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In the context of an increasing interest towards the safe introduction of technologies that can help human drivers while performing their driving tasks in the ordinary traffic, the proposed work provides a contribution, investigating safety-related aspects of assisted vehicles in a simulated environment, by considering other driving behavioral parameters. In particular, the influence of Adaptive Cruise Control (ACC), combined with driver distraction and route familiarity, on the driving behavior was investigated using a driving simulator. A sample of 37 drivers, aged between 21 and 34 years performed the driving tests in the simulator environment, with different scenarios applied to one road section. The interactions between a lead vehicle and a follower were investigated, collecting the kinematic parameters of the vehicle (speed, acceleration) and its position in the road (lateral position and distance from the lead vehicle) either with or without active ACC conditions. Driving simulation scenarios differed among each other not only for the ACC turned on or off, but also for the secondary tasks presented to the drivers in specific time frames of the test. Moreover, tests were repeated to induce a route familiarity effect and to study its influence on driving behavior, thus on safety. Results highlighted that the active ACC was correlated with more cautious behaviors, in terms of speed, deceleration/acceleration and distance from the lead vehicle. Women drivers were associated to a greater variability in the driving behavior. The visual distraction induced by secondary tasks appeared to have negative effects on safety-related driving performances, especially when the secondary task was highly demanding. On the other hand, when drivers became familiar with the route, they performed more aggressive driving patterns. [ABSTRACT FROM AUTHOR]

In this driving simulation study, we evaluated the change in driver behaviour of forty young drivers (20 females) following the conversion of a 2-way local street into a 1-way one with a cycle lane in a typical residential neighbourhood. Twenty drivers who were also used to commuting/travelling by bicycle were selected for the study. We compared three road layouts: (i) a standard 2-way urban street with no cycle lane (baseline), (ii) a 1-way street with a cycle lane on the right side, and (iii) a 1-way street with a left-side cycle lane. Driver behavioural metrics when interacting with cyclists including overtaking speed, lateral position in the lane, and vehicle-cyclist clearance were analysed using linear mixed-effect models. Speed and lateral position in the lane were also analysed in the non-overtaking phase. Significantly better driver behaviour was observed in layouts with a dedicated cycle lane, manifesting itself in greater lateral distance values for safer overtaking and better trajectory control. Drivers were more likely to stay within their lanes, eliminating the need to alter trajectory when passing cyclists, thus promoting better longitudinal and lateral control. The presence of a cycle lane significantly improves the interaction between drivers and cyclists, regardless of the position of the cycle lane, i.e., Ieftvs. right side Withrespectto the vehicle lane. Whiletheresults do not favour a particular side, designers should also consider cyclist preferences and driver expectations at intersections. Further research is crucial to validate findings and explore lane positioning effects near intersections, and greater emphasis should be placed on determining cyclist preferences in terms of infrastructure design. [ABSTRACT FROM AUTHOR]

Objective: Distracted driving is a primary contributor to for motor vehicle crashes, the leading cause for injuries and fatalities for youth. Although attention and working memory clearly underlie driving abilities, few studies explore these functions on the brain-level under the cognitive load of driving. To understand the load driving has on auditory attention processing, we examined the differences in dynamic brain response to auditory stimuli during LOAD (while driving in a high-fidelity driving simulator) and No-LOAD conditions (seated in simulator, parked on the side of the road). Methods: Twenty-seven young adult drivers (18–27 y/o; 15 = women) completed a Selective Auditory Attention Task during both a LOAD (driving) and No-LOAD condition in a ½ cab miniSim® high-fidelity driving simulator. During the task, participants responded by pressing the volume control button on the steering wheel when a target tone was presented to a target ear. Electroencephalography-recorded event-related brain responses to the target tones were evaluated through alpha and theta oscillations for two response windows (early: 150–330ms; late: 350–540ms). Results: During an early time window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD theta power in the right frontal cortical region (F(1, 24)= 5.4, p=.03, partial η2=.18). During the later window, we observed a significant interaction between attended/unattended and LOAD/No-LOAD alpha response in the posterior cortical region (F(1, 24)=11.81, p=.002, partial η2=.15) and in the right temporal cortical region during the window (F(1, 24)=4.3, p=.05, partial η2=.33). Conclusions: Our data provide insight into the demand that driving has on cognitive faculties and how dual task engagement may draw resources away from driving. We suggest future research directly incorporate vehicle control abilities into study design to understand how brain-based measures relate to driving behaviors. [ABSTRACT FROM AUTHOR]

