Driver Reaction Research Articles

Determination of driver’s reaction time in expert studies of road traffic accidents using software and hardware complex

The article addresses the psychophysiological state of vehicle drivers, analyzed in expert studies of road traffic accidents (RTAs). The purpose of this article is to publish the results of research on reaction time and visual perception of drivers under the influence of alcohol. The authors experimentally obtained the values of simple reaction time of drivers able to work as usual and drivers in a state of alcoholic intoxication (0.3‰, 0.5‰), as well as the values of the volume of their visual perception. The influence of different degrees of alcohol intoxication on the driver reaction time, which affects the ability to timely prevent accidents, is derived and scientifically justified.

Driver-pedestrian interaction under legal and illegal pedestrian crossings

Pedestrian crossing outside of a zebra crossing (jaywalking) is one of those pedestrian behaviors that may highly affect safety and operations. Unlike permissible crossings at zebra crossings, jaywalking events are not often anticipated by drivers, which may result in less driver reaction time and different vehicle operation dynamics.The objective of the research was to study driver’s behavior as well as to model the interaction with a pedestrian, who was crossing at and outside (jaywalker) of designated zebra crossings. Data were collected through an instrumented vehicle study.Sixteen participants took part in the survey. Each participant’s vehicle was instrumented with a video camera and a global positioning system (GPS) device. Each participant drove his vehicle along two streets of the center of Rome, during which numerous events of crossings at and outside the zebra crossing were detected. The acquired data allowed obtaining the driver’s speed profiles in approach to the pedestrian crossing.The analysis was based on variables that were obtained from the speed profiles of drivers. The driver’s behavior under legal and illegal crossings was analyzed in terms of driver yielding rates and yielding types, relationship between speed and distance of the vehicle from the conflict point at the decision point, and for different times left for the vehicle to get to the conflict point at the moment the pedestrian reaches the curb as well as pedestrian origin (form the right or left).Significant differences were detected in driver’s behavior during the interaction with the pedestrian crossing at and outside of the zebra crossing. The main results highlighted that the average yield rate to jaywalkers was lower than that to pedestrians at permissible crossings, the average deceleration rates were higher in the case of illegal crossing and driver yielding decision point to jaywalkers was closer to the conflict point.The obtained results provide the basis for modeling interactions between pedestrians crossing at and outside of crosswalks and approaching drivers in a micro-simulation environment.

Macroscopic Model on Driver Physiological and Psychological Behavior at changes in Traffic

A model is presented which can accurately characterize macroscopic traffic evolution. This model is based on analogies with the ideal gas law. A traffic constant is proposed which considers both physiological and psychological driver behavior. Physiological behavior is the time taken to observe and process local traffic conditions and then initiate actions while psychological behavior includes the attitude and awareness of a driver. Thus, the traffic constant encompasses the perception, awareness, attitude, and reaction of a driver. The proposed model is evaluated for changes in traffic flow caused by an inactive bottleneck. The results are compared with those for the Payne-Whitham (PW) model. This shows that the temporal and spatial evolution of traffic with the proposed model is more realistic.

Analyzing the ability of crash-prone highways to handle stochastically modelled driver demand for stopping sight distance

Stopping Sight Distance (SSD) is the distance defined in most highway design guides as the distance required by drivers to safely come to a complete stop in case of an emergency. Accordingly, design guides define theoretical values for SSD and recommend that these requirements are satisfied at all points along a highway corridor. SSD is estimated as a function of speed, driver reaction time, and deceleration rate, which are all factors that vary by both driver and driving conditions. Despite the anticipated uncertainty in those variables, they are all modelled deterministically. Unfortunately, this is an inaccurate assumption and provides no information about the extent to which roads designed to meet SSD requirements are able to satisfy road user demand for SSD. Design guides also fail to provide information about the impact a segment that fails to meet driver needs has on safety. To overcome those limitations, this paper assesses the ability of existing roads to satisfy stochastically modelled road user demand for SSD. The Available Sight Distance (ASD) was first quantified for a group of top crash-prone segments, and a Monte Carlo Simulation was used to model demand for SSD. The proportion of the test highways that failed to meet driver demands for SSD was then quantified by comparing the ASD to the required SSD at different levels of driver demand. Furthermore, the paper also compares the safety performance between regions that meet SSD and those that fail to do so. Among other findings, the paper shows that, on average, 6.8 % of the length of the test segments are noncompliant to the SSD demands of 70 % of the driving population. On the other hand, the average percent noncompliance for 30 % of the driving population (the 30 % with limited abilities) was 12.1 %. It was also found that, on average, crash rates in the noncompliant regions were two to three times higher than those in the compliant regions at the 70 % level (i.e., in regions that fail to meet the SSD demands of 70 % of the driving population).

