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Background: Metabolic syndrome (MetS) is especially prevalent among US truck drivers. However, there has been limited research exploring associations between MetS conditions with roadway crashes among truck drivers. The objective of this paper is to assess relationships between specific combinations of individual MetS components and crashes and near-misses., Methods: Survey, biometric, and anthropometric data were collected from 817 truck drivers across 6 diverse US states. Survey data focused on demographics and roadway safety outcomes, and anthropometric/biometric data corresponded to five MetS conditions (waist circumference blood pressure, hemoglobin A1c, triglycerides, and high-density lipoprotein [HDL] cholesterol). Logistic regression was used to calculate odds ratios of lifetime crashes and near-miss 1-month period prevalence associated with: 1) specific MetS conditions regardless of presence or absence of other MetS conditions, and 2) specific MetS conditions and counts of other accompanying MetS conditions., Results: Hypertension was the MetS characteristic most strongly associated with lifetime crash and 1-month near-miss outcomes, while high triglycerides, low HDL cholesterol, and large waist circumference were most commonly present among groups of conditions associated with crashes and near-misses. Overall, an increasing number of specific co-occurring MetS conditions were associated with higher reporting of roadway crashes., Conclusions: Specific combinations and higher prevalence of MetS conditions were associated with increased frequency of reported crashes. Moreover, when the co-occurrence of MetS conditions is aggregated, a dose-response relationship with crashes appears. These results suggest that policy changes and interventions addressing MetS may increase driver health and reduce crash risk., (© 2022 Wiley Periodicals LLC.)

Previous studies showed that the use of haptic feedback can improve user's experience. However, quantitative data on driver performance are missing. This paper examines potential benefits of various feedbacks on driving performance and user's preference: Visual feedback M, Visual and Auditory feedback (VA), and Visual, Auditory and Haptic feedback (VAH). 24 participants completed a dual-task approach, using the Lane Change Test (LCT) as a driving task and two use cases of touchscreen tasks (Slide or Push task). Moreover, in a given condition participants had to performed a third task to increase mental workload. Results showed that the presence of the trimodal feedback VAH significantly improved driving performances compared to other feedbacks. This was observed through a decrease in standard deviations of lateral. Haptic feedbacks reduce error rates in the dual-task, and that participants significantly preferred this trimodal haptic feedback Therefore, haptic feedback use have an objective positive effect on driver performance and on subjective user's experience while interacting with a touchscreen interface for both Slide and Push tasks conditions. [ABSTRACT FROM AUTHOR]

Excess health and safety risks of commercial drivers are largely determined by, embedded in, or operate as complex, dynamic, and randomly determined systems with interacting parts. Yet, prevailing epidemiology is entrenched in narrow, deterministic, and static exposure-response frameworks along with ensuing inadequate data and limiting methods, thereby perpetuating an incomplete understanding of commercial drivers' health and safety risks. This paper is grounded in our ongoing research that conceptualizes health and safety challenges of working people as multilayered "wholes" of interacting work and nonwork factors, exemplified by complex-systems epistemologies. Building upon and expanding these assumptions, herein we: (a) discuss how insights from integrative exposome and network-science-based frameworks can enhance our understanding of commercial drivers' chronic disease and injury burden; (b) introduce the "working life exposome of commercial driving" (WLE-CD)-an array of multifactorial and interdependent work and nonwork exposures and associated biological responses that concurrently or sequentially impact commercial drivers' health and safety during and beyond their work tenure; (c) conceptualize commercial drivers' health and safety risks as multilayered networks centered on the WLE-CD and network relational patterns and topological properties-that is, arrangement, connections, and relationships among network components-that largely govern risk dynamics; and (d) elucidate how integrative exposome and network-science-based innovations can contribute to a more comprehensive understanding of commercial drivers' chronic disease and injury risk dynamics. Development, validation, and proliferation of this emerging discourse can move commercial driving epidemiology to the frontier of science with implications for policy, action, other working populations, and population health at large., (© 2024 Wiley Periodicals LLC.)

When we drive a car in the high luminance environment, to comply with drivers and passengers' requests for better visibility, the display should be low reflection. In this report, the Advanced Reflection Invisible technology with Embedded System (ARIES) will be introduced for automotive wide‐view LCD with CTP to get super low reflection and improve ambient contrast ratio. It makes excellent sunlight readability without increasing the backlight luminance [ABSTRACT FROM AUTHOR]

The article reports that the periodical has announced public release of position papers as part of the work of its National Practice and Standards Reference Group related to occupational therapy and driver assessment and support of allied health assistants towards occupational therapy practice.

The Austroads Fitness to Drive Guidelines were updated in 2022. Most of the focus to date has been on Part 2 of the Guidelines, which provide guidance as to specific medical conditions. Less attention has been paid to Part 1 of the Guidelines, which cover a medical practitioner's ethical and legal obligations. This paper addresses the imbalance by considering and amplifying the obligations discussed in Part 1 of the Guidelines. [ABSTRACT FROM AUTHOR]

In urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two‐objective train operation optimization based on eco‐driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware‐in‐the‐loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow. [ABSTRACT FROM AUTHOR]

This paper investigates the potential impacts of dynamic wireless power transfer (DWPT) systems on the urban road network with a particular focus on charging and energy consumption implications. The paper is motivated by the recent advancement of connected and autonomous vehicle (CAV) technology and innovations on smart transportation infrastructure. An eco‐driving model aiming at energy consumption reduction is developed and a DWPT system layout suitable for charging eco‐driving CAVs at a signalised intersection is proposed. The idea is demonstrated in a simulated environment with different market penetration rate (MPR) of CAVs travelling through a signalized intersection equipped with the proposed DWPT system. The results suggest that as MPR of CAV increases, the energy savings for both CAVs and human‐driven vehicles (HVs) will increase significantly. At full MPR of CAVs, the reduction of average energy consumption of all types of vehicles can reach up to 62%. The average energy transferred and stored per battery electric vehicle can increase by up to 5% and 10%, respectively. These promising results indicate that a well‐designed DWPT system layout together with the eco‐driving behaviour of CAVs can have a positive impact on the urban transportation system in terms of resulting energy consumption and emissions. [ABSTRACT FROM AUTHOR]

