Assessing advanced driver assistance systems in police vehicles under demanding conditions.

Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers' safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers' driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers' driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.

A driving simulation study was conducted to assess the effect of ADAS in police vehicles under various driving and non-driving related task conditions. The results can help vehicle manufacturers improve the design and usability of ADAS in police cars.

A novel approach for predicting usability of upper limb prostheses.

Limb amputation can lead to significant functional challenges in daily activities, prompting amputees to use prosthetic devices (PDs). However, the cognitive demands of PDs and usability issues have resulted in user rejections. This study aimed to create a Human Performance Model for Upper-Limb Prosthetic Devices (HPM-UP). The model used formulations of learnability, error rate, memory load, efficiency, and satisfaction to assess usability. The model was validated in an experiment with 30 healthy participants using a bypass prosthetic device. Findings indicated that the HPM-UP successfully predicted the usability of prosthetic devices, aligning with human subject data. This research proposes a quantitative approach to predict upper limb prosthetic device usability by quantifying each dimension and computationally connecting them. The model, available on Github and executable with Rstudio, could enable clinicians to assess and analyze the human performance of various commercial prostheses, aiding in recommending optimal devices for patients.

Cognitive workload classification of law enforcement officers using physiological responses.

Motor vehicle crashes (MVCs) are a leading cause of death for law enforcement officers (LEOs) in the U.S. LEOs and more specifically novice LEOs (nLEOs) are susceptible to high cognitive workload while driving which can lead to fatal MVCs. The objective of this study was to develop a machine learning algorithm (MLA) that can estimate cognitive workload of LEOs while performing secondary tasks in a patrol vehicle. A ride-along study was conducted with 24 nLEOs. Participants performed their normal patrol operations while their physiological responses such as heartrate, eye movement, and galvanic skin response were recorded using unobtrusive devices. Findings suggested that the random forest algorithm could predict cognitive workload with relatively high accuracy (>70%) given that it was entirely reliant on physiological signals. The developed MLA can be used to develop adaptive in-vehicle technology based on real-time estimation of cognitive workload, which can reduce the risk of MVCs in police operations.

Automated vehicles for older adults with cognitive impairment: a survey study.

As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square - structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults' perception of AVs including education and adaptive driving simulation-based training.Practitioner summary: This study investigated the perception of older adults and other stakeholders regarding AVs. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided guidelines to improve older adults' perception of AVs.

Assessing workload in using electromyography (EMG)-based prostheses.

Using prosthetic devices requires a substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross-validation were applied to select the most important features and find the optimal models. Classification accuracy, the area under the receiver operation characteristic curve (AUC), precision, recall, and F1 scores were calculated to compare the models' performance. The findings suggested that task performance measures, pupillometry data, and CPM outcomes, combined with the naïve bayes (NB) and random forest (RF) algorithms, are most promising for classifying cognitive workload. The proposed algorithms can help manufacturers/clinicians predict the cognitive workload of future EMG-based prosthetic devices in early design phases.Practitioner summary: This study investigated the use of machine learning algorithms for classifying the cognitive workload of prosthetic devices. The findings suggested that the models could predict workload with high accuracy and low computational cost and could be used in assessing the usability of prosthetic devices in the early phases of the design process.Abbreviations: 3d: 3 dimensional; ADL: Activities for daily living; ANN: Artificial neural network; AUC: Area under the receiver operation characteristic curve; CC: Continuous control; CPM: Cognitive performance model; CPM-GOMS: Cognitive-Perceptual-Motor GOMS; CRT: Clothespin relocation test; CV: Cross validation; CW: Cognitive workload; DC: Direct control; DOF: Degrees of freedom; ECRL: Extensor carpi radialis longus; ED: Extensor digitorum; EEG: Electroencephalogram; EMG: Electromyography; FCR: Flexor carpi radialis; FD: Flexor digitorum; GOMS: Goals, Operations, Methods, and Selection Rules; LDA: Linear discriminant analysis; MAV: Mean absolute value; MCP: Metacarpophalangeal; ML: Machine learning; NASA-TLX: NASA task load index; NB: Naïve Bayes; PCPS: Percent change in pupil size; PPT: Purdue Pegboard Test; PR: Pattern recognition; PROS-TLX: Prosthesis task load index; RF: Random forest; RFE: Recursive feature selection; SHAP: Southampton hand assessment protocol; SFS: Sequential feature selection; SVC: Support vector classifier.

A novel autonomous vehicle interface for older adults with cognitive impairment.

The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs' usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.

Effects of unreliable automation, non-driving related task, and takeover time budget on drivers' takeover performance and workload.

