Driver Reaction Research Articles

Predicting driver reaction time and deceleration: Comparison of perception-reaction thresholds and evidence accumulation framework

Prediction of driver reaction to the lead vehicle motion based on the perception-reaction time (PRT) is critical for prediction of rear-end crash risk. This study determines PRT at various spacings in approaching and braking conditions, and examines the association of PRT and deceleration rate with crash risk. For these tasks, a total of 50 drivers’ behavior was observed in a driving simulator experiment with 4 different scenarios – reaction to a decelerating lead vehicle, reaction to a stopped lead vehicle, perception of a lead vehicle’s speed change, and perception of a slow-moving lead vehicle. The study tested three hypotheses of PRT including perception and reaction thresholds and the evidence accumulation framework using a visual variable (tau-inverse). It was found that the drivers neither reacted after a specific PRT from the start of perception nor reacted at a specific value of tau-inverse. Rather, the drivers generally reacted when the accumulation of evidence (tau-inverse) over time reached a threshold. It was also found that the magnitude of deceleration rate depends on the tau-inverse at the start of braking and hence, higher crash risk was associated with higher level of urgency and insufficient brake force rather than longer PRT. This study demonstrates that the evidence accumulation framework is a promising method of predicting driver reaction in approaching and braking conditions for different types of lead vehicle, and the level of urgency is important for predicting the probability of crash.

Dynamics of traffic flow affected by the future motion of multiple preceding vehicles under vehicle-connected environment: Modeling and stabilization

Connected vehicles that communicate via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies have become a hot topic in the transportation control field. Benefit from these new technologies, this work constructs a modified intelligent microscopic model considering driver reaction to future motion of preceding vehicles. First, we mimic the impact of anticipative motion of multiple preceding vehicles on the traffic stream, developing a new model, which is further investigated by applying the linear and nonlinear analytical methods. Also, the corresponding neutral and coexisting stability conditions are determined. Analytical results indicate that the information about the future motion of multiple preceding vehicles can improve traffic stability significantly. Besides, the phase diagram is presented by plotting the neutral stability lines and coexisting curves; three zones are created by partitioning the phase diagram with the neutral line and coexisting line, namely, the unsteady zone under the coexisting line, the stable zone above the neutral line, and the metastable zone between the two lines. Finally, the emulation experiments are conducted to validate the analytical conclusions. The results show that the information on a changing trend of multiple headways obviously smooths the traffic flow and restrains the formation of traffic jams.

Comparative assessment of drivers' stress induced by autonomous and manual driving with heart rate variability parameters and machine learning analysis of electrodermal activity

Abstract Background Nowadays research on Autonomous Driving Systems (ADS) and attention towards novel technology allowing real-time assessment of car drivers' psycho-physiological status is growing, to quantify driver's stress during semi-autonomous or autonomous driving assistance and to investigate human reaction to different types of ADS. We present a system for automatic stress detection with combined machine learning analysis of Skin Potential Response (SPR) and electrocardiographic (ECG) recordings to compare the driver's stress reaction during both manual and autonomous driving sessions carried out in a dynamic professional drive simulator (PDS). Methods All data were acquired after informed consent from 14 healthy volunteers (HVs) in the Vi-grade (Udine) PDS. Two SPR signals (one from each hand) and three chest ECG leads were recorded. A Motion Artifact (MA) removal algorithm was used to remove motion artifacts from SPR signals. A cleaned, single SPR signal, obtained as the RMS value by combining the two original signals, was then sent together with the time-variation of heart rate (HR) to a Machine Learning (ML) classification algorithm, i.e., a Support Vector Machine (SVM), based on some specific features of this signal. The output of the SVM provides a series of labels, that indicate the presence or lack of stress episodes during the driving experiment. Stress occurrence was also independently quantified with heart rate variability (HRV) analysis in the time (TD) and frequency (FD) domains and with non-linear (NL) methods. Results All participants completed the driving protocol consisting of two subsequent sessions, one with conventional manual (MD) and the other with autonomous (AD) driving settings, along a highway where some unexpected events occurred, inducing different level stress response. Figure 1 shows an example of time variant changes of the RMS SPR signal and of the HR of one tested individual during both experimental settings. A simultaneous increase of both SPR and HR signal is apparent during the stress episodes correctly identified by the SVM (gray shadows). Discriminant analysis of FD (VLF, LF and HF) and NL (SD1, SD2, Entropy and Recurrence Plot) HRV parameters, independently assessed by two researchers blind to SVM results, differentiated between stress induced by MD and AD (accuracy: 88,4% cross-correlated) in good agreement with automatic SVM assessment. In general stress level was lower during the AD, being all HRV parameters not significantly modified from baseline rest. SPR amplitude eventually increased also during AD, but SVM efficiently differentiated between AD and MD stress anyhow. Conclusions The proposed method for automatic assessment of stress reactions of car drivers with SVM of SPR and HR signals is reliable, in both MD and AD scenarios. The results seem to evidence that MD is in general more demanding than AD inducing higher activation of sympathetic nervous system, especially in critical situations. Figure 1. Time variance of SPR and HR. Funding Acknowledgement Type of funding source: None