The primary aim of this systematic review was to examine the efficacy of driving interventions with regard to a reduction in motor vehicle crashes and improvements in driving skills among older people. The secondary aim was to identify the optimal type (on‐road or off‐road) and dosage (period, sessions, and duration) of driving interventions for improving driving skills in older people. We searched MEDLINE, EMBASE, PsycINFO, and Scopus of Systematic Reviews for papers published from their inception to December 1, 2020, as well as the reference lists of the included papers. The selected studies were randomized controlled trials examining the effects of driving interventions among community‐dwelling older drivers aged 65 years and over. A meta‐analysis of two studies (n = 960) showed that driving interventions significantly reduced the number of motor vehicle crashes per person‐years. Ten studies (n = 575) were included in the meta‐analysis showing that the interventions significantly improved the driving skill scores. Driving skill scores significantly improved after on‐road training, and in interventions of at least 3 h, 3 sessions, and 3 weeks. Driving interventions significantly improve driving skills and reduce motor vehicle crashes among older drivers aged 65 years and over. On‐road training is more efficacious than off‐road training and driving interventions of at least 3 h taking place in 3 sessions over a period of 3 weeks may be required to improve driving skills in older drivers. Geriatr Gerontol Int 2023; 23: 771–778. [ABSTRACT FROM AUTHOR]

With the continuous development of automated driving, the path planning algorithm of unmanned vehicles has been continuously innovated. First, some basic concepts of path planning are briefly introduced. Secondly, starting with the classification idea of path planning algorithm based on graph structure and sampling, the paper introduces four typical basic algorithms: Dijkstra algorithm, A* algorithm, PRM algorithm and RRT algorithm. In addition, the Apollo algorithm, which has been studied most at present, is also introduced. The principle and application scenario of each algorithm are summarized. Finally, several new algorithms and their development prospects are introduced. [ABSTRACT FROM AUTHOR]

Policy PointsThe health care sector is increasingly investing in social conditions, including availability of safe, reliable, and adequate transportation, that contribute to improving health.In this paper, we suggest ways to advance the impact of transportation interventions and highlight the limitations of how health services researchers and practitioners currently conceptualize and use transportation.Incorporating a transportation justice framework offers an opportunity to address transportation and mobility needs more comprehensively and equitably within health care research, delivery, and policy. [ABSTRACT FROM AUTHOR]

Motion planning, decision making, and control are vital functions in autonomous driving for accomplishing the desired driving task while considering passenger comfort, road infrastructure, and surrounding traffic participants. Model predictive control (MPC) is a promising method for simultaneously realizing these functions. However, formulating a single MPC that can run through all driving scenarios is difficult, and previous research has often been conducted to design an MPC for a specific driving task. To extend the availability of MPC for all driving tasks, smooth switching between different MPCs designed for each driving task must be addressed. One of the difficulties in switching between MPCs is guiding the state to a feasible set of optimization problems after switching. In this paper, we present a new framework to realize the smooth connection of MPCs, that is, to reduce the optimization infeasibility at the time of MPC switching. In our proposed method, two general nonlinear MPCs with different state spaces, cost functions, constraints, and formulations can be systematically switched via automatically generated intermediate‐MPCs without requiring any particular alterations. This can help reduce the system complexity of the hybrid MPC system. [ABSTRACT FROM AUTHOR]

As human society continues to progress and develop, people's means of transport are becoming more and more advanced and road conditions more and more complex. In the case of complex traffic conditions, the amount of information available to people driving cars in a short period of time is relatively small for the overall traffic system. Based on the development of the Internet of vehicles technology, this paper proposes a method based on the Internet of vehicles system to present the out‐of‐line‐of‐sight traffic information to vehicle drivers by receiving the out‐of‐line‐of‐sight vehicle information from the roadside unit and obtaining the roadside pedestrian information combined with visual perception. Firstly, the roadside unit receives information on all traffic participants in the traffic node, where vehicle information is obtained by connecting the vehicle unit to the roadside unit, and pedestrian and non‐motorised vehicle information is obtained by using an improved target detection algorithm to convert pedestrian and non‐motorised vehicle coordinates into world coordinates using the principle of camera imaging, thus obtaining the location information of pedestrians and non‐motorised vehicles. Then, after obtaining the information of each traffic participant, the vehicle is informed by the roadside unit, and after the position conversion between the roadside unit and the vehicle, the information of the position of each traffic participant in the view of that vehicle is obtained and the road condition is displayed through the display system. [ABSTRACT FROM AUTHOR]

Highly automated vehicles (HAVs) are expected to fundamentally change the on‐road transportation field by increasing traffic flow efficiency, reducing road crashes caused by human errors, and increasing comfort and driving productivity. With the help of recent developments in cognitive management techniques and machine learning analysis, intelligent on‐board computing services are gaining acceptance. The main goal of the present paper is to introduce and develop a novel on‐board cognitive decision‐making functionality that dynamically and automatically enables HAVs to operate each time in the best available level of driving automation (LoDA). The proposed functionality utilizes several attributes associated with the driving environment and the driver's personality characteristics and personal preferences. The cognitive nature of the proposed functionality is based on previous knowledge, turned into experience, by implementing the Naive Bayes classifier supervised machine learning method. The effectiveness of the proposed cognitive functionality, in terms of accuracy and speed of convergence, in proactively identifying the optimal LoDA, is illustrated by modelling and analysing three scenarios with regards to drivers with different profile data and to driving scenes with different environment characteristics. Therefore, it can operate as an in‐car intelligent personal assistant for the drivers. [ABSTRACT FROM AUTHOR]

