On-road Study Research Articles

Assessing driver distraction from in-vehicle information system: an on-road study exploring the effects of input modalities and secondary task types

In-vehicle information system (IVIS) use is prevalent among young adults. However, their interaction with IVIS needs to be better understood. Therefore, an on-road study aims to explore the effects of input modalities and secondary task types on young drivers' secondary task performance, driving performance, and visual glance behavior. A 2 × 4 within-subject design was undertaken. The independent variables are input modalities (auditory-speech and visual-manual) and secondary task types (calls, music, navigation, and radio). The dependent variables include secondary task performance (task completion time, number of errors, and SUS), driving performance (average speed, number of lane departure warnings, and NASA-TLX), and visual glance behavior (average glance duration, number of glances, total glance duration, and number of glances over 1.6 s). The statistical analysis result showed that the main effect of input modalities is significant, with more distraction during visual-manual than auditory-speech. The main impact of secondary task types was also substantial across most metrics, aside from average speed and average glance duration. Navigation and music were the most distracting, followed by calls, and radio came in last. The distracting effect of input modalities is relatively stable and generally not moderated by the secondary task types, except radio tasks. The findings practically benefit the driver-friendly human–machine interface design, preventing IVIS-related distraction.

Does users’ experience and evaluation of level 3 automated driving functions predict willingness to use: Results from an on-road study

To ensure the successful deployment of Automated Vehicles (AVs), it is important to understand users’ acceptance and willingness to use. The majority of previous studies which have investigated Willingness to Use of automated functionalities in vehicles, have relied on users’ imagination of system operational features, without any actual hands-on experience. To close this research gap, we conducted an extensive questionnaire study with participants who had experienced either a Motorway (N = 294) or an Urban (N = 144) Automated Driving Function (ADF) in an on-road study using prototype vehicles, conducted as part of the large-scale European L3Pilot project. We investigated the impact of participants’ demographic characteristics, and their evaluation of the ADFs, on their Willingness to Use these ADFs again in the future. Findings revealed that age was a significant predictor but had a small effect, with younger users showing a higher Willingness to Use the Motorway ADF, but not the Urban ADF. Other user characteristics, such as safety drivers versus ordinary drivers, system familiarity, and gender, did not predict Willingness to Use, for either system. Factors related to users’ Feelings of Ease (e.g., perceived safety, fun, comfort, trust) and Perceived Usefulness of the system, were the most important predictors, with more positive ratings for these factors leading to a higher Willingness to Use, for both systems. Other factors that showed an effect on Willingness to Use include System Monitoring Requirements (i.e., willingness to engage in other tasks instead of monitoring the system), System Information (i.e., wanting to know more about the system’s limitations) and System Expectations being met. This study shows the importance of ensuring that users have a good experience and perceive the system as useful to increase their Willingness to Use the ADF.

Does in-vehicle automation help individuals with Parkinson's disease? A preliminary analysis.

PD is a progressive neurodegenerative disorder that affects, according to the ICF, body systems (cognitive, visual, and motor), and functions (e.g., decreased executive functions, decreased visual acuity, impaired contrast sensitivity, decreased coordination)-all which impact driving performance, an instrumental activity of daily living in the domain of "Activity" and "Participation" according to the ICF. Although there is strong evidence of impaired driving performance in PD, few studies have explored the real-world benefits of in-vehicle automation technologies, such as in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), for drivers with PD. These technologies hold potential to alleviate driving impairments, reduce errors, and improve overall performance, allowing individuals with PD to maintain their mobility and independence more safely and for longer periods. This preliminary study aimed to fill the gap in the literature by examining the impact of IVIS and ADAS on driving safety, as indicated by the number of driving errors made by people with PD in an on-road study. Forty-five adults with diagnosed PD drove a 2019 Toyota Camry equipped with IVIS and ADAS features (Toyota Safety Sense 2.0) on a route containing highway and suburban roads. Participants drove half of the route with the IVIS and ADAS systems activated and the other half with the systems deactivated. The results suggest that systems that assume control of the driving task, such as adaptive cruise control, were most effective in reducing driving errors. Furthermore, individual differences in cognitive abilities, particularly memory, were significantly correlated with the total number of driving errors when the systems were deactivated, but no significant correlations were present when the systems were activated. Physical capability factors, such as rigidity and bradykinesia, were not significantly correlated with driving error. Taken together, these results show that in-vehicle driver automation systems can benefit drivers with PD and diminish the impact of individual differences in driver cognitive ability.

