Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

In the near future, conditionally automated vehicles (CAVs; SAE Level 3) will travel alongside manual drivers (≤ SAE level 2) in mixed traffic on the highway. It is yet unclear how manual drivers will react to these vehicles beyond first contact when they interact repeatedly with multiple CAVs on longer highway sections or even during entire highway trips. In a driving simulator study, we investigated the subjective experience and behavioral reactions of N = 51 manual drivers aged 22 to 74 years (M = 41.5 years, SD = 18.1, 22 female) to driving in mixed traffic in repeated interactions with first-generation Level 3 vehicles on four highway sections (each 35 km long), each of which included three typical speed limits (80 km/h, 100 km/h, 130 km/h) on German highways. Moreover, the highway sections differed regarding the penetration rate of CAVs in mixed traffic (within-subjects factor; 0%, 25%, 50%, 75%). The drivers were assigned to one of three experimental groups, in which the CAVs differed regarding their external marking, (1) status eHMI, (2) no eHMI, and (3) a control group without information about the mixed traffic. After each highway section, drivers rated perceived safety, comfort, and perceived efficiency. Drivers were also asked to estimate the penetration rate of CAVs on the previous highway section. In addition, we analyzed drivers’ average speed and their minimum time headways to lead vehicles for each speed zone (80 km/h, 100 km/h, 130 km/h) as well as the percentage of safety critical interactions with lead vehicles (< 1 s time headway). Results showed that manual drivers experienced driving in mixed traffic, on average, as more uncomfortable, less safe and less efficient than driving in manual traffic, but not as dangerous. A status eHMI helps manual drivers identify CAVs in mixed traffic, but the eHMI had no effect on manual drivers’ subjective ratings or driving behavior. Starting at a level of 25% Level 3 vehicles in mixed traffic, participants' average speed decreased significantly. At the same time, the percentage of safety critical interactions with lead vehicles increased with an increasing penetration rate of CAVs. Accordingly, additional measures may be necessary in order to at least keep the existing safety level of driving on the highway.     

How the initial level of trust in automated driving impacts drivers’ behaviour and early trust construction

Trust in Automation is known to influence human-automation interaction and user behaviour. In the Automated Driving (AD) context, studies showed the impact of drivers’ Trust in Automated Driving (TiAD), and linked it with, e.g., difference in environment monitoring or driver’s behaviour. This study investigated the influence of driver’s initial level of TiAD on driver’s behaviour and early trust construction during Highly Automated Driving (HAD). Forty drivers participated in a driving simulator study. Based on a trust questionnaire, participants were divided in two groups according to their initial level of TiAD: high (Trustful) vs. low (Distrustful). Declared level of trust, gaze behaviour and Non-Driving-Related Activities (NDRA) engagement were compared between the two groups over time. Results showed that Trustful drivers engaged more in NDRA and spent less time monitoring the road compared to Distrustful drivers. However, an increase in trust was observed in both groups. These results suggest that initial level of TiAD impact drivers’ behaviour and further trust evolution.     

Assessing the effect of long-automated driving operation,repeated take-over requests,and driver’s characteristics on commercial motor vehicle drivers’ driving behavior and reaction time in highly automated vehicles

Automated Commercial Motor Vehicles (CMVs) have the potential to reduce the occurrence of crashes, enhance traffic flow, and reduce the stress of driving to a larger extent. Since fully automated driving (SAE Level 5) is not yet available, automated driving systems cannot perform all driving tasks under all road conditions. Drivers need to regain the vehicle’s control when the system reaches its maximum operational capabilities. This transition from automated to manual is referred to as Take-Over Request (TOR). Evaluating driver’s performance after TORs and assessing effective parameters have gained much attention in recent years. However, few studies have addressed CMV drivers’ driving behavior after TOR and the effect of long-automated driving and repeated TORs. This paper aims to address this gap and gain behavioral insights into CMV drivers’ driving behavior after TOR and assess the effect of the duration of automated operation before TOR, repeated TORs, and driver characteristics (e.g., age, gender, education, and driving history). To accomplish this, we designed a 40-minutes experiment on a driving simulator and assessed the responses of certified CMV drivers to TORs. Drivers’ reaction time and driving behavior indices (e.g., acceleration, velocity, and headway) are compared to continuous manual driving to measure driving behavior differences. Results showed that CMV drivers’ driving behavior changes significantly after the transition to manual regardless of the number of TORs and the duration of automated driving. Findings suggest that 30 min of automated operation intensifies the effect of TOR on driving behaviors. In addition, repeated TOR improves reaction times to TOR and reduces drivers' maximum and minimum speed after TORs. Driver’s age and driving history showed significant effects on reaction time and some driving behavior indices. The findings of this paper provide valuable information to automotive companies and transportation planners on the nature of driver behavior changes due to the carryover effects of manual driving right after automated driving episodes in highly automated vehicles.     

