A driving simulator study to assess driver performance during a car-following maneuver after switching from automated control to manual control

A review of the literature on autonomous vehicles has shown that they offer several benefits, such as reducing traffic congestion and emissions, and improving transport accessibility. Until the highest level of automation is achieved, humans will remain an important integral of the driving cycle, which necessitates to fully understand their role in automated driving. A difficult research topic involves an understanding of whether a period of automated driving is likely to reduce driver fatigue rather than increase the risk of distraction, particularly when drivers are involved in a secondary task while driving. The main aim of this research comprises assessing the effects of an automation period on drivers, in terms of driving performance and safety implications. A specific focus is set on the car-following maneuver. A driving simulator experiment has been designed for this purpose. In particular, each participant was requested to submit to a virtual scenario twice, with level-three driving automation: one drive consisting of Full Manual Control Mode (FM); the other comprising an Automated Control Mode (AM) activated in the midst of the scenario. During the automation mode, the drivers were asked to watch a movie on a tablet inside the vehicle. When the drivers had to take control of the vehicle, two car-following maneuvers were planned, by simulating a slow-moving vehicle in the right lane in the meanwhile a platoon of vehicles in the overtaking lane discouraged the passing maneuver. Various driving performances (speeds, accelerations, etc.) and surrogate safety measures (PET and TTC) were collected and analysed, focusing on car-following maneuvers. The overall results indicated a more dangerous behavior of drivers who were previously subjected to driving automation; the percentage of drivers who did not apply the brakes and headed into the overtaking lane despite the presence of a platoon of fast-moving vehicles with unsafe gaps between them was higher in AM drive than in FM drive. Conversely, for drivers who preferred to brake, it was noted that those who had already experienced automated driving, adopted a more careful behavior during the braking maneuver to avoid a collision. Finally, with regard to drivers who had decided to overtake the braking vehicle, it should be noted that drivers who had already experienced automated driving did not change their behavior whilst overtaking the stopped lead vehicle.     

Obstacle avoidance under automated steering: Impact on driving and gaze behaviours

Within the context of more and more autonomous vehicles, an automatic lateral control device (AS: Automatic Steering) was used to steer the vehicle along the road without drivers’ intervention. The device was not able to detect and avoid obstacles. The experiment aimed to analyse unexpected obstacle avoidance manoeuvres when lateral control was delegated to automation. It was hypothesized that drivers skirting behaviours and eye movement patterns would be modified with automated steering compared with a control situation without automation. Eighteen participants took part in a driving simulator study. Steering behaviours and eye movements were analysed during obstacle avoidance episodes. Compared with driving without automation, skirting around obstacles was found to be less effective when drivers had to return from automatic steering to manual control. Eye movements were modified in the presence of automatic steering, revealing further ahead visual scanning of the driving environment. Resuming manual control is not only a problem of action performance but is also related to the reorganisation of drivers’ visual strategies linked to drivers’ disengagement from the steering task. Assistance designers should pay particular attention to potential changes in drivers’ activity when carrying out development work on highly automated vehicles.     

Evaluating driver comprehension of the roadway environment to retain accountability of safety during driving automation

Driver comprehension is a substantial component of situation awareness that involves the ability of an individual to understand the significance of an object, traffic sign, or hazard while driving. An increase in crashes related to autonomous driving systems has raised a concern regarding the safety of other roadway users due to the diminishing accountability resulting from a general lack of understanding of the limitations or disregard of the safety protocols by users. To keep drivers vigilant when engaged in partial automated systems, a methodology to monitor real-time driver comprehension was proposed. A driving simulator study consisting of 90 participants, equally split between males and females, was executed to establish driver comprehension in six different variations of driving difficulty. Joint probability density functions were created by considering percent time spent gazing, answers to probe questions, and driving performance. Based on these density functions, five levels of comprehension were devised and assigned thresholds. Overall, as task difficulty increased, a non-linear deterioration in driving speed along with an increase in total gaze duration was observed before comprehension was attained. A two-step validation protocol was also proposed to ensure similar levels of comprehension to non-automated driving from the human driver, when engaged in early forms of automation. The proposed real-time driver comprehension monitoring constitutes a first step toward developing a methodology to reinstate the accountability of safety of other roadway users when engaged in driver-in-the-loop automation systems.     