This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving.

This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement. Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect "edge cases" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time. Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions-with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded. DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload-though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation. Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level. These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.

Using Detection Response Task and NASA-TLX to Measure the Difference in Cognitive Workload Between Partially Automated Mode and Manual Mode: An On-Road Study

The purpose of this study is to investigate how partially automated vehicles affect cognitive load. The study involved an on-road experiment where 29 participants drove a Tesla in both partially automated and manual mode for up to 45 minutes. The researchers objectively measured the participants' cognitive workload using the Detection Response Task (DRT) and subjectively assessed it using NASA Task Load Index (NASA-TLX). The findings revealed that there was no significant difference in cognitive workload between the partially automated mode and manual mode in both objective and subjective measures. Our study expands the literature on the effects of partially automated vehicles on cognitive workload by using DRT and NASA-TLX. Further studies should adopt similar methodology with the addition of physiological and ocular measures.

Driving Automation for Older Adults with Preclinical Alzheimer’s Disease

Introduction: Older adults with preclinical Alzheimer’s disease (AD) show changes in on-road driving performance. The impact of preclinical AD on using automated vehicle (AV) technology is unknown. The aim was to evaluate safety and cognitive workload while operating AV technology in drivers with preclinical AD. Introduction: This cross-sectional study included 40 participants: 19 older adults (age 74.16 ± 4.78; MOCA scores 26.42 ± 2.52) with preclinical AD, evidenced by elevated cortical beta-amyloid; and 21 controls (age 73.81 ± 5.62; MOCA scores 28.24 ± 1.67). All participants completed two scenarios in a driving simulator. Scenario 1 included conditional automation with an emergency event that required a manual take-over maneuver. Scenario 2 was identical but with a cognitive distractor task. Emergency response time was the main safety outcome measure. Cognitive workload was calculated using moment-to-moment changes in pupillary size and converted into an Index of Cognitive Activity (ICA). Mann-Whitney U and independent t tests were used to compare group differences. Results: Emergency response times were similar between drivers with preclinical AD and controls in scenario 1 (20.85 s ± 1.08 vs. 20.52 s ± 3.18; p = 0.83) and scenario 2 (14.83 s ± 7.37 vs. 13.45 s ± 10.43; p = 0.92). Likewise, no differences were found in ICA between drivers with preclinical AD and controls in scenario 1 (0.34 ± 0.08 vs. 0.33 ± 0.17; p = 0.74) or scenario 2 (0.30 ± 0.07 vs. 0.29 ± 0.17; p = 0.93). Conclusions: Older drivers with preclinical AD may safely operate AV technology, without increased response times or cognitive workload. Future on-road studies with AV technology should confirm these preliminary results in drivers with preclinical AD.

Overestimation of on-road driving performance is associated with reduced driving safety in older drivers

ObjectivesOlder drivers often show less precise self-ratings with a tendency to overestimate themselves. It is unclear, however, how overestimators differ from underestimators or drivers with adequate self-ratings. Methods59 healthy older drivers participated in this on-road study. Besides standardized on-road driving assessment, the study protocol included the collection of neuropsychological and driving-related data as well as different self-ratings. Statistical analyses involved correlations between different subjective and objective ratings as well as statistical comparisons between drivers who overestimated and drivers who adequately rated their on-road driving performance (no drivers underestimated their performance). ResultsDespite positive correlations between different self- and expert ratings, our results revealed that 25 % of the participants overestimated their on-road driving skills. Among other things, overestimators showed poorer on-road driving performances, more prospective near and minor at-fault accidents, poorer cognitive performances in specific driving-related domains and reduced annual mileage. DiscussionOur results suggest that older drivers who overestimate their driving skills show poorer performances within a broad range of skills that directly reflect or are closely related to driving safety. Against the background that an adequate self-assessment is required by German traffic regulations, our findings suggest that overestimators represent a risk group among the population of older drivers.

