Driver Reaction Research Articles

Priority Design in Multi-Target AR-HUD Warning: Evidence from Eye Movement and Behavior of the Novice Driver

Technically, Augmented Reality Head-Up Display (AR-HUD) technology can augment multiple targets in the scene. However, existing literature predominantly focuses on scenarios involving single-target augmentation. The understanding of multi-target augmentation and studies exploring effective presentation methods under such conditions are notably scarce. This study evaluates the efficacy of integrating color-based warning priority design across multi-target scenarios. 45 Participants in different warning modes (Equivalent, Hierarchical, and Baseline) view AR-augmented driving videos and respond to risky targets. Their behavioral performance and eye-tracking data are compared. Findings indicate that the equivalent warning mode, lacking in priority design, adversely affects driver performance, prolongs reaction times, and elevates saccade counts, and gaze entropy. Conversely, the hierarchical warning mode significantly ameliorates driver reaction times and the time to first fixation, while also reducing saccade counts and gaze entropy, demonstrating the efficacy of the warning priority design. The findings provide insight into the design of AR-HUD with multi-target augmentation.

Optimal Adaptive Cruise Control in Mixed Traffic With Communication Latence and Driver Reaction

Optimal Adaptive Cruise Control in Mixed Traffic With Communication Latence and Driver Reaction

Red Light Reaction – A Statistics Project with Real Life Application

We describe a hands-on project in which students collect data on the impact of distracted driving on driver reaction time. Initially they do this in class via a virtual driving applet, using themselves and fellow students as test subjects. Different applet versions simulate driving with and without distraction and measure the time it takes to apply brakes after the red brake lights of the car ahead appear. Students use the collected data to practice a range of statistical techniques, such as assessing data for outliers, creating a hypothesis to be tested, performing an appropriate test, and then translating their results to determine a safe driving distance. In the second part of the project, students work in groups outside of class. Each group recruits a category of test subjects (e.g., athletes, video gamers, STEM majors) of their choosing, collects data, and performs statistical analysis. Finally, students develop hypotheses as to whether different categories of drivers have better or worse reaction times, collect additional relevant data, and perform the appropriate statistical test. The applets, the project guide, the dataset, and supporting videos can be downloaded from https://osf.io/h8yxw/files/osfstorage. For users of the Canvas LMS, we also provide an export cartridge that contains the various project components. Supplementary materials for this article are available online.

Method for Determining the Conditions for Ensuring Stable Rearward Movement of a Semi-trailer Combination with Non-turning Semi-trailer Wheels

The efficiency of organising freight transport and using motor transport in a Smart City depends on a set of its properties, which in the process of operation determine its suitability for use in given operating conditions. Vehicles have different overall dimensions and designs, which determine their maneuverability and directional stability, their ability to make a curve-line movement in the city, as well as to move safely in reverse. Nowadays, tractor-trailer combinations with non-turning semi-trailer wheels are widely used for freight transport. Such a scheme of vehicle construction does not ensure its directional stability when reversing, which can lead to the folding of the tractor and semi-trailer and loss of mobility of the combination. In the absence of additional special systems, the driver controls the rearward movement of the road train using the steering wheel and rear-view mirrors, and the accuracy of delivery to the object depends on the level of the driver's training. The research of driver's reaction time spent on situation assessment, decision making, and realisation has been carried out. It has been revealed that with manual control, there are difficulties connected with training of drivers to estimate the parameters of movement and control of the road train, in particular, with the process of parking and its accurate delivery to the object. With the help of the developed mathematical model of the rearward movement of the road train and the proposed new turn control law, the research of maneuvering of the road train during its delivery to the object has been carried out and the results confirming the possibilities of building an automated system allowing to reduce the influence of the human factor on the parking process, as well as to reduce the time required to perform the maneuver have been obtained. The conducted analysis of system stability with the help of Routh–Hurwitz criterion has determined the conditions of providing stability of the system moving in reverse. The method of controlling the backward movement of the road train and the design of the device realising it has been developed. The results of simulation modeling allowed us to find the necessary values for the control law for the movement of road trains of different lengths, as well as the preferred variants of the initial positions of the road train for the realisation of its precise delivery to the object.

