Drivers’ driving style and their take-over-control judgment

Advanced driver assistance systems (ADASs), which help a driver drive a car safely and easily (e.g., warning alerts, steering control, and brake/acceleration pedal operation), have increased in popularity. However, such systems have not yet been perfected. Sometimes, humans must take over control from the systems; otherwise, they can cause an accident. In this study, we focused on one of the ADASs, adaptive cruise control (ACC), which automatically maintains a selected distance from the preceding car, and investigated individual differences in take-over-control judgment and related factors. The candidate factors included driver’s manual driving style, driving performance without the ACC, and the usability evaluation of ACC. Ten participants repeated the short, strictly controlled trials in a driving simulator (DS), with a varying value of only one parameter (deceleration of the preceding car) affecting the need for intervention. First, we confirmed that the participants made the judgment based on the dangerousness of the situation and that there were individual differences in the take-over-control judgments. Some participants intervened in the ACC control in less dangerous trials, whereas other participants did not, even if their own car got very close to the preceding car. We conducted a correlation analysis and confirmed the results with the estimation of the confidence interval using a bootstrap method. As a result, we found that driving style and driving performance without ACC had a stronger relationship to the number of interventions, rather than the usability evaluation. In particular, methodical drivers, who obeyed traffic rules and manners, began to intervene in less dangerous situations. The tendency to avoid utilizing brake operations was also related to take-over-control judgment. This might be because the participants intervened by pressing the brake pedal. Our study showed that drivers’ driving style could affect the usage of ACC independently from the performance of the ACC.     

Secondary task engagement and vehicle automation – Comparing the effects of different automation levels in an on-road experiment

Recent and upcoming advances in vehicle automation are likely to change the role of the driver from one of actively controlling a vehicle to one of monitoring the behaviour of an assistant system and the traffic environment. A growing body of literature suggests that one possible side effect of an increase in the degree of vehicle automation is the tendency of drivers to become more heavily involved in secondary tasks while the vehicle is in motion. However, these studies have mainly been conducted in strictly controlled research environments, such as driving simulators and test tracks, and have mainly involved either low levels of automation (i.e., automation of longitudinal control by Adaptive Cruise Control (ACC)) or Highly automated driving (i.e., automation of both longitudinal and lateral control without the need for continuous monitoring). This study aims to replicate these effects during an on-road experiment in everyday traffic and to extend previous findings to an intermediate level of automation, in which both longitudinal and lateral control are automated but the driver must still monitor the traffic environment continuously (so-called Partial automation). N = 32 participants of different age groups and different levels of familiarity with ACC drove in rush-hour traffic on a highway segment. They were assisted by ACC, ACC with steering assistance (ACC+SA), or not at all. The results show that while subjective and objective driving safety were not influenced by the degree of automation, drivers who were already familiar with ACC increased the frequency of interactions with an in-vehicle secondary task in both assisted drives. However, participants generally rated performing the secondary task as less effortful when being assisted, regardless of the automation level (ACC vs. ACC+SA). The results of this on-road experiment thus validate previous findings from more-controlled research environments and extend them to Partially automated driving.     

Drivers’ engagement level in Adaptive Cruise Control while distracted or impaired

Adaptive Cruise Control (ACC) is designed for convenience to maintain a set speed and specified distance from a lead vehicle. However, use of ACC may change driving patterns and perceptions over time. Many drivers perceive safety benefits associated with ACC even though the safety implications are not always clear. This study examined the factors that can influence the frequency of ACC use with surveys distributed to ACC owners in Washington State. A cluster analysis was conducted to group drivers based on how often they activated and used ACC under various driving scenarios. Four clusters emerged that showed a range of use from those who rarely used ACC in any situation (low engagement group) to those who used it for almost all situations regardless of whether it is appropriate or not (high engagement group). An ordered logit model was used to predict the likelihood of being in one of the four clusters. Drivers that were less likely to use ACC in distracting or impaired situations tend to be older, were not willing to re-purchase a similar vehicle with ACC, and were generally confused on how to use the cruise speed setting. Drivers who reported higher overall use of ACC also used the system in situations that can be considered distracting or risky, which can negate the overall benefits of ACC.     