Facilitators,barriers and needs in the use of adaptive driving strategies to enhance older drivers’ mobility: Importance of openness,perceptions, knowledge and support

This study aimed to explore facilitators, barriers and needs for the use of adaptive driving strategies (i.e., means used to adjust driving for diminished abilities) which can optimize the community mobility of older drivers. An exploratory qualitative clinical research design was conducted with 11 older drivers, 7 relatives and 14 driving professionals. Five focus group discussions were audio recorded, transcribed and analyzed. Facilitators for the use of adaptive driving strategies were: being a woman; perceiving dangers; recognizing the usefulness of strategies and abilities as diminished; having disabilities or discomfort when driving; experiencing complex driving situations; receiving help of relatives and services of professionals; and having other transportation options. Barriers were: not knowing strategies; being proud; lack of self-criticism; unwillingness of relatives and physicians to intervene; having costs to the use of adaptive strategies; recognizing driving as important; perceiving the complexity of using other transportation options; and lack of proximity to facilities and services. To foster the use of adaptive strategies, TV, radio, newspapers and information sessions need increasing older drivers’ awareness about the age-related changes, the community resources, and the strategies themselves, including their importance in safe driving. Furthermore, to support older drivers in changing their driving habits and using adaptive strategies, results demonstrated that it is important to involve their relatives and professionals. While promoting safe driving and the prevention of collisions and injuries on the road, knowledge about facilitators, barriers and needs for the use of adaptive driving strategies could ultimately allow seniors to optimize their community mobility.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.     

The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior

The goal of the present study was to assess the effectiveness of eye blink behavior in measuring drivers’ mental workload. Previous research has shown that when mental workload increases with the primary task difficulty, blink frequency drops. On the opposite, the number of blinks increases when a cognitive secondary task has to be performed concurrently. However, the combined effects of the primary task difficulty and dual-tasking on blink rate have not been investigated. The present study was thus designed to vary systematically both the primary driving task and the cognitive secondary task demand to examine their combined effects on blink rate. The driving task was manipulated by varying the complexity of a simulated driving environment. The cognitive load was manipulated using a concurrent simple reaction time task or a complex calculation task. The results confirmed that eye blink frequency was a sensitive measure to elicit increased mental workload level coming from the driving environment. They also confirmed that blink rate increased with the introduction of a cognitive secondary task while blink duration was not affected. However, eye blink behavior did not provide a clear mental workload signature when driving task demands and dual-task conditions were varied simultaneously. The overall picture goes against the suitability of eye blink behavior to monitor drivers’ states at least when external and internal demands interact.     

Effects of explanation-based knowledge regarding system functions and driver’s roles on driver takeover during conditionally automated driving: A test track study

The present study was designed to examine the influence of explanation-based knowledge regarding system functions and the driver’s role in conditionally automated driving (Level 3, as defined in SAE J3016). In particular, we studied how safely and successfully drivers assume control of the vehicle when encountering situations that exceed the automation parameters. This examination was conducted through a test-track experiment. Thirty-two younger drivers (mean age = 37.3 years) and 24 older drivers (mean age = 71.1 years) participated in Experiments 1 and 2, respectively. Adopting a between-participants design, in each experiment the participants were divided into two age- and sex-matched groups that were given differing levels of explanation-based knowledge concerning the system limitations of automated driving. The only information given to the less-informed groups was that, during automated driving, drivers may be required to occasionally assume control of the vehicle. The well-informed groups were given the same information, as well as details regarding the auditory-visual alerts produced by the human–machine interface (HMI) during requests to intervene (RtIs), and examples of situations where RtIs would be issued. Ten and nine RtI events were staged for each participant in Experiment 1 and 2, respectively; the participants performed a non-driving-related task while the automated driving system was functioning. For both experiments it was found that, for all RtI events, more participants in the well-informed groups than the less-informed groups successfully assumed control of the vehicle. These results suggest that, in addition to providing information regarding the possible occurrence of RtIs, explanations of HMI and RtI-related situations are effective for helping both younger and older drivers safely and successfully negotiate such events.     