Validating risk behavior in driving simulation using naturalistic driving data

The absence of physical accident risk in driving simulation, which allows for safely studying critical driving situations, also reduces the driver's risk perception, which may result in unrealistic driver behavior. Validation studies in this context are rare for ethical reasons, making it difficult to assess the extent of this issue at present. The present study addresses this gap by utilizing naturalistic driving data. Four critical cut-in situations on German highways were extracted from naturalistic data and replicated in a driving simulator study with N = 58 participants. Both in-situ self-ratings on subjective criticality and post-hoc video-based ratings (from the driver and objective observers), as well as presence ratings, were collected to supplement driver behavior. Although driver reactions in the simulator and the field were not equivalent in absolute terms, drivers in both the simulation and the real world exhibited accident-avoidance behavior through braking reactions, indicating relative validity. No clear mediating role of the sense of presence towards a more careful driver behavior was found. This work shows that drivers exhibit accident-avoiding behavior in the simulator and tend to react to hazards in the simulator similarly as they would in a real situation, while absolute numerical values should only be interpreted with caution.

Determination of rates of occurrence for hydroplaning events with naturalistic driving data

Introduction: The degree to which hydroplaning occurs in real-world conditions is not entirely known. Naturalistic driving data can be helpful in addressing some of the limitations of existing data sources related to the incidence of hydroplaning. Method: Data from the Second Strategic Highway Research Program Naturalistic Driving Study were leveraged to estimate the incidence of hydroplaning. Two hydroplaning detection algorithms were used for candidate hydroplaning event generation. Hard braking events were also identified and analyzed as normative comparisons to the hydroplaning events. Results: A total of 1,141 hydroplaning events were found in the naturalistic driving data and utilized for analysis, including 650 hydroplaning events that were unnoticeable by the driver based on lack of observable reactions, 13 events that were deemed to be of “critical” severity, and only 3 events that resulted in crash events during more than 30 million miles of driving. Hard braking events occurred nearly four times more often, and at comparatively lower speeds, than hydroplaning events. Observable driver reactions also differed between event types. For example, more drivers changed their posture after a hydroplaning event than after a hard braking event and drivers maintained both hands on the wheel at higher rates after experiencing a hydroplaning event than after a hard braking event. Suspension of secondary tasks during hard braking and hydroplaning events was also observed. Conclusions: Overall, these findings suggest that drivers perceive hydroplaning events as more harmful than hard braking events, despite the large discrepancy and incompatibility in how often these driving situations lead to vehicular crashes. Practical application: The findings of this research will provide vehicle and tire designers with empirical data that quantifies the important tradeoffs they must make in balancing vehicle and tire performance in wet and dry environments, and in tradeoffs related to tire wear performance and grip.

Attention-based cross-frequency graph convolutional network for driver fatigue estimation.

Fatigue driving significantly contributes to global vehicle accidents and fatalities, making driver fatigue level estimation crucial. Electroencephalography (EEG) is a proven reliable predictor of brain states. With Deep Learning (DL) advancements, brain state estimation algorithms have improved significantly. Nonetheless, EEG's multi-domain nature and the intricate spatial-temporal-frequency correlations among EEG channels present challenges in developing precise DL models. In this work, we introduce an innovative Attention-based Cross-Frequency Graph Convolutional Network (ACF-GCN) for estimating drivers' reaction times using EEG signals from theta, alpha, and beta bands. This method utilizes a multi-head attention mechanism to detect long-range dependencies between EEG channels across frequencies. Concurrently, the transformer's encoder module learns node-level feature maps from the attention-score matrix. Subsequently, the Graph Convolutional Network (GCN) integrates this matrix with feature maps to estimate driver reaction time. Our validation on a publicly available dataset shows that ACF-GCN outperforms several state-of-the-art methods. We also explore the brain dynamics within the cross-frequency attention-score matrix, identifying theta and alpha bands as key influencers in fatigue estimating performance. The ACF-GCN method advances brain state estimation and provides insights into the brain dynamics underlying multi-channel EEG signals.

