Perception-reaction Time Research Articles

Can retroreflective rings enhance drivers’ safety perception of spatial right-of-way in freeway tunnels? A simulation exploration

In order to investigate whether retroreflective rings can enhance drivers’ perception of spatial right-of-way in freeway tunnels, this paper explores a simulation test. The characteristics of spatial right-of-way in tunnels are elucidated, and a comparative test is conducted using commonly used delineators and raised pavement markers against retroreflective rings to enhance the perception of spatial right-of-way. The test employs the perception of lateral deviation and longitudinal distance as indicators to reflect the lateral and longitudinal right-of-way. Video scenarios, incorporating different facilities and spacing, are created using 3Ds Max software following the design standards of freeway tunnels. The indicators of Stimulation of Subjectively Equal Distance (SSED), lateral deviation, and perception reaction time (PRT) are chosen to assess the effects of different facilities on drivers under varying spacing conditions. Fifty-two participants, divided into two groups of novice drivers and experienced drivers, underwent perception testing in a simulated driving environment. The results indicate that drivers exhibit the highest overestimation of longitudinal distance and the longest PRT of lateral deviation in the absence of facilities. Installing retroreflective rings with a spacing of 50–200 m significantly mitigates the overestimation of longitudinal distance, while reducing the PRT of lateral deviation. On the other hand, setting up delineators and raised pavement markers with a spacing of 6–12 m significantly reduces the PRT of lateral deviation, while there is no significant enhancement to the perception of longitudinal distance. A spacing of 200 m for retroreflective rings and 10 m for delineators and raised pavement markers in the straight section is recommended as a safer and more economical setting scheme. The combination of these facilities can enhance drivers’ safety perception of spatial right-of-way in freeway tunnels, facilitating rapid perception, correct judgment, and timely decision-making for the safe passage of vehicles.

Identifying drivers’ perception-reaction time (PRT) in car-following processes via two different methods using vehicle trajectory data

To judge whether the connected and automated vehicles (CAVs) could perform better than human, this study proposes two methods to identify perception-reaction time (PRT) of human drivers using vehicle trajectory data - the calibrated-based and duration-based methods. The calibrated-based method utilises an intelligent driver model (IDM) to capture the longitudinal driving behaviours and delineate the PRT as a model parameter. While the duration-based approach collects PRT by manually extracting the PRT value based on the trajectory data. To investigate the factors influencing these types of PRTs, two regression models are proposed. Results reveal important contributing factors affecting the PRT values of human drivers, including the mean and standard deviation of the leading vehicle’s speed, and the speed and gap difference between leading and following vehicles, as well as the mean speed of the following vehicle. Besides, the same factor has different effects on the calibrated and selected PRTs. For instance, the std of the gap between leading and following vehicles manifests negative and positive impacts on the calibrated and selected PRTs, respectively. In summary, findings of this study can provide valuable insights into the identification of the PRT values of human drivers, potentially improving future design of CAVs.

Difference in perception-reaction time of plain and plateau drivers at expressway exit ramps

The low-pressure (and low-oxygen) environment in plateau regions significantly impacts the driver’s perception-reaction time (PRT) and, in turn, road safety. However, drivers faced increased workloads at interchange exit areas while the Tibet region experienced a significant improvement in high-grade highway construction. Therefore, this study aimed to quantitatively analyze the influence of the plateau environment on PRT. Using a UC/win-road simulator, 6 scenarios of expressway exit ramps were constructed. Then, a total of 50 participants (35 males, 15 females) in Nanjing (altitude of 50 m) and 50 participants (36 males, 14 females) in Lhasa (altitude of 3650 m) were recruited for the simulation experiments. The driver’s PRT was measured according to the visual fixation characteristics (using an eye-tracking device) and vehicle running data. The no-difference in PRT for various scenarios was validated using the one-way-ANOVA test. Then, the Spearman bivariate correlation and generalized linear regression model were used to analyze the influence of driver’s age, driving experience, gender, and acclimation period on PRT. In addition, the difference in PRT of different groups of plain and plateau drivers was further analyzed using the Mann-Whiteney U and Kruskal-Wallis tests. Although driving experience and gender affected the PRT of the plain and plateau drivers, the effect was greater in the plain areas. The low-pressure environment at high altitude had a more significant impact on young novice male drivers. These findings serve as valuable references for refining the design of expressway exit ramps and improving road safety in the plateau areas.