Difficulties experienced by older drivers during their regular driving and their expectations towards Advanced Driving Aid Systems and vehicle automation

The aim of this study was to analyse the difficulties experienced by older drivers during their regular driving, and to identify their needs and their expectations regarding Advanced Driving Aid Systems (ADAS) and vehicle automation. More than 100 items were investigated by using a Focus Group method based on a Collective Questionnaire (named FoG-CoQS). Thirty elderly drivers, 15 females and 15 males aged from 70 to 81 years (mean age of 73.3; S.D. = 3.18) were recruited among a representative sample of 76 older drivers living in the Rhône area and having previously participated to an on-road experiment, in order to collect from this Focus Group method further information about the driving difficulties they experienced in their everyday life and their expectations towards driving aids. Seven main topics were more particularly investigated, recovering at last all the main dimensions of the driving task (from navigation to speed control, through intersection crossing).Regarding driving difficulties, one of the most interesting result collected is the high contrast between the literature review, identifying Left Turn (LT) manoeuvres at crossroads as a risky driving situation for elderly drivers, and the relatively low values of perceived difficulties (i.e. compared to other driving sub-tasks) collected during this Focus Group among our sample of older drivers. Regarding the driving aid functions investigated, 10 of them obtained high scores of “perceived utility” (i.e. overpassing 60% on scales ranging from 0% [no utility] to 100% [high utility]), and they concerned assistances liable to support all the main components of the driving task investigated in this study.Additional results are related to the differences between the elderly female and male drivers. Several driving situations were assessed as significantly more difficult to perform by the older female than by the older male drivers, like intersection crossing, entering expressways, or implementing a lane change manoeuver. By contrast, this gender effect is more limited regarding driving aids: synthetically, men and women have a positive attitude towards driving aid systems and their expectations for future ADAS are quite similar (for instance, “informative systems” are preferred than driving aids based on “vehicle automation”).Finally, from two transversal items (i.e. “difficulties” to perform a driving sub-task and “perceived utility” of ADAS), it was possible to rank older drivers’ difficulties experienced during their everyday life (from lowest difficulties to “navigate on a familiar itinerary” to highest when “interacting with bicyclists”) and their expectations towards driving aids (from lowest utility score given to “Automatic Lane Change systems” to highest utility value provided to “Speed Informer systems”). At last, older drivers’ acceptance and expectations towards highly automated cars was also investigated: full automation was assessed as an interesting solution to ensure the self-mobility of elderly peoples in their circle, but also for themselves in the future, in case of impairments of their own cognitive or physical capacities.     

Developing human-machine trust: Impacts of prior instruction and automation failure on driver trust in partially automated vehicles

To prompt the use of driving automation in an appropriate and safe manner, system designers require knowledge about the dynamics of driver trust. To enhance this knowledge, this study manipulated prior information of a partial driving automation into two types (detailed and less) and investigated the effects of the information on the development of trust with respect to three trust attributions proposed by Muir (1994): predictability, dependability, and faith. Furthermore, a driving simulator generated two types of automation failures (limitation and malfunction), and at six instances during the study, 56 drivers completed questionnaires about their levels of trust in the automation. Statistical analysis found that trust ratings of automation steadily increased with the experience of simulation regardless of the drivers’ levels of knowledge. Automation failure led to a temporary decrease in trust ratings; however, the trust was rebuilt by a subsequent experience of flawless automation. Results showed that dependability was the most dominant belief of drivers’ trust throughout the whole experiment, regardless of their knowledge level. Interestingly, detailed analysis indicated that trust can be accounted by different attributions depending on the drivers’ circumstances: the subsequent experience of error-free automation after the exposure to automation failure led predictability to be a secondary predictive attribution of drivers’ trust in the detailed group whilst faith was consistently the secondary contributor to shaping trust in the less group throughout the experiment. These findings have implications for system design regarding transparency and for training methods and instruction aimed at improving driving safety in traffic environments with automated vehicles.     