Relationship between driving styles and biological behavior of drivers in negative emotional state

ObjectiveThis paper explores the links between driving style and the biological behavior of people while driving with dangerous negative emotions (such as anger, anxiety, and fear).BackgroundIt is highly important to study the behavior of humans from varying aspects to discover the factors affecting it. Driving style, as one of the critical aspects of the human factor, and biological behavior, as a factor influencing the performance of individuals, motivate us to examine the relationship between the two.MethodFor this purpose, a test was designed to record the biological signal data, namely, the Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), and Electrodermal activity (EDA), in a driving simulator with driving events prompting negative emotions. The Multidimensional Driving Style Inventory (MDSI) was employed to determine the driving style of participants.ResultsCorrelation analysis was engaged for data analysis. The results showed, firstly, a significant relationship between the participants’ driving style and their biological behavior and, secondly, the highest correlation between the EEG signal and driving style. Moreover, participants with a nervous and anxious style showed maximum change in biological behavior, while those with a reckless style displayed minimum alterations in biological behavior at the time of unpleasant events during driving.ConclusionConduction of such research can help better understand the behavior of different people while facing unpleasant driving events.     

Driving errors,estimated performance and individual characteristics under simulated and real road traffic conditions – A validation study

BackgroundThe suitability of driving simulators for the prediction of driving behaviour in road traffic has been able to be confirmed in respect of individual assessment parameters. However, there is a need for overarching approaches that take into account the interaction between various influencing factors in order to establish proof of validity. The aim of this study was to explore the validity of our driving simulator in respect of its ability to predict driving behaviour based on participants‘ observed driving errors and driver’s individual characteristics.Method41 healthy participants were assessed both in a Smart-Realo-Simulator and on the road. By means of linear modelling, the correlation between observed driving errors was investigated. In addition, the influence of self-reported and externally assessed driving behaviour as well as individual parameters (education and training; driving history) were analysed.ResultsBy including these factors, 58% of the variance could be explained. For observed driving errors, a relative validity was established. For self-reported and externally assessed driving behaviour, an absolute to relative validity emerged. The amount of time spent in education and training proved to have a significant influence on driving performance in the simulator, but not on the road.DiscussionIn general, our results confirmed the validity of our driving simulator with regard to observed and self-reported driving behaviour. It emerged that education and training as potential indicators of cognitive resources played a differential role regarding the study conditions. Since real road driving is considerably automated in experienced drivers, this result suggests that simulation-related behavioural regulation is challenged by additional cognitive demands as opposed to behavioural regulation extending to real road driving. However, the source of these additional cognitive demands remains currently elusive and may form the subject of future research.     

Modeling the development of vehicle lateral control skills in a cognitive architecture

The development of lateral control skills is crucial to driving safety. The current study examined a computational method using a cognitive architecture to model the learning process of vehicle lateral control. In a fixed-base driving simulator, an experiment compared the lateral control performance of non-drivers, novices, and experienced drivers. A cognitive model using Adaptive Control of Thought-Rational (ACT-R) was built to model the learning process of lateral control skills. The modeling results were compared with the human results. The drivers with more experience had better lateral control performance. The model produced similar results as the human results and modeled the progress of learning. The model provided a computational explanation for the mechanisms of lateral control skill learning. Implication and future studies were discussed.     

Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.     

Mapping drivers’ mental models of adaptive cruise control to performance

Driver support features (DSF) have the potential to improve safety, but they also change the driver-vehicle relationship —as well as their respective roles and responsibilities. To maximize safety, it is important to understand how drivers’ knowledge and understanding of these technologies—referred to as drivers’ mental models—impact performance and safety. This simulator study examined how drivers with different mental models of adaptive cruise control performed in edge cases. The study compared the responses of groups of drivers, with strong and weak mental models of ACC, established through a combination of screening, training, and exposure, in edge case situations in a high-fidelity driving simulator. In general, participants with strong mental models were faster than those with weak mental models to respond in edge-case situations—defined as cases where the ACC did not detect an approaching object, such as a slow-moving motorcycle. The performance deficits observed for drivers with weak mental models appear to reflect uncertainty surrounding how ACC will behave in edge cases.     