A novel approach to the investigation and quantification of the stop/start process for pedestrian traffic using motion capture devices

Characterising the stop/start walking process of individuals in a crowded and congested space is an important consideration in modelling pedestrian movement. However, the reaction of pedestrians to speed changes, especially to the person in front, has not been fully characterised nor quantified for full adoption in computer models. This study, therefore, explored the different phases of the stop/start process through a series of novel experiments conducted at University College Dublin (UCD) in which individual movements were captured precisely using motion capture equipment. The overall aim of the study was to develop a novel methodology (inspired from vehicle traffic flow) to break down and quantify the components of the stop/start walking process, i.e., the perception-reaction time, slow-down time, and start-up time of individuals walking in a single-file. These times, together with the total stopping distance and inter-person distances of each follower to their leader at the beginning and the end of each phase were quantified successfully and their inter-relationships were explored. The results showed the mean perception-reaction time, slow-down time, and start-up time delay were 0.48, 0.58, and 0.39 s, respectively. Where applicable, normal, lognormal, Weibull, gamma, and log-logistic probability distributions were fitted to the data to determine the best fit. The novel methodology developed in this study can be used in the future to investigate pedestrians’ behaviour in response to any changes in leaders’ speed, i.e. quantify the reaction of individuals in different phases. The results of this study can inform the representation of the stop/start process in microscopic pedestrian models.

A Systematic Review of Emergency Braking Assistant System to Avoid Accidents Using Pulse Width Modulation and Fuzzy Logic Control Integrated with Antilock Braking

The increasing number of fatalities in nations across the world because of a deficiency of protective technology in automobiles has created a chaotic scene in recent years. However, the car driver’s Perception-Reaction Time (PRT) plays an important variable during such accidents and emergencies. The Anti-Lock Braking System (ABS) seems a viable technology, which today is used to prevent tires from sliding during quick brakes, whereas EBS is designed for braking assistance during tuning or emergency braking. Physical weight, rotational rigidity, diameter, and tire material strength of the vehicle are all used to simulate them. Advance Driver Assistance Systems (ADAS), which include Anti-lock brakes (ABS) and Emergency Braking Systems (EBS), are the foremost viable technique for minimizing the environmental impact and uncertainties of driving road transportation. The presented systematic review aims to deliver a ground-level analysis that can be used to enhance the safety of motor vehicle driving, reduce wheel slip to achieve the best possible stopping distance in commercial and specialized vehicles, and influence future transportation. In this study, the most widely utilized technologies for ADAS have been reviewed and discussed. Various sensors used to improve braking and vehicle performance have been systematically studied in the context of low power-consuming techniques like pulse width modulation. An analysis of emergency braking procedures performed by riders with varying degrees of braking expertise was conducted using previously collected experimental data, and the results were used to conclude potential loss of control situations.

Plateau effect on driver’s hazard perception response mode: Graph construction approach

It is crucial for drivers to conduct rapid and effective risk perception and response processes when faced with hazardous driving situations. The low pressure and oxygen environment in the plateau results in a greater workload of drivers, contributing to a significant decline in perception and response ability. This study proposes a graph construction approach to model drivers’ hazard response modes (HRMs) in plateau areas. A total of 31 drivers (23 males) aged 21 to 55 years (M [age] = 28.0 years, M [driving experience] = 6.5 years) were recruited to participate in four hazard perception experiments using a UC-WIN/ROAD driving simulator. The experiments were successively conducted in five cities with different altitudes, including Nanjing (50 m), Nyingchi (2,995 m), Lhasa (3,650 m), Nagqu (4,460 m), and Yanghu Scenic Spot (4,998 m). Then, according to the graph construction approach, four HRMs for drivers were extracted. In addition, two series of generalized linear models were proposed to analyze the relationships between the perception reaction time (PRT), HRM, altitude, age, acclimation period, gender, and driving experience. The effects of significant variables, including scenario types, altitude, acclimation period, driving experience, and gender, were used in the construction of HRM and risk perception ability of plateau drivers. These results showed that constructing HRMs to model the driving styles of plateau drivers is feasible and effective, enabling future driving assistance systems to be better customized for drivers in such a particular condition.

