Bayesian modeling of the microscopic traffic characteristics of overtaking in two-lane highways

Bayesian networks are employed to model the uncertainty hindering in the overtaking behavior of young drivers in two-lane highways and reveal the traffic related microscopic characteristics that may influence the decision to overtake. Results using data from an experiment conducted on driving simulator show that male drivers, on average, accept smaller gaps for overtaking than female drivers. For both male and female drivers, the spacing with the lead and the opposing vehicle is more influential to the probability to overtake compared to vehicle speed. Moreover, a thorough look at the relationships between the microscopic traffic characteristics and the probability to overtake reveals differences between male and female drivers regarding the road traffic scene appraisal mechanism on the emergence of an opportunity to overtake.     

The effects of adaptive cruise control (ACC) headway time on young-experienced drivers' overtaking tendency in a driving simulator

The preference to maintain a certain desired speed is perhaps the most prevalent explanation for why a driver of a manually driven car decides to overtake a lead vehicle. Still, the motivation for overtaking is also affected by other factors such as aggressiveness, competitiveness, or sensation-seeking caused by following another vehicle. Whether such motivational factors for overtaking play a role in partially automated driving is yet to be determined. This study had three goals: (i) to investigate whether and how a driver's tendency to overtake a lead vehicle changes when driving a vehicle equipped with an adaptive cruise control (ACC) system. (ii) To study how such tendencies change when the headway time configuration of the ACC system varies. (iii) To examine how the manipulation of the speed and speed variance of the lead vehicle affect drivers' tendencies to overtake a lead vehicle. We conducted two different experiments, where the second experiment followed the first experiment's results. In each experiment, participants drove three 10–12 min simulated drives under light traffic conditions in a driving simulator under manual and level one (L1) automation driving conditions. The automation condition included an ACC with two headway time configurations. In the first experiment, it was 1 sec and 3 secs, and in the second, it was 1 sec and 2 secs. Each drive included six passing opportunities representing three different speeds of the lead vehicle (−3 km/h, +3 km/h, +6 km/h relative to the participant), with or without speed variance. Results show that drivers tended to overtake a lead vehicle more often in manual mode than in automated driving modes. In the first experiment, ACC with a headway time of 1 sec led to more overtaking events than ACC with 3 secs headway time. In addition, the relative speed of the lead vehicle and its speed variability affected overtaking tendencies. In the second experiment, the relative speed of the lead vehicle and its speed variability affected overtaking tendencies only when interacting with each other and with driving configuration. When the speed of the lead vehicle was +3 km/h and included variability, more overtaking events occurred in manual mode than both automation modes. This work has shown that driving with ACC might help reduce overtaking frequencies and more considerable when the headway time is set to 3 secs.     

User-related assessment of a Driver Assistance System for Continuous Support – A field trial

A Driver Assistance System for Continuous Support continuously evaluates the status of the host vehicle as well as the surrounding traffic based on information from on-board sensors. When the system detects a hazard, it issues a warning to the driver, depending on the degree of the hazard. The effects of this system on driver behaviour and acceptance were evaluated in a field trial carried out in 2013. Twenty-four drivers took part in test drives with a within-subject design along a 53 km test route containing motorway and rural-road sections. Driving data was logged and the test drivers were observed by means of an in-car observation method (Wiener Fahrprobe); in this case by two observers in the car along with the driver. Questionnaires were used to assess the drivers’ comprehension of and reaction to the system. The system was successful in affecting driver behaviour in terms of lower speed when negotiating curves. Positive effects were found in the form of better speed adaptation to the situation during driving with the system activated. Also, lane choice and lane change improved with the system on. When it came to speed limit compliance, driving speed in general and longitudinal and lateral positioning, no effects could be found. No major differences were found regarding distance to the vehicle in front, overtaking manoeuvres, stopping behaviour at intersections, driving against yellow at traffic lights and interaction behaviour with other road users while driving with or without the system. On the negative side, it was noted that only during driving with the system activated did the test drivers make turns at intersections at too high speeds. In addition, more errors associated with dangerous distance to the side were observed with the system activated. In terms of the emotional state of the driver, the only difference found was that the drivers felt an increase in irritation. Regarding subjective workload, the drivers only assessed one item, i.e. whether their performance decreased statistically significantly while driving with the system. The test drivers were of the opinion that the system was useful, and that it would enhance safety especially in overtaking manoeuvres on motorways. The blind-spot warning was found especially useful in the overtaking process. The drivers appreciated the fact that the system did not give information all the time.     

