A variable speed limit control approach for freeway tunnels based on the model-based reinforcement learning framework with safety perception.

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel variable speed limit (VSL) control strategy based on the model-based reinforcement learning framework (MBRL) with safety perception. The MBRL framework is designed by developing a multi-lane cell transmission model for freeway tunnels as an environment model, which is built so that agents can interact with the environment model while interacting with the real environment to improve the sampling efficiency of reinforcement learning. Based on a real-time crash risk prediction model for freeway tunnels that uses random deep and cross networks, the safety perception function inside the MBRL framework is developed. The reinforcement learning components fully account for most current tunnels' application conditions, and the VSL control agent is trained using a deep dyna-Q method. The control process uses a safety trigger mechanism to reduce the likelihood of crashes caused by frequent changes in speed. The efficacy of the proposed VSL strategies is validated through simulation experiments. The results show that the proposed VSL strategies significantly increase traffic safety performance by between 16.00% and 20.00% and traffic efficiency by between 3.00% and 6.50% compared to a fixed speed limit approach. Notably, the proposed strategies outperform traditional VSL strategy based on the traffic flow prediction model in terms of traffic safety and efficiency improvement, and they also outperform the VSL strategy based on model-free reinforcement learning framework when sampling efficiency is considered together. In addition, the proposed strategies with safety triggers are safer than those without safety triggers. These findings demonstrate the potential for MBRL-based VSL strategies to improve traffic safety and efficiency within freeway tunnels.

Outcome evaluation of the p drivers Program: Randomised controlled trial of a program to improve safe driving among novice drivers.

BACKGROUND: Globally, road traffic crashes are the leading cause of death for young adults. The P Drivers Project was a trial of a behavioural change program developed for, and targeted at, young Australian drivers in their initial months of solo driving when crash risk is at its highest. METHODS: In a parallel group randomised controlled trial, drivers (N = 35,109) were recruited within 100 days of obtaining their probationary licence (allowing them to drive unaccompanied) and randomised to an intervention or control group. The intervention was a 3 to 6-week multi-stage driving behaviour change program (P Drivers Program). Surveys were administered at three time points (pre-Program, approximately one month post-Program and at 12 months after). The outcome evaluation employed an on-treatment analysis comprising the 2,419 intervention and 2,810 control participants who completed all required activities, comparing self-reported crashes and police-reported casualty crashes (primary outcome), infringements, self-reported attitudes and behaviours (secondary outcomes) between groups. RESULTS: The P Drivers Program improved awareness of crash risk factors and intentions to drive more safely, relative to the controls; effects were maintained after 12-months. However, the Program did not reduce self-reported crashes or police-reported casualty crashes. In addition, self-reported violations, errors and risky driving behaviours increased in the intervention group compared to the control group as did recorded traffic infringements. This suggests that despite the Program increasing awareness of risky behaviour in novice drivers, behaviour did not improve. This reinforces the need to collect objective measures to accompany self-reported behaviour and intentions. CONCLUSIONS: The P Drivers Program was successful in improving attitudes toward driving safety but the negative impact on behaviour, lack of effect on crashes, and the large loss to follow-up fail to support the use of a post-licensing behaviour change program to improve novice driver behaviour and reduce crashes. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: 363,293 (ANZCTR, 2012).

Integrated driving risk surrogate model and car-following behavior for freeway risk assessment.

Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.

Effects of executive load on crashes and near-crashes for young versus older drivers.