Book Review: Driver Reactions to Automated Vehicles: A Practical Guide for Design and Evaluation

Book Review: Driver Reactions to Automated Vehicles: A Practical Guide for Design and Evaluation

Implications of Crashes Involving Roadway Objects for Machine Vision-Based Driving Systems

Crashes involving roadway objects can cause severe injuries and property damage. Utilizing data from the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS), this study investigated crashes involving roadway objects and their implications for the potential of machine vision-based driving systems in preventing such crashes. A comprehensive statistical and machine learning analysis was first conducted to identify major factors affecting the occurrence and severity of such events. Machine vision performance metrics (based on the SHRP 2 NDS cameras) and human driving decisions were then analyzed to identify opportunities where machine vision systems could particularly mitigate risk factors. The results suggest that driver behaviors/errors, speed, reaction time, and object characteristics played the most significant role in the occurrence and severity outcome of the SHRP 2 events. The average object detection distance based on the SHRP 2 cameras was approximately 20 m for all objects. The average reaction time provided by the SHRP 2 cameras was 1.5 s for all events but 1.1 s for events involving animals and roadway debris. In general, the machine vision reaction time was longer than the driver reaction time for approximately 95% of all analyzed events and 75% of the events in which drivers reacted before collisions. Drivers were able to make safe evasive maneuvers in 56% of all analyzed events and 72% events involving roadway debris and animals. Based on these results, the paper discusses in detail when and how machine vision systems could assist in preventing crashes involving roadway objects.

Driver Reactions to Uphill Grades: Inference from a Stochastic Car-Following Model

This paper analyzes the impact of uphill grades on the acceleration drivers choose to impose on their vehicles. Statistical inference is made based on the maximum likelihood estimation of a two-regime stochastic car-following model using Next Generation SIMulation (NGSIM) data. Previous models assume that the loss in acceleration on uphill grades is given by the effects of gravity. We find evidence that this is not the case for car drivers, who tend to overcome half of the gravitational effects by using more engine power. Truck drivers only compensate for 5% of the loss, possibly because of limited engine power. This indicates not only that current models are severely overestimating the operational impacts that uphill grades have on regular vehicles, but also underestimating their environmental impacts. We also find that car-following model parameters are significantly different among shoulder, median and middle lanes but more data is needed to understand clearly why this happens.

Framework of wind-traffic-bridge coupled analysis considering realistic traffic behavior and vehicle inertia force

The performance of a long-span bridge under strong wind and traffic loads is of great importance to ensure the functionality and the serviceability of the bridge as well as the safety of running vehicles. In this paper, an innovative wind-traffic-bridge (WTB) interaction model for predicting the dynamic performance of the long-span bridge and running vehicles under the crosswind is proposed, considering realistic traffic behavior and vehicle inertia force. In this regard, the LAI-E traffic simulation model aiming to faithfully reproduce realistic traffic flow is developed by integrating the kinetics theory, vehicle capabilities, and driver reactions. The results indicate that using the traditional NaSch traffic model results in a significant overestimation of the vehicle lateral response under a windy environment, whereas the LAI-E traffic model can overcome this issue by ensuring a smooth speed transition during the vehicular acceleration/deceleration process. Additionally, when the vehicle accelerates or decelerates at the emergency capability, the additional pitching moment caused by the vehicle inertia force can result in remarkable increase of vertical wheel contact forces. The present study suggests the necessity of considering both realistic traffic behavior and vehicle inertia force during the WTB analysis.