Transportation-Related Human Factors in High-Altitude Regions: Review, Needs, and Novelties

The low pressure at high altitudes (above 2500 m) causes hypoxia (decreased oxygen) that affects people’s physiological and psychological characteristics. Specifically, hypoxia may affect the neural function of the brain, leading to severe cognitive deficits and a significant decline in memory function and attention. This article addresses the effect of human factors on transportation design and operation at high altitudes (HA), with some details on the Tibet-China region. Specifically, the paper first reviews the basic transportation-related concepts for high altitude, including oxygen and temperature levels, driver perception-reaction time, hazard perception, vehicle speed, and walking speed. Then, the transportation users affected by high altitudes are discussed, including drivers, pedestrians, cyclists, passengers, and others. Next, the impacts of human factors on highway design and operation for HA regions are discussed along with the research needs. Finally, recent innovations to address the challenges of HA transportation are presented, along with case studies comparing some human factors of the plateau and plain areas. This article represents a valuable reference for future research in HA regions to improve transportation design and safety

Novel algorithm for multivariate time series crash risk prediction using CNN-ATT-LSTM model

Multivariate Time Series Crash Risk Prediction is essential in the development of Collision Avoidance Systems (CASs), which are vital components of the Intelligent Transportation System. The crash risk prediction performance is degraded with high computational cost and low accuracy. To address this issue, Attention based CNN-LSTM Hybrid model is proposed for Multivariate time series crash risk prediction through the augmentation of Convolutional Neural Network (CNN) with Attention based Long Short Term Memory (ATT-LSTM). Attention mechanism is incorporated with LSTM to learn long-term dependencies of ultra-long sequences. Modified Crash Risk Index (MCRI) is developed to label the crash and non-crash events considering Adaptive Perception Reaction Time (APRT) which enables the enhancement of the accuracy of the Multivariate time series crash risk prediction system. The problem is formulated as multivariate time series prediction and validity of proposed model is evaluated with Next Generation Simulation (NGSIM) dataset. The proposed model outperforms state-of-the-art models where MCRI and CNN-ATT-LSTM enhance accuracy of the crash risk prediction. 98.1% of accuracy has been achieved in the proposed model. The result demonstrates that the proposed model requires less computational cost, high accuracy and minimum preprocessing. The proposed model presents warning to the driver at the time of collision accurately and can be implemented in Collision Avoidance Systems.

Vision Transformer for Detecting Critical Situations and Extracting Functional Scenario for Automated Vehicle Safety Assessment

Automated Vehicles (AVs) are attracting attention as a safer mobility option thanks to the recent advancement of various sensing technologies that realize a much quicker Perception–Reaction Time than Human-Driven Vehicles (HVs). However, AVs are not entirely free from the risk of accidents, and we currently lack a systematic and reliable method to improve AV safety functions. The manual composition of accident scenarios does not scale. Simulation-based methods do not fully cover the peculiar AV accident patterns that can occur in the real world. Artificial Intelligence (AI) techniques are employed to identify the moments of accidents from ego-vehicle videos. However, most AI-based approaches fall short in accounting for the probable causes of the accidents. Neither of these AI-driven methods offer details for authoring accident scenarios used for AV safety testing. In this paper, we present a customized Vision Transformer (named ViT-TA) that accurately classifies the critical situations around traffic accidents and automatically points out the objects as probable causes based on an Attention map. Using 24,740 frames from Dashcam Accident Dataset (DAD) as training data, ViT-TA detected critical moments at Time-To-Collision (TTC) ≤ 1 s with 34.92 higher accuracy than the state-of-the-art approach. ViT-TA’s Attention map highlighting the critical objects helped us understand how the situations unfold to put the hypothetical ego vehicles with AV functions at risk. Based on the ViT-TA-assisted interpretation, we systematized the composition of Functional scenarios conceptualized by the PEGASUS project for describing a high-level plan to improve AVs’ capability of evading critical situations. We propose a novel framework for automatically deriving Logical and Concrete scenarios specified with 6-Layer situational variables defined by the PEGASUS project. We believe our work is vital towards systematically generating highly reliable and trustworthy safety improvement plans for AVs in a scalable manner.