Enhancing Safety in Autonomous Vehicles: The Impact of Auditory and Visual Warning Signals on Driver Behavior and Situational Awareness

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver–vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect drivers’ behavior using virtual reality (VR). We measured drivers’ reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. The results showed that visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. The analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the “out-of-the-loop” problem and fostering human–vehicle cooperation.

A Sequence-to-Sequence Car-Following Model for Addressing Driver Reaction Delay and Cumulative Error in Multi-Step Prediction

A Sequence-to-Sequence Car-Following Model for Addressing Driver Reaction Delay and Cumulative Error in Multi-Step Prediction

Large Scale Evaluation of Normalized Hard-Braking Events Derived from Connected Vehicle Trajectory Data at Signalized Intersections, Roundabouts, and All-Way Stops

Intersection safety has been traditionally evaluated using three to five years of crash data. Recent literature suggests that connected vehicle (CV)-derived hard braking (HB) events can provide a surrogate for crashes with only a few weeks or months of data collection. This study used CV trajectories to derive HB events. Then, the HB events were normalized as the ratio of HB events to sampled CV trajectories. The normalized HB ratios were evaluated and compared at 435 signalized intersections, roundabouts, and all-way stops in Indiana. The analysis showed that signalized intersections and roundabouts had the highest counts of HB events, and all-way stops had the highest HB ratios. Through movements at signalized intersections showed the lowest HB ratios, whereas left turns at all-way stops had the highest ratios. A density analysis of the geospatial occurrence of HB events concluded that they tend to occur closest to the intersection center at all-way stops, but are more evenly distributed at signalized intersections. Additionally, a speed analysis indicated that HB events at signalized intersection through movements tend to occur at higher speeds, roughly between 26 and 36 MPH, perhaps due to the driver reaction during the onset of yellow. The findings presented in this study provide transportation agencies with insights on the occurrence of normalized HB ratios at three different intersection types. The data provided in this paper provide a framework for agencies to use HB ratios to screen different types of intersections for further evaluation.

Bus drivers’ anger and anger expression in driver–passenger conflicts

Bus driver anger due to passenger misbehavior may lead to serious bus accidents, yet there are no mature tools available to capture bus driver reactions during driver–passenger conflicts. The bus driver anger scale (BDAS) and the bus driver anger expression inventory (BDAX) were developed to measure the anger levels and expressions of drivers in such conflicts. A bus driver anger model was developed based on 400 questionnaires in Changsha, China. The findings indicate that drivers are most likely to be angered by passenger violations and quarrels among passengers. Drivers irritated by passenger irregularities tend to employ personal aggressive expression or adaptive/constructive expression. Disputes among passengers may lead drivers to resort to unreasonable methods of venting their emotions. Moreover, passengers’ rude behaviors can trigger bus drivers’ aggressive personal expressions. Therefore, it is necessary to establish passenger regulations and encourage drivers to express their anger reasonably in driver–passenger conflicts.

Modeling Pedestrian Near-Crash Events at a Rectangular Rapid Flashing Beacon-Controlled Intersection Using Video Analytics and Long Short-Term Memory Neural Network

Pedestrian safety is a long-standing issue in urban areas, where pedestrian near-crash events are more frequent than in suburban or rural areas. To address the pedestrian safety problem, a proactive approach was explored to assess and predict the severity of these events, which are valuable indicators of potential crashes. Object detection and tracking techniques were used to establish the temporal relationship of pedestrian near-crash events involving vehicles at an intersection controlled with rectangular rapid flashing beacons. A long short-term memory (LSTM) neural network model is proposed to warn a driver 2 s before the vehicle reaches the conflict zone. However, this scenario can be considered optimistic, as the 2 s interval represents an ideal driver’s reaction time, which is more likely to happen in a connected and automated vehicle environment where vehicles receive real-time information about their surroundings and perform some basic tasks such as braking without waiting for the driver reaction. The results demonstrate the effectiveness of the proposed LSTM neural network model, with an area under the curve value of 78.5% on the training data and an overall recall of 71.1% on the test data. The practical significance of this model is its potential to provide timely information about near-crash events, thereby enhancing pedestrian safety at critical points such as intersections.