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots. • This study showed how driver compensates differently according to weather conditions to avoid crash event. • This study provided a discrimination threshold between normal and risky driving patterns in both rainy and clear weather conditions. • This study showed how the trajectory analysis helped in better discriminating driving patterns during a specific event. • The usage of an advanced machine learning classification method helped in detecting risky driving patterns more efficiently. • It was found that using a cluster analysis helped in detecting the risky driving pattern over the whole events. • The cluster analysis succeeded to provide results in the same context as the trajectory analysis.

Macroscopic Traffic Flow Characterization at Bottlenecks

Traffic congestion is a significant issue in urban areas. Realistic traffic flow models are crucial for understanding and mitigating congestion. Congestion occurs at bottlenecks where large changes in density occur. In this paper, a traffic flow model is proposed which characterizes traffic at the egress and ingress to bottlenecks. This model is based on driver response which includes driver reaction and traffic stimuli. Driver reaction is based on time headway and driver behavior which can be classified as sluggish, typical or aggressive. Traffic stimuli are affected by the transition width and changes in the equilibrium velocity distribution. The explicit upwind difference scheme is used to evaluate the Lighthill, Whitham, and Richards (LWR) and proposed models with a continuous injection of traffic into the system. A stability analysis of these models is given and both are evaluated over a road of length 10 km which has a bottleneck. The results obtained show that the behavior with the proposed model is more realistic than with the LWR model. This is because the LWR model cannot adequately characterize driver behavior during changes in traffic flow.

Safety Facilities Technology of Eight-Lane Highway based on Driver's Stress Reaction

Safety Facilities Technology of Eight-Lane Highway based on Driver's Stress Reaction

Investigation on vehicle lateral instability when crossing a curved highway bridge during an earthquake

This paper describes a numerical study on the vehicle instability when crossing curved highway bridge during an earthquake. Analytical framework using two-axle vehicle dynamics model is developed to describe vehicle vibration when moving under seismic excitation from the curved bridge girder. The model includes out-of-plane and in-plane motion along with the effect of curved girder with specific radius, camber angle and longitudinal grade. Two criteria for critical vehicle stability are evaluated namely the rollover and sideslip stability. As case study, numerical simulations were conducted using three-dimensional finite element model of an elevated highway junction consisting of four curved sections and two straight sections. Effects of curved bridge radii, level of earthquake, vehicle velocity and driver reaction on the vehicle stability were investigated. It was found that a vehicle moving on a curved bridge during earthquake has a larger possibility for rollover and sideslip instability than a vehicle moving on a straight section for the same driving velocity and level of earthquake. Results of simulations demonstrate that normal forces on vehicle’s wheels are significantly influenced by the curved bridge effects, steering angle, effect of road camber at the curved section. These conditions contributed to reduction of normal forces on vehicle’s wheels and increased the risk of vehicle instability.

System-Level Reliability Analysis of Cooperative Driving with V2X Communication for Intersection Collision Avoidance

Cooperative driving with vehicle-to-everything (V2X) communication is a promising technique to improve traffic safety and efficiency. Intersection collision avoidance (ICA) is a typical safety application of it. This paper analyzes reliability of ICA with cooperative manual driving at the system level. First, the reliability of an ICA system is defined as the probability of the ICA system avoiding collisions or near-misses at intersections without failure under conditions that collisions or near-misses are about to happen. Post-encroachment time is used in the expression of this definition. Then, components of the ICA system are classified into four types: hardware, software, maneuver, and V2X communication, and a reliability block diagram (RBD) is applied to reveal how these components contribute to system reliability. Five ICA system patterns with different V2X communication modes and strategy types are compared based on RBD analysis. This shows that centralized strategies are more reliable than decentralized ones for V2I communication if software reliability of these two strategies is the same. Furthermore, reliabilities of ICA components are analyzed in detail, and they are classified into two categories based on their different impact modes on the system. Finally, a numerical example shows how to test reliability of an ICA system using reliabilities of its components by Monte Carlo simulation. Results show that closer distances from vehicles to their conflict point when alerted, longer driver reaction time, and smaller vehicle deceleration rates are more likely to lead to system failure, whereas communication latency has little effect on it.