Which visual cues do drivers use to anticipate and slow down in freeway curve approach? An eye-tracking, think-aloud on-road study

Although much research is done on speed and gaze behaviour inside curves, there is little understanding of which cues drivers use to anticipate and slow down while approaching curves. Therefore, an on road experiment was conducted in which 31 participants drove through six freeway curves in their own car. During the experiment, look-ahead fixations and speed were recorded using an eye-tracker and a GPS tracker, respectively. In addition to these measurements, the participants verbalised their reasons for changing speed. The distribution of fixations over various areas of interest was investigated around the start of deceleration before each curve and around the start of each curve. Verbalisation data were analysed to infer the number and types of reasons for changing speed and when these were mentioned together with mentions of deceleration before a curve. The results showed that before starting to decelerate, the participants fixated mostly on the Focus of Expansion and edges parallel to the curve trajectory, whereas most fixations on warning or speed signs were recorded mostly after participants started to decelerate. These findings suggest that drivers use information from the Focus of Expansion, be it a change in optical flow or the presence of a kink in the alignment, as the main cue to start decelerating. Parallel edges are also important cues, whereas warning and speed signs are primarily used to confirm that a speed change is needed.

Driver’s Attention Allocation and Mental Workload at Different Random Hazard Points on Prairie Highway

To identify the characteristics of driver’s visual perception and measure the mental workload at different random hazard points on prairie highway, an on-road study was conducted with 28 drivers. The I view X HED eye tracker and MP150 multichannel physiological recorder were used to collect the driver’s eye movement and ECG data at different hazard scenarios synchronously. The gaze transfer theory and statistical methods were used to make comparative analysis of typical visual and mental workload evaluation indicators of drivers at different random risk points. The results show that no matter what kind of random risk is confronted, the percentage of drivers’ fixation duration to the current lane drops, where random risk belongs to increase. The distribution of eye glance transition proportions shows that drivers highly bias their scanning attention by only focusing on transferring between forward and the areas where the random belongs to. Compared with off-road risk points, the driver’s gaze transfer is more frequent when facing on-road risk points, and the gaze transfer path is fixed, indicating that on-road risks have higher requirements for drivers’ perception and greater information processing load. There are obvious differences in the degree of influence of the types of random risk points on driver’s psychology. The heart rate growth rate is the largest when drivers were confronted with overtaking cut-in (37.9%) and forward parking (38%), whereas the index RMSSD changes in the opposite way. It reaches the minimum value when the random risks are overtaking cut-in (22.679 ms) and forward parking (22.907 ms). Meanwhile, the driving speed shows larger fluctuation at risk points on the road. This study reveals that on-road hazards pose greater threats to drivers, and it can contribute to a better understanding of the potential hazards on the prairie highways and provide suggestions for future application of advanced driver assistance systems which can warn drivers about potential hazards.