ENHANCING ROAD SAFETY FOR VULNERABLE ROAD USERS – THE ROLE OF AUTONOMOUS EMERGENCY BRAKING SYSTEMS

Vulnerable road users are the largest group of road accident victims in the world. At the same time, it should be noted that most road accidents are caused by the motor vehicle driver. An opportunity to increase the level of road safety is emergency braking systems installed in vehicles. Their task is to detect the risk of a collision with another object, issue a warning to the driver and, in the absence of reaction, perform emergency braking. The publication presents the results of research conducted by the Motor Transport Institute as part of the PEDICRASH project. As part of the work, a series of tests of the autonomous emergency braking system were carried out to check unusual but probable cases of events in which it should work. The tests were carried out in a closed area using a few selected models of popular passenger car brands. An additional data acquisition system and dummies imitating vulnerable road users: pedestrians and cyclists were used. The constructions represent the silhouette of an adult person and meet the requirements of the Euro NCAP AEB Protocol. The time of the collision at the time of issuing a warning to the driver was analyzed. Assuming that the vehicle was moving uniformly, the distance from the obstacle was calculated at the time of issuing the warning and after the driver's reaction time (0.7 s and 1 s were assumed) from this signal. The deceleration necessary to brake the vehicle was calculated and it was determined whether the driver would have a chance to brake the vehicle before a pedestrian or a cyclist in a given situation. For cases in which the driver would not be able to brake in time, the speed of the vehicle with which it would hit the vulnerable road users was calculated. Possible injuries and accident costs were then estimated.

A new car-following model with incorporation of Markkula's framework of sensorimotor control in sustained motion tasks

This study develops a car-following model called the “Markkula Intelligent Driver Model (MIDM)” based on Markkula's Framework of Sensorimotor Control. The MIDM predicts the start time of driver's reaction based on the evidence accumulation within Markkula's framework unlike the existing car-following models that use a constant reaction time parameter. The MIDM also accurately represents the actual shape and duration of acceleration maneuvers. Fifty drivers’ car-following behavior was observed in 2 different scenarios using a driving simulator – reaction to a decelerating lead vehicle and reaction to a stopped lead vehicle. Trajectory data from the NGSIM project were also used for the evaluation. Compared to the Gipps Model, the Wiedemann Model and the IDM, the MIDM realistically reproduced trajectories of speed, acceleration, jerk and spacing for both simulator and NGSIM data. The MIDM can also incorporate the effects of lead vehicle brake lights for more accurate estimation of the driver reaction time.

Experimental road safety study of the actual driver reaction to the street ads using eye tracking, multiple linear regression and decision trees methods

The article describes the results of a naturalistic driving study done in Kuwait performed by 34 participants wearing a mobile eye tracker to monitor the effect of roadside advertisements on user attention. Eye-tracking (fixations) are the main dependent variable and examined as a function of driving/roadside characteristics, particularly billboards, speed, and so forth. The results obtained were analyzed using traditional statistics (ANOVA test and multiple linear regression) and machine learning (decision tree estimation methods). From the results, it was found that road advertisements negatively affect driver attention and thus road safety. How the level of safety varies with the type and size of advertisement was also investigated. As a consequence, all of the estimates revealed that different aspects of advertising have a detrimental impact on drivers' behavior, and that the duration of fixation and the rate of acceleration before viewing an ad are impacted by advertising type and size, respectively.

Driver fatigue detection method based on facial features using deep learning

Fatigue driving can result in delayed driver reactions and lack of concentration, increasing the risk of traffic accidents. Therefore, finding effective fatigue detection methods is crucial for accident prevention and ensuring public safety. Traditional driver fatigue detection methods based on facial features are limited by their reliance on manually designed features and classifiers, leading to low accuracy, slow detection speed, and inefficient performance. Hence, methods based on deep learning have gradually become a trend in fatigue-driving detection research. This paper focuses on driver fatigue detection methods based on facial features using deep learning. In this study, we analyze numerous research outcomes on how to accurately extract, analyze, and integrate various facial features, such as eye features, mouth features, facial expressions, and head postures, to precisely determine the driver's fatigue state. Then, we compare the latest deep learning-based technologies. This paper provides an information-rich repository to help researchers select more promising designs, that can drive the widespread development of fatigue-driving detection technologies.