Autonomous driving systems hardware and software architecture exploration: optimizing latency and cost under safety constraints

AbstractWith the recent progress of techniques in computer vision and processor design, vehicles are able to perform a greater number of functions, and are reaching higher levels of autonomy. As the list of autonomous tasks that the car is supposed to perform grows, two design questions arise: how to group these tasks into modules and which processors and data buses should instantiate these modules and their links in the physical architecture. Both questions are linked, as the processing capacity of the processors influences how centralized the architecture can be, and the modularization influences the overall system latency as well. Furthermore, our interest lies in designing architectures that perform the tasks rapidly, while minimizing cost. This multiobjective problem is intractable without architecture exploration and an analysis tool. This paper presents a linear optimization formulation to capture these tradeoffs, and to systematically find relevant architectures with optimal latency and cost. The results show that enforcing all safety constraints on the architecture leads to a worst case increase of 17% in latency and 18% component cost per vehicle. The increase in latency is significant at the scale of human driver reaction times.

Prospective assessment of the effectiveness of autonomous emergency braking in car-to-cyclist accidents in France

Objective: This study aimed to assess the effectiveness of autonomous emergency braking (AEB) systems in car-to-cyclist frontal collisions by simulating their effects, in terms of crash avoidance and injury mitigation, on a representative target population of real-world accidents. Identifying effectiveness-critical AEB–cyclist design parameters through a sensitivity analysis was also targeted.Methods: The analysis is based on a representative set of real-world car-to-cyclist frontal collisions gathered from French police reports. AEB–cyclist-relevant accident cases were first selected and used to build injury risk curves for fatal, severe, and slight cyclist injuries. The effect of AEB–cyclist on these cases was then simulated by means of a car kinematic model involving sensor detection strategies and actuator actions. Combining the resulting simulated impact speed distributions with the injury risk curves allowed to assess AEB–cyclist’s effectiveness in terms of lives saved and mitigated injuries. Using design of experiments methods, the sensitivity of this effectiveness with regards to AEB–cyclist design parameters could be assessed.Results: Cyclist injury risks curves were built, along with their confidence intervals, for fatal, severe, and slight injuries using a polytomous complementary log–log regression model, with squared impact speed as an independent variable. A sensitivity analysis on an ideal bisensor AEB–cyclist setting highlighted influential design parameters such as maximal braking intensity or crucial decision algorithm parameters such as maximal time and distance to collision thresholds. AEB–cyclist effectiveness was nevertheless shown to range from 35% to 59% in fatalities, 14% to 54% in severe injuries, and 11% to 42% in slight injuries, depending on field of view parameters alone, once reference values of decision algorithm parameters had been set.Conclusions: This study illustrates the potential benefits and limits of AEB–cyclist systems. High-end systems show acceptable effectiveness rates, but road safety performance strongly depends on external factors such as road surface conditions or has to be tuned in order to avoid unnecessary activations and driver discomfort. Limits of the system’s everyday use (lack of maintenance, driver reaction time to collision warnings, etc.) were not taken into account, thus resulting in optimistic evaluations of AEB–cyclist effectiveness.