Do adaptive cruise control and lane keeping systems make the longitudinal vehicle control safer? Insights into speeding and time gaps shorter than one second from a naturalistic driving study with SAE Level 2 automation

Advanced driver assistance systems such as adaptive cruise control (ACC) and lane keeping system (LKS) potentially contribute to reducing crash rates and traffic congestion. On-road studies based on early ACC systems operational at medium–high speeds only have shown that the system reduces the proportion of short time gaps when activated. Despite the effects on driver behaviour, most mathematical models assessing the impact of ACC and LKS systems on crash rates and traffic congestion are not based on empirical findings.This study examines the factors that influence changes in the longitudinal vehicle control when driving with ACC and LKS. The data were collected in a naturalistic driving experiment with full-range ACC and LKS and two different vehicle brands (BMW and Tesla) in the Netherlands. To capture changes that are relevant for traffic safety, speeding and a time gap shorter than one second were investigated. The factors influencing speeding and short time gaps were analysed using statistical tests and logistic regression models with random effects, that allow to control for the impact of different explanatory variables and correlations between repeated 10-s intervals over time.The findings revealed that, overall, drivers were less likely to speed and they were also less likely to have a time gap shorter than one second in the experimental condition with the ACC and the LKS than in the baseline condition in manual driving. Drivers were likely to speed in the following 10-s interval when the current speed was close to the speed limit, and/or when the next speed limit was lower than the current speed limit, and/or when the acceleration was high. Drivers were likely to have a short time gap in the following 10-s interval when approaching a slower leader, and/or when the current time gap was short and/or when the acceleration was high. Controlled for these main factors, drivers were less likely to speed and to have a short time gap when the ACC and the LKS were active. However, drivers were more likely to speed when overruling the ACC by pressing the gas pedal. When the systems were active, one vehicle brand showed a smaller probability of a short time gap than the other brand, suggesting differences in ACC system settings between brands. In addition, the speeding probability increased while the probability of a short time gap decreased over time during the trip after the activation of the systems. Although further studies including a larger sample of participants and a wider range of traffic situations are needed, the results are useful to the design of automated vehicles that prevent speeding and short time gaps, and to the implementation of traffic simulations that evaluate the impact of ACC and LKS on crash rates and traffic congestion according to realistic on-road data.

Drivers' Performance in Non-critical Take-Overs From an Automated Driving System-An On-Road Study.

The objective of this semi-controlled study was to investigate drivers' performance when resuming control from an Automated Driving System (ADS), simulated through the Wizard of Oz method, in real traffic. Research on take-overs has primarily focused on urgent scenarios. This article aims to shift the focus to non-critical take-overs from a system operating in congested traffic situations. Twenty drivers drove a selected route in rush-hour traffic in the San Francisco Bay Area, CA, USA. During the drive, the ADS became available when predetermined availability conditions were fulfilled. When the system was active, the drivers were free to engage in non-driving related activities. The results show that drivers' transition time goes down with exposure, making it reasonable to assume that some experience is required to regain control with comfort and ease. The novel analysis of after-effects of automated driving on manual driving performance implies that the after-effects were close to negligible. Observational data indicate that, with exposure, a majority of the participants started to engage in non-driving related activities to some extent, but it is unclear how the activities influenced the take-over performance. The results indicate that drivers need repeated exposure to take-overs to be able to fully resume manual control with ease. Take-over signals (e.g., visuals, sounds, and haptics) should be carefully designed to avoid startle effects and the human-machine interface should provide clear guidance on the required take-over actions.

Hey, watch where you’re going! An on-road study of driver scanning failures towards pedestrians and cyclists

The safety of Vulnerable Road Users (VRUs), such as pedestrians and cyclists, is a serious public health concern, especially at urban intersections. A major reason for vehicle-VRU collisions is driver attentional errors. Prior studies suggest that cross-modal transportation experiences (e.g., being a driver who also cycles) improve visual attention allocation toward VRUs. However, these studies were conducted in simulators or in a laboratory, limiting their generalizability to real world driving. We utilized an instrumented vehicle equipped with eye tracking technology to examine (a) the prevalence of drivers’ visual scanning failures toward VRUs at real intersections and (b) whether there is an effect of cycling experience on this prevalence. Twenty-six experienced drivers (13 cyclists and 13 non-cyclists), between the ages of 35 and 54, completed 18 different turns at urban Toronto intersections, for which gaze and video data were utilized to determine drivers’ visual scanning failures towards areas where conflicting VRUs could approach. Among the 443 unique turn events, 25% were identified as having a visual scanning failure. Results from a mixed effects logit model showed that the odds of committing visual scanning failures towards VRUs during a turning maneuver at an intersection were 2.01 times greater for drivers without cycling experience compared to drivers with cycling experience. Given that our participants represented a low crash-risk age group, this study suggests that the rate at which VRUs are unattended to may be much higher.