With the increasing use of infotainment systems in vehicles, secondary tasks requiring executive demand may increase crash risk, especially for young drivers. Naturalistic driving data were examined to determine if secondary tasks with increasing executive demand would result in increasing crash risk. Data were extracted from the Second Strategic Highway Research Program Naturalistic Driving Study, where vehicles were instrumented to record driving behavior and crash/near-crash data. executive and visual-manual tasks paired with a second executive task (also referred to as dual executive tasks) were compared to the executive and visual-manual tasks performed alone. Crash/near-crash odds ratios were computed by comparing each task condition to driving without the presence of any secondary task. Dual executive tasks resulted in greater odds ratios than those for single executive tasks. The dual visual-manual task odds ratios did not increase from single task odds ratios. These effects were only found in young drivers. The study shows that dual executive secondary task load increases crash/near-crash risk in dual task situations for young drivers. Future research should be conducted to minimize task load associated with vehicle infotainment systems that use such technologies as voice commands.

Evaluation of power wheelchair driving performance in simulator compared to driving in real-life situations: the SIMADAPT (simulator ADAPT) project-a pilot study.

OBJECTIVE: The objective of this study was to evaluate users' driving performances with a Power Wheelchair (PWC) driving simulator in comparison to the same driving task in real conditions with a standard power wheelchair. METHODS: Three driving circuits of progressive difficulty levels (C1, C2, C3) that were elaborated to assess the driving performances with PWC in indoor situations, were used in this study. These circuits have been modeled in a 3D Virtual Environment to replicate the three driving task scenarios in Virtual Reality (VR). Users were asked to complete the three circuits with respect to two testing conditions during three successive sessions, i.e. in VR and on a real circuit (R). During each session, users completed the two conditions. Driving performances were evaluated using the number of collisions and time to complete the circuit. In addition, driving ability by Wheelchair Skill Test (WST) and mental load were assessed in both conditions. Cybersickness, user satisfaction and sense of presence were measured in VR. The conditions R and VR were randomized. RESULTS: Thirty-one participants with neurological disorders and expert wheelchair drivers were included in the study. The driving performances between VR and R conditions were statistically different for the C3 circuit but were not statistically different for the two easiest circuits C1 and C2. The results of the WST was not statistically different in C1, C2 and C3. The mental load was higher in VR than in R condition. The general sense of presence was reported as acceptable (mean value of 4.6 out of 6) for all the participants, and the cybersickness was reported as acceptable (SSQ mean value of 4.25 on the three circuits in VR condition). CONCLUSION: Driving performances were statistically different in the most complicated circuit C3 with an increased number of collisions in VR, but were not statistically different for the two easiest circuits C1 and C2 in R and VR conditions. In addition, there were no significant adverse effects such as cybersickness. The results show the value of the simulator for driving training applications. Still, the mental load was higher in VR than in R condition, thus mitigating the potential for use with people with cognitive disorders. Further studies should be conducted to assess the quality of skill transfer for novice drivers from the simulator to the real world. Trial registration Ethical approval n ∘ 2019-A001306-51 from Comité de Protection des Personnes Sud Mediterranée IV. Trial registered the 19/11/2019 on ClinicalTrials.gov in ID: NCT04171973.

Understanding risky driving behaviors among young novice drivers in Nigeria: A latent class analysis coupled with association rule mining approach.

Traffic crashes are significant public health concern in Nigeria, particularly among young drivers. The study aims to explore the underlying pattern of risky driving behaviors and the associations with demographic factors among young drivers in Nigeria. A combined approach of Latent Class Analysis (LCA) and Association Rule Mining is applied to the dataset comprising responses from 684 young drivers who complete the "Behavior of Young Novice Drivers Scale" (BYND) questionnaires. The LCA identifies four distinct classes of drivers based on the risky behavior profiles: Reckless-Speedsters, Cautious Drivers, Distracted Multitaskers, and Emotion-impacted Drivers. Association rule mining further connects these driver classes to demographic and driving history variables, uncovering intriguing insights. Reckless-Speedsters predominantly consist of young males who engage in riskier driving behaviors, including exceeding speed limits and disregarding traffic rules. Conversely, Cautious Drivers, also predominantly young males, exhibit a safer driving profile marked by rule adherence and a notably lower crash rate. Distracted Multitaskers, sharing a demographic profile with Cautious Drivers, diverge significantly due to their higher crash involvement, hinting at a propensity for distracted driving practices. Lastly, Emotion-Impacted Drivers, primarily comprising young employed males, display behaviors influenced by emotions, shorter driving distances, and prior unsupervised driving experience. Most of the behaviors are attributed to inadequate traffic control, absence of traffic signs in most of the roads, preferential treatment, and lack of strict law enforcement in the country. The findings hold substantial implications for road safety interventions in Nigeria, urging targeted approaches to address the unique challenges presented by each driver class. With acknowledging the study limitations and advocating for future research in objective measures and emotion-behavior interactions, the comprehensive approach provides a robust foundation for enhancing road safety in the Nigerian context.