Objective and perceived risk in overtaking: The impact of driving context

The assistance and autonomous performance of overtaking manoeuvres can offer significant safety benefits. The impact of driving context on perceived risk emphasises the benefits of using contextual information to adjust the manoeuvring behaviour. This paper follows a mixed approach, addressing two main objectives: identifying factor combinations related to overtaking crashes (objective risk) and exploring their relationship to perceived risk. Factor combinations were extracted from a multi-year dataset, acquired from the UK in-depth study RAIDS (Road Accident In-depth Studies). Selected factors were used to create motorway overtaking scenarios with different manoeuvring behaviour (pull-out distance, manoeuvre duration, speed) and driving context (day/night, overtaking car/truck), while 237 participants assessed their impact on perceived risk through an online survey. The findings highlight the strong impact of manoeuvre characteristics on perceived risk, mediated or intensified by the driving context. Long pull-out distance and short manoeuvre duration time were preferred; under night conditions, short pull-out distances were perceived as riskier compared to daytime, while the opposite effect appeared for high speed, which was considered safer. The results can inform future research on motorway overtaking safety perception and acceptability, as well as the design of systems that assist or autonomously perform overtaking. Specifically, they can be used as guidelines for incorporating context related information to adjust overtaking behaviour according to user preferences and create a positive passenger experience.     

Who overtakes more? Explanatory analysis of the characteristics of drivers from low/middle and high-income countries on passing frequency

The passing manoeuvre requires a driver to make decisions and take actions which are dependent on his/her behavioural characteristics and driving ability. However, previous works on passing rate models have exclusively considered geometric and traffic-related variables. This study aims at bridging this gap by investigating the influence of driver profile (i.e., age, gender, nationality - Italian or Iranian - aggressive driving scores, driving exposure) on passing frequency. A driving simulation experiment involving 54 drivers (36 Italians, 18 Iranians) was conducted along a 6.67 km segment of a two-lane rural highway with passing manoeuvres permitted along 25% of its length. Controlled factors included traffic flow and speed in the oncoming direction, and speed in the driver direction, with a total of 27 scenarios assigned to drivers based on a 3³ confounded factorial design. A Poisson regression model was used to investigate the significance of independent variables. Age and gender and their interaction term were significant, thus the effects of age and gender on the number of passing manoeuvres are mutually interdependent. Furthermore, drivers who drive less often completed fewer overtaking manoeuvres. Sensitivity analyses were carried out to understand the magnitude of change in passing frequency attributable to a variation in the explanatory variables. The findings suggest that driver characteristics have a significant effect on passing frequency and should be considered when conducting a performance and safety evaluation of two-lane roads.     

Can prosocial attitude reduce the risk behavior in simulated driving?

High traffic density may lead to more traffic accidents because of more frequent lane change and overtaking behaviors, but drivers with different characteristics may exhibit different driving behaviors. The present study explored the difference in driving behaviors between drivers with a high/low prosocial attitude under high/low traffic density. In this study, a 2 (high/low prosocial attitude) *2 (high/low traffic density) mixed design was used to investigate the interaction between prosocial attitude and traffic density on lane change and overtaking behavior. The implicit association test paradigm was used to measure prosocial attitude, and drivers were divided into two groups. Forty subjects were asked to complete simulated driving tasks under the two conditions of high and low traffic density, and driving behaviors were recorded by driving simulators. The results show that high traffic density leads to more lane change and overtaking behavior. Drivers with a high prosocial attitude have better driving performance under both high and low traffic density, but drivers with a low prosocial attitude maintain a smaller transverse distance from adjacent vehicles in high traffic density, which may increase risk. This study provides support for the selection, training and intervention of professional drivers.     