Due to the effects of low-pressure and hypoxic environments at high altitudes, drivers in high-altitude areas exhibit increased perceived reaction times, leading to challenges in accurate speed estimation and handling judgment. This study aims to quantitatively analyze the impact of plateau environments on operating speeds at interchange exit ramps. Utilizing a UC/win-road simulator, six scenarios of expressway exit ramps were constructed. The simulation experiments involved 50 participants (35 males, 15 females) from Nanjing, China (altitude of 50 m) and 50 participants (36 males, 14 females) from Lhasa, China (altitude of 3,650 m). This research focused on examining the influence of the plateau environment on drivers' operating speeds, investigating variations in speed between drivers in plain and plateau areas, across genders, and during different acclimation periods. It also aimed to predict operating speeds at the midpoint and exit of the curve on the exit ramp for drivers in both plain and plateau areas. Based on these predictions, the study elucidated the trend of operating speed as influenced by the low-pressure and hypoxic conditions of the plateau, as well as the characteristics of the exit ramp's horizontal curve. Additionally, the research uncovered the internal correlations and potential reasons linking operating speed to drivers' perception and response abilities, physiological and visual load levels, and driving styles. [ABSTRACT FROM AUTHOR]

Objectives: This research aims to: (i) compare the effects of different sidewall entrainment facilities on drivers' visual behavior; (ii) compare the effects of the same sight entrainment facilities on drivers in different lanes; (iii) give recommendations for engineering applications based on the results of the study; Methods: The study designed four different scenes, each with symmetrically designed visual facilities on the both sidewalls of the tunnel, scene a represents a typical urban tunnel in China (horizontal stripes on sidewalls), scene b includes vertical stripes on sidewalls in addition to scene a, scene c introduces an LED-arch based on scene b, and scene d features a rhythmic pattern (Wave pattern on sidewalls). 30 participants, 21 men and 9 women, aged 21–54, drove the four scenes. Eye movement data of participants in each lane for different scenes were collected using an eye-tracking device. Visual performance indicators including fixation duration, number of fixations, saccade duration, and saccade amplitude were utilized to comprehensively evaluate drivers' visual behavior. Factor analysis was employed to analyze the impact of different visual guiding facilities on drivers' visual searching abilities. Results: There is a significant effect of sidewall guiding facilities and lane location on drivers' visual behavior and loading. Across scenes, drivers' visual load is ranked as follows, from highest to lowest: scene a (baseline) > scene b (horizontal stripes added to scene a) > scene c (LED-arch added to scene b) > scene d (Wave pattern). Furthermore, under the same scene, drivers' visual load in each lane is ranked in descending order: Middle lane > Right lane > Left lane. Conclusion: Due to the effect of the tunnel structure on the drivers' visual field, drivers in the left lane have the highest visual load in any scenario compared to the other two lanes, which can be ameliorated but not eliminated. Traditional guiding facilities and decorated pattern both improve the visual behavior and reduce drivers' visual load in urban tunnels, especially in scene c and scene d, but scene d should not be used for the entire length of the tunnel in order to prevent driver distraction. In engineering practice, scene c (LED-arch added to scene b) can be set up in general sections of urban tunnels, and decorated pattern can be added to fatigue reminder regions to alleviate driving fatigue. [ABSTRACT FROM AUTHOR]