Driving anger in Germany: Validation of the driving anger scale for German drivers

In 1994, Deffenbacher et al. published the Driving Anger Scale (DAS), a tool for assessing a driver’s propensity to experience anger in road traffic. Since then, much research has used this scale to measure the driving anger experienced in various countries around the world. This study examines the scale’s validity for German drivers. It also relates their experiences of anger while driving to their experiences and expressions of anger in general, as well as to certain demographic variables. In addition, it compares German drivers’ experiences of driving anger to those reported by drivers from other countries. We distributed a German version of the DAS and the State-Trait Anger Expression Inventory (STAXI) to a sample of 1136 German drivers. Results showed that a 22-items version of the DAS with six factors produced good fit indices for German drivers. Furthermore, data analysis revealed small to moderate significant relationships between German drivers’ driving anger experiences and their experiences and expressions of anger in general, underlining the idea that driving anger is a personality characteristic that is related to one's general experience and expression of anger. Finally, German drivers' driving anger experiences differed from those of drivers from other countries in that German drivers reported less driving anger than drivers from Spain and New Zealand, comparable levels to those from Turkey, Malaysia, and the United States, and more driving anger than drivers from France, Australia, China, and the United Kingdom. In addition, discourteous driver actions and hostile gestures consistently triggered highest driving anger ratings whereas police presence was rated lowest. Given these results, we conclude that the DAS can be applied to German drivers in its modified version.     

Driving next to automated vehicle platoons: How do short time headways influence non-platoon drivers’ longitudinal control?

Advanced driver assistance systems (ADAS) are taking over an increasing part of the driving task and are supporting the introduction of semi- and fully automated vehicles. As a consequence, a mixed traffic situation is developing where vehicles equipped with automated systems taking over the lateral and longitudinal control of the vehicle will interact with unequipped vehicles (UV) that are not fitted with such automated systems. Different forms of automation are emerging and it appears that regardless of which form is going to become popular on our roads, there is a consensus developing that it will be accompanied by a reduction in time headway (THW). The present simulator study examined whether a ‘contagion’ effect from the short THW held in platoons on the UV drivers would occur. Thirty participants were asked to follow a lead vehicle (LV) on a simulated motorway in three different traffic conditions: surrounding traffic including (1) platoons with short following distance (THW = 0.3 s), (2) large following distance (THW = 1.4 s) or (3) no platoons at all. Participants adapted their driving behaviour by displaying a significant shorter average and minimum THW while driving next to a platoon holding short THWs as when THW was large. They also spent more time keeping a THW below a safety threshold of 1 s. There was no carryover effect from one platoon condition to the other, which can be interpreted as an effect that is not lasting in time. The results of this study point out the importance of examining possibly negative behavioural effects of mixed traffic on UV drivers.     

Semi-automated versus highly automated driving in critical situations caused by automation failures

The purpose of this study was to examine the effects of vehicle automation and automation failures on driving performance. Previous studies have revealed problems with driving performance in situations with automation failures and attributed this to drivers being out-of-the-loop. It was therefore hypothesized that driving performance is safer with lower than with higher levels of automation. Furthermore, it was hypothesized that driving performance would be affected by the extent of the automation failure. A moving base driving simulator was used. The design contained semi-automated and highly automated driving combined with complete, severe, and moderate deceleration failures. In total the study involved 36 participants. The results indicate that driving performance degrades when the level of automation increases. Furthermore, it is indicated that car drivers are worse at handling complete than partial deceleration failures.     

Secondary task engagement and disengagement in the context of highly automated driving

During highly automated driving (level 3 automation according to SAE International, 2014) people are likely to increase the frequency of secondary task interactions. However, the driver must still be able to take over control within a reasonable amount of time. Previous studies mainly investigated take-over behavior by forcing participants to engage in secondary tasks prior to take over, and barely addressed how drivers voluntarily schedule secondary task processing according to the availability and predictability of automated driving modes. In the current simulator study 20 participants completed a test drive with alternating sections of manual and highly automated driving. One group had a preview on the availability of the automated driving system in upcoming sections of the track (predictive HMI), while the other drivers served as a control group. A texting task was offered during both driving modes and also prior to take-over situations. Participants were free to accept or reject a given task, taking the situational demands into account. Drivers accepted more tasks during highly automated driving. Furthermore, tasks were rejected more often prior to take-over situations in the predictive HMI group. This was accompanied by safer take-over performance. However, once engaged in a task, drivers tended to continue texting even in take-over situations. The results indicate the need to discriminate different aspects of task handling regarding self-regulation: task engagement and disengagement.     