This paper explores the importance of two forms of interactions, namely marketer–traveler interaction and traveler–traveler interaction in driving destination brand engagement (DBE) dimensions, and their effect on travelers' short‐term and long‐term behavioral intentions based on impulse buying and external search behavior. Using multi‐group analysis, the impact of DBE dimensions on external search behavior and impulse buying across Generation‐Z travelers (aged between 18 and 25) and Generation‐Y travelers (aged between 26 and 40) was also explored. The results provide meaningful implications for tourism marketers to drive relationships between travelers and destinations on digital tourism platforms. [ABSTRACT FROM AUTHOR]

When errors of automated vehicles (AVs) occur, drivers' trust can easily be destroyed, resulting in the reduction of the use of AVs. This study aims to examine how error of AVs declines driver's trust by impacting their subjective perceptions. A driving simulator experiment is conducted, in which 104 participants (male = 58; female = 46) experienced automated driving with automation errors and rated their trust. The results indicate that automation error will affect the driver's perceived predictability, perceived reliability, and perceived safety, which will lead to the decline of trust and abandonment of automated driving. With the occurrence of automation error of AVs, perceived safety plays a more critical role in drivers' trust. In addition, when automation errors occur in specific tasks with low risk, the trust of drivers will drop faster than that in high‐risk tasks. This paper has explored the internal effects of the decline of driver's trust after automation errors of AVs, and further considers the influence of different external risks on these perception factors and trust. This study can help AVs manufacturers to formulate different degrees of trust repair strategies according to different driving tasks and accident severity. [ABSTRACT FROM AUTHOR]

The high incidence of traffic accidents brings immeasurable losses to life. In order to avoid such crises, researchers and automakers have used many methods to solve this problem. Among them, technology based on visual features is widely used in driver fatigue detection. As fatigue detection plays a vital role in the driving process, the high accuracy of fatigue monitoring is very important. This paper focuses on the method based on convolutional neural network to detect driver fatigue. First, in the face detection part, the Single‐Shot Multi‐Box Detector algorithm is used to improve the speed and accuracy of face detection to extract the eye and mouth regions; second, the VGG16 network is used to learn fatigue features, which is performed on the NTHU‐Drowsy Driver Detection (NTHU‐DDD) data set and the other two modified data sets Training test. The main result of this work is that the accuracy of fatigue monitoring is higher than other methods including the original method, with an accuracy rate of over 90%. And it has better generalization ability than the multi‐physical feature fusion detection method. At the same time, we propose the fatigue detection method based on convolutional neural network to improve the advanced driver assistance system (ADAS) to make it more robust and reliable decision making. [ABSTRACT FROM AUTHOR]

We propose a new digital driving technique using pulse‐density modulation (PDM) with a random dither matrix for evenness of luminance uniformity and higher motion image quality. As a digital driving technique, pulse‐width modulation (PWM) is conventionally used for active‐matrix organic light‐emitting diode (AMOLED) displays in order to improve luminance uniformity. However, in PWM driving, it is difficult to realize high‐frame‐rate driving for improving motion image quality. On the other hand, it is considered that PDM driving method is effective to obtain high motion image quality because of non‐frame‐refreshing property. In the present paper, we proposed a PDM driving technique using a random dither matrix and evaluated the relationship between the display image quality and the driving frequency in the proposed method through simulations. As a result, it is confirmed that good image quality can be obtained by using a blue‐noise mask as a random dither matrix at a practical driving frequency. Moreover, we demonstrated the proposed technique using an actual AMOLED panel. [ABSTRACT FROM AUTHOR]

The control scheme for an electric motor drive is generally implemented by involving proportional‐integral (PI) controllers in current and speed feedback control loops. A good design of PI controller parameters has always been a challenging task for control system design engineers. This paper describes the D‐decomposition method for the design of the parameters of involved DC‐link current and speed PI controllers in closed‐loop scheme of a pulse width‐modulated, current source inverter (CSI)‐fed squirrel‐cage induction motor drive. The PI controllers ensure the stable operation of CSI‐fed induction motor in natural unstable operating region of its torque‐slip characteristic. In order to achieve smoother speed tracking performance with minimum overshoot/undershoot, the damping ratio has been pre‐specified while designing the PI controller parameters using the D‐decomposition method. Simulation results have been presented to confirm the design and have been compared with the results already published. [ABSTRACT FROM AUTHOR]

Autonomous driving decision is a critical component of automatic driving system, which informs and updates the unmanned vehicle of object movements. However, end‐to‐end autonomous driving decision is still a great challenge due to the different scales of traffic target in the wild dynamic traffic scenes. To solve these problems, this paper proposes a novel model with attention mechanism and spatiotemporal features extraction. Specifically, for the important spatial information of traffic targets with scale differences, the spatial dimensions of height H, width W and channel C are independent of each other to build sparse spatial attention map. Moreover, different sparse masks are trained for spatial network by pruning elements of feature maps at the end of each block of backbone, which improves the accuracy of the two subnetworks of spatial network by 2.3% and 3.9%, respectively. Then the extracted spatial information is introduced jointly to the time sequence network with previous speed as input to obtain the vehicle steering angle and speed. Experiments on public virtual datasets show that the prediction accuracy of the model reaches 85.8%. Compared with other state‐of‐the‐art models, our model increased by 4.8% and 2.2%, respectively. [ABSTRACT FROM AUTHOR]

In the field of automatic driving, identifying vehicles and pedestrians is the starting point of other automatic driving techniques. Using the information collected by the camera to detect traffic targets is particularly important. The main bottleneck of traffic object detection is due to the same category of targets, which may have different scales. For example, the pixel‐level of cars may range from 30 to 300 px, which will cause instability of positioning and classification. In this paper, a multi‐dimension feature pyramid is constructed in order to solve the multi‐scale problem. The feature pyramid is built by developing a U‐shaped module and using a cascade‐method. In order to verify the effectiveness of the U‐shaped module, we also designed a new one‐shot detector U‐FPNDet. The model first extracts the basic feature map by using the basic network and constructs the multi‐dimension feature pyramid. Next, a pyramid pooling module is used to get more context information from the scene. Finally, the detection network is run on each level of the pyramid to obtain the final result by NMS. By using this method, a state‐of‐the‐art performance is achieved on both detection and classification on commonly used benchmarks. [ABSTRACT FROM AUTHOR]