On the impact of on-road partially-automated driving on drivers' cognitive workload and attention allocation.

The use of partially-automated or SAE level-2 vehicles is expected to change the role of the human driver from operator to supervisor, which may have an effect on the driver's workload and visual attention. In this study, 30 Ontario drivers operated a vehicle in manual and partially-automated mode. Cognitive workload was measured by means of the Detection Response Task, and visual attention was measured by means of coding glances on and off the forward roadway. No difference in cognitive workload was found between driving modes. However, drivers spent less time glancing at the forward roadway, and more time glancing at the vehicle's touchscreen. These data add to our knowledge of how vehicle automation affects cognitive workload and attention allocation, and show potential safety risks associated with the adoption of partially-automated driving.

Effects of sleep deprivation and hazard types on the hazard perception of young novice drivers: An ERP study.

OBJECTIVE: The present study aimed to explore the effects of sleep deprivation on young novice drivers' cognitive neural processing of different hazard types. METHOD: A 2 (sleep deprivation group, control group) × 3 (no hazard, covert hazard, overt hazard) mixed experimental design was used. Twenty-eight young drivers were sleep-deprived (no sleep within the past 24 h), while 28 drivers were in the control group (maintaining a normal schedule throughout the week). Eighty pictures containing a covert hazard (20 pictures), overt hazard (20 pictures) and no hazard (40 pictures) were presented. Participants were asked to press the keyboard quickly if they detected a hazard situation. The reaction time, accuracy, and changes in the N1 (100-150 ms) and N2 (250-350 ms) components of event-related potentials (ERP) measured using electroencephalography (EEG) were obtained. RESULTS: Compared to the control group, the response accuracy of sleep-deprived drivers was higher in the cover-hazard situation and their N1 latency was longer in the no-hazard situation. Compared to the no-hazard and overt-hazard situations, the participants' reaction times and N2 amplitudes were significantly greater, and the response accuracy was significantly lower in the covert-hazard situation. CONCLUSION: Hazard perception is compromised when drivers are sleep-deprived, especially when they are confronted with covert hazard situations. The findings help understand the negative effects of sleep deprivation in the early stage of young novice drivers' hazard perception.

The impact of environmental noise on drivers' cognitive abilities: A case study on in-vehicle voice interaction interfaces.

To investigate the impact of environmental noise on the cognitive abilities of drivers, this study, using in-vehicle voice interaction as an example, conducted laboratory experiments to assess the effects of road traffic noise, entertainment noise, and white noise stimuli on drivers' attention and short-term memory. The noise levels simulated to mimic acoustic conditions during car driving ranged from 35 dB(A) to 65 dB(A). The conclusions drawn were as follows: (1) Noise levels directly influenced subjective annoyance levels, with annoyance linearly increasing as noise levels escalated; (2) Both attention and short-term memory task reaction times of drivers were significantly influenced by noise types. Compared to traffic noise and white noise, drivers' cognitive efficiency was lower under entertainment noise. (3) Performance in complex cognitive tasks was more susceptible to noise levels compared to simple cognitive tasks; (4) Experimentally, it was found that drivers exhibited the highest cognitive efficiency in cognitive tasks when the environmental noise level was 55 dB(A), as opposed to noise levels of 35 dB(A), 45 dB(A), and 65 dB(A).