Powered-Two-Wheelers kinematic characteristics and interactions during filtering and overtaking in urban arterials

The present paper focuses on the Powered-Two-Wheelers (PTWs) kinematic characteristics and their interactions with the rest of traffic in urban arterials. The factors that may affect the likelihood of PTW drivers to accept critical spacing during filtering and overtaking are also investigated using trajectory data collected from video recordings. The distributional characteristics of the PTW kinematic parameters showed that the patterns of filtering and overtaking have several differences. Further results using Logit models show that PTW speed difference with the rest of traffic, spacing, the existence of heavy vehicles and the occurrence of platoon of moving PTWs (in which the leader is the reference PTW) are significant factors related to the probability of driving in critical spaces through traffic. The likelihood of accepting critical lateral distance from the vehicle being overtaken may be related to the adjacent lane spacing, the speed difference and the existence of a platoon of PTWs. A comparative study between Logit models and equivalent structures of neural networks showed that, in the specific application, neural networks were found to perform better than the Logit models in terms of the model’s discrimination power.     

Modelling the influence of time pressure on reaction time of drivers

Speeding is a major traffic violation and time pressure is one of the leading contributors to speeding. High-speed driving requires an immediate response to perilous events from the driver to avoid a crash. Reaction time is one of the important driving performance measures to assess the driver’s response to the event. Therefore, the current study examined the influence of time pressure on reaction times of the drivers measured for two different perilous events (pedestrians crossing and obstacle overtaking). Eighty-five Indian licensed drivers participated in a driving simulation study designed for three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP). The survival analysis technique was used to model the effect of time pressure and driver characteristics with reaction times of the drivers. It was observed that drivers’ reaction times decreased by 18% and 9% in LTP and 28% and 16% in HTP during the pedestrians crossing and obstacle overtaking events, respectively. Further, 1 m/second increase in approach speed resulted in 2% and 4% reduction in reaction times of the drivers in pedestrians crossing and obstacle overtaking events, respectively. Young drivers responded 21% faster than mature drivers during the pedestrians crossing event. Interestingly, sleeping hours and physical fitness played an important role in driver’s reaction to the events. The drivers performing regular physical exercise and having minimum eight-hours of overnight sleep reacted 16% and 17% earlier in pedestrians crossing and obstacle overtaking events, respectively. The overall findings from this study showed enhanced stimulus-response behaviour of the drivers under time pressure driving conditions. The results obtained from the study can give new insight into various safety-related ITS applications.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.     

Safety at first sight? – Manual drivers’ experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway

Soon, manual drivers will interact with conditionally automated vehicles (CAVs; SAE Level 3) in a mixed traffic on highways. As of yet, it is largely unclear how manual drivers will perceive and react to this new type of vehicle. In a driving simulator study with N = 51 participants aged 20 to 71 years (22 female), we examined the experience and driving behavior of manual drivers at first contact with Level 3 vehicles in four realistic driving scenarios (highway entry, overtaking, merging, introduction of a speed limit) that Level 3 vehicles may handle alone once their operational domain extends beyond driving in congested traffic. We also investigated the effect of an external marking via a visual external human–machine interface (eHMI), with participants being randomly assigned to one of three experimental groups (none, correct, incorrect marking). Participants experienced each driving scenario four times, twice with a human-driven vehicle (HDV), and twice with a CAV. After each interaction, participants rated perceived driving mode of the target vehicle as well as perceived safety and comfort. Minimum time headways between participants and target vehicles served as an indicator of safety criticality in the interactions. Results showed manual driver can distinguish CAVs from HDVs based on behavioral differences. In all driving scenarios, participants rated interactions with CAVs at least as safe as interactions with HDVs. The driving data analysis showed that manual driver interactions with CAVs were largely uncritical. However, the CAVs’ strict rule-compliance led to short time headways of following manual drivers in some cases. The eHMI used in this study neither affected the subjective ratings of the manual drivers nor their driving behavior in mixed traffic. Thus, the results do not support the use of eHMIs on the highway, at least not for the eHMI design used in this study.     