Background: Visuo-spatial neglect (VSN) is generally assessed with neuropsychological paper-and-pencil tasks, which are often not sensitive enough to detect mild and/or well-compensated VSN. It is of utmost importance to develop dynamic tasks, resembling the dynamics of daily living., Objective: A simulated driving task was used to assess (1) differences in performance (i.e., position on the road and magnitude of sway) between patients with left- and right-sided VSN, recovered VSN, without VSN, and healthy participants; (2) the relation between average position and VSN severity; and (3) its diagnostic accuracy in relation to traditional tasks., Methods: Stroke inpatients were tested with a cancellation task, the Catherine Bergego Scale and the simulated driving task., Results: Patients with left-sided VSN and recovered VSN deviated more regarding position on the road compared to patients without VSN. The deviation was larger in patients with more severe VSN. Regarding diagnostic accuracy, 29% of recovered VSN patients and 6% of patients without VSN did show abnormal performance on the simulated driving task. The sensitivity was 52% for left-sided VSN. Right-sided VSN was not well detected, probably due to the asymmetric layout., Conclusions: Based on these results, the simulated driving task should not be the only task to assess VSN, especially in its current form. Given the heterogenic nature of VSN, the assessment should always consist of several tasks varying in nature and complexity and include a dynamic task to detect mild and/or recovered VSN. A symmetric design should be used when designing novel tasks to assess right-sided VSN., (© 2018 The Authors. Journal of Neuropsychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Few if any sanctions have been imposed on doctors for inappropriate certification of fitness to drive. This paper reviews a recent Tribunal decision on inappropriate certification of fitness to drive. This paper reviews the facts of the case of Medical Board of Australia v Andrew and discusses the legal implications for doctors who certify patients as fit to drive. The paper offers a necessary and timely warning that the Medical Board sought harsh sanctions, more severe than were imposed by the Tribunal, but that the Tribunal also felt compelled to impose 'general deterrence' to prevent inappropriate certification. The paper emphasises the need for scrupulous attention to detail and an absolute need for circumspection when assessing patients who claim to be seizure free, or who minimise impairment from a range of other conditions, insisting on clearance to be able to drive [ABSTRACT FROM AUTHOR]

This paper presents a novel switching controller incorporated with backlash and friction compensations, which is utilized to achieve speed synchronization among multi-motor and load position tracking. The proposed controller consists of two parts: synchronization and tracking control in contact mode and robust control in backlash mode, where a function characterizing whether backlash occurs is used for switching between two modes. Using the proposed switching controller, several control objectives are achieved. Firstly, the coupling problem of speed synchronization and load tracking in contact mode is addressed by introducing a switching plane. Secondly, based on the switching plane, an improved prescribed performance function is introduced to attain load tracking with prescribed performances, and L ∞ performance of speed synchronization is guaranteed by initialization method, maintaining the transient performance of synchronization behavior. Thirdly, the lumped uncertain nonlinearity including friction and other uncertain functions is compensated by Chebyshev neural network in contact mode. Furthermore, a robust control is adopted in backlash mode to make system traverse backlash at an exponential rate and simultaneously eliminate low-speed crawling phenomenon of LuGre friction. Finally, comparative simulations on four-motor driving servo system are provided to verify the effectiveness and reliability. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Currently, traffic sign recognition techniques have been brought into the assistive driving of automobiles. However, small traffic sign recognition in real scenes is still a challenging task due to the class imbalance issue and the size limit of the traffic signs. To address the above issues, a feature‐enhanced hybrid attention network is proposed based on YOLOv5s for a small, fast, and accurate traffic sign detector. First, a series of online data augmentation strategies are designed in the preprocessing module for the model training. Second, the hybrid channel and spatial attention module CSAM are integrated into the backbone for a better feature extraction ability. Third, the channel attention module CAM is used in the detection head for a more efficient feature fusion ability. To validate the approach, extensive experiments are conducted based on the Tsinghua‐Tencent 100K dataset. It is found that the novel method achieves state‐of‐the‐art performance with only negligible increases in the model parameter and computational overhead. Specifically, the mAP@0.5$mAP@0.5$, parameters, and FLOPs are 85.8%, 7.13 M, and 16.1 G, respectively. [ABSTRACT FROM AUTHOR]

Background: Population ageing will lead to an increase in the number of older drivers. The ageing process can affect older adults' driving ability. Nurses can play a major role in identifying potentially at‐risk drivers and providing guidance about driving safety and cessation, but their role remains somewhat unknown. Objective: This scoping review aims to present the scientific literature in relation to the nursing profession in the field of road safety. Design: CINAHL and PubMed databases were used to identify quantitative, qualitative, mixed methods or clinical practice guideline articles that referred to the role of nurses in older people's road safety and were published in English or French languages between January 1990 and August 2020. Ten (10) articles met the inclusion criteria and were analysed. Results: Analysis of included articles revealed one main theme: Nurses' and NPs' roles in the mobility continuum. Results showed that nurses and nurse practitioners (NPs) often see older drivers in their clinical practice and that they have the competencies to screen and assess their fitness to drive. They are well positioned to discuss age‐related changes, fitness to drive and a driving retirement plan with their older patients, but they are not confident when addressing this issue. Few older adults have discussed their driving abilities with healthcare professionals, but they would be willing to discuss this if the subject were brought up. Conclusions: This scoping review highlighted the paucity of research addressing the role of the nurse in road safety. More research is needed to adequately document this role. [ABSTRACT FROM AUTHOR]