Higway Criuse Control System for Vehicles using Low Power RF and Can Protocol

Nowadays mishaps are happening much of the time, causing destruction of numerous individuals by committing unassuming errors while driving (in school zone, slopes region, and roadways). In any case, once in a while it may not be conceivable to see the billboards put by the Highway Department to caution the drivers in such sort of spots and there is an opportunity for mishap. The headway in the processor innovation and microcontrollers has opened another framework intended to forestall the mishaps caused because of carelessness of drivers in observing rush hour gridlock flags close by the street and different abnormalities on the streets. So to suggest the driver about the zones and to consequently keep up the speed is cultivated by methods for low power RF innovation. The primary target is to plan an Electronic Display controller implied for vehicle's speed control and screens the zones, which runs on an implanted framework and can be hand crafted to fit into a vehicle's dashboard to show data on the vehicle. This framework whenever received by some state can successfully diminish the quantity of street mishaps brought about by speeding vehicles losing control of the vehicle at speed breakers or by driver's carelessness towards traffic signals. This paper presents another structure to control the speed of the vehicles at clumsy zones and security zone places for fixed time. The undertaking is made out of two separate units: Zone status transmitter unit, Electronic Display and Control unit. When the street sign is gotten from the zones, the vehicle's Electronic Display Controller Unit cautions the driver, to lessen the speed as indicated by the zone; it hangs tight for driver's reaction and diminishes the speed of vehicle consequently with CAN Protocol.

Efficacy of automatic emergency braking among risky drivers using counterfactual simulations from the SHRP 2 naturalistic driving study

Motor vehicle crashes remain a significant problem in the US and worldwide. Automatic emergency braking (AEB) is designed to mitigate the most common crash mode: rear-end striking crashes. However, assessing the efficacy of AEB in real-world crash scenarios is challenging given that avoided crashes are rarely documented except during naturalistic driving studies. In the absence of such data, AEB can be evaluated in real-world crash scenarios by retrospectively adding AEB to naturalistic crashes using counterfactual simulations. AEB was retrospectively applied to rear-end striking crashes (n = 40) from the SHRP 2 database among teen (16–19 yrs), young adult (20–24 yrs), adult (35–54 yrs), and older (70+ yrs) drivers. Real-world AEB deceleration profiles from IIHS AEB tests were paired with SHRP 2 vehicles based on vehicle make and class. AEB onset for SHRP 2 crashes was based on a brake threat number (BTN) algorithm. AEB curves were adjusted to match the speed of the vehicle at AEB onset. AEB deceleration curves were scaled based on road surface conditions. Driver reaction was accounted for by beginning the deceleration curve at the current driver-initiated braking level. Overall, AEB was found to be very effective, preventing 83% (n = 33) of rear-end striking crashes. However, AEB was less effective for crashes that occurred at higher speeds and during inclement weather conditions. These data provide a counterfactual evaluation of AEB that can be used by OEMs to prioritize AEB optimization for higher speed crashes and sub-optimal road conditions.

Investigation of the instinctive reaction of human drivers in social dilemma based on the use of a driving simulator and a questionnaire survey

Objective：The moral and ethical issue is a great challenge to the development of autonomous vehicles. There may be distinctions between the choices made by an observer and a participant. The paper is designed to investigate whether drivers will sacrifice the fewest people to save more people in social dilemma, and whether human drivers would give priority to protecting pedestrians or self-protection in an emergency.Methodology: The experiment was conducted with a total of 50 participants assigned to three groups. Three experimental scenarios were designed and each of them contained a social dilemma. A driving simulator was used in this study to explore the choices of human drivers in social dilemma. In addition, the simulator results were compared with those of questionnaire survey.Result: In study 1, 73% of 22 participants swerved into the right lane to hit only one pedestrian for the safety of other five. In study 2 and 3, more participants chose to hit the barrier to protect the pedestrian.Conclusion: A conclusion can be drawn from the second and third group of experiments that most drivers consider not only their own safety, but the safety of pedestrians. Most of the participants intended to minimize the total amount of harm in social dilemma. The choice of crashing into barriers to protect a pedestrian can also be seen as a way to minimize the total amount of harm.

Modelling fundamental diagrams according to different water film depths from the perspective of the dynamic hydraulic pressure

In this paper, we propose enhanced fundamental diagrams based on different water film depths by considering the effects of hydroplaning using a physical method. Various factors are calculated to describe the total safe distance headway of main vehicle components. These factors include the driver reaction times, reaction distances, vehicle braking times, and vehicle braking distances corresponding to different water film depths. An excellent match is found between the computed braking distance, the braking time calculated using the proposed numerical model, and the results published in other papers. These calculations are performed to estimate the distance headway and quantitatively analyse the relationships between the speed, density, and water film depth. By using three road-specific parameters estimated by our proposed model, namely, the free-flow speed, jam density, and capacity flow, a link transmission model is developed to analyse the dynamic impact of the water film depth.