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.

Impact of interface design on drivers’ behavior in partially automated cars: An on-road study

In partially automated vehicles, the driver and the automated system share control of the vehicle. Consequently, the driver may have to switch between driving and monitoring activities. This can critically impact the driver’s situational awareness. The human–machine interface (HMI) is responsible for efficient collaboration between driver and system. It must keep the driver informed about the status and capabilities of the automated system, so that he or she knows who or what is in charge of the driving. The present study was designed to compare the ability of two HMIs with different information displays to inform the driver about the system’s status and capabilities: a driving-centered HMI that displayed information in a multimodal way, with an exocentric representation of the road scene, and a vehicle-centered HMI that displayed information in a more traditional visual way. The impact of these HMIs on drivers was compared in an on-road study. Drivers’ eye movements and response times for questions asked while driving were measured. Their verbalizations during the test were also transcribed and coded. Results revealed shorter response times for questions on speed with the exocentric and multimodal HMI. The duration and number of fixations on the speedometer were also greater with the driving-centered HMI. The exocentric and multimodal HMI helped drivers understand the functioning of the system, but was more visually distracting than the traditional HMI. Both HMIs caused mode confusions. The use of a multimodal HMI can be beneficial and should be prioritized by designers. The use of auditory feedback to provide information about the level of automation needs to be explored in longitudinal studies.

Post-Drive Standing Balance of Vehicle Passengers Using Wearable Sensors: The Effect of On-Road Driving and Task Performance.

Postural sway has been demonstrated to increase following exposure to different types of motion. However, limited prior studies have investigated the relationship between exposure to normative on-road driving conditions and standing balance following the exposure. The purpose of this on-road study was to quantify the effect of vehicle motion and task performance on passengers’ post-drive standing balance performance. In this study, trunk-based kinematic data were captured while participants performed a series of balance exercises before and after an on-road driving session in real-time traffic. Postural sway for all balance exercises increased following the driving session. Performing a series of ecologically relevant visual-based tasks led to increases in most post-drive balance metrics such as sway position and velocity. However, the post-drive changes following the driving session with a task were not significantly different compared to changes observed following the driving session without a task. The post-drive standing balance performance changes observed in this study may increase vulnerable users’ risk of falling. Wearable sensors offer an opportunity to monitor postural sway following in-vehicle exposures.

No Difference in Arousal or Cognitive Demands Between Manual and Partially Automated Driving: A Multi-Method On-Road Study.

IntroductionPartial driving automation is not always reliable and requires that drivers maintain readiness to take over control and manually operate the vehicle. Little is known about differences in drivers’ arousal and cognitive demands under partial automation and how it may make it difficult for drivers to transition from automated to manual modes. This research examined whether there are differences in drivers’ arousal and cognitive demands during manual versus partial automation driving.MethodWe compared arousal (using heart rate) and cognitive demands (using the root mean square of successive differences in normal heartbeats; RMSSD, and Detection Response Task; DRT) while 39 younger (M = 28.82 years) and 32 late-middle-aged (M = 52.72 years) participants drove four partially automated vehicles (Cadillac, Nissan Rogue, Tesla, and Volvo) on interstate highways. If compared to manual driving, drivers’ arousal and cognitive demands were different under partial automation, then corresponding differences in heart rate, RMSSD, and DRT would be expected. Alternatively, if drivers’ arousal and cognitive demands were similar in manual and partially automated driving, no difference in the two driving modes would be expected.ResultsResults suggest no significant differences in heart rate, RMSSD, or DRT reaction time performance between manual and partially automated modes of driving for either younger or late-middle-aged adults across the four test vehicles. A Bayes Factor analysis suggested that heart rate, RMSSD, and DRT data showed extreme evidence in favor of the null hypothesis.ConclusionThis novel study conducted on real roads with a representative sample provides important evidence of no difference in arousal and cognitive demands. Younger and late-middle-aged motorists who are new to partial automation are able to maintain arousal and cognitive demands comparable to manual driving while using the partially automated technology. Drivers who are more experienced with partially automated technology may respond differently than those with limited prior experience.