Driving distraction at night: The impact of cell phone use on driving behaviors among young drivers

Currently, young drivers are more likely than other drivers to use cell phones while driving at night, which has become a major cause of road crashes. However, limited attention has been given to distracted nighttime driving. Therefore, the aim of this study was to explore the interaction effect of cell phone use and time of day (daytime and nighttime) on young drivers’ car-following performance. Forty-three young drivers engaged in a driving simulator experiment with a within-subject design that included three distractions (no distraction, talking and texting on a cell phone) and two times of day. This paper applied non-parametric tests to analyze the data and obtained the following results: (1) the standard deviation of lane position (SDLP) did not significantly differ at either time of day under no distraction, but it was significantly higher at night on straight roads and large-radius curves after introducing distractions. In addition, participants drove faster and gave less headway on small-radius curves under both distractions at night; (2) texting significantly increased the SDLP, while there was less lateral variation during the talking tasks than under no distraction on simple road sections; and (3) compared with the experienced drivers, the novice drivers drove faster during the talking tasks on small-radius curves, but there was no significant difference between groups during the texting tasks. These findings provide both theoretical and practical implications for related policy makers to enhance traffic safety.     

Cognitive function and young drivers: The relationship between driving,attitudes, personality and cognition

Young drivers (aged 17–25 years) are the highest risk age group for driving crashes and are over-represented in car crash statistics in Australia. A relationship between cognitive functioning and driving in older drivers (60 years and older) has been consistently supported in previous literature, however, this relationship has been neglected in research regarding younger drivers. The role of cognitive functioning in young people’s driving was investigated both independently and within a current model of younger peoples driving performance. With young drivers as participants, driving behaviour, attitudes, personality and cognitive functioning were tested and driving performance was operationalised through two measures on a driving simulator, speeding and lane deviations. Cognitive functioning was found to contribute to driving behaviour, along with driving attitudes and personality traits, in accounting for young people’s driving performance. The young drivers who performed better on cognitive functioning tasks engaged in less speeding behaviour and less lane deviation on the driving simulator than those who performed worse on these tasks. This result was found independent of the role of driving behaviour, driving attitudes and personality traits, accounting for unique variance in driving ability.     

Comparative assessment of the behaviour of drivers with Mild Cognitive Impairment or Alzheimer’s disease in different road and traffic conditions

The objective of this research was the analysis of the driving performance of drivers with Mild Cognitive Impairment (MCI) or Alzheimer’s disease (AD), in different road and traffic conditions, on the basis of a driving simulator experiment. In this experiment, healthy “control” drivers, patients with MCI, and patients with AD, drove at several scenarios at the simulator, after a thorough neurological and neuropsychological assessment. The scenarios include driving in rural and urban areas in low and high traffic volumes. The driving performance of healthy and impaired drivers was analysed and compared by means of Repeated Measures General Linear Modelling techniques. A sample of 75 participants was analysed, out of which 23 were MCI patients and 14 were AD patients. Various driving performance measures were examined, including longitudinal and lateral control measures. The results suggest that the two examined cerebral diseases do affect driving performance, and there were common driving patterns for both cerebral diseases, as well as particular characteristics of specific pathologies. More specifically, cognitively impaired drivers drive at lower speeds and with larger headway compared to healthy drivers. Moreover, they appear to have difficulties in positioning the vehicle on the lane. The group of patients had difficulties in all road and traffic environments, and especially when traffic volume was high. Most importantly, both cerebral diseases appear to significantly impair reaction times at incidents. The results of this research suggest that compensatory behaviours developed by impaired drivers are not adequate to counterbalance the direct effects of these cerebral diseases on driving skills. They also demonstrate that driving impairments increase as cognitive impairments become more severe (from MCI to AD).     