To address the issues of high workload, poor visualization, and weak dynamic verification support in train control data verification, this paper proposed a building information modelling (BIM)-based dynamic verification method for train control engineering data. A data model-driven method in scene design was first applied to automatically integrate 3D railway stations on the Unity platform, generating realistic railway station environments. Then, continuous collision detection was employed to visually simulate the dynamic operations of trains based on relevant railway specifications. Finally, dynamic verification of train control engineering data was completed integrating 3D station model and train simulation operations. For 3D station integration, this method could automatically and efficiently retrieve railway infrastructure models, accurately constructing 3D stations, including tracks, turnouts, and signal machines. During the 3D simulation operations, the system could automatically detect track section occupancy, operate signal lights, calculate and execute driving permission, and read balise data via vehicle-ground communication. It also could perform over-speed protection according to the permission, controlling train operations within 3D simulation scenes and replicating scenes of commissioning and integrated testings. If any anomalies occurred during the simulation, the system would flag potential risks in data design, ending validation with a warning. In an experiment using data from the Xianfeng Station and adjacent sections of the Qianjiang -Changde Railway, the results showed that: (1) The length of 1LQG was insufficient to meet the requirement of 80 km/ h for initial speed. It was recommended to increase the length from 305 m to 525 m to meet the requirements for all train types. (2) The 350 m distance between the neutral zone and the adjacent signal machine was inadequate for the CRH-200 train passing through the zone. Some data issues occurred, suggesting a need to extend the distance to over 524 m. These findings provide valuable references for the application of BIM method in the verification of train control engineering data. [ABSTRACT FROM AUTHOR]

The development mode of transportation infrastructure construction has shifted from pursuing speed and scale to prioritizing quality and efficiency. As a result, there has been an increasing demand for fine-tuning and adapting the tunnel lighting environment, which should prioritize the safety and comfort of drivers. Based on tunnel lighting theory and driving behavior researches, eight indicators that affect driver safety and comfort were selected to establish a driving safety and comfort evaluation system. Indoor simulation was conducted through tunnel driving simulation environment and hardware facilities. A virtual simulation model of the tunnel was established using UC-win／Road software, and data corresponding to the eight indicators were obtained through driving simulators and physiological instruments. Then the entropy method was used to determine the weights of the indicators, and the driving safety and comfort in the tunnel under different lighting brightness and color temperature environments were evaluated. The target value of brightness and color temperature in the tunnel were obtained with the goal of achieving the optimal value of driver safety and comfort. The experimental results confirm the effectiveness of the driver safety and comfort evaluation system, propose an improved tunnel lighting scheme, and provide reference for tunnel operators. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For autonomous driving, drivers’ intervention may be required when vehicles fail or are in a dilemma to detect emergent and unprogrammed events. In such situations, non-driving related tasks may have a great impact on the safety of drivers’ critical intervention behavior thus leading to traffic accidents. Therefore, exploring the impacts of non-driving-related tasks on drivers’ critical intervention behavior, quantifying and predicting the corresponding risks have become important. In this paper, driving simulation experiments are carried out to obtain the vehicle driving state data and visual behavior information of drivers during the autonomous driving scenarios that require critical interventions. To construct the risk quantification model for drivers’ critical intervention behavior, the fuzzy comprehensive evaluation method and the criteria importance though intercriteria correlation (CRITIC) weighting method are employed. Then, for risk prediction, a model is constructed based on the visual behavior information before the occurrences of intervention. Multivariate logistic regression (MLR) and support vector machine are compared. The results show that non-driving tasks significantly postpone driver's critical intervention responses, increasing crash risks of the driving. For prediction, SVM performs better than the MLR in terms of metrics including the precision, the recall, and the overall accuracy. This paper examines the risks during situations requiring drivers’ critical intervention, associated with different non-driving tasks, which has remained much unexplored in the previous research. The methodology of this paper can be applied to smart vehicle systems in alerting vehicles for take-over reactions, with recognizing and predicting potential risks.