Research on driver's anger recognition method based on multimodal data fusion.

OBJECTIVES: This paper aims to address the challenge of low accuracy in single-modal driver anger recognition by introducing a multimodal driver anger recognition model. The primary objective is to develop a multimodal fusion recognition method for identifying driver anger, focusing on electrocardiographic (ECG) signals and driving behavior signals. METHODS: Emotion-inducing experiments were performed employing a driving simulator to capture both ECG signals and driving behavioral signals from drivers experiencing both angry and calm moods. An analysis of characteristic relationships and feature extraction was conducted on ECG signals and driving behavior signals related to driving anger. Seventeen effective feature indicators for recognizing driving anger were chosen to construct a dataset for driver anger. A binary classification model for recognizing driving anger was developed utilizing the Support Vector Machine (SVM) algorithm. RESULTS: Multimodal fusion demonstrated significant advantages over single-modal approaches in emotion recognition. The SVM-DS model using decision-level fusion had the highest accuracy of 84.75%. Compared with the driver anger emotion recognition model based on unimodal ECG features, unimodal driving behavior features, and multimodal feature layer fusion, the accuracy increased by 9.10%, 4.15%, and 0.8%, respectively. CONCLUSIONS: The proposed multimodal recognition model, incorporating ECG and driving behavior signals, effectively identifies driving anger. The research results provide theoretical and technical support for the establishment of a driver anger system.

Psychosocial factors, psychological well-being and safety incidents among long-distance bus drivers in Ghana: A cross-sectional survey.

BACKGROUND: Commercial bus drivers account for most road traffic crashes and related mortality. The psychosocial working conditions of these drivers have been found precarious. However, road safety initiatives in Ghana still focus on correcting risky driving behaviours, ignoring the conditions under which these drivers operate. Hence, the purpose of this study was to examine whether psychosocial work factors can predict the psychological well-being and risky driving behaviours of long-distance bus drivers in Ghana. METHODS: This quantitative cross-sectional survey recruited 7315 long-distance bus drivers that operate from Accra to other parts of Ghana and cities in other West African countries. Hypotheses were tested using Partial Least Squares Structural Equation Modelling (PLS-SEM). RESULTS: We found that job demands and job resources are direct and significant predictors of psychological well-being and safety incidents among these drivers. Moreover, psychological well-being of the drivers had a significant inverse relationship with their safety incidents. Psychosocial safety climate (PSC) had a negative association with safety incidents, and a positive but non-significant association with psychological well-being. PSC had a negative and significant association with job resources contrary to the notion of the PSC theory. CONCLUSION: Psychosocial work factors are predictors of psychological well-being and safety incidents of long-distance bus drivers. Owners and managers of bus transport businesses in Ghana, driver unions and station masters need to highly prioritise psychological health and safety of this bus drivers by providing suitable job resources and adopting bottom-up communication that might help the drivers effectively cope with their job demands.

Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment.

Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.

Psychosocial work conditions and traffic safety among minibus and long-bus drivers.

OBJECTIVE: This study sought to examine the association between psychosocial work factors and road traffic crashes (RTCs), and test the differences in psychosocial work factors between minibus and long-bus drivers. METHODS: This cross-sectional survey employed a convenient sampling method to collect data from 7315 long-distance minibus and long-bus drivers who operate between the Ghanaian cities, Accra and Tema and other parts of the country. The drivers answered a job content questionnaire, psychosocial safety climate scale (PSC-12), work-family conflict scale, and demographic questions on age, education, driving hours, and RTC history. RESULTS: The correlational analysis showed a significant association between psychosocial work factors and RTCs for the previous 2 years. Hierarchical multiple linear regression found that supervisor support, skill discretion, decision autonomy, psychological demands, PSC, and work-family conflict significantly contributed to explaining RTC rates among the drivers. Also, significant differences were found between minibus and long-bus drivers in driving hours, occurrence of near misses, RTCs, and all psychosocial work factors explored in this study except work-family conflict. CONCLUSIONS: Psychosocial work factors directly predict RTCs among minibus and long-bus drivers. Policymakers, driver unions, and owners and managers of bus transport businesses should prioritize integrating occupational health and safety into road transport activities. Furthermore, managers and bus owners should use bottom-up communication, provide access to support services and work-family balance initiatives, flexible work schedules, and a supportive work environment to improve road safety.

Modulating Driver Alertness via Ambient Olfactory Stimulation: A Wearable Electroencephalography Study.

Poor alertness levels and related changes in cognitive efficiency are common when performing monotonous tasks such as extended driving. Recent studies have investigated driver alertness decrement and possible strategies for modulating alertness with the goal of improving reaction times to safety critical events. However, most studies rely on subjective measures in assessing alertness changes, while the use of olfactory stimuli, which are known to be strong modulators of cognitive states, has not been commensurately explored in driving alertness settings. To address this gap, in the present study we investigated the effectiveness of olfactory stimuli in modulating the alertness state of drivers and explored the utility of electroencephalography (EEG) in developing objective brain-based tools for assessing the resulting changes in cortical activity. Olfactory stimulation induced a significant differential effect on braking reaction time. The corresponding effect to the cortical activity was characterized using EEG-derived metrics and the devised machine learning framework yielded a high discriminating accuracy (92.1%). Furthermore, neural activity in the alpha frequency band was found to be significantly associated with the observed drivers' behavioral changes. Overall, our results demonstrate the potential of olfactory stimuli to modulate the alertness state and the efficiency of EEG in objectively assessing the resulting cognitive changes.

Could driving help us to "see better"? A comparative assessment of saccadic efficiency, visual speed, and attention.

BACKGROUND: This study aimed at comparing drivers' and non-drivers' results in the Adult Developmental Eye Movement with Distractors test (ADEMd) and the Useful Field of View test (UFOV). METHODS: One hundred and twenty Spaniards (mean age 50.90 ± 17.32 years) without eye disease voluntarily participated in this cross-sectional descriptive study. Participants in a single experimental session completed a questionnaire on sociodemographic, health, eyesight, and driving information. They also performed the ADEMd and UFOV tests randomly following standardized protocols. The ADEMd is a visual-verbal test that measures saccadic efficiency and visual attention. Brown-Forsythe (B-F) tests with Games-Howell post-hoc adjustments were conducted to assess differences between groups. Groups were formed according to sex, age (young adults, adults, and older adults), and driver/non-driver for further analysis. Additionally, associations between dependent variables were assessed through Spearman's correlations. RESULTS: Drivers obtained significantly better results in the ADEMd compared with non-drivers. Non-significant differences between drivers and non-drivers were encountered in the UFOV. Additionally, significant differences were observed between sexes and age groups. It is worth highlighting that non-driver's age significantly correlated with worse ADEMd performance (rho = .637 to .716). This correlation was non-significant in drivers. Similarly, reading hours significantly correlated with better ADEMd performance in non-drivers (rho = - .291 to - .363), but not in drivers. The only significant correlations between ADEMd and UFOV tests were found in drivers (rho = .307 to .410). CONCLUSION: Considering all the discussed results, it could be hypothesized that the driving task promotes abilities, such as oculomotor and cognitive function, which are relevant for the performance in the ADEMd. However, this hypothesis is based on correlational outcomes and further studies should causally assess this possible relation.

Using Serial Trichotomization to Determine Fitness to Drive in Medically At-Risk Drivers.

IMPORTANCE: Clinical tests that identify fit and unfit drivers with 100% sensitivity and specificity would reduce uncertainty and improve efficiency of occupational therapists performing comprehensive driving evaluations (CDEs). OBJECTIVE: To examine whether serial trichotomization of clinical tests predicts pass-fail outcomes with 100% sensitivity and specificity in a sample of medically at-risk drivers and in drivers with and without cognitive impairment (CI) referred for a CDE. DESIGN: Retrospective data collection and analysis of scores on the Montreal Cognitive Assessment; Trail Making Test, Part A and Part B; and the Useful Field of View® Subtests 1 to 3 and outcomes on the CDE (pass-fail or indeterminate requiring lessons and retesting). Receiver operating characteristic curves of clinical tests were performed to determine 100% sensitivity and specificity cut points in predicting CDE outcomes. Clinical tests were arranged in order from most to least predictive to identify pass-fail and indeterminate outcomes. SETTING: A driving assessment clinic. PARTICIPANTS: Among 142 medically at-risk drivers (M age = 69.2 yr, SD = 14.1), 66 with CI, 46 passed and 39 failed the CDE; 57 were indeterminate. OUTCOMES AND MEASURES: On-road pass-fail outcomes. RESULTS: Together, the six clinical tests predicted 62 pass and 49 fail outcomes in the total sample; 21 pass and 34 fail outcomes in participants with CI; and 58 pass and 14 fail outcomes in participants without CI. CONCLUSIONS AND RELEVANCE: Serial trichotomization of clinical tests increases the accuracy of making informed decisions and reduces the number of drivers undergoing unnecessary on-road assessments. Plain-Language Summary: Clinical tests and their cut points that identify fit and unfit drivers vary substantially across settings and research studies. Serial trichotomization is one method that could help control for this variation by combining clinical test scores showing 100% sensitivity and specificity to identify pass (fit drivers) and fail outcomes (unfit drivers) and to reduce the number of drivers undergoing unnecessary on-road assessments.

Parkinson's Disease and Driving Fitness: A Systematic Review of the Existing Guidelines.

BACKGROUND: Motor/nonmotor symptomatology and antiparkinsonian drugs deteriorate the driving ability of Parkinson's disease (PD) patients. OBJECTIVES: Treating neurologists are frequently asked to evaluate driving fitness of their patients and provide evidence-based consultation. Although several guidelines have been published, the exact procedure along with the neurologist's role in this procedure remains obscure. METHODS: We systematically reviewed the existing guidelines, regarding driving fitness evaluation of PD patients. We searched MEDLINE and Google Scholar and identified 109 articles. After specified inclusion criteria were applied, 15 articles were included (nine national guidelines, five recommendation papers, and one consensus statement). RESULTS: The treating physician is proposed as the initial evaluator in 8 of 15 articles (neurologist in 2 articles) and may refer patients for a second-line evaluation. The evaluation should include motor, cognitive, and visual assessment (proposed in 15, 13, and 8 articles, respectively). Specific motor tests are proposed in eight articles (cutoff values in four), whereas specific neuropsychological and visual tests are proposed in seven articles each (cutoff values in four and three articles, respectively). Conditional licenses are proposed in 11 of 15 articles, to facilitate driving for PD patients. We summarized our findings on a graphic of the procedure for driving fitness evaluation of PD patients. CONCLUSIONS: Neurological aspects of driving fitness evaluation of PD patients are recognized in most of the guidelines. Motor, neuropsychological, visual, and sleep assessment and medication review are key components. Clear-cut instructions regarding motor, neuropsychological, and visual tests and relative cutoff values are lacking. Conditional licenses and periodical reevaluation of driving fitness are important safety measures.

The effect of rear bicycle light configurations on drivers' perception of cyclists' presence and proximity.