Exploring the effects of EEG signals on collision cases happening in the process of young drivers’ braking

Detecting mental states in drivers offers an opportunity to reduce accidents by triggering alerts and signaling the need for rest or renewed focus. Here we used electroencephalography (EEG) to measure brain signals in young drivers operating a driving simulator to detect mental states and predict accidents. We measured reaction times to unexpected hazardous events and correlated them with EEG signals measured from the frontal, parietal, and temporal cortices as well as the central sulcus (corresponding to motor cortex). We found that EEG signals in the relative beta (power in beta (13–30 Hz) relative to total power of the EEG (0.5–30 Hz)), alpha/delta, alpha/theta, beta/delta, beta/theta frequency bands were higher for collisions than successful collision avoidance, and that the key decision-making period is the 2nd second before braking. Importantly, a decision tree classifier trained on these neural signals predicted collision avoidance outcomes. Then based on random forest model, we extracted three critical neural signals (beta/delta_frontal, relative beta_parietal and relative beta_central Sulcus) to classify collision avoidance outcomes. Our findings suggest measuring EEG during driving may provide useful signals for enhancing driver safety.     

The likelihood of becoming a pedestrian fatality and drivers’ knowledge of pedestrian rights and responsibilities in the Commonwealth of Virginia

Pedestrian–vehicle crashes represent a small percentage of all Virginia crashes (less than 2% over the past 10 years). However, approximately 10% of crash fatalities are pedestrians. We analyzed pedestrian crash trends from 1990–1999 and investigated variables believed to predict these crashes, such as location (urban versus rural setting), sex, age, pedestrian drinking, driver drinking, driver violation, and time of day. A logistic regression analysis, controlling for year, found all of these variables significantly predicted the odds of dying versus being injured in a pedestrian crash. The typical fatality victim was an older male who had been drinking and was walking in a rural area between 12:00 and 5:59 a.m. A driver who had been drinking but would not be cited for a violation more likely struck this pedestrian. Crash data do not tell us about the knowledge drivers and pedestrians have regarding pedestrian laws, and how such knowledge suggested typical self-reported behaviors. Thus, we conducted a telephone survey of licensed Virginia drivers to assess such self-reported knowledge and behaviors. Most respondents reported knowing and following state laws regarding driver yielding and walking across streets. However, we found one area of particular concern. Respondents tended to believe pedestrians had the right-of-way at all times even when not crossing at crosswalks or intersections when Virginia law does not yield right-of-way to pedestrians in all cases. Virginia does not assign right-of-way in all cases to any group; rather the context determines who may proceed while others yield. Additional analyses from the crash trends and survey are reported in the text. The authors also draw parallels between their US research and that of the international community.     

Assessing the feasibility of the theory of planned behaviour in predicting drivers’ intentions to operate conditional and full automated vehicles

Guided by the Theory of Planned Behaviour (TPB), this study examined the beliefs underpinning, and feasibility of the factors in predicting, individuals’ intentions to use a conditional (Level 3) automated vehicle (AV) and a full (Level 5) AV. Australian drivers (N = 505) aged 17–81 years (Mean age = 33.69, SD = 18.79) were recruited and completed a 20 min online survey which featured both quantitative and qualitative items. For the quantitative data, two linear regressions revealed that the TPB standard constructs of attitudes, subjective norm, and perceived behavioural control (PBC) accounted for 66% of the variance in intentions to use a conditional AV and 68% of the variance in intentions to use a full AV. Of the TPB constructs, attitudes and subjective norms were significant positive predictors of future intentions to use conditional and full AVs. For the qualitative data, some differences emerged for the underlying behavioural beliefs that underpinned intentions to use conditional and full AVs. For example, having beliefs about control over the conditional AV was identified by many participants as an advantage, while not being in full control of the full AV was identified as a disadvantage. For underlying control beliefs, participants identified similar barriers for both vehicle types, including; high costs, lack of trust, lack of control over the vehicle, lack of current legislation to support the mainstream introduction of these vehicles, and concerns of safety for self and for other road users when operating AVs. Overall, these findings provide some support for applying the TPB to understand drivers’ intended use of AVs. However, while the current study showed that the constructs of attitudes and subjective norms might reflect intended use of AVs, more research is required to further examine the role of PBC. Additionally, the findings provide initial insights into the underlying behavioural and control beliefs that may motivate drivers to use AVs and highlight the similarities and differences in drivers’ perceptions towards two levels of vehicle automation.     