Abstract: Assessing fitness to drive in applicants with a historical or current substance use disorder presents a specific clinical challenge. The Australian guidelines require evidence of remission and absence of cognitive change when considering applications for re‐licensing driver or individuals applying to reengage in safety‐sensitive work. This paper reviews some of the clinical and biochemical indicators that determine whether a particular person is in ‘remission’ and meets the criteria for return to driving or other safety‐sensitive occupation. It provides an overview of the challenges in establishing an evidence‐based approach to determining fitness for safety critical activities. There is no internationally accepted definition of ‘remission’. Review of the literature and examination of assessment protocols from other national jurisdictions are available for alcohol and the more important drugs of interest in road safety. Assessing fitness to drive when there is a history of substance misuse and/or substance use disorders is a complex issue that requires assessment of biomarkers, clinical findings and clinical assessment before the person returns to driving. We propose that hair testing provides a reliable and reproducible way to demonstrate remission and provide cost‐effective monitoring. Standardised psychological tests could provide a reproducible assessment of the cognitive effects of drug use and suitability to resume driving. We recommend that AustRoads amend the national guidelines to reflect an evidence‐based approach to assessing fitness to drive after conviction for offences related to alcohol and drug use. [ABSTRACT FROM AUTHOR]

The paper deals with instructions in a specific instructional setting, in which a driver learns to drive on a race circuit, assisted by a coach helping him to improve his performance and speed. This setting opens up both original issues for the study of learning to drive a car and general issues concerning the praxeological, multimodal, and sequential analysis of instructions in an ethnomethodological and conversation analytic (EMCA) perspective. On the basis of a systematic analysis of the coach's instructions and the driver's responses, the paper reveals the situated methodic production and understanding of these instructions and their organization in sequences and series of sequences. Training to drive at high speed appears as a perspicuous setting for investigating the ecology and the indexicality of instructed action, its temporal features, and the way these feature affect sequentiality—in particular action formation and sequence organization. [ABSTRACT FROM AUTHOR]

This paper proposes a personalized output‐feedback path‐following control strategy for automated vehicles. A personalized path generation approach is created to obtain the expected paths of drivers with different driving styles. Historical driving data and road features are used to calculate the waypoints that characterize driving styles. Then, a robust H∞ output‐feedback controller is designed to follow the generated paths. A solution based on matrix partition is provided to compute the output‐feedback control gains without using the vehicle lateral velocity signal, which facilitates the practical implementation of the designed controller. The proposed driving strategy is validated in CarSim® simulations and scaled car experiments. The results show the personalized paths can match with drivers' driving styles and can be tracked by the designed output‐feedback path‐following controller. [ABSTRACT FROM AUTHOR]

Background: Road Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low‐ and middle‐income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high‐income countries have successfully reduced RTI by using a public health approach and implementing evidence‐based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high‐income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge. Objectives: The objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels. Search Methods: The EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019. Selection Criteria: The interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post‐crash pre‐hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non‐fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes. Data Collection and Analysis: The EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case‐control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2. Main Results: The EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post‐crash pre‐hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non‐fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety‐six percent of the studies were reported from high‐income countries (HIC), 4.5% from upper‐middle‐income countries, and only 1.4% from lower‐middle and low‐income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality. Authors' Conclusions: The EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence‐synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions. [ABSTRACT FROM AUTHOR]

There are continuing concerns about the experiences offered to older adolescents being looked after (‘in care’) in the UK and, especially, to care leavers. Questions are asked about the limitations of State care compared with normal family life. This paper reports on an initiative to provide driving lessons to a group of six young men living in residential homes in one city. It links with resilience theory – how individuals can have relatively good outcomes despite early adversity. A qualitative study was undertaken to explore the effects of the initiative, including individual interviews with young men, heads of homes in which they lived and children's services managers. The overall results indicated that the initiative was very worthwhile. The lessons were a significant part of young people's lives. Possible effects on young people were divided into personal, instrumental and social. Benefits were reported from all parties concerning young people's self‐esteem and self‐confidence, as well as in forging close relationships with supportive adults. Driving would not be seen as a panacea for complex personal histories and structural problems, yet this small experiment suggests that driving lessons could be of disproportionate benefit and there is a moral obligation to provide them in any case. [ABSTRACT FROM AUTHOR]

ETRI's Co-Pilot project is aimed at the development of an automated vehicle that cooperates with a driver and interacts with other vehicles on the road while obeying traffic rules without collisions. This paper presents a core block within the Co-Pilot system; the block is named "Co- Pilot agent" and consists of several main modules, such as road map generation, decision-making, and trajectory generation. The road map generation builds road map data to provide enhanced and detailed map data. The decision-making, designed to serve situation assessment and behavior planning, evaluates a collision risk of traffic situations and determines maneuvers to follow a global path as well as to avoid collisions. The trajectory generation generates a trajectory to achieve the given maneuver by the decision-making module. The system is implemented in an open-source robot operating system to provide a reusable, hardware-independent software platform; it is then tested on a closed road with other vehicles in several scenarios similar to real road environments to verify that it works properly for cooperative driving with a driver and automated driving. [ABSTRACT FROM AUTHOR]

The significance of occupational choice, in particular those that involve driving, has not yet been studied or investigated in relation to serial murder. This paper, which adopts a case study approach, attempts to shed light on how driving as an occupation may instrumentally influence the offending behaviour of one North American serial murderer-Dennis Rader. Attention is given towards how spending such significant amounts of time driving may have held deep psychological importance for Rader, with regard to the development of his offending-oriented fantasies. In particular, the 'offending space model' is used to examine the relationship between 'thinking and doing'. In providing this micro-level analysis, we suggest that transient oriented occupations provide a rich array of practical advantages that may aid serial murderers in avoiding detection, whilst also holding deep psychological influence in the nurturing and development of their fantasies. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive multimodal in-vehicle information system (AMiVIS) for safe driving. The proposed system first filters input information based on the priority and driving situation, to effectively manage input information and then intelligently provide the information to the driver, and then interacts with the driver using an adaptive multimodal interface by considering both driving workload and the driver's cognitive reaction. This system consists of four stages: information-priority determination, driving-situation awareness, optimized-workload based modality selection, and driver's cognitive-reaction based modality-level control. It was shown experimentally that the proposed system can promote driver safety and enhance information understanding by filtering the input information. Also the system can reduce the driving workload by selecting the proper modality and its level. In addition, an analysis of subjective questionnaires regarding the proposed system shows that more than 85% of the respondents were satisfied with it. The proposed system is expected to provide prioritized information through a modality that is very easy to understand. [ABSTRACT FROM AUTHOR]