OESDs in an on-road study of semi-automated vehicle to human driver handovers

Design of appropriate interaction and human–machine interfaces for the handover of control between vehicle automation and human driver is critical to the success of automated vehicles. Problems in this interfacing between the vehicle and driver have led, in some cases, to collisions and fatalities. In this project, Operator Event Sequence Diagrams (OESDs) were used to design the handover activities to and from vehicle automation. Previous work undertaken in driving simulators has shown that the OESDs can be used to anticipate the likely activities of drivers during the handover of vehicle control. Three such studies showed that there was a strong correlation between the activities drivers represented in OESDs and those observed from videos of drivers in the handover process, in driving simulators. For the current study, OESDs were constructed during the design of the interaction and interfaces for the handover of control to and from vehicle automation. Videos of drivers during the handover were taken on motorways in the UK and compared with the predictions from the OESDs. As before, there were strong correlations between those activities anticipated in the OESDs and those observed during the handover of vehicle control from automation to the human driver. This means that OESDs can be used with some confidence as part of the vehicle automation design process, although validity generalisation remains an important goal for future research.

The Development of an Automated Vehicle Simulator for On-Road Study


 
 
 In the current development of Automated Vehicle (AV) technology, one of the challenges yet to be resolved is when the AV is driving in urban environments. There will be mixed traffic scenarios where the AV and Vulnerable Road Users (VRU) such as pedestrians, cyclists, and motorcyclists, will share the road infrastructures. In this study, an AV simulator is developed and will be used to study the interaction between an AV and motorcyclist when they encounter a specific situation on the real roads. The study begins with developing a test vehicle based on the three main systems: the vehicle's interior, outer appearance, and behavior. The vehicle's interior comprises four subsystems: data acquisition, sensor, monitoring, and power management system. The outer appearance system has a look-alike LiDAR system, while the driving style sub-system monitors the behavior system. Future improvement will include developing ghost drivers to be implemented together with this system to enhance this on- road simulator's saliency.
 
 

Are Double-Lane Roundabouts Safe Enough? A CHAID Analysis of Unsafe Driving Behaviors

This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.

Development of Tire-Wear Particle Emission Measurements for Passenger Vehicles

In this study, we aimed to develop a new method for measuring tire-wear particles of less than 2.5 μm generated from vehicle use. We also aimed to devise a method for evaluating the emission factor of tire-wear particles. To develop an evaluation method for tire-wear particles, we examined several factors, such as how tire components in airborne particles collected on a sampling filter were measured, the comparison of tire-wear particles obtained in a laboratory study and an on-road study, a method for measuring tire-wear particles using a test vehicle, and a method for evaluating tire-wear mass using a weighting balance. Measurements of tire-wear particles were carried out using the measurement method proposed herein. The amount of tire wear that the particles generated was almost constant in a vehicle speed range of 20–40 km/h but was influenced by a change in lateral acceleration in the range of 0–0.4G. Furthermore, the relationship between the emission of tire-wear particles and the lateral acceleration force can be shown by a quadratic polynomial. We estimated the emission factor of tire-wear particles by applying the relational equation to the speed profile of the JC08 used in Japanese exhaust gas tests. The emission factor of the test tire used in this study was 3.7 mg/km-vehicle. The ratio of the tire-wear particles to tread wear mass was about 3.3% at PM2.5 and 3.7% at PM10.