Incorporation of Human Factors to a Data-Driven Car-Following Model

In recent years, there have been intensive efforts to consider human factors (HFs) in the modeling of human driver behavior. In particular, “engineering” car-following models widely used in traffic simulation have been extended to include HFs. This extension is needed to generate critical situations, which are often attributable to human error. However, incorporation of reaction processes requires advanced models that take driver predictions and delayed responses into account. In this paper, a methodology for integrating HFs into driver behavior modeling is developed based on a long short-term memory architecture. The proposed methodology employed a three-layer psychological concept: perception, information processing, and action. The perception layer modeled (imperfect) estimation of visually received stimuli. Information processing included short-term memory and the projection of perceived stimuli into the near future. The executed action, based on the sensed as well as anticipated dynamic driving state, was delayed by the perception–reaction time. To represent individual differences among driver types, the available training dataset was classified in four clusters according to observable driver characteristics. The methodology was demonstrated with data recorded at an urban signalized intersection. Model performance was compared with that of two established engineering models, the intelligent driver model and the (extended) full velocity difference model. The results indicated that the human driver model developed here showed superior performance in replicating real-world trajectories compared with existing models and was able to represent the varying driving strategies of different groups.

Analysis on hazard perception ability of drivers in plateau areas: by different elevations

The low-pressure and low-oxygen environment of plateaus results in a greater workload for drivers, which contributes to the serious road safety situation in plateau areas in China. This study conducted four hazard perception experiments in Nanjing (50 m above sea level (asl)), Nyingchi (2995 m asl), Lhasa (3650 m asl), Nagqu (4460 m asl), and Yanghu Scenic Spot (4998 m asl) using UC-WIN/ROAD driving simulation software. A total of 31 drivers (23 males, 8 females) were recruited in this study, with a mean age of 28.0 years old and mean driving experience of 6.5 years. A bivariate correlation test was adopted to analyze the impacts of the elevation, age, acclimation period, gender, and driving experience on the perception–reaction time. Then, the drivers were divided into three groups (Good, Medium, and Poor) using K-means clustering. Finally, the marginal effects of the linear regression models for the three groups were calculated to augment the comparison. As expected, the elevation showed a positive correlation with the perception–reaction time but indicated a variance in influence effects in the three groups. Consistent with previous evidence, experienced drivers had better perception ability in the plateau. These findings could help in the design of high-elevation roads and guide the definition of suitability to drive in the plateau, thereby improving driving safety.

Self-explaining performance of visual guiding facilities in urban road tunnels based on speed perception

A low-cost and effective speed control method is to be developed for urban road tunnels in order to enhance traffic safety and improve operation in the tunnels. The self-explaining performance of the visual guiding facilities on driver’s speed perception and reaction time in the interior zone of urban tunnels was investigated. Using 3D Max to build video simulation model, and referring to the common visual guiding facilities in urban tunnels, six scenes were constructed, namely no facility, horizontal stripe, edge marker, retroreflective arch, vertical stripe and multiple facilities. Stimulus of subjectively equal speed (SSES) and perception reaction time (PRT) were selected as the basic performance indicators to evaluate the different scenes. The experimental results showed that the SSES in multiple facilities scene was closest to the standard speed, followed by the vertical stripe, retroreflective arch, horizontal stripe, and no facility, while speed over-estimation was the most serious in edge marker scene. The results of PRT also showed that self-explaining performance of PRT in multiple facilities and vertical stripe scenes was better than other facilities. Vertically spaced facilities had better SSES and PRT self-explaining performance. The reasonable combination of different types of visual guiding facilities can integrate the advantages of each facility, which is more conducive to realizing the self-explaining effect of rapid perception, accurate judgment, timely decision and easy manipulation.

Analysis of Stopping Sight Distance (SSD) Parameters: A Review Study

Stopping sight distance (SSD) is one of the important elements in geometric highway design, which relies on the two key parameters, i.e., vehicle braking characteristics and drivers’ reaction time. Previous studies have shown higher discrepancies in the coefficients of the SSD parameters, and therefore, making it difficult for practitioners what values to be followed. Therefore, this review study aims to compare deceleration rates and PRT values in different situations that can be applied to geometric highway design. To this end, two electronic databases were searched and relevant articles that reported drivers’ perception reaction time (PRT) or deceleration rates in different situations were identified and included in the review. The obtained results showed that deceleration rates of vehicles ranged from 0.49 m/s2 to 8.76 m/s2 with a total weighted average of 2.82 m/s2. On the other hand, PRT of drivers ranged from 0.48 seconds to 2.01 seconds, with a total weighted average of 1.21 seconds. The key factors that were assessed on deceleration rate of vehicles are surface condition; vehicle type; stimulus; and initial speed. Results of this study suggest that all these factors, except for initial speeds greater than 80 km/h, have a significant effect on deceleration rates of vehicles. The findings of this study could be used as inputs in geometric highway design calculations under different conditions.