The influence of alcohol (0.5‰) on the control and manoeuvring level of driving behaviour,finding measures to assess driving impairment: A simulator study

ObjectiveThe influence of psychoactive substances on driving performance and traffic safety has been extensively studied. Research on the influence of alcohol at the control level of behaviour (i.e. automated processes) has been well established and has shown that the ability to operate a vehicle decreases with rising alcohol levels. However, results one level higher at the manoeuvring level (i.e. conscious processes), are inconsistent. The current study aimed to replicate findings on the influence of alcohol on the control level of behaviour and investigate effects on the manoeuvring level in order to find suitable measures to assess driving impairment.MethodThe study was double-blind, placebo-controlled with a counterbalanced treatment order and a two-way crossover design. Thirty participants performed tasks in a driving simulator under the influence of alcohol (0.5‰) and a placebo. In the driving tasks the control level of behaviour (swerving, average speed, and speed variation) was investigated, as well as the manoeuvring level of behaviour (distance to other traffic during an overtaking manoeuvre, reaction time to a traffic light turning amber, and response to a suddenly merging car).ResultsAs expected, alcohol affected the control level of behaviour negatively. Participants swerved more and showed more speed variation after alcohol intake. The manoeuvring level of driving behaviour was also affected by alcohol. The distance to other drivers during an overtaking manoeuvre was smaller under the influence of alcohol. Results on reaction time were however less straightforward. Reaction time increased significantly under the influence of alcohol when reacting to a traffic light but not in reaction to a car unexpectedly merging into traffic. When analysing behaviour in reaction to these different events in more detail it became clear that they were responded to in varying manners, making it difficult to find an average impairment measure.ConclusionsThe deteriorating effect of alcohol at the control level of driving behaviour was replicated, confirming the suitability of the standard deviation of lateral position and the variation in speed as measures of impairment. At the manoeuvring level, the kept distance to the leading car during an overtaking manoeuvre appeared to be a suitable measure to assess impairment as well as reaction time to a traffic light. The current study also confirms the difficulties in evaluating complex driving behaviour and the need for more research on this subject.     

Performance and presence with head-movement produced motion parallax in simulated driving

Driving simulator studies can reveal relevant and valid aspects of driving behavior, but underestimation of distance and speed can negatively affect the driver’s performance, such as in performance of overtaking. One possible explanation for the underestimation of distance and speed is that two-dimensional projection of the visual scene disrupts the monocular-based illusory depth because of conflicting binocular and monocular information of depth. A possible solution might involve the strengthening of the monocular information so that the binocular information becomes less potent. In the present study, we used an advanced high-fidelity driving simulator to investigate whether adding the visual depth information of motion parallax from head movement affects sense of presence, judgment of distance and speed, and performance measures coupled with overtaking. The simulations included two types of driving scenario in which one was urban and the other was rural. The main results show no effect of this head-movement produced motion parallax on sense of presence, head movement, time to collision, distance judgment, or speed judgment. However, the results show an effect on lateral positioning. When initiating the overtaking maneuver there is a lateral positioning farther away from the road center as effect of the motion parallax in both types of scenario, which can be interpreted as indicating use of naturally occurring information that change behavior at overtaking. Nevertheless, only showing tendencies of effects, absent is any clear additional impact of this motion parallax in the simulated driving.     