Analysis of Driver Reaction During Braking and Avoidance Maneuver

One of the parts of the accident analysis is also the objective assessment of the responsibility of accident participants related to their behavior and objective ability to avert the imminent critical situation. During a critical situation, the driver can respond by avoidance maneuver, critical braking or a combination of both. However, most of the research studies have been focused on the analysis of the driver´s brake-reaction time. The aim of this paper is to analyze the driver´s reaction time in detail during the avoidance maneuver and critical braking. For the purpose of this study, the electromyography has been used. The combination allows detailed and accurate determination of the onset of the muscle activation during the reaction. For the elimination of the result distortion, the measurements were carried out in a real vehicle on a predetermined route and also in simulated condition. Participants responded to the various types of the critical incidents, mostly sudden braking of the leading vehicle. The obtained results demonstrate differences in the time necessary for the activation of the lower and the upper limb during a critical situation.

Development and evaluation with MIL and HIL simulations of a LQR-based upper-level electronic stability control

This paper proposes an electronic stability control and shows MIL and HIL simulations performed for evaluation. Two designs are presented, one based on two-degrees-of-freedom linear model that includes side-slip and yaw motion and other based on three-degrees-of-freedom linear model that includes roll motion. Model-in-the-loop and Hardware-in-the-loop simulations were performed to test ESC on vehicle represented by a nonlinear model that considers lateral, yaw and roll motions and an intelligent driver model to generate the steering wheel command as driver reaction to vehicle motion in respect with desired path. It’s found from simulations for double lane change maneuver that the proposed controller is effective in reducing of side-slipping, rolling and yaw rate errors, keeping the steering stable in scenarios where the driver loses control of the vehicle, even in presence of disturbances in vehicle response in relation with response predicted by linear model used for control design.

A Macroscopic Traffic Model based on Driver Reaction and Traffic Stimuli

A new macroscopic traffic flow model is proposed, which considers driver presumption based on driver reaction and traffic stimuli. The Payne–Whitham (PW) model characterizes the traffic flow based on a velocity constant C 0 which results in unrealistic density and velocity behavior. Conversely, the proposed model characterizes traffic behavior with velocities based on the distance headway. The performance of the proposed and PW models is evaluated over a 300 m circular road for an inactive bottleneck. The results obtained show that the traffic behavior with the proposed model is more realistic.

A Combined Driver-Station Interactive Algorithm for a Maximum Mutual Interest in Charging Market

This paper investigates the problem of a combined study on both electric vehicle (EV) drivers’ behavior in charging station selection (DSS) and station’s behavior in charging service management (CSM) that are considered in the proposed DSS-CSM algorithm. The motivation behind the proposed algorithm is to comprehensively describe a charging market from both drivers’ point of view to reduce range anxiety and enhance customer satisfaction and charge station providers’ point of view to manage charging station properly and improve profitability. The proposed DSS algorithm obtains all the required data to find the best station in terms of the influential parameters given by the candidate stations. A fuzzy multi-criteria decision-making (FMCDM) method that is utilized in the proposed DSS algorithm is deployed for evaluating the parameters and deciding for the best charging station. Moreover, the proposed CSM algorithm of each charging station is utilized to maximize its profitability by taking into account the charge, reserve, and discount prices with respect to the reaction of EV drivers. The Monte Carlo simulations are applied to demonstrate the effectiveness of the proposed algorithm in comparison with a designed benchmark algorithm.

Residual road departure crashes after full deployment of LDW and LDP systems

Objective: Road departures are one of the most severe crash modes in the United States. To help reduce this risk, vehicles are being introduced in the United States with lane departure warning (LDW) systems, which warn the driver of a departure, and lane departure prevention (LDP) systems, which assist the driver in steering back to the roadway. Previous studies have estimated that LDW/LDP systems may prevent one third of drift-out-of-lane road departure crashes. This study investigates the crashes that were not prevented, to potentially set research priorities for next-generation road departure prevention systems.Methods: The event data recorder (EDR) data from 128 road departure crashes in the National Automotive Sampling System Crashworthiness Data System (NASS-CDS) from 2011 to 2015 were mapped onto the vehicle trajectory and simulated with LDW/LDP to assess the potential for crash avoidance. The model predicted that 63–83% of single-vehicle road departure crashes may not be prevented by an LDW system and 49% may not be prevented by an LDP system.Results and Conclusions: For LDP systems, which were assumed to have zero latency, no crashes were avoided if the time-to-collision (TTC) from lane crossing to impact was less than 0.55 s. Obstacles such as guardrails and traffic barriers, which tend to be very close to the road, were more common among the remaining crashes. The study shows that LDW/LDP systems are limited by two factors, driver reaction time and TTC to the roadside object. Thus, earlier driver response and longer TTC may help in these situations.