Although driving to and from work is an unavoidable part of the working life of many people, little is known about the relationship between work-related and drive-related cognitive load in daily commuting. To explore those relationships, Study 1 examined the effect of a demanding driving period on the performance of a subsequent cognitive task (mimicking a home-to-work commuting scenario). That driving-related higher cognitive load than the control condition was associated with an increased accuracy in the following cognitive task. Study 2 examined the effects of a period of demanding cognitive tasks on the performance of a subsequent driving task (mimicking a work-to-home commuting scenario). Although no reliable effect on speed or lane keeping ability in a virtual motorway scenario was observed, the completion of tasks under the higher cognitive load condition before driving led to a modest increase in the distance kept from the car ahead. The two sets of findings suggest that moderate levels of cognitive load could modulate the performance in timely contiguous tasks. The process underpinning possible spillover effects with such timestamp is unknow but might be linked to the activation of long-lasting attentional processes involving alertness. Hence, this exploratory study can be a catalyst for future studies investigating the interplay between cognitive load and driving in scenarios of daily commuting. [ABSTRACT FROM AUTHOR]

The guide signs at expressway play an important role in conveying road information to drivers. To study the reasonable setting of guide signs at expressway with consecutive dense exits, firstly, a bidirectional eight-lane expressway segment model were constructed with three exit ramp scenarios: "single exit", "consecutive double exits", and "consecutive triple exits". Two groups of schemes (standard group and optimized group) for guide signs were set, resulting in 10 different experimental comparison schemes. Secondly, thirty-two licensed drivers were recruited to conduct experimental tests using a driving simulation platform. Then four types of experimental data related to driving operations are collected: participants' subjective perception, vehicle operation safety, driving maneuvers smoothness, and cumulative eye movement comfort. These data were used to compare and analyze the 10 different design schemes of guide signs, determining the optimal design alternatives for expressway segment with three exit ramp scenarios. The comparative analysis of the experimental data across the three exit ramp scenarios revealed that factors such as ramp spacing, guide signs content, and placement distance of guide signs significantly impact vehicle safety and comfort. There is an inverse relationship between ramp spacing and both vehicle safety and driving comfort. The participants' visual recognition efficiency is enhanced by optimizing the content and placement distance of the guide sign effectively, further ensuring the safe and smooth operation of vehicles. This study can effectively reduce traffic conflicts at expressway exit ramps and decrease the incidence of traffic accidents. Additionally, it provides solid theoretical support for the design and sustainable development of expressway traffic facilities. [ABSTRACT FROM AUTHOR]

We present a method to automatically calculate time to fixate (TTF) from the eye-tracker data in subjects with neurological impairment using a driving simulator. TTF presents the time interval for a person to notice the stimulus from its first occurrence. Precisely, we measured the time since the children started to cross the street until the drivers directed their look to the children. From 108 neurological patients recruited for the study, the analysis of TTF was performed in 56 patients to assess fit-, unfit-, and conditionally-fit-to-drive patients. The results showed that the proposed method based on the YOLO (you only look once) object detector is efficient for computing TTFs from the eye-tracker data. We obtained discriminative results for fit-to-drive patients by application of Tukey's honest significant difference post hoc test (p < 0.01), while no difference was observed between conditionally-fit and unfit-to-drive groups (p = 0.542). Moreover, we show that time-to-collision (TTC), initial gaze distance (IGD) from pedestrians, and speed at the hazard onset did not influence the result, while the only significant interaction is among fitness, IGD, and TTC on TTF. Obtained TTFs are also compared with the perception response times (PRT) calculated independently from eye-tracker data and YOLO. Although we reached statistically significant results that speak in favor of possible method application for assessment of fitness to drive, we provide detailed directions for future driving simulation-based evaluation and propose processing workflow to secure reliable TTF calculation and its possible application in for example psychology and neuroscience. [ABSTRACT FROM AUTHOR]

As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The dataset encompasses electrooculography signals, with a focus on standardizing and processing the data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology. [ABSTRACT FROM AUTHOR]