Measuring being lost in thought: An exploratory driving simulator study

In driver behaviour research there is considerable focus on distraction caused by specific external systems, such as navigation systems or mobile telephones. However, it is not clear whether self-paced actions such as daydreaming have the same negative effects on driving behaviour. In a driving simulator study, the effects of an internal cognitive process (internal distraction) on driving behaviour and physiological data were compared to the effects of a sound and speech task (external distraction). Three groups of participants made two drives on a motorway, with one control group, one internal distraction group and one external distraction group. Dependent measures included driving behavioural measures, physiological measures and a subjective indication of participants’ experienced involvement in the driving task.The effects of both the internal and external distraction task were reflected in speed, number of lane changes, deceleration, glances and subjective ratings. When an effect was found for both the internal and the external distraction task, the results indicated similar (negative) effects. Participants also indicated that they had the feeling they were less involved in the driving task with both secondary tasks.     

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.     

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.     

A multitasking general executive for compound continuous tasks

As cognitive architectures move to account for increasingly complex real-world tasks, one of the most pressing challenges involves understanding and modeling human multitasking. Although a number of existing models now perform multitasking in real-world scenarios, these models typically employ customized executives that schedule tasks for the particular domain but do not generalize easily to other domains. This article outlines a general executive for the Adaptive Control of Thought–Rational (ACT–R) cognitive architecture that, given independent models of individual tasks, schedules and interleaves the models' behavior into integrated multitasking behavior. To demonstrate the power of the proposed approach, the article describes an application to the domain of driving, showing how the general executive can interleave component subtasks of the driving task (namely, control and monitoring) and interleave driving with in-vehicle secondary tasks (radio tuning and phone dialing).     

Use of Level 1 and 2 driving automation on horizontal curves on interstates and freeways

Little is known about how the actual use of Level 1 and 2 driving automation systems may be affected by geometric road characteristics in naturalistic driving environments. This study examined the use of these systems on horizontal curves on interstates and freeways. It used travel data collected in a field operational test conducted with two 2016 Land Rover Range Rover Evoque vehicles equipped with adaptive cruise control (ACC) and two 2017 Volvo S90 vehicles equipped with ACC and Pilot Assist (PA). Logistic regression models estimated changes in the likelihood of ACC use associated with horizontal curvature in the Evoque vehicles, and of PA and ACC use in the S90 vehicles, while accounting for traffic conditions. Drivers were less likely to drive with ACC or PA on as horizontal curves became sharper. In the Evoque vehicles, the likelihood of using ACC was 71.6% lower on the sharpest category of horizontal curves (those with a degree of curvature>2.5 degrees per 100 feet of arc or a radius smaller than 2,292 feet), compared with straight road segments or the flattest horizontal curve category (those with a degree of curvature <= 1.5 degrees per 100 feet of arc or a radius no <3,820 feet). In the S90 vehicles, the likelihood of using PA and ACC declined 74.6 and 66.3%, respectively, on the sharpest curves. Many driving automation systems face challenges on horizontal curves, even within their operational design domain. Future implementations that improve functionality may enhance driver experience and boost drivers’ confidence in these systems, which should increase their use and maximize the safety benefits these systems might offer.     

Disengagement from driving when using automation during a 4-week field trial

IntroductionA small body of research on the real-world use of commercially available partial driving automation suggests that drivers may struggle with or otherwise lapse in adequately monitoring the system and highway environment, and little is known about key issues such as how behavior associated with system use changes over time. The current study assessed how driver disengagement, defined as visual-manual interaction with electronics or removal of hands from the wheel, differed as drivers became more accustomed to partial automation over a 4-week trial.MethodsTen volunteers drove a Volvo S90 with adaptive cruise control (ACC), which automates speed and headway, and Pilot Assist, which combines ACC and continuous lane centering. Instrumentation captured automation use, secondary task activity, hands-on-wheel status, vehicle speed, and GPS location during all trips.ResultsThe longer drivers used the Pilot Assist partial automation system, the more likely they were to become disengaged, with a significant increase in the odds of observing participants with both hands off the steering wheel or manipulating a cell phone relative to manual control. Results associated with use of ACC found comparable or lower levels of disengagement compared to manual driving as the study progressed.DiscussionThis study highlights concerns about vehicle control and the degree to which drivers remain actively in the loop when using automation. Calls for implementing more robust driver monitoring with partial automation appear warranted—particularly those that track head or eye position.     

Evaluating the impact of real-time coaching programs on drivers overtaking cyclists

Innovative motor insurance schemes involve the use of on-board devices to collect kinematic driving data as part of the so-called ‘Pay-How-You-Drive’ schemes, which charge premiums based on drivers’ behavior. Some of these schemes also involve on-board coaching programs, which give real-time feedback to users.Here, we aimed to investigate the influence of motor insurance on-board real-time coaching programs on drivers’ behavior while overtaking cyclists, as motor vehicle/bicycle interactions are a relevant issue in road safety. The tested programs give real-time feedback to users on their acceleration, promoting smoother and safer driving styles.Data were collected with a driving simulator experiment involving 67 young drivers. The experiment was divided into two trials: in the first, participants drove as normally as possible without receiving any type of feedback; in the second, which took place one month later, they received feedback based on their driving behavior. Using data from the first trial, participants were clustered (k-mean approximation) into two groups, according to their driving style (aggressive vs. defensive). For each group, half of the drivers received contingent positive feedback (when a smooth driving event occurred) and the other half received contingent negative feedback (when a harsh driving event occurred). Feedback was presented in the form of auditory cues (for half of one group) or as visual cues (for the others). Thus, there were eight groups based on driving style, feedback type, and feedback modality.Multiple kinematic variables were studied with mixed ANOVA, and included not only clearance distances, speeds, and acceleration, but also the chosen overtaking strategy (accelerative vs. flying). Driving style, gender, car usage, feedback type and modality were considered as factors in the analysis.Results showed that the coaching programs had a significant positive effect, in terms of safety, reducing acceleration and speeds during the overtaking and inducing drivers to adopt the safer accelerative strategy. It was also particularly effective in improving the performance of aggressive drivers. These results are of high interest for real-world applications because they were obtained with a general-purpose coaching program; conversely, it might be impractical to develop dedicate programs for specific situations such as drivers overtaking cyclists.     

Evidence that implementation intentions reduce drivers’ use of mobile phones while driving

Implementation intentions are IF-THEN plans that have the potential to reduce mobile phone use while driving and thus contribute towards the prevention of road traffic crashes. We tested whether an intervention, designed to promote the formation of implementation intentions, could reduce drivers’ use of mobile phones. A randomized controlled design was used. The participants (N = 136) were randomised to an implementation intention or a control condition. Self-report questionnaires were administered to all participants at both pre- and one-month post-intervention to measure the use of mobile phones while driving, goal intentions and the theoretically derived motivational pre-cursors of goal intentions (attitudes, subjective norm and perceived behavioural control). Immediately following the pre-intervention questionnaire, the participants in the implementation intention condition (n = 67) were given a volitional help sheet, which asked them to form implementation intentions by specifying target driving situations that tempted them the most to use a mobile phone and linking them with goal-directed responses that could be used to resist the temptation. The participants in the control condition (n = 69) were asked to specify target situations that tempted them the most to use a mobile phone while driving and to generally try to avoid using a mobile phone in those situations. One-month post-intervention, the participants in the implementation intention condition reported using a mobile phone less often while driving in their specified target driving situations than did the participants in the control condition. As expected, no differences were found between the conditions in the reported frequency of mobile phone use in unspecified driving situations, goal intentions or any motivational pre-cursor of goal intentions. The implementation intention intervention that was tested in this study is a potentially effective tool for reducing mobile phone use while driving in target driving situations, where behaviour-change is most needed.     