Adaptive forward collision warnings: The impact of imperfect technology on behavioral adaptation, warning effectiveness and acceptance

Adaptive ADAS that adjust warnings according to the driver´s current need for support offer a great potential to increase safety. However, it is crucial to understand how drivers deal with dynamically adapting technologies particularly in situations in which driver state monitoring fails and the system shows unexpected behavior. To better understand the consequences of unreliable adaptive ADAS on safety and to assess how failures of an adaptive FCW influence driving behavior, we conducted a driving simulator study with N = 48 participants. Participants experienced critical brake events in situations with and without a distracting secondary task. An adaptive FCW provided visual warnings to undistracted drivers but highly supportive visuo-haptic warnings (brake jerks or vibration) to distracted drivers. In 20% of brake events, however, the system unexpectedly provided incorrectly adapted warnings in which the combination of warning type and distraction was reversed. This adaptive FCW was compared to a non-adaptive standard FCW that provided visual warnings only. We found that incorrect warnings impaired driver reactions and safety in distracted drivers, and these adverse behavioral effects had two sources: (1) Violations of the drivers´ expectancies about the warning, and hence, behavioral adaptation. (2) The absence of the compensatory effect of the highly supportive warning in case of distraction. In contrast, correctly adapted warnings reduced decrements in brake reaction times and fully offset safety deficits associated with driver distraction. Crucially, however, an effectiveness evaluation of the adaptive system’s potential to support drivers when correct warnings were elicited failed to demonstrate a benefit of the adaptive FCW over the non-adaptive FCW. Our results thus emphasize that a high reliability is crucial for adaptive ADAS to improve safety and to prevent adverse effects due to behavioral adaptation.

Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

AbstractIn urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.

Posttraumatic stress reactions of underground drivers after suicides by jumping to arriving trains; feasibility of an early stepped care outpatient intervention

ABSTRACTObjective: Underground drivers face a considerable risk of running over suicide jumpers on the tracks during their career. These traumatic exposures may lead to major psychological sequelae.Methods: Within an outpatient setting, 50 drivers were consecutively enrolled in a prospective non-controlled trial. A low-intensity, stepped-care approach included: emergency care immediately after the critical accident, comprehensive assessment with a structured clinical interview using the following scales within three days: Composite International Diagnostic Interview (CIDI), Impact of Event Scale (IES), Screening for Somatoform Disorders (SOMS), and Cologne Trauma Inventory (KTI).Results: During a 2-year period, 50 subway drivers were exposed to 66 serious critical accidents (deaths: 39, severe injuries: 27). Rate of acute stress reactions was 48%; rate of acute stress disorders was 30%. Scores of IES and SOMS were significantly increased correspondingly. At 1-month follow-up, PTSD was diagnosed in 24 (ICD-10) and in 9 drivers (DSM-IV), respectively. Major depression (n = 15) and somatoform disorder (n = 10) were diagnosed as coexistent to PTSD. Acute stress reaction/acute stress disorder, IES- and SOMS-scores, and previous traumatic exposures during adulthood, but not during childhood, were significantly associated with the risk of PTSD. A majority of drivers (n = 43) succeeded in reaching complete symptomatic remission and returning to work again within a 6-month period. Seven drivers suffered from long-lasting posttraumatic symptoms causing severe social impairment.Conclusions: A low-intensity, outpatient stepped-care approach may provide support to traumatized underground drivers in their process of posttraumatic remission and recovery.