Measuring drivers’ takeover performance in varying levels of automation: Considering the influence of cognitive secondary task

Mixed control by driver and automated system will remain in use for decades until fully automated driving is perfected. Thus, drivers must be able to accurately regain control of vehicles in a timely manner when the automated system sends a takeover request (TOR) at its limitation. Therefore, determining the factors that affect drivers’ takeover quality at varying levels of automated driving is important. Previous studies have shown that visually distracting secondary tasks impair drivers’ takeover performance and increase the subjective workload. However, the influence of purely cognitive distracting secondary tasks on drivers’ takeover performance and how this influence varies at different levels of automation are still unknown. Hence, a 5 (driving modes) × 3 (cognitive secondary tasks) factorial design with the within-subject factors was adopted for this driving simulator experiment. The sample consisted of 21 participants. The participants’ subjective workloads were recorded by the NASA-Task Load Index (NASA-TLX). Results showed that compared to manual driving conditions, the drivers’ subjective workloads were significantly reduced in both partially and highly automated driving conditions, even with a TOR, confirming the benefit of the automated driving system in terms of reducing the driving workload. Moreover, the drivers exhibited a lower takeover behavior quality at high levels of automation than manual driving in terms of increased reaction time, abnormal performance, standard deviation of lane position, lane departure probability, and reduced minimum of time to collision. However, at the highly automated driving condition, the drivers’ longitudinal driving safety and ability to follow instructions improved when performing a highly cognitive secondary task. This phenomenon possibly occurred because automated driving conditions lead to an underload phenomenon, and the execution of highly cognitive tasks transfers drivers into moderate load, which helps with the drivers’ takeover performance.     

Self-perception of driving abilities in older age: A systematic review

Older adults have become the fastest growing age group worldwide and will continue to make up a more significant portion of the driving population. Given the increased potential for age-related perceptual, cognitive, and physical declines, it is important to understand the perception that older drivers have regarding their own driving abilities. This awareness often motivates their driving behavior and patterns.A systematic review was conducted to synthesize the literature regarding the self-perception of driving abilities in older age. The PRISMA method was used and 10 databases (SCOPUS, TRID, PsycINFO, AgeLine, Web of Science, Abstracts in Social Gerontology, Inspec, Compendex, PubMed, and Medline) were searched to identify relevant articles. A total of 25 articles met the search criteria and were included in the qualitative synthesis.Overall, methods used to assess self-perception of driving abilities in older adults vary considerably. Some studies employ only subjective questionnaires, while others administer questionnaires in addition to on-road driving or simulated evaluations. Nonetheless, the studies overwhelmingly report that older drivers tend to rate highly, and often overestimate, their driving abilities. They perceive their driving abilities to be better than themselves at a younger age, their cohorts, and all other drivers. However, more work is needed to develop improved subjective assessments that guide drivers in more accurately estimating their true driving abilities, as well as to compare subjective ratings to objective performance-based measures of driving abilities.This review may assist researchers in better understanding the characteristics that influence driving self-perception and may inform the development of interventional strategies that help older drivers to better calibrate how they perceive their driving.     

The contribution of memory to differences in situation awareness in expert and non-expert drivers

Situation awareness (SA) is knowing what is going on in the environment: identifying objects, understanding how they interact and predicting future events. It is important in the context of driving as it is related to hazard perception. Driving-related SA may help explain expert drivers’ superior driving skill, but it is important to understand whether this is because expert drivers have better memory for driving-related tasks, whether superior memory performance is task specific, and the degree to which any effect is attributable to experience vs. expertise. On-road paramedics were compared with non-expert drivers. The participants engaged in an SA driving task where they were required to describe a vide taped driving situation after the screen cut to black. We measured their SA, memory and demographic driving variables. The starting SA of World, Action and Schema was re-developed to better reflect driving SA, into World, Action, Other-Agent Action, Projection, and Rationale. Driving expertise predicted each category of SA, except the Action category, independently of other experience variables. Similarly, expertise also predicted SA categories independently of any of the memory tasks. We concluded that expert drivers have better driving-SA than non-expert drivers and this is not due to better memory for driving tasks, or ‘time-on-road’. This finding is important in driver training because if we can harness the SA skills that expert drivers demonstrate, we could potentially implement them in better driver training programs.     