The Driving Anger Expression Inventory: A Validity Study with Community College Student Drivers

This study explored the relationship of driving anger expression to driving anger, trait anger, general anger expression, and aggressive and risky behavior while driving. Verbal, physical, and vehicular forms of expressing anger while driving correlated positively with each other, driving and trait anger, anger-in, and anger-out and negatively with adaptive/constructive driving anger expression and general anger-control. Adaptive/constructive expression formed small negative correlations with these measures, except for a positive correlation with anger-control. Regression models controlling for demographic variables and driving anger, trait anger, or general anger expression demonstrated forms of driving anger expression added variance to predicting aggressive and risky behavior. Forms of driving anger expression partially mediated the effects for driving anger, trait anger, and general anger expression on aggressive and risky behavior. No moderation effects were found for age, gender, or miles driven. Findings provided evidence for convergent and incremental validity for the Driving Anger Expression Inventory.     

Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task

Forward Collision Warning Systems (FCWS) have been designed to enhance road safety by reducing the number of rear-end collisions. Nevertheless, little is known about how drivers adapt their behaviour over time when using this kind of system. In addition, these systems are expected to aid particularly distracted drivers. However, previous research has suggested that the effectiveness of the system could depend on the difficulty level of the secondary task. The objective of this study on driving simulator was twofold. Firstly, it consisted in evaluating the behavioural adaptation to an FCWS as well as analysing the possible consequences of driving without the system after a short period of adaptation. Secondly, it was to evaluate the effectiveness of the system according to two different difficulty levels of a cognitive secondary task. The results showed that drivers adapted their behaviour positively when the system was introduced. Nevertheless, both the effectiveness and the behavioural adaptation in the short term were dependent on the cognitive load induced by the secondary task. These findings suggest that the warning needs some attentional resources to be processed. Finally, no negative or transfer effect was observed following the removal of the system after a short period of adaptation.     

How do the recognizability and driving styles of automated vehicles affect human drivers’ gap acceptance at T- Intersections?

Future traffic will be composed of both human-driven vehicles (HDVs) and automated vehicles (AVs). To accurately predict the performance of mixed traffic, an important aspect is describing HDV behavior when interacting with AVs. A few exploratory studies show that HDVs change their behavior when interacting with AVs, being influenced by factors such as recognizability and driving style of AVs. Unsignalized priority intersections can significantly affect traffic flow efficiency and safety of the road network. To understand HDV behavior in mixed traffic at unsignalized priority T-intersections, a driving simulator experiment was set up in which 95 drivers took part in it. The route in the driving simulator included three T-intersections where the drivers had to give priority to traffic on the major road. The participants drove different scenarios which varied in whether the AVs were recognizable or not, and in their driving style (Aggressive or Defensive). The results showed that in mixed traffic having recognizable aggressive AVs, drivers accepted significantly larger gaps (and had larger critical gaps) when merging in front of AVs as compared to mixed traffic having either recognizable defensive AVs or recognizable mixed AVs (composed of both aggressive and defensive). This was not the case when merging in front of an HDV in the same scenarios. Drivers had significantly smaller critical gaps when driving in traffic having non-recognizable aggressive AVs compared to non-recognizable defensive AVs. The findings suggest that human drivers change their gap acceptance behavior in mixed traffic depending on the combined effect of recognizability and driving style of AVs, including accepting shorter gaps in front of non-recognizable aggressive AVs and changing their original driving behavior. This could have implications for traffic efficiency and safety at such priority intersections. Decision makers must carefully consider such behavioral adaptations before implementing any policy changes related to AVs and the infrastructure.     

Keep on cruising: Changes in lifestyle and driving style among male drivers between the age of 18 and 23

Despite recent improvements in general road safety levels, young male drivers in most western countries continue to be overrepresented in road traffic accidents. Lifestyle related motivational factors are a key element in the young male driver problem. Based on 379 posted questionnaires completed by the same male drivers at the age of 18 and again at the age of 23, this study examined changes in the relationship between lifestyle and driving style over a 5 year period. A number of changes in car use, driving style and engagement in different leisure time activities were found. Cruising was related to an extrovert social life as well as problem behaviours such as drink driving. At the age of 18 cruising was a part of the normal social life of the majority of the participants. However, while most drivers reduced their level of cruising as well as related problem behaviour over time, a smaller group still showed a similar life style at the age of 23. The study confirmed the importance of lifestyle related motivational factors for driving behaviour among young drivers.