Change of driver's reaction time depending on the amount of alcohol consumed by the driver - the case study

Each driver should drive according to the actual road traffic rules of the country in which is he currently located. The law systems of various states of Europe allow an increased maximal blood alcohol level of driver. As the fact that no small number of road accidents is caused because of the influence of alcohol on the driver, it is necessary to focus the attention of the society on this issue. The paper deals with the influence of alcohol to change of the reaction time of the driver. During the project of the alcohol effect on the driver and its behaviour, the reaction time of several drivers of a selected group of people was measured. At first (before alcohol drinking) it was assessed the physical and mental condition of the drivers, using a series of question. Subsequently were carried out the measurements by car with sober driver. After this initial measurement the driver began to drink an alcohol and after 20 minutes the level of alcohol in driver´s breath was determined by certificated equipment. After finding of this value was the driver re-tested by the car, where he had to stop the vehicle before simulated barrier. During the tests the stopping distance, the mean fully developed deceleration was explored as the supporting variables. As the most important variable was the driver´s reaction time. It was researched by using of the video-analysing from two synchronized cameras. The aim of this paper is to focus on the fact that there is a dependence between the amount of consumed alcohol and the driver´s reaction time, which significantly affects the road safety. The reaction time of the driver directly influences to the stopping distance thus it is resolute for stopping the vehicle before a barrier

Driver emotion recognition of multiple‐ECG feature fusion based on BP network and D–S evidence

Driving emotion is considered as driver's psychological reaction to a change in traffic environment, which affects driver's cognitive, judgement and behaviour. In anxiety, drivers are more likely to get engaged in distracted driving, increasing the likelihood of vehicle crash. Therefore, it is essential to identify driver's anxiety during driving, to provide a basis for driving safety. This study used multiple-electrocardiogram (ECG) feature fusion to recognise driver's emotion, based on back-propagation network and Dempster-Shafer evidence method. The three features of ECG signals, the time-frequency domain, waveform and non-linear characteristics were selected as the parameters for emotion recognition. An emotion recognition model was proposed to identify drivers' calm and anxiety during driving. The results show after ECG evidence fusion, the proposed model can recognise drivers' emotion, with an accuracy rate of 91.34% for calm and 92.89% for anxiety. The authors' findings of this study can be used to develop the personalised driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance and application value for improving road traffic safety.

Driver-behavior-based robust steering control of unmanned driving robotic vehicle with modeling uncertainties and external disturbance

In this paper, a steering robust control method based on professional driver behavior with modeling uncertainties and external disturbance is proposed for an unmanned driving robotic vehicle, to realize the accurate and stable steering control like a professional human driver. An unmanned driving robotic vehicle nonlinear dynamics model considering modeling uncertainties and external disturbance is established. A driver behavior model composed of an adaptive preview model, a driver path planning model, and a driver desired yaw rate model is established, with the influences on preview time and driver path planning strategy, respectively. On the basis of this, a robust steering controller based on professional driver behavior is presented, by taking road information, driver reaction delay time, and vehicle driving status as inputs and the servo motor rotation angle of the steering mechanical arm as output. The stability of the control system with modeling uncertainties and external disturbance is proved. A comparison of the analysis results of simulation and experiment among the proposed control method, other existing control methods, and professional human driver demonstrates the effectiveness of the proposed method.

The prevalence of cognitive disengagement in automobile crashes

SummaryThe current study analysed crashes identified in a large‐scale naturalistic driving database to assess the prevalence of cognitive disengagement (i.e., purely cognitive distraction and mind wandering/microsleep) or episodes wherein the driver did not look away from the roadway during secondary task completion or wherein another clearly observable contributing crash factor was not present, and the driver's reaction to the crash showed symptoms of cognitive disengagement. The study found that <1% (95% CI [0.45, 1.66]) of higher severity crashes had a potential contributing crash factor of mind wandering/microsleep; ~1.5% (95% CI [0.83, 2.32]) had a potential contributing factor of purely cognitive distraction. The results suggest a relatively low prevalence of cognitive disengagement among automotive crashes compared with visual/manual secondary tasks. However, the current methodology is limited in that it does not account for instances where cognitive disengagement, in combination with other factors, may have led to crashes, potentially leading to conservative estimates.