Objective: To investigate how the visual complexity of head-up displays (HUDs) influence the allocation of driver's attention in two separate visual domains (near and far domains). Background: The types and amount of information displayed on automobile HUDs have increased. With limited human attention capacity, increased visual complexity in the near domain may lead to interference in the effective processing of information in the far domain. Method: Near-domain and far-domain vision were separately tested using a dual-task paradigm. In a simulated road environment, 62 participants were to control the speed of the vehicle (SMT; near domain) and manually respond to probes (PDT; far domain) simultaneously. Five HUD complexity levels including a HUD-absent condition were presented block-wise. Results: Near domain performance was not modulated by the HUD complexity levels. However, the far domain detection accuracies were impaired as the HUD complexity level increased, with greater accuracy differences observed between central and peripheral probes. Conclusion: Increased HUD visual complexity leads to a biased deployment of driver attention toward the central visual field. Therefore, the formulation of HUD designs must be preceded by an in-depth investigation of the dynamics of human cognition. Application: To ensure driving safety, HUD designs should be rendered with minimal visual complexity by incorporating only essential information relevant to driving and removing driving-irrelevant or additional visual details. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BackgroundDriving simulators are established tools used for different research purposes. Either head-mounted displays (HMDs) or monitors are applied. However, the extent to which the setting (HMD vs monitors) or the interactivity in driving (active behaviour as a driver or passive behaviour as a co-driver) influences the experience of presence, realism or simulator sickness is not yet entirely clear.ObjectiveWe aimed to compare different conditions to assess the influence of interactivity and presentation setting on the experience of presence, realism, and simulator sickness. Further, different driving performance parameters (e.g., speed perception or sign detection) were aimed to be assessed between the two settings.MethodsNinety participants were recruited. This was an experimental study (2 × 2 within-between-subject design) with interactivity as within-subject factor (2 levels: active vs passive) and setting as between-subject factor (2 levels: HMD vs monitors). As driving scenarios, we selected four specific driving maneuvers that we assume would affect driving behaviour differently depending on the setting. Differences in the experience of presence as well as in the development of simulator sickness were assessed using standardised questionnaires.ResultsThere was no difference between the two settings regarding presence, realism or simulator sickness. Regarding interactivity, presence was significantly higher in the active compared to the passive condition. The most prominent difference between the two settings was found in the sign detection task where participants wearing an HMD performed worse.ConclusionThe choice of interactivity appears to have a stronger effect on the experience of presence than the setting. Sign detection was worse in the HMD setting probably due to the lower display resolution. These results are relevant for the interpretation of driving simulator studies and the implementation of future studies.

Expressway exit areas experience traffic diversion and complex road conditions, making them accident-prone areas. In this study, transverse and fishbone visual illusion deceleration markings were selected to optimize the induction facilities at expressway exits. The research aims to investigate the impact of these markings on the driving behavior, cognitive load, and physiological characteristics of drivers in various distracted scenarios at expressway exit areas. Furthermore, a comprehensive evaluation of each experimental scheme is conducted using the Matter-Element Extension Model. The study found that the implementation of deceleration markings can effectively enhance driver alertness and lane change awareness, enabling drivers to reduce their speed to near the speed limit in exit areas without compromising driving comfort. Compared to the situation without markings, drivers begin to decelerate approximately 600 m earlier and exit the ramp when markings are present. Fishbone deceleration markings, in contrast to transverse markings, result in lower vehicle speeds, smoother deceleration, and more effectively stimulate drivers' intention to change lanes, guiding them to make the final lane change earlier. Based on the comprehensive evaluation results, it is recommended that transverse or fishbone deceleration markings be considered in engineering practice. These markings have not produced significant effects on driver visual fatigue and driving load, with fishbone markings demonstrating superior comprehensive evaluation outcomes. These research findings can provide valuable insights for future expressway exit area marking design schemes, further enhancing driver safety.

AbstractMental workload recognition is of great significance in preventing human errors and accidents. This study constructed a multimodal recognition scheme to recognize three mental workload states: underload, moderate load, and overload. Based on driving scenarios, these three states were induced in this study by changing the driving modes and situations. Multimodal recognition of underload, moderate load, and overload was performed using electroencephalography (EEG), electrocardