wearable Lights on Driver Responses

Retroreflective markings provide important visual information to drivers about emergency vehicles and the incidents at which they are working. A stationary outdoor field study to investigate the impacts of emergency vehicle marking color, retroreflectivity level, and spatial patterns on drivers’ ability to see nearby emergency responders was carried out. The study also examined the impacts of a wearable flashing light-emitting diode (LED) light. Based on the results, agencies should use materials with higher levels of retroreflectivity carefully, especially when they will be covering large-area vehicle surfaces. Limiting the maximum retroreflectivity level to no greater than American Society for Testing and Materials Type III may help lessen negative impacts of bright reflective materials on drivers’ ability to see emergency responders. As long as the average reflectivity of different color combinations is similar to that of red and yellow reflective markings, chevron patterns with those color combinations will not reduce emergency responder visibility. Outline patterns of reflective markings on vehicles performed similarly at night to patterns covering most of the vehicle surface. The effectiveness of using high-retroreflectivity materials in an outline pattern in combination with lower- (or non-) retroreflectivity materials elsewhere on the surface should be studied. Wearable flashing LED lights can make emergency responders easier to see at night, provided they do not increase glare to approaching drivers.

Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP

Abstract. The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts.

Microbial and environmental medium-driven responses to phosphorus fraction changes in the sediments of different lake types during the freezing period.

The comparative study of the transformation among sediment phosphorus (P) fractions in different lake types is a global issue in lake ecosystems. However, interactions between sediment P fractions, environmental factors, and microorganisms vary with the nutrient status of lakes. In this study, we combine sequential extraction and metagenomics sequencing to assess the characteristics of P fractions and transformation in sediments from different lake types in the Inner Mongolian section of the Yellow River Basin. We then further explore the response of relevant microbial and environmental drivers to P fraction transformation and bioavailability in sediments. The sediments of all three lakes exhibited strong exogenous pollution input characteristics, and higher nutritional conditions led to enhanced sediment P fraction transformation ability. The transformation capacity of the sediment P fractions also differed among the different lake types at the same latitudes, which is affected by many factors such as lake environmental factors and microorganisms. Different drivers reflected the mutual control of weakly adsorbed phosphorus (WA-P), potential active phosphorus (PA-P), Fe/Al-bound phosphorus (NaOH-P), and Ca-bound phosphorus (HCl-P) with the bio-directly available phosphorus (Bio-P). The transformation of NaOH-P in reducing environments can improve P bioavailability, while HCl-P is not easily bioavailable in weakly alkaline environments. There were significant differences in the bacterial community diversity and composition between the different lake types at the same latitude (p < 0.05), and the role of P fractions was stronger in the sediments of lakes with rich biodiversity than in poor biodiversity. Lake eutrophication recovery was somewhat hindered by the microbial interactions of P cycling and P fractions within the sediment. This study provides data and theoretical support for exploring the commonalities and differences among different lake types in the Inner Mongolian section of the Yellow River Basin. Besides, it is representative and typical for promoting the optimization of ecological security patterns in ecologically fragile watersheds.

Evaluating the effectiveness of engineering countermeasures at yellow dilemma zone: a driving simulator-based study

ABSTRACT The indecisiveness of drivers at the yellow onset creates a dilemma zone (DZ) upstream of the signalized crossings. The yellow light dilemma negatively influences intersection safety, often leading to rear-end and right-angle collisions. This study explores the impact of upstream road marking and active in-vehicle warnings designed based on DZ boundaries. The prototype concepts of in-vehicle warning systems and upstream road marking were designed and implemented in the driving simulator environment. The driver responses from the experimental setup were captured to evaluate the effectiveness of the designed DZ countermeasures. Study results indicate that implemented DZ countermeasures (i.e. DZ road marking, in-vehicle display warning, and in-vehicle audio warning) significantly decreased the drivers’ crossing probability, resulting in a shorter dilemma zone. However, the proposed countermeasures did not show any improvement in the drivers’ red-light running (RLR) behavior. The driving simulator study showed promising results in reducing aggressive crossing responses at the yellow onset.

The role of Advance Amber Warning Signal in enhancing driver decision-making: A comparative study in Brunei Darussalam

The current study examines the impact of Advance Amber Warning Signal (AAWS) on driver behaviour and decision-making processes at two junctions on a roadway. Through on-road observations and decision tree analysis, data was collected to assess the effects of AAWS on driver responses to a yellow dilemma. The results indicated that AAWS led to a significant increase in the percentage of drivers who accelerated before stopping at the junction, while reducing uncertain changes between braking and accelerating. Moreover, AAWS resulted in improved stopping propensity, and a substantial reduction in red light violations. These findings demonstrate the positive influence of AAWS on driver behaviour, improved decision-making, and enhanced compliance with traffic regulations. The presence of AAWS equips drivers with the necessary tools to navigate challenging scenarios on the road and make safe decisions during critical moments. As a result, the implementation of AAWS has the potential to significantly enhance junction safety, optimise driver behaviour, and contribute to overall road safety measures.