INFLUENCE OF THE SCENARIO COMPLEXITY AND THE LIGHTING CONDITIONS ON THE DRIVER BEHAVIOUR IN A CAR-FOLLOWING SITUATION

The aim of this study was to analyse the driver behaviour in simple and complex car-following situations under day- and night-time lighting conditions (four scenarios). Nearly 70 drivers participated in the tests, each taking several trials, during which they were exposed to randomly selected situations. The tests involved driving along a two-lane motorway with a vehicle in front and responding to its sudden braking. Different distances between the vehicles were simulated. The scenarios varied in complexity ranging from none to some vehicles around the subject vehicle. The study involved measuring different reaction times, i.e. the time to release the accelerator pedal, the time to apply the brake pedal and the time to start steering, to find out how the particular emergency manoeuvres contribute to the occurrence of collisions in the four scenarios. The results show that both the complexity of a road situation as well as the lighting conditions determine the type of emergency manoeuvre undertaken and the time of the driver reaction.

Feedback Game-Based Shared Control Scheme Design for Emergency Collision Avoidance: A Fuzzy-Linear Quadratic Regulator Approach

Driver-machine shared control scheme opens up a new frontier for the design of driver assistance system, especially for improving active safety in emergency scenario. However, the driver's stress response to steering assistance and strong tire nonlinearity are main challenges suffered by controller designing for collision avoidance. These unfavorable factors are particularly pronounced during emergency steering maneuvers and sharply degrade shared control performance. This paper proposes a fuzzy-linear quadratic regulator (LQR) game-based control scheme to simultaneously enhance vehicle stability while compensating driver's inappropriate steering reaction in emergency avoidance. A piecewise linear-based Takagi–Sugeno (T–S) fuzzy structure is presented to mimic driver's knowledge about vehicle lateral nonlinearity, and the control authority is shared between driver and emergency steering assistance (ESA) system through steer-by-wire (SBW) assembly. An identical piecewise internal model is chosen for ESA and the shared lane-keeping problem is modeled as a fuzzy linear quadratic (LQ) problem, where the symmetrical fuzzy structure further enhances vehicle's ability to handle extreme driving conditions. In particular, the feedback Stackelberg equilibrium solutions of the fuzzy-LQ problem are derived to describe the interactive steering behavior of both agents, which enables the ESA to compensate driver's irrational steering reaction. Hardware-in-the-loop (HIL) experiment demonstrates the ESA's capability in compensating driver's aggressive steering behavior, as well as the copiloting system's excellent tracking and stabilizing performance in emergency collision avoidance.

Impact of heterogeneity of car-following behavior on rear-end crash risk

An increasing number of vehicles travel on freeways result not only in traffic congestions but also accidents. Rear-end crashes in freeways can be collectively attributed to drivers, vehicles, and road infrastructure, but driving behavior plays a key role in influencing car-following safety. This study aims to investigate the impact of heterogeneity of driving behavior on rear-end crash risk. Driving behavior depends on perceived risk levels, acceleration and deceleration habits, and driver reaction characteristics. Thus, the influencing factors of rear-end crash risk were initially analyzed by using the desired safety margin (DSM) model. Subsequently, five driving behavior parameters, including upper and lower limits of DSM, sensitivity coefficients of acceleration and deceleration, and response time, were calibrated by using the vehicle trajectories from the Next Generation Simulation I-80 datasets. Simulation experiments were designed to evaluate the impact of heterogeneity of car-following behavior on rear-end crash risk. Results showed that decreasing the lower (or upper) limit of the DSM, increasing the response time, increasing the sensitivity coefficient for acceleration, or decreasing the sensitivity coefficient for deceleration can increase rear-end crash risk. In addition, if stable and unstable driving styles coexist, then their proportions have important influences on rear-end crash risk. These results imply that two critical factors affect shock waves, namely, driving behavior characteristics and proportion of different driving styles. Thus, a potential strategy for the adjustment of the proportions of unstable driving styles can attenuate shock waves and reduce rear-end crash risk to a certain extent. Moreover, a wide extent of driving behavior heterogeneity can attenuate shock waves and subsequently reduce rear-end crash risk. Overall, driving behavior heterogeneity has an important impact on rear-end crash risk. Exploring the effect of each driving behavior parameter on rear-end crash probability is useful for urban road traffic control, and it can provide improved understanding of abnormal driving behavior characteristics to minimize rear-end crash risks.