A fuzzy-based driver assistance system using human cognitive parameters and driving style information

Reducing the number of traffic accidents due to human errors is an urgent need in several countries around the world. In this scenario, the use of human-robot interaction (HRI) strategies has recently shown to be a feasible solution to compensate human limitations while driving. In this work we propose a HRI system which uses the driver’s cognitive factors and driving style information to improve safety. To achieve this, deep neural networks based approaches are used to detect human cognitive parameters such as sleepiness, driver’s age and head posture. Additionally, driving style information is also obtained through speed analysis and external traffic information. Finally, a fuzzy-based decision-making stage is proposed to manage both human cognitive information and driving style, and then limit the maximum allowed speed of a vehicle. The results showed that we were able to detect human cognitive parameters such as sleepiness –63% to 88% accuracy–, driver’s age –80% accuracy– and head posture –90.42% to 97.86% accuracy– as well as driving style –87.8% average accuracy. Based on such results, the fuzzy-based architecture was able to limit the maximum allowed speed for different scenarios, reducing it from 50 km/h to 17 km/h. Moreover, the fuzzy-based method showed to be more sensitive with respect to inputs changes than a previous published weighted-based inference method.     

Analysis of the factors influencing overtaking in two-lane highways: A driving simulator study

BackgroundHuman factors are among the leading causes of frontal collision accidents. Therefore, understanding the factors that affect driver behaviour during overtaking is essential.ObjectiveThis research proposed a methodology to observe passing manoeuvres on two-lane highways in a driving simulator and to investigate the effects of the speed of an impeding vehicle, the type of the vehicle to be overtaken and the passing sight distance on the following gap distance as an indicator of driver behaviour.MethodThe repeated measures experiment allowed for 640 possible overtakings with a sample of 80 participants. The speed of the impeding vehicle, the type of the impeding vehicle and the passing sight distance were used as within-subject factors with eight treatments. The driver’s age, gender, and experience were considered the between-subject factors.ResultsWhen the speed of the impeding vehicle was 60 km/h, the participants adopted a following gap in passing sight distance of 446 m (M = 81.32 m), which was larger than the following gap in passing sight distance of 560 m (M = 70.84 m).ConclusionAmong the factors that were considered, the effect of the speed of an impeding vehicle on the following gap at the beginning of overtaking was higher than the effect of the type of the impeding vehicle or the passing sight distance. Together, these combination values can describe the driver behaviour and help to improve the standards-based design values to increase safety.     

Does surrounding traffic benefit from an assisted driver with traffic light assistance system?

Traffic light assistance systems enable drivers more energy and time efficient driving behavior at signalized intersections. However, most vehicles will not be equipped with such systems in the next years. These unequipped vehicles’ drivers (UVDs) may benefit from assisted drivers, if they would adapt their behavior. This paper outlines how UVDs (N = 60) interpreted and reacted to a driver with traffic light assistance system. We used a multi-driver simulator with three drivers driving in a car-following scenario. The lead driver was not a participant, but a confederate who was followed by two UVDs. The confederate was apparently equipped either with or without a traffic light assistance system. The traffic light assistance system consisted of two functionalities: a Green Light Optimal Speed Advisory and a start-up assistance system with two different parametrizations. These functionalities aimed at preventing unnecessary changes in speed and reducing the start-up lost time after signal change. The results showed that UVDs benefited from the driving behavior of the confederate with traffic light assistance system. However, the assisted driving behavior was hardly understood and partly rated as aversive by the UVDs. We discuss how to enhance behavioral adaptation of UVDs. We also outline which negative consequences may result from encounters of driver with systems and UVDs. We assume that how UVDs react towards drivers with systems may be one factor contributing to a successful launch of such systems.     

Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification

Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression.     

Drivers’ and cyclists’ safety perceptions in overtaking maneuvers

Drivers overtaking cyclists on rural roads are a safety concern, as drivers need to handle the interaction with the cyclist and possibly an oncoming vehicle. Improving the maneuver’s outcome requires an understanding of not only the objective, measurable safety metrics, but also the subjective, perceived safety of each road user. Previous research has shown that the perceived safety of the cyclist is most at risk at the passing moment, when driver and cyclist are closest to each other. However, to develop safety measures, it is necessary to know how both road users perceive safety, by understanding the factors that influence their perceptions during the overtaking maneuver. This study measured the perceived safety of drivers in a test-track experiment in Sweden and the perceived safety of cyclists in a field test in Spain. For both drivers and cyclists, we developed Bayesian ordinal logistic regression models of perceived safety scores that take as input objective safety metrics representing the different crash risks at the passing moment. Our results show that while drivers’ perceived safety decreases when there is an oncoming vehicle with a low time-to-collision, cyclists’ perceived safety is reduced by a small lateral clearance and a high overtaking speed. Although our datasets are heterogeneous and limited, our results are in line with previous research. In addition, the Bayesian models presented in this paper are novel and may be improved in future studies once more naturalistic data become available. We discuss how our models may support infrastructure development and regulation, policymaking, driver coaching, the development of active safety systems, and automated driving by providing a possible method for predicting perceived safety.     

Characterisation of motorway driving style using naturalistic driving data

The study of measurable differences between drivers has ramifications for several sub-fields in traffic and transportation research. Better understanding of the variability in individual driving styles would be especially useful for understanding driver preferences, psychological mechanisms for vehicle control and for developing more realistic traffic simulations. In our study based on a large naturalistic data set, we investigated the driving style of 76 individuals driving in a motorway setting. We discovered that the majority of between-driver variation in keeping longitudinal and lateral safety margins, lane changing frequency, acceleration and speed preference, can be reduced to two dimensions, which we interpret as habitualised motives centred around mental effort and expediency.     

Temporal fluctuations in driving demand: The effect of traffic complexity on subjective measures of workload and driving performance

Traffic density has been shown to be a factor of traffic complexity which influences driver workload. However, little research has systematically varied and examined how traffic density affects workload in dynamic traffic conditions. In this driving simulator study, the effects of two dynamically changing traffic complexity factors (Traffic Flow and Lane Change Presence) on workload were examined. These fluctuations in driving demand were then captured using a continuous subjective rating method and driving performance measures. The results indicate a linear upward trend in driver workload with increasing traffic flow, up to moderate traffic flow levels. The analysis also showed that driver workload increased when a lane change occurred in the drivers’ forward field of view, with further increases in workload when that lane change occurred in close proximity. Both of these main effects were captured via subjective assessment and with driving performance parameters such as speed variation, mean time headway and variation in lateral position. Understanding how these traffic behaviours dynamically influence driver workload is beneficial in estimating and managing driver workload. The present study suggests possible ways of defining the level of workload associated with surrounding traffic complexity, which could help contribute to the design of an adaptive workload estimator.     

Investigating the influence of time pressure on overtaking maneuvers and crash risk

The research conducted on overtaking maneuver for evaluating drivers’ safety showed adverse effects of urgency on driving performance and decision making. Therefore, a driving simulator study was designed to examine driving performance of the drivers and its implication on overtaking and crash probabilities under increasing time pressure conditions. Eighty-eight participants data were analyzed in the current study. Three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) were considered for analyzing driving performance of the drivers while executing overtaking maneuvers. The driving performance was assessed using minimum time-to-line crossing and coefficient of variation in speed to dissect the safety margin adopted by the drivers while overtaking the lead vehicle. Further, minimum time-to-line crossing and coefficient of variation in speed were considered as explanatory variables to investigate their influence on overtaking and crash probabilities. Parametric survival analysis and Generalized Linear Mixed Models (GLMM) were used to assess the driving performance, overtaking and crash probabilities. The parametric survival analysis showed that minimum time-to-line crossing reduced by 36.7% and 63.8% in LTP and HTP driving conditions, respectively. The GLMM results revealed that coefficient of variation in speed increased by 3.437% in HTP (no significant effect in LTP) as compared to NTP driving conditions. Further, the GLMM results showed that overtaking and crash probabilities decreased with increment in minimum time-to-line crossing and coefficient of variation in speed values. Additionally, it was observed that male drivers took risky decisions than female drivers. Nevertheless, the comparative analysis revealed that male drivers were less prone to crashes than female drivers. Overall, it can be inferred that the drivers take risky decisions with increment in time pressure to complete the driving task, even at the expense of their own safety which exposed them to high likelihood of crashes.