Better Rested than Sorry: Data-Driven Approach to Reducing Drowsy Driving Crashes on Interstates

AbstractDriver perception-reaction time and ability to sustain attention decrease as drivers become drowsy. The consequences of drowsy driving crashes are often worse on interstate highways due to ...

Virtual Reality Application on Road Markings’ Visibility Analysis

The most important function of road markings is to guide road users; therefore, their visibility, in relation to retroreflectivity (RL), plays an essential role in the markings’ performance at nighttime. Researchers have applied field experiments to determine the minimum acceptable levels of markings’ RL. However, field studies usually involve a large investment of time, personnel, space, and budget. This paper presents a preliminary study that adopted virtual reality (VR) techniques to establish an immersive driving environment for the RL analysis of yellow markings. The objective is to evaluate the minimum required marking visibility for road users, especially senior drivers, while driving at nighttime. The brightness feature created in the VR model does not simulate the optical behavior of markings’ glass beads, but it simulates the marking’s visibility in relation to detection distance at each test drive. This is an alternative method to correlate the field RL and the VR simulation model through the equivalent detection distances that were recorded by the observers who participated in both field and VR driving tests. The relationship between the field markings’ RL, 80 to 130 mcd/m2/lx, and simulated markings’ brightness was successfully established with its limitations of RL between 80 and 130 mcd/m2/lx. Three age clusters are involved in this study, namely 20–30, 40–50, and 60+ years old, with 30 people in each cluster. The simulation study found that the driver’s behavior in relation to reaction time is highly related to age cluster but not driving speed or RL. The average reaction times for the three age clusters are 0.438 s, 0.499 s, and 0.522 s, respectively. However, the perception reaction time 2.5 s was considered while calculating the required RL for markings. Results found that the minimum required yellow markings’ RL increases with driving speed, and it requires 80 mcd/m2/lx for 50 km/h and 130 mcd/m2/lx for 65 km/h to satisfy the 85 percentiles of the driving population in Taiwan.

AN ANALYSIS OF TYPE I DILEMMA ZONE AT SIGNALISED INTERSECTIONS

Intersections, for vehicles coming from different directions, are conflict points in road networks. When a driver approaching a signalised intersection encounters the yellow light, he/she is in a dilemma either to safely stop or to pass through the intersection during clearance time. The decision to stop or to pass may change depending on some factors such as duration of yellow light, deceleration and acceleration rate, width of intersection, speed and length of vehicle, etc. This study aims to put forth the effects of some related factors affecting the length of the Type I dilemma zone. To perform this study, five factors including vehicle speed, maximum deceleration rate, perception-reaction time, clearance time, the total intersection width-vehicle length were considered and a total of 648 different traffic cases were investigated. The study results showed that the Type I dilemma zone length increased with the increase of speed, total intersection width-vehicle length and perception-reaction time, but decreased with the increase of clearance time and deceleration rate.

Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology

Data-based research approaches to generate crash scenarios have mainly relied on conventional vehicle crashes and naturalistic driving data, and have not considered differences between the autonomous vehicle (AV) and conventional vehicle crashes. As the AV’s presence on roadways continues to grow, its crash scenarios take on new importance for traffic safety. This study therefore obtained crash patterns using the United States Department of Transportation pre-crash scenario typology, and used statistical analysis to determine the differences between AV and conventional vehicle pre-crash scenarios. Analysis of 122 AV crashes and 2084 conventional vehicle crashes revealed 15 types of scenario for AVs and 26 for conventional vehicles. The two groups showed differences in type of scenario, and differed in the proportion of crashes when the scenario was the same. The most frequent AV pre-crash scenarios were rear-end collisions (52.46%) and lane change collisions (18.85%), with the proportion of AVs rear-ended by conventional vehicles occurring with a frequency 1.6 times that of conventional vehicles. An in-depth crash investigation was conducted of the characteristics and causes of four AV pre-crash scenarios, summarized from the perspectives of perception and path planning. The perception-reaction time (PRT) difference between AVs and human drivers, AV’s inaccurate identification of the intention of other vehicles to change lanes, and AV’s insufficient path planning combining time and space dimensions were found to be important causes for the AV crashes. By increasing understanding of the complex characteristics of AV pre-crash scenarios, this analysis will encourage cooperation with vehicle manufacturers and AV technology companies for further study of crash causation toward the goals of improved test scenario construction and optimization of the AV’s automated driving system (ADS).

Feasibility study of highway alignment design controls for autonomous vehicles

In recent years, the development and testing of autonomous driving technology have become widespread around the world. However, due to differences in perception abilities between autonomous vehicles and human drivers, the current geometric design controls for highway alignments, designed for the human driver, may not be applicable to the autonomous vehicle (AV). Few studies, however, have systematically investigated the design controls for autonomous vehicles, though we face full driving automation in the next few decades. Because the range of modern AV sensors reaches 250 m, with expected further improvements in the near future, there is a need to determine how the sensors’ perception field and perception-reaction time may affect the current road design standards developed for human drivers. This study therefore tested the feasibility of the current design controls for fully-autonomous vehicles by separately computing controls for vertical alignments and combined horizontal and vertical alignments, considering the AV’s perception abilities of perception-reaction time (PRT), sensor height, and upward angle from the horizontal. The required stopping sight distance (SSD) and minimum length of sag and crest vertical curves were derived and compared with those for human drivers. Computations for combined alignments were based on Green Book coordination guidelines: as the minimum length of horizontal curve can be used for alignments adhering to guidelines, preview sight distance (PVSD) was computed for alignments that do not. Results showed that 1) AV-based design controls on vertical curves were more tolerant than those based on human drivers; and 2) the dominating criterion of sag vertical curve design control was comfort for autonomous vehicles, versus required SSD for human drivers.

Sacred components of hunting and fishing technologies of the indigenous peoples of the Amur-Sakhalin Region

The article, by means of the comparative-historical method, critical analysis of scholarly concepts, and use of ethnographic materials, deals with the study of the complex of beliefs and rituals of indigenous peoples of the Russian Far East (Nanais, Negidals, Nivkhs, Orochs, Udeges, Uilta, Ulchs, and Evenks) as a sacred component of their traditional and modern hunting and fishing technologies. The term ‘technology’ originates from the Ancient Greek philosophy by the development of the doctrine of ‘techne’ as an art by which things are made. Technology is based on notion and supersedes the role of the chance in human life and activities, which expedites the proc-ess of adaptation to the nature. The ritual preceding production of a tool or a vehicle is performed to improve qual-ity of the item to ensure its more productive use in hunting and fishing. In the culture of indigenous peoples of the Russian Far East, there are known examples of invocation of magic, cults, beliefs, and rituals to secure hunter’s luck in fabrication of hunter carriers, tools for hunting marine and terrestrial animals, and traps. Beliefs and rituals serve as the sacred components of the hunting and fishing technologies, which have the utmost importance for sustainable life of the indigenous population. The main conclusion is that, in spite of some differences in the economy, degree of settlement and mobility, and the level of influence of nonethnic cultures, undoubtedly, the results of hunting, fishing, off-shore seal catching, deer breeding, and foraging depend on personal experience, rational knowledge of the qualities of plants, weather signs, and migration times and habits of animals, and on the quality of the trade equipment, transport, clothes and footwear. However, these aspects are not sufficient and the hunters resort to the sacral components of the hunting and fishing technologies — transport means are ‘enli-vened’, by magic means they are imparted with the qualities of living beings — people or animals. With the help of amulets, the hunters strengthen their trade qualities — agility, perception-reaction time, and intuition. Prohibitions are observed, which are aimed at decreasing dependence on chance and increasing hunting productivity. The rational technologies, aimed at the survivance of the ethnos, are complemented by the sacred components, so that people cling to the help of supernatural powers.

A system to determine advisory speed limits for horizontal curves based on mental workload and available sight distance

This study proposes a framework that accounts for mental workload and available sight distances to estimate advisory speed limits on horizontal curves. To achieve this goal, automated scripts were used to extract data on horizontal curve elements (i.e., the degree of curvature and the deflection angle), to detect crest vertical curves, and to compute the available sight distance from remote sensing data collected on highways in Alberta, Canada. Mental workload ratings were then assigned to each horizontal curve to calculate the perception–reaction time needed by drivers to maintain control of their vehicles while negotiating these curves. Finally, a curve advisory speed was calculated based on the available sight distance and the mental workload perception–reaction time needed to ensure a safe driving environment. This study presents a unique approach that incorporates human factors, including the responses of drivers based on their perception of the driving environment, in the development of speed advisory systems.