The objective of this study was to assess the effects of unreliable automation, non-driving related tasks (NDRTs), and takeover time budget (TOTB) on drivers' takeover performance and cognitive workload when faced with critical incidents. Automated vehicles are expected to improve traffic safety. However, there are still some concerns about the effects of automation failures on driver performance and workload. Twenty-eight drivers participated in a driving simulation study. The findings suggested that drivers require at least 8 s of TOTB to safely take over the control of the vehicle. In addition, drivers exhibited safer takeover performance under the conditionally automated driving situation than negotiating the critical incident in the manual driving condition. The results of drivers' cognitive workload were inconclusive, which might be due to the individual and recall biases in subjective measures that could not capture subtle differences in workload during takeover requests.Practitioner Summary: A driving simulation study was conducted to assess the effect of unreliable automation, non-driving related tasks, and different takeover time budgets on drivers' performance and workload. The results can provide guidelines for vehicle manufacturers to improve the design of automated vehicles.

Effect of Secondary Tasks on Police Officer Cognitive Workload and Performance Under Normal and Pursuit Driving Situations.

OBJECTIVE: The objective of this study was to assess the effects of single and multiple secondary tasks on officers' performance and cognitive workload under normal and pursuit driving conditions. BACKGROUND: Motor vehicle crashes are a leading cause of police line of duty injuries and deaths. These crashes are mainly attributed to the use of in-vehicle technologies and multi-tasking while driving. METHOD: Eighteen police officers participated in a driving simulation experiment. The experiment followed a within-subject design and assessed the effect of single or multiple secondary tasks (via the mobile computer terminal (MCT) and radio) and driving condition (normal vs. pursuit driving) on officers' driving performance, cognitive workload, and secondary task accuracy and reaction time. RESULTS: Findings suggested that police officers are protective of their driving performance when performing secondary tasks. However, their workload and driving performance degraded in pursuit conditions as compared to normal driving situations. Officers experienced higher workload when they were engaged with secondary tasks irrespective of the task modality or type. However, they were faster but less accurate in responding to the radio as compared to the MCT. CONCLUSION: Police officers experience high mental workload in pursuit driving situations, which can reduce their driving performance and accuracy when they are engaged in some secondary tasks. APPLICATION: The findings might be helpful for police agencies, trainers, and vehicle technology manufacturers to modify the existing policies, training protocols, and design of police in-vehicle technologies in order to improve police officer safety.

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology.

OBJECTIVE: This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology. BACKGROUND: HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored. METHOD: A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method. RESULTS: The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs. CONCLUSION: The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables. APPLICATION: The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.

Effects of levels of automation and non-driving related tasks on driver performance and workload: A review of literature and meta-analysis.

This study assessed the effects of different levels of automation and non-driving related tasks (NDRT) on driver performance and workload. A systematic literature review was conducted in March 2021 using Compendex, Google Scholar, Web of Science, and Scopus databases. Forty-five studies met the inclusion criteria. A meta-analysis was conducted and Cochrane risk of bias tool and Cochran's Q test were used to assess risk of bias and homogeneity of the effect sizes respectively. Results suggested that drivers exhibited safer performance when dealing with critical incidents in manual driving than partially automated driving (PAD) and highly automated driving (HAD) conditions. However, drivers reported higher workload in the manual driving mode as compared to the HAD and PAD conditions. Haptic, auditory, and visual-auditory takeover request modalities are preferred over the visual-only modality to improve takeover time. Use of handheld NDRTs significantly degraded driver performance as compared to NDRTs performed on mounted devices.

Effect of variable priority training on police officer driving performance and workload.

Motor vehicle crashes are a leading cause of police injuries and deaths in line of duty. These crashes have been mainly attributed to the use of in-vehicle technologies while driving. Police officers receive extensive training on driving skills; however, limited training is provided on the use of in-vehicle technologies. Variable priority training (VPT) is a computer-based training that has shown promising results in improving multi-tasking performance. Eighteen police officers participated in a driving simulation study to assess the effect of VPT on officers' performance and workload. Findings suggested that although VPT was effective in improving officers' performance in dual and multi-task simulations across the training sessions, this effect was not generally transferred to driving. However, the VPT might be effective for training of high-demand situations involving pursuit driving and multiple secondary tasks. The findings can be beneficial for police agencies to improve training protocols. Practitioner summary: A driving simulation study was conducted to assess the effect of a computer-based training approach on police officers' driving performance and cognitive workload. The findings suggested that the proposed training approach might be effective for training of high-demand situations involving pursuit driving and multi-tasking.