Numerous scientific studies show that risk factors can interact to synergistically increase the likelihood of certain adverse and life-threatening outcomes. Yet, the extent to which individuals know that specific risk factor combinations present 'synergistic risks' is unclear and little is known about the determinants of such knowledge. This is largely because epistemological progress concerning this topic has been frustrated by a reliance on metrics that have latterly been judged to be of questionable validity. To address this issue, this paper presents two studies that assess an alternative approach (i.e., risk model judgements) which requires respondents to judge the risk for a factor combination relative to, rather than in isolation from, the risk attributable to each constituent factor. Results from both studies indicate that risk model judgements overcome the drawbacks of traditional metrics. More importantly, the results provide epistemological insights into what can determine whether an individual understands that a factor combination presents a synergistic risk; these determinants include experiential and intuitive insights into the effects of combining specific risk factors, domain-specific judgemental experience and exposure to effective learning opportunities. These findings can be utilized in interventions aimed at helping individuals to make better decisions concerning multiple risk factors. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of China's economy and the growth of people's travel demand, road transport industry has developed rapidly. However, at the same time, the traffic safety problems caused by the accelerated growth of transport vehicles are becoming more and more serious. Driver's driving behavior is closely related to traffic safety. With the help of data mining technology, we can make full use of the system data collected by road transportation industry to identify the bad driving behavior of transport vehicles and get seven secondary indicators of traffic safety evaluation indicators: overspeed, accelerated, decelerated, fatigue driving, idle preheating, overspeed idling, and flameout taxiing. According to the indicators, the occurrence times and duration are further subdivided into 14 three‐level indicators. Then, by using the analytic hierarchy process, through the construction of comparison judgment matrix and consistency checking, the corresponding weight of each evaluation index is determined with the help of the entropy method, and the formation safety of transport vehicles is evaluated. The results show that drivers with more fatigue driving, acceleration, and deceleration have higher driving risk. Transportation enterprises and relevant departments need to focus on their monitoring. The combination of data mining, data analysis algorithm, and traffic safety field in machine learning provides a demonstration for the study of safe driving behavior of transport vehicles in China and has a guiding significance for the supervision and training of drivers' driving behavior and reducing road safety risks. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) provide a promising alternative to conventional engine-powered vehicles, with less emission and better fuel economy. This study proposes a hierarchical control for a power-split HEV over a driving cycle, featuring pre-trip optimisation and en-route speed adaption. Constraints including vehicle powertrain boundaries, road gradients, and speed limits, are taken into consideration. In the first stage, the HEV operating conditions, including the optimal vehicle SOC, speed profiles, and total driving time, are generated for the entire trip before departure. Based on the pre-trip results, the second stage adapts the vehicle speed for a short horizon when driving, while taking the safety spacing to the preceding vehicle into consideration, which acts as an indicator of actual traffic conditions and guarantees safe driving. Both optimisations are conducted under the distance domain for realising localisation in the optimal speed profile due to frequent changes in traffic conditions. An estimation of distribution algorithm is used to run the simulation so that the feasibility, robustness, and effectiveness of the proposed hierarchical control can be demonstrated. [ABSTRACT FROM AUTHOR]

Planning for autonomous vehicles to merge into high‐density traffic flows within limited mileage is quite challenging. Specifically, the driving trajectory will inevitably have intersections with other vehicles whose driving intentions can't be directly observed. Herein, a two‐stage algorithm framework that is decomposed into the longitudinal and lateral planning processes for online merging planning is proposed. An improved particle filter is used to estimate the driving models of surrounding vehicles for predicting their future driving intentions. Based on Monte Carlo tree search (MCTS), different action spaces are evaluated for longitudinal merging gap selection and lateral interactive merging operation, while heuristic pruning is used to reduce the computation cost. Moreover, the coefficients related to the driving styles are introduced, and their influences on merging performance are analyzed. Finally, the proposed algorithm is implemented in a two‐lane simulation environment. The results show that the proposal has outperformed other baseline methods. [ABSTRACT FROM AUTHOR]

Urban vehicle trajectory prediction positively alleviates traffic congestion, avoids traffic accidents, and optimizes the urban transportation system. Since taxi trajectories are influenced by the driving intention, it is significant to consider the Points of Interest (POI) as the spatial features for trajectory prediction. A Knowledge Graph Convolutional Network Long Short‐Term Memory (KGCN‐LSTM) model is proposed here to improve the accuracy and robustness of trajectory prediction. POI information is considered as the prior‐knowledge of the trajectory by the Graph Convolutional Network (GCN). Under multiple comparison experiments, Shopping POI gains the highest positive effect weight of 15% in holidays, and Hospital POI gains the highest weight of 16% in working days. In holidays, higher accuracy and robustness are achieved compared with benchmarks when performing the KGCN‐LSTM model with POI of shopping, food, life service, scenic spots, and entertainment classes, while the performance is not improved with the rest of the POI classes. In working days, higher accuracy and stronger robustness are achieved compared with benchmarks when performing the KGCN‐LSTM model with POI of hospital, life service, and exercise. While the performance is not improved with the rest of the POI classes. [ABSTRACT FROM AUTHOR]