The optimal cycle light configuration for maximizing cyclists' conspicuity to drivers is not clear. Advances in sensor technology has led to the development of 'reactive' cycle lights that detect changes in the environment and consequently increase their flashing speed and brightness in risky situations - for example, when a rearward car is approaching - but no research has examined the effect of such lights on driver perception. The aim of the present study is to compare different cycle light configurations, including 'reactive' light technology, on drivers' ability to detect cyclists and estimate their proximity. We recruited 32 drivers to participate in two experiments, in which they viewed life-size real-world stimuli filmed from a driver's perspective in daytime and at dusk. The footage showed a cyclist on a bicycle with a rear light mounted on the seat post, in various configurations: static light, steady flashing, reactive flashing and no light. In Experiment 1, the drivers were required to detect the presence or absence of a cyclist on the road ahead as quickly as possible. In Experiment 2, they were required to estimate the distance of the cyclist from their vehicle, and to rate their confidence in their estimates. Experiment 1 revealed that drivers were quicker to detect the cyclist's presence in all rear cycle light conditions relative to the no light condition, but there were no differences in speed or accuracy across rear light conditions. Experiment 2 showed that drivers were more accurate in estimating the cyclist's proximity in the steady flashing and reactive flashing conditions, compared to static and no light conditions. Drivers were also more confident in their judgements in all rear light conditions compared to the no light condition. In conclusion, flashing rear cycle lights, regardless of reactive technology, enhanced drivers' perception of a cyclist ahead, notably in terms of their judgements of distance to that cyclist. Further investigation is needed to fully understand the impact of cycle light technology on driver perception, as well as the use of drivers' distance-to-cyclist estimates as an index of cyclists' cognitive conspicuity.

Visual-Motor Processing Speed and Reaction Time Differences between Medically-At-Risk Drivers and Healthy Controls.

This cross-sectional study compared visual-motor processing speed and reaction times between medically-at-risk drivers and normal controls to determine if the time in seconds distinguished between drivers who pass, fail, or need restrictions based on a road test. The medically-at-risk drivers' data (N = 35, 28-89 years) were collected as part of a comprehensive driving evaluation and coded by diagnosis (e.g., cognitive, neurological, medical) and driving outcome. The healthy control (N = 121, 21-79 years) data were collected in previous studies. The Vision Coach™ Full Field 60 task was used to collect reaction times in seconds between the two groups. Independent t-tests showed a significant difference (p < .001) in trial times between healthy controls and medically-at-risk adults. No significant difference (p = .141) was found between the three diagnoses groups. The resulting scores from the Vision Coach™ demonstrated a significant different (p < .001) between those who were determined fit to drive without restrictions and those who were determined not fit to drive after a comprehensive driving evaluation, showing the potential to be used as a screening tool for determining driving risk.

Driving Cessation and Late-Life Depressive and Anxiety Symptoms: Findings from the National Health and Aging Trends Study.

OBJECTIVES: To describe the association between driving cessation and depressive and anxiety symptoms over time by assessing depression and anxiety at 1- and 4-years follow-up. METHODS: The study examined community-dwelling adults aged 65 years and older from the National Health and Aging Trends Study who were driving at the 2015 interview and completed 1-year (N = 4,182) and 4-year (N = 3,102) follow-up interviews. Outcomes were positive screens for depressive and anxiety symptoms in 2016 or 2019, and the primary independent variable was driving cessation within one year of the baseline interview. RESULTS: Adjusting for socio-demographic and clinical characteristics, driving cessation was associated with depressive symptoms at 1 year (OR = 2.25, 95% CI: 1.33-3.82) and 4-year follow-up (OR = 3.55, 95% CI: 1.72-7.29). Driving cessation was also associated with anxiety symptoms at 1 year (OR = 1.71, 95% CI: 1.05-2.79) and 4 year follow up (OR = 3.22, 95% CI: 1.04-9.99). CONCLUSIONS: Driving cessation was associated with an increased risk of developing depressive and anxiety symptoms in later life. However, reasons for this association remain unclear. CLINICAL IMPLICATIONS: Although the mechanism linking driving cessation with worse mental health symptoms is uncertain, driving facilitates many important activities. Clinicians should monitor the well-being of patients who stop or intend to stop driving.