Physical and cognitive demands associated with police in-vehicle technology use: an on-road case study.

Motor vehicle crashes are a leading cause of police officers' deaths in line of duty. These crashes have been mainly attributed to officers' driving distraction caused by the use of in-vehicle technologies while driving. This paper presents a 3-h ride-along study of 20 police officers to assess the physical and cognitive demands associated with using in-vehicle technologies. The findings suggested that the mobile computer terminal (MCT) was the most frequently used in-vehicle system for the officers. In addition, officers perceived the MCT to significantly increase their visual, cognitive, and physical demands compared to other in-vehicle technologies. Evidence from electromyography and eye-tracking measures suggested that officers with more experience as a patrol officer and those who were working in more congested areas experienced higher cognitive workload. Furthermore, it was found that as the ride-along duration increased, there were indications of muscle fatigue in medial deltoid and triceps brachii muscles. Practitioner summary: This study assessed the impact of police in-vehicle technology use in an on-road case study. The findings provide new data and knowledge for police agencies and vehicle manufacturers to develop administrative measures and in-vehicle technology innovations to improve police officers' health and safety.

Effects of Mobile Computer Terminal Configuration and Level of Driving Control on Police Officers' Performance and Workload.

OBJECTIVE: The objective of this study was to assess police officers' performance and workload in using two mobile computer terminal (MCT) configurations under operational and tactical driving conditions. BACKGROUND: Crash reports have identified in-vehicle distraction to be a major cause of law enforcement vehicle crashes. The MCT has been found to be the most frequently used in-vehicle technology and the main source of police in-vehicle distraction. METHOD: Twenty police officers participated in a driving simulator-based assessment of driving behavior, task completion time, and perceived workload with two MCT configurations under operational and tactical levels of driving. RESULTS: The findings revealed that using the MCT configuration with speech-based data entry and head-up display location while driving improved driving performance, decreased task completion time, and reduced police officers' workload as compared to the current MCT configuration used by police departments. Officers had better driving but worse secondary task performance under the operational driving as compared to the tactical driving condition. CONCLUSION: This study provided an empirical support for use of an enhanced MCT configuration in police vehicles to improve police officers' safety and performance. In addition, the findings emphasize the need for more training to improve officers' tactical driving skills and multitasking behavior. APPLICATION: The findings provide guidelines for vehicle manufacturers, MCT developers, and police agencies to improve the design and implementation of MCTs in police vehicles considering input modality and display eccentricity, which are expected to increase officer and civilian safety.

Human factors in police mobile computer terminals: A systematic review and survey of recent literature, guideline formulation, and future research directions.

The objectives of this research were to: (1) identify Mobile Computer Terminal (MCT) human factors issues, (2) formulate guidelines and an enhanced MCT for improving interface design and implementation in police patrols, and (3) identify areas of future research to fill gaps in the literature. A systematic literature search was conducted leading to results categorized in four groups including: productivity, physical discomfort, interface usability, and driving distraction. Although MCT use has increased officer productivity, several usability issues need to be resolved. The MCT has also increased officer physical discomfort and distraction. MCT design and implementation guidelines that resolve human factors issues in police patrols were identified along with an enhanced design concept. Guidelines for MCT design were validated with an online survey completed by 81 police officers. Future research directions were proposed to recognize police officer needs and work context.

Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures.

There are about 44 million licensed older drivers in the U.S. Older adults have higher crash rates and fatalities as compared to middle-aged and young drivers, which might be associated with degradations in sensory, cognitive, and physical capabilities. Advanced driver-assistance systems (ADAS) have the potential to substantially improve safety by removing some of driver vehicle control responsibilities. However, a critical aspect of providing ADAS is educating drivers on their operational characteristics and continued use. Twenty older adults participated in a driving simulation study assessing the effectiveness of video-based and demonstration-based training protocols in learning ADAS considering gender differences. The findings revealed video-based training to be more effective than demonstration-based training in improving driver performance and reducing off-road visual attention allocation and mental workload. In addition, female drivers required lower investment of mental effort (higher neural efficiency) to maintain the performance relative to males and they were less distracted by ADAS. However, male drivers were faster in activating ADAS as compared to females since they were monitoring the status of ADAS features more frequently while driving. The findings of this study provided an empirical support for using video-based approach for learning ADAS in older adults to improve driver safety and supported previous findings on older adults' learning that as age increases there is a tendency to prefer more passive and observational learning methods.

Identification of task demands and usability issues in police use of mobile computing terminals.