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.     

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.     

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.     

Supporting the changing driver’s task: Exploration of interface designs for supervision and intervention in automated driving

Driving automation leads to a changing role for drivers, that is from manual vehicle control to supervising automation. Supervision of partial automation requires now and then intervention. Since the automation causes low vigilance and out-of-the-loop performance problems, this changing role is not well suited for human operators. To explore how driver-vehicle interfaces can support drivers in their changed role, we tested three concepts. Concept A was a baseline reference, providing only acoustic warnings. Concept B presented status-information and warnings behind the steering wheel. Concept C used illumination and haptic feedback in the seat-pan to direct attention outside the vehicle and to stimulate response. Concept C only provided vibrotactile feedback when intervention was needed. Results of our study show improved support for supervision with the illumination-concept, i.e. better hazard-detection and raised levels of Situation Awareness in some scenarios relevant for supervisory control. Knowing that supervision will be the dominating driver’s responsibility during partially automated driving, the illumination-concept is a recommended solution for support of the driver’s changing role. Nonetheless, neither concept B, nor C, showed additional support for intervention compared to the baseline. It was hypothesised that the combination of concept C’s stimuli for intervention-support caused counter-productive levels of annoyance. Furthermore, we concluded that intervention and supervision benefit from different interface-features and discussed possible causes underlying ambiguity between support for supervision and support for intervention shown with concept C. Therewith, the considerations in this paper contribute to further development of – and knowledge about – appropriate driver-vehicle interaction while vehicle-operation advances into operating partially automated driving systems.     

Effect of external HMI for automated vehicles (AVs) on drivers’ ability to infer the AV motion intention: A field experiment

A field experiment was conducted aiming to shed light on how drivers negotiate an ambiguous traffic situation when encountering an autonomous vehicle (AV) in the presence of a yielding intention signal (AV with eHMI) or not (AV without eHMI). A traffic conflict scenario was created with two opposing vehicles instructed to perform a left turn at a four-way junction, at the same point in time. Forty participants were assigned to two groups encountering either an AV with eHMI or an AV without eHMI. To check for equivalence across the two groups, both groups also encountered a conventional vehicle (CV). Results showed that the two groups performed similarly during encounters with a CV. During encounters with AV, however, participants provided with the eHMI maintained higher speed before the AV and finished their maneuver quicker than when they were not provided with eHMI. Additionally participants provided with eHMI rated higher their ability to infer the AV intention before coming to a full-stop than those provided without eHMI. The above findings indicate that the presence of eHMI on AV can accelerate drivers’ inferences about yielding intention of an AV, and have the potential to optimize AV-driver interaction in ambiguous traffic situations.     

Drunk driving offenders’ knowledge and behaviour in relation to alcohol-involved driving in Yinchuan and a comparison with Guangzhou,China