Enhancing Test Driver Response Skills through Accident Scenario Analysis in Real-World Evaluation of Autonomous Vehicles

Enhancing Test Driver Response Skills through Accident Scenario Analysis in Real-World Evaluation of Autonomous Vehicles

Asian Anthropogenic Aerosol Forcing Played a Key Role in the Multidecadal Increase in Australian Summer Monsoon Rainfall

Abstract Observations show a significant increase in Australian summer monsoon (AUSM) rainfall since the mid-twentieth century. Yet the drivers of this trend, including the role of anthropogenic aerosols, remain uncertain. We addressed this knowledge gap using historical simulations from a suite of Coupled Model Intercomparison Project phase 6 (CMIP6) models, the CESM2 Large Ensemble, and idealized single-forcing simulations from the Precipitation Driver Response Model Intercomparison Project (PDRMIP). Our results suggest that Asian anthropogenic aerosol emissions played a key role in the observed increase in AUSM rainfall from 1930 to 2014, alongside the influence of internal variability. Sulfate aerosol emissions over Asia led to regional surface cooling and strengthening of the climatological Siberian high over eastern China, which altered the meridional temperature and sea level pressure gradients across the Indian Ocean. This caused an intensification and southward shift of the Australian monsoonal westerlies (and the local Hadley cell) and resulted in a precipitation increase over northern Australia. Conversely, the influence of increased greenhouse gas concentrations on AUSM rainfall was minimal due to the compensation between thermodynamically induced wettening and transient eddy-induced drying trends. At a larger scale, aerosol and greenhouse gas forcing played a key role in the climate response over the Indo-Pacific sector and eastern equatorial Pacific, respectively (coined the “tropical Pacific east–west divide”). These findings contribute to an improved understanding of the drivers of the multidecadal trend in AUSM rainfall and highlight the need to reduce uncertainties in future projections under different aerosol emission trajectories, which is particularly important for northern Australia’s agriculture. Significance Statement Australian summer monsoon (AUSM) rainfall plays a vital role in sustaining northern Australia’s unique biodiversity and extensive agricultural industry. While observations show a significant increase in AUSM rainfall since the mid-twentieth century, the causes remain uncertain. We find that anthropogenic aerosol emissions from Asia played a key role in driving this multidecadal AUSM rainfall trend by inducing dynamic adjustments over the Indo-Pacific sector. These findings highlight the need to consider different aerosol emission trajectories when assessing future projections of AUSM rainfall.

A Virtual Vehicle–Based Car‐Following Model to Reproduce Hazmat Truck Drivers’ Differential Behaviors

Enhancing hazmat truck safety through advanced driving assistance systems (ADAS) relies on both system efficacy and driver reactions. This study investigates the driving behaviors of hazmat truck drivers in response to forward collision warnings (FCWs). Traditional warning triggering methods struggle to capture diverse and immediate driver responses; therefore, our research employs a vision‐based framework for driving data extraction and utilizes the K‐means++ clustering method for response‐based classification. Moreover, we propose an enhanced version of the intelligent driver model (IDM) based on the concept of a virtual vehicle to reproduce hazmat truck drivers’ differential behaviors during risky car‐following periods, achieving results that depict improved driving simulations. This model is compared with classic benchmarks, including the IDM, optimal velocity model (OVM), and full velocity difference (FVD) model, demonstrating superior performance in terms of traffic stability and safety in extreme scenarios. Our findings highlight that preaction drivers tend to accelerate before receiving warnings, opting to overtake rather than maintain safe distances. In contrast, calm drivers decelerate in anticipation of the warning, showcasing their awareness of maintaining safety. The analysis reveals that aggressive drivers are predominantly in the 41–45 age group, indicating a higher skill level, while calm drivers are more commonly older, reflecting a trend in cautious driving behaviors. Overall, our research contributes to the development of effective ADAS by considering real‐time driver responses and emphasizes the potential of our model to revolutionize commercial ADAS adoption and enhance road safety for hazmat operations.

An Exponential Decay Model for Decaying of Contact Patch Friction Steering Moment with Rolling Speed

ABSTRACT Steering torque is a very important quantity for the driver's response. In fact, it gives the driver an idea of the road adherence condition during driving. Several models have been developed to simulate shear forces at the contact patch; most of them are based on semi-empirical tire models that account for slip and slip angles. They have good reliability when speed is high enough, but at very low speed, such as in parking, these models suffer from reliability and numerical issues. This paper presents a model to compute the steering moment due to contact patch friction at any longitudinal speed including pivot steering condition. In particular, it supplements the pivot steering model with a novel exponential decay of moment model to simulate steering moment for various wheel rolling speeds. The decay rate was found to be dependent upon contact patch geometry and rolling speed.