In December 2022, the new guideline for evaluating the effect of psychotropic drugs on the performance to drive a motor vehicle was issued by the Ministry of Health, Labour and Welfare (MHLW) and implemented in Japan. Of the safety information, information on the influence of medications on driving performance is particularly important because it can be relevant to the social functioning of patients. In principle, the package inserts of medications are designed based on evidence and provide precautions regarding the operation of heavy machinery such as automobiles in Japan, the United States, and Europe. The effects of medications on driving performance are generally evaluated in a tiered approach involving nonclinical and clinical studies. Because of the wide variety of functional domains involved in automobile driving, the selection of evaluation methods for a given medication depends on their characteristics, which is a complicated method. Therefore, to evaluate the effects of psychotropic drugs on driving performance efficiently and appropriately, we developed the MHLW guideline that specifically defines the evaluation methods used in pharmacological studies, the neuropsychological tests used in pharmacodynamic studies, and the situations in which driving studies are necessary. Regarding the planning of appropriate drug development strategies, we review the background of the MHLW guideline and its differences from the US Food and Drug Administration (FDA) guideline. [ABSTRACT FROM AUTHOR]

Building on a corpus of 70 hours of German driving lessons, this paper studies the use of declaratives vs. imperatives for instruction. It shows how these linguistic resources are adapted to different praxeological, temporal and participant‐related environments. Declaratives are used for first instructions, task‐setting and post‐trial discussions. They exhibit complex syntax and do not call for immediate compliance. Their high degree of explicitness conveys how the action is to be carried out. Imperative instructions overwhelmingly correct ongoing actions of students or respond to their failure to produce expected actions. They exhibit minimal argument structure. They are reminders which presuppose that the student monitors the scene and can perform the action unproblematically. They index that requests have to be complied with immediately or even urgently. [ABSTRACT FROM AUTHOR]

Abstract: Background: Driving ability is a key function for the majority of patients with multiple sclerosis (MS) to help maintain daily interactions. Both physical and cognitive disability, as well as treatments, may affect the ability to drive. Spasticity is a common symptom associated with MS, and it may affect driving performance either directly or via the medications used to treat it. In this article, we review the evidence relating the antispasticity medicine, Δ9‐tetrahydrocannabinol:cannabidiol (THC:CBD) oromucosal spray (Sativex®), and its potential impact on driving performance. Methods: Articles were identified by searching PubMed from 1/1/2000 to 30/6/2017 using a specified list of search terms. The articles identified using these search terms were augmented with relevant references from these papers and other articles known to the authors. Results: The results from THC:CBD oromucosal spray driving studies and real‐world registries did not show any evidence of an increase in motor vehicle accidents associated with THC:CBD oromucosal spray. The majority of patients reported an improvement in driving ability after starting THC:CBD oromucosal spray, and it was speculated that this may be related to reduced spasticity and/or better cognitive function. It should be noted that THC blood levels are significantly lower than the levels associated with recreational use of herbal cannabis. Conclusions: THC:CBD oromucosal spray was shown not to impair driving performance. However, periodic assessment of patients with MS driving ability is recommended, especially after relapses and changes in treatment. Blood THC measurements might be above authorized thresholds for some countries following administration of THC:CBD oromucosal spray, thus specific knowledge of each country's driving regulations and a medical certificate are recommended. [ABSTRACT FROM AUTHOR]

Although transportation safety has greatly improved over the past 2 decades, motor vehicle crash injuries remain a leading cause of morbidity and mortality, particularly among young drivers. Driver errors and behaviors such as speeding and distraction contribute disproportionately to crashes among inexperienced novices, who develop safe driving judgment only with substantial driving experience, commonly described as the "young driver problem." Research on young drivers has applied a range of research methods, including analyses of national archival data (mainly from police reports), crash analyses, observation of driver behavior, surveys of driver behavior and dispositions, and experimental research on driver behavior and vehicle crash worthiness. Prominent research questions regarding young driver safety include what and how do novices learn to drive safely, what are the predictors of young driver crashes, what is the variability and overtime trajectories of young driver performance and outcomes, and to what extent is the young driver problem due mainly to average population risk or high-risk groups? Current research on young drivers is complicated by small sample sizes, relatively rare events, high within and between group variability, missing data, the need to estimate exposure, and the lack of longitudinal and experimental designs, problems that require complex analytic methods. In this paper, we provide an overview of driving research methods, examples of research addressing the young driver problem, and examples of statistical collaboration on young driver research, focusing particularly on estimating prediction of crash risk and estimating variability in young driver performance and outcomes. [ABSTRACT FROM AUTHOR]

Policy PointsPublic health science regarding alcohol consumption and problems, alcohol's role in equity and social justice, and identification of effective policy interventions has grown steadily stronger in the past 30 years.Progress on effective alcohol policies has stalled or gone backward in the United States and much of the world.Because alcohol influences at least 14 of the 17 sustainable development goals, as well as more than 200 disease and injury conditions, reducing alcohol problems should offer a platform for collaboration across public health silos but will require that public health itself respect and follow its own science. [ABSTRACT FROM AUTHOR]

With the development of vehicle telematics and data mining technology, usage‐based insurance (UBI) has aroused widespread interest from both academia and industry. The extensive driving behavior features make it possible to further understand the risks of insured vehicles, but pose challenges in the identification and interpretation of important ratemaking factors. This study, based on the telematics data of policyholders in China's mainland, analyzes insurance claim frequency of commercial trucks using both Poisson regression and several machine learning models, including regression tree, random forest, gradient boosting tree, XGBoost and neural network. After selecting the best model, we analyze feature importance, feature effects and the contribution of each feature to the prediction from an actuarial perspective. Our empirical study shows that XGBoost greatly outperforms the traditional models and detects some important risk factors, such as the average speed, the average mileage traveled per day, the fraction of night driving, the number of sudden brakes and the fraction of left/right turns at intersections. These features usually have a nonlinear effect on driving risk, and there are complex interactions between features. To further distinguish high−/low‐risk drivers, we run supervised clustering for risk segmentation according to drivers' driving habits. In summary, this study not only provide a more accurate prediction of driving risk, but also greatly satisfy the interpretability requirements of insurance regulators and risk management. [ABSTRACT FROM AUTHOR]