ObjectiveThis study was conducted after a legislative amendment criminalising drunk driving (BAC > 80 mg/100 ml) had been in force for a year and investigated whether drunk driving offenders in Yinchuan, China were aware of the law, and whether their knowledge of and exposure to enforcement and the existence of alcohol use disorders were related to their alcohol-involved driving behaviour. The results were compared with results from an earlier study in Guangzhou, China.MethodA survey was conducted from July to October 2012 in Yinchuan to collect information on drunk driving offenders’ knowledge and practice in relation to alcohol-involved driving. The Alcohol Use Disorders Identification Test (AUDIT) was used to assess hazardous drinking levels. In total, 106 drunk driving offenders were recruited while in detention. The findings were compared with those of the Guangzhou study, where the same procedure was used to recruit 101 drunk driving offenders.ResultsThe mean age of the sample was 31.7 years (SD = 8.1; range 17–59 years). Males constituted 96% of the sample. The mean age at which offenders reported starting to drink alcohol was 18.7 years (SD = 3.2; range 10–26 years). Driver’s licenses had been held for an average of 8.5 years. The status of knowledge in relation to alcohol-involved driving in Yinchuan was slightly lower in proportion than in Guangzhou. The rate of alcohol-involved driving reported in the previous 12 months in Yinchuan (43%) was slightly higher than in Guangzhou (39%). The proportion of recidivists in Guangzhou (21.8%) was higher than Yinchuan’s (10.4%). On average, offenders had experienced 1.6 police alcohol breath tests in the previous year (SD = 1.3; range 1–10). AUDIT scores indicated that a substantial proportion of the offenders had high levels of alcohol use disorders.DiscussionLimited awareness of legal alcohol limits might contribute to drunk driving offences. The high level of alcohol consumption by many offenders suggests that hazardous drinking levels may be a contributor. Recidivist drivers also had higher AUDIT scores, which suggest there may be benefit in using the AUDIT to identify potential drink drivers and recidivists, subject to further research.     

Effects of route familiarity on drivers’ psychological conditions: Based on driving behaviour and driving environment

Route familiarity affects a driver’s mental state and indirectly affects traffic safety; however, this important factor is easily overlooked. Previous research on route familiarity has only analysed psychological states in terms of unfamiliarity and familiarity, the influence of driving behaviour and driving environment on psychological states has been ignored. As a result, the mechanisms through which the route familiarity influence driver psychological states, and vice versa, are unclear. This study proposes a quantitative framework for studying driver psychological condition and route familiarity using experimental data from a real driving task and driving environment data. The experimental data included 1022 observations obtained by 23 participants over 7 consecutive trials on 6 unfamiliar experimental routes with large differences in scenarios; environmental data were automatically extracted after segmenting a driving video through the Dilated Residual NetWorks model. The results reveal that (1) the relationship between the driver’s psychological condition and route familiarity is not monotonic and is different for straight and turning sections; (2) the driver’s psychological condition is influenced by the visual scene elements and the type of road section, and the results of the multivariate regression analysis quantified the variability of the influence; and (3) unlike a majority of findings on distracted driving, our study suggest that the driver’s attention to the external environment in the urban distracted driving state will gradually approach a ‘distraction threshold’, and the time and size of the ‘distraction threshold’ are influenced by the driver. This study can further the development of urban traffic safety research and help urban designers plan and improve urban landscapes to ensure drivers maintain stable mental states when they drive.     

Developmental changes in consistency of autobiographical memories: adolescents’ and young adults’ repeated recall of recent and distance events

Autobiographical memories contribute continuity and stability to one’s self yet they also are subject to change: they can be forgotten or be inconsistently remembered and reported. In the present research, we compared the consistency of two reports of recent and distant personal events in adolescents (12- to 14-year-olds) and young adults (18- to 23-year-olds). In line with expectations of greater mnemonic consistency among young adults relative to adolescents, adolescents reported the same events 80% of the time compared with 90% consistency among young adults; the significant difference disappeared after taking into consideration narrative characteristics of individual memories. Neither age group showed high levels of content consistency (30% vs. 36%); young adults were more consistent than adolescents even after controlling for other potential predictors of content consistency. Adolescents and young adults did not differ in consistency of estimating when their past experiences occurred. Multilevel modelling indicated that the level of thematic coherence of the initial memory report and ratings of event valence significantly predicted memory consistency at the level of the event. Thematic coherence was a significant negative predictor of content consistency. The findings suggest a developmental progression in the robustness and stability of personal memories between adolescence and young adulthood.     

Barriers to primary prevention efforts in the schools: Are we the biggest obstacle to the transfer of knowledge?

Dialogue and debate regarding the definition of prevention has subsided and has been replaced by research and information regarding pathways to dysfunction and effective primary prevention strategies. In spite of the solid research base and the strong desire of practitioners to apply prevention strategies, there continues to be sporadic implementation in schools. Barriers to the implementation of primary prevention programs include traditional professional practice within psychology, lack of communicating pertinent information between professions and policymakers, commitment of resources, and limited understanding of the pathways to successful and widespread implementation. Recommendations to address these barriers are discussed.