Crash reports from various states in the U.S. have shown high numbers of emergency vehicle crashes, especially in law enforcement situations. This study identified the perceived importance and frequency of police mobile computing terminal (MCT) tasks, quantified the demands of different tasks using a cognitive performance modeling methodology, identified usability violations of current MCT interface designs, and formulated design recommendations for an enhanced interface. Results revealed that "access call notes", "plate number check" and "find location on map" are the most important and frequently performed tasks for officers. "Reading plate information" was also found to be the most visually and cognitively demanding task-method. Usability principles of "using simple and natural dialog" and "minimizing user memory load" were violated by the current MCT interface design. The enhanced design showed potential for reducing cognitive demands and task completion time. Findings should be further validated using a driving simulation study.

Effect of police mobile computer terminal interface design on officer driving distraction.

Several crash reports have identified in-vehicle distraction to be a primary cause of emergency vehicle crashes especially in law enforcement. Furthermore, studies have found that mobile computer terminals (MCTs) are the most frequently used in-vehicle technology for police officers. Twenty police officers participated in a driving simulator-based assessment of visual behavior, performance, workload and situation awareness with current and enhanced MCT interface designs. In general, results revealed MCT use while driving to decrease officer visual attention to the roadway, but usability improvements can reduce the level of visual distraction and secondary-task completion time. Results also suggest that use of MCTs while driving significantly reduces perceived level of driving environment awareness for police officers and increases cognitive workload. These findings may be useful for MCT manufacturers in improving interface designs to increase police officer and civilian safety.

Driver performance and attention allocation in use of logo signs on freeway exit ramps.

The objective of this research was to quantify the effects of driver age, ramp signage configuration, including number of panels, logo format and sign familiarity, on driver performance and attention allocation when exiting freeways. Sixty drivers participated in a simulator study and analysis of variance models were used to assess response effects of the controlled manipulations. Results revealed elderly drivers to demonstrate worse performance and conservative control strategies as compared to middle-aged and young drivers. Elderly drivers also exhibited lower off-road fixation frequency and shorter off-road glance durations compared to middle-aged and young drivers. In general, drivers adopted a more conservative strategy when exposed to nine-panel signs as compared to six-panel signs and were more accurate in target detection when searching six-panels vs. nine and with familiar vs. unfamiliar logos. These findings provide an applicable guide for agency design of freeway ramp signage accounting for driver demographics.

Enhanced Hazard Analysis and Risk Assessment for Human-in-the-Loop Systems.

OBJECTIVE: The objective of this study was to enhance the existing system hazard analysis (SHA) technique by introducing the concepts of human and automation reliability quantification as well as fuzzy classification of system risks. These enhancements led to formulation of a new overall system risk-reliability score. BACKGROUND: Many system safety analysis methods focus on individual physical component failure. Some human reliability analyses (HRA) consider human-automation interaction in determining system failure rates. There is no system safety analysis technique that quantifies the impact of human and automation reliability on the risk of hazard exposure. METHOD: Classification of the probability and severity of hazard exposure is typically made in terms of linguistic rather than numerical variables. Fuzzy sets are applicable for transforming linguistic classifications to numerical quantities. We focused on using fuzzy sets to define overlapping bands of system risk exposure with reference to the hazard risk categories defined in MIL-STD 882B. Fuzzy sets were also used for human-automated system reliability classification. RESULTS: Introduction of human and automation reliability assessment in the SHA allows for definition of a system risk-reliability modeling space. The enhanced SHA (E-SHA) technique yields a mishap risk index, which is projected based on a composite assessment of human-automated system reliability at the time of operation. The E-SHA was compared with one of the most advanced HRA techniques. CONCLUSION: The E-SHA technique supports broader safety control recommendations and provides comparable, if not more detailed, results than prior systems safety and HRA techniques.

The role of driver age in performance and attention allocation effects of roadway sign count, format and familiarity.

White-on-blue logo signs are used to inform drivers of food, gas, lodging, and attraction businesses at highway interchanges. In this study, 60 drivers were asked to look for food and attraction targets on logo signs while driving in a realistic freeway simulation. The objective of the study was to quantify effects of the number of sign panels (six vs. nine), logo familiarity (familiar vs. unfamiliar), logo format (text vs. pictorial), and driver age (young, middle, and elderly) on performance, attention allocation and target identification accuracy. Results revealed elderly drivers to exhibit worse performance in comparison to middle-age and young groups even though they adopted a more conservative driving strategy. There was no significant effect of the number of panels, logo familiarity, and logo format on driver performance or attention allocation. In target identification, drivers were more accurate with familiar or text-based panels appearing in six-panel signs.