Purpose/Background: Firearm injury, particularly self‐directed, is a major source of preventable morbidity and mortality among older adults. Older adults are at elevated risk of serious illness, cognitive impairment, and depression—all known risk factors for suicide and/or unintentional injury. Healthcare providers are often the first to identify these conditions and, although they commonly deliver safety guidance to such patients, little is known about how they approach firearm safety conversations with older adults. Methods: We conducted semi‐structured interviews with healthcare providers who care for older adults (November 2020–May 2021). We used inductive and deductive thematic analyses to develop themes. We present themes and representative quotes from our analysis. Results: We interviewed 13 healthcare providers who regularly care for older adult firearm owners. Emergent themes were: circumstances that prompt firearm safety conversations; strategies for addressing firearm safety in routine and acute circumstances; barriers to addressing firearm safety; and available or desired resources. Conclusion: Planning for firearm safety should occur "early and often" as part of a longitudinal relationship with older adult patients. Age‐related safety issues such as driving are regularly addressed with older adult patients, likely because there are standard processes and established pathways. Establishing processes and provider/ patient resources would help improve provider efficacy to address firearm safety and relinquishment for older adult firearm owners. Integrating firearm safety conversations into routine encounters (e.g., Medicare Annual Wellness Visit, problem‐focused visits) templates could be a promising initial step but resources for follow‐up to the firearm screening must be available to both provider and patient. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study is to examine age, period and birth cohort trends in the prevalence of any alcohol‐related risky behaviour and to compare these trends between men and women. Design and setting: We used an age–period–cohort analysis of repeated cross‐sectional survey data from the Australian National Drug Strategy Household Survey from 2001 to 2016. Participants: Participants were 121 281 people aged 14–80 years who reported consuming alcohol in the past 12 months. Measurements: Any risky behaviour undertaken while under the influence of alcohol in the past 12 months (e.g. operating a motor vehicle) was measured: male or female. Findings Controlling for age and cohort, cubic spline models showed that any alcohol‐related risky behaviour declined with time among participants who consumed alcohol [2016 versus 2007 rate ratio (RR) = 0.80, 95% confidence interval (CI) = 0.76–0.84]. Risky behaviour peaked in the 1954 birth cohort (1954 versus 1971 RR = 1.42, 95% CI = 1.30–1.55) and then steadily declined with more recent birth cohorts (2002 versus 1971 RR = 0.32, 95% CI = 0.27–0.39). Risky behaviour peaked at age 21 years, followed by steady decline and stabilization at approximately age 70 years. Males were overall twice as likely as females to report alcohol‐related risky behaviour (RR = 2.10, 95% CI = 1.84–2.39), but this effect was smaller in cohorts born after 1980 [1980 prevalence rate ratios (PRR) = 2.09, 95% CI = 1.81–2.43; 2002 PRR = 1.31, 95% CI = 1.03–1.68]. Conclusions: Alcohol‐related risky behaviour in Australia has declined generally since 2001, with rates for recent cohorts having the sharpest decline. Risky behaviour remains most prevalent in young adults, and the male–female gap in risky behaviour is closing for more recent birth cohorts. These trends are consistent with alcohol consumption trends observed in Australia and world‐wide. [ABSTRACT FROM AUTHOR]

The vehicle suspension system is considered to have tremendous potential in energy harvesting and to be applicable for improving the efficiency of energy use of the vehicle. A novel piezoelectric energy harvester (PEH) with noncontact magnetic force has the feature of low friction loss and is designed based on the vibration characteristic of the suspension system. The research comprises two main components: the motion conversion component and the energy conversion component. A screw‐nut mechanism that can convert linear vibration between the vehicle body and the wheel into rotational motion is used as the motion conversion component. The energy conversion component is a core component of the PEH; its function is to convert rotational energy into electrical energy through noncontact magnetic force and piezoelectric effect. The magnetic excitation model's credibility and the modeling method's feasibility are verified by experiment and simulation. Through vehicle field tests, the influence of the driving speed, road surface roughness, and cargo state on the harvested power is obtained. Driving on a random road, the maximum of the generated power is 24.28 W, at 60 km/h, under the laden state. Driving on a pulse road, the harvested power is 3346 W, at 30 km/h, under the unladen state. It is indicated that the PEH is a promising and practical option for harvesting energy for the vehicle's suspension system. [ABSTRACT FROM AUTHOR]

The authors comment on the revised advise issued by pharmaceutical company AbbVie regarding the duration of the effects of isoflurane on cognition and psychomotor functions of patients, particularly in driving. They analyze previous reports that might have influenced the modifications in the product specification of AbbVie for its isoflurane. The authors also reveal research proving that psychomotor functions return 24 hours after inhalation.

Vehicles with SAE Level 2 automated features are already in active use on the road, and vehicles with Level 3 or 4 will be with us soon. Although the vehicles provide support for longitudinal and lateral control, partially automated driving experience is sometimes more demanding than manual driving. However, the effects of automated driving on workload in naturalistic conditions have not been extensively investigated, as most studies have been undertaken in driving simulators. This study aims to extend the current understanding about workload in partially automated driving on public roads. Drivers' perceived workload was assessed after conducting manual and automated driving activities using a small sample (N = 8). They performed driving tasks in three contemporary vehicles with SAE Level 2 features, in highway and urban environments. The comparative findings revealed that drivers' perceived workload was higher in partially automated driving than manual driving. Furthermore, perceived workload was higher in urban environments than highway environments and in less experienced drivers than more experienced drivers. Although the findings may need to be interpreted with caution due to the small sample size, they provide a future research agenda that can be built upon. [ABSTRACT FROM AUTHOR]