Car following with an inertia-oriented driving technique: A driving simulator experiment

BackgroundFor many decades, car-following (CF) and congestion models have assumed a basic invariance: drivers’ default driving strategy is to keep the safety distance. The present study questions that Driving to keep Distance (DD) is a traffic invariance and, therefore, that the difference between the time required to accelerate versus decelerate must necessarily determine the observed patterns of traffic oscillations. Previous studies have shown that drivers can adopt alternative CF strategies like Driving to keep Inertia (DI) by following basic instructions. The present work aims to test the effectiveness of a DI course that integrates 4 tutorials and 4 practice sessions in a standard PC computer designed to learn more adaptive driving behaviors in dense traffic. Methods. Sixty-eight drivers were invited to follow a leading car that varied its speed on a driving simulator, then they took a DI course on a PC computer, and finally they followed a fluctuating leader again on the driving simulator. The study adopted a pretest-intervention-posttest design with a control group. The experimental group took the full DI course (tutorials and then simulator practice). The control group had access to the DI simulator but not to the tutorials. Results. All participating drivers adopted DD as the default CF mode on the pre-test, yielding very similar results. But after taking the full DI course, the experimental group showed significantly less accelerations, decelerations, and speed variability than the control group, and required greater CF distance, that was dynamically adjusted, spending less fuel in the post-test. A group of 8 virtual cars adopting DD required less space on the road to follow the drivers that took the DI course.     

Effects of different non-driving-related-task display modes on drivers’ eye-movement patterns during take-over in an automated vehicle

Highly automated vehicles relieve drivers from driving tasks, allowing them to engage in non-driving-related-tasks (NDRTs). However, drivers are required to take over control in certain circumstances due to the limitations of highly automated vehicles. This study focused on drivers’ eye-movement patterns during take-overs when an NDRT (watching videos) was presented via a head-up-display (HUD) and a mobile device display (MDD), compared to no NDRT as the baseline. The experiment was conducted in a high-fidelity driving simulator with real-world driving videos scenarios. Forty-six participants took part in the experiment by completing three drives in three counterbalanced conditions (HUD, MDD and baseline). A take-over-request was issued towards the end of automated driving requesting drivers to stop the NDRT and take over control. Eye-movement data including pupil diameter, blinks, glance duration and number of AOI (Area of Interest) were collected and analysed. The results show that during automated driving, drivers were more engaged in the MDD NDRT with smaller pupil diameter and shorter glance duration on the front scenario compared to the HUD and baseline modes. The number of AOI was reduced during automated driving in both MDD and HUD modes. The take-over-request redirected drivers’ visual attention back to the driving task from NDRT by increasing drivers’ pupil diameter, glance duration and number of AOI. However, the effect of MDD NDRT on pupil diameter and glance duration continued even after the take-over-request when the NDRT was terminated. The study demonstrated HUD is a better display to help maintain drivers’ attention on the road.     

Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes

Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers’ takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants’ takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants’ takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers’ visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants’ attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers’ situation awareness in critical situations.     

Anger,aggression, risky behavior,and crash-related outcomes in three groups of drivers

High anger drivers who acknowledged problems with driving anger and were interested in treatment were compared to high and low anger drivers who did not acknowledge problems with driving anger or want treatment. Although high anger drivers who acknowledged problems reported greater anger on two measures than high anger drivers who did not acknowledge problems, both high anger groups tended not to differ from one another and were more frequently and intensely angered when driving, reported more aggressive and less adaptive/constructive forms of expressing anger while driving, engaged in more aggressive and risky behavior on the road, and experienced more of some accident-related outcomes than low anger drivers. High anger groups did not differ from each other, but reported more trait anxiety and anger and more outward negative and less controlled general anger expression than the low anger group. The two groups of high anger drivers, however, require different types of interventions given their state of readiness for driving anger reduction. Results were also interpreted as supportive of the state-trait model of anger and construct validity of the Driving Anger Scale.     

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.