Assessing the impact of reaction time on the crossing and merging conflicts and identifying suitable reaction time to detect the critical conflict

ABSTRACT Due to the increased vehicular traffic in developing countries, safety in mixed traffic conditions has become a concern. When critical speed was used with surrogate safety measures to identify critical conflicts, studies assumed some driver reaction time without any analysis. This study aims to assess the impact of different reaction times on the Percentage of Critical Conflicts (PCC) and identify the suitable reaction time for critical conflict identification. Post Encroachment Time was used in this study to assess the safety of crossing and merging traffic at unsignalized T-intersections. It was observed that an increase of 1 s reaction time increases 26.92% of PCC. PET extremes of different reaction times were modelled using Generalized Extreme Value distribution. The 1-second reaction time was found suitable as the estimated crash frequencies were similar to the actual field crashes. This approach could also be employed in the homogenous traffic conditions for critical conflict identification.

A multimodal driver monitoring benchmark dataset for driver modeling in assisted driving automation

In driver monitoring various data types are collected from drivers and used for interpreting, modeling, and predicting driver behavior, and designing interactions. Aim of this contribution is to introduce manD 1.0, a multimodal dataset that can be used as a benchmark for driver monitoring in the context of automated driving. manD is the short form of human dimension in automated driving. manD 1.0 refers to a dataset that contains data from multiple driver monitoring sensors collected from 50 participants, gender-balanced, aged between 21 to 65 years. They drove through five different driving scenarios in a static driving simulator under controlled laboratory conditions. The automation level (SAE International, Standard J3016) ranged from SAE L0 (no automation, manual) to SAE L3 (conditional automation, temporal). To capture data reflecting various mental and physical states of the subjects, the scenarios encompassed a range of distinct driving events and conditions. manD 1.0 includes environmental data such as traffic and weather conditions, vehicle data like the SAE level and driving parameters, and driver state that covers physiology, body movements, activities, gaze, and facial information, all synchronized. This dataset supports applications like data-driven modeling, prediction of driver reactions, crafting of interaction strategies, and research into motion sickness.

Drivers' use of different types of multimedia devices while driving - a survey study

Accidents and collisions continue to be an ongoing problem in road safety. While driving, drivers are influenced by stimuli that can affect their behaviour in various ways. Nowadays, various types of multimedia devices have become popular, which, when used by drivers while driving, can influence driver attention. The literature undertakes studies of the impact of such devices on the driver's degree of distraction as measured, for example, by the driver's reaction time. A survey was conducted to identify sources of driver distraction. A total of 237 drivers aged between 18 and 78 years participated in the sample. Of the respondents, 64.6% use a mobile phone while driving, 20.3% use a smartwatch, 33.3% use a multimedia control system for vehicle functions. Respondents indicated that they had either witnessed (18.1%) or been involved (6.3%) in a collision/accident when the driver used various types of multimedia devices while driving. Of those surveyed, 2.1% had caused a collision or accident by using a multimedia device while driving. Respondents are aware of the risks of using different types of media devices while driving, but they still use them. The analyses of the survey data will be used in future research using a car simulator.

A microscopic traffic flow model for explaining nonlinear traffic phenomena: Modeling, stability analysis and validation

As the core of reproducing the real nonlinear phenomena of traffic congestion, the investigation of car-following models, which take into account human factors, has consistently assumed a central role within the domain of transportation science. Nevertheless, it is noteworthy that prior studies have invariably employed a fixed value for driver reaction time to stimuli, whereas it is imperative to recognize the dynamic nature of this parameter, closely associated with the instantaneous vehicle speed. To address this issue, this paper constructs a dynamic sensitivity coefficient (DSC), and further develops a nonlinear human factoring car-following model to reveal the relation between the reaction time and the current speed. Firstly, the linear critical stability condition and the mKdV equation of the model are derived by the linear stability analysis and kink–antikink solution analytic method, respectively. Then, the numerical experiments are conducted to demonstrate that the proposed model is more practical and can reproduce the real driving behavior and phenomena. Finally, calibration and validation results exhibit the actual vehicle trajectory can be simulated well.