A preliminary investigation of the effectiveness of high visibility enforcement programs using naturalistic driving study data: A grouped random parameters approach

This paper seeks to assess the effectiveness of high-visibility enforcement (HVE) programs in terms of reducing aggressive driving behavior. Using Strategic Highway Research Program 2 (SHRP2) Naturalistic driving study (NDS) data, behavioral reactions of drivers before, during, and after the conduct of high-visibility enforcement programs are analyzed, in order to identify the potential effect of high-visibility enforcement in driving behavior. In this context, two fundamental aspects of aggressive driving behavior (speeding and tailgating) are employed and analyzed. To simultaneously explore the intensity and the duration of these behavioral patterns, novel metrics are defined and used in the analysis. To investigate the effect of high-visibility enforcement programs, and at the same time, to control for the effect of driver-, trip-, vehicle-, and weather-specific characteristics on the extent of speeding and tailgating, univariate grouped random parameters linear regression models are estimated. In addition, likelihoods of speeding and tailgating occurrences are analyzed simultaneously, within a grouped random parameters bivariate probit modeling framework. The results of this preliminary analysis show that even though the implementation of the high-visibility enforcement has mixed effects on the extent and the likelihood of the driving behavior metrics, it demonstrates a promising potential in modifying driving behavior.

A hybrid approach for identifying factors affecting driver reaction time using naturalistic driving data

The National Transportation Safety Board (NTSB) estimates that 80% of the deaths and injuries resulting from rear-end collisions could be prevented by the use of advanced collision avoidance systems. While autonomous or higher-level vehicles will be equipped with this technology by default, most of the vehicles on our roadways will lack these advances, so rear-end crashes will dominate accident statistics for many years to come. However, a simple and cost-effective in-vehicle device that uses predictive tools and real-time driver-behavior and roadway data can significantly reduce the likelihood of these crashes. In this paper, we propose a hybrid physics/data-driven approach that can be used in a kinematic-based forward-collision warning system. In particular, we use a hierarchical regularized regression model to estimate driver reaction time based on individual driver characteristics, driving behavior, and surrounding driving conditions. This personalized reaction time is input into the Brill’s one-dimensional car-following model to calculate the critical distance for collision warning. We use the Second Strategic Highway Research Program (SHRP-2)’s Naturalistic Driving Study (NDS) data, the largest and most comprehensive study of its kind, to model driver brake-to-stop response time. The results show that the inclusion of driver characteristics increases model precision in predicting driver reaction times.

Effects of warning systems on the reactions of male and female drivers: A systematic review

Background: Warning systems are widely used as a safety option in cars. The aim of this study was to systematically investigate the reactions of male and female drivers after receiving the warnings. Methods: A literature search was done using Science Direct, Scopus and PubMed databases. The included articles have been published in 2003-2018. Articles were first screened reviewing their titles in order to exclude non-relevant articles. Then, the abstracts and full texts of the retained articles were reviewed. A manual search of journals was also performed. Results: Of 915 retrieved articles, 29 articles have been included in this review. In the included articles, 11 articles use forward collision warning (FCW) system, 6 articles use lane departure warning (LDW) system and 21 articles use other types of warning systems including intersection collision warning system, red light running collision warning, adaptive cruise control, etc. Most of the studies showed a significant difference between genders in the case of reaction-type variables. The variables that are significant in more studies are warning rate, crash rate and brake reaction time. Conclusion: Most of the included articles indicate that there are significant differences between male and female drivers after receiving warnings. This can be considered for customers to be more vigilant about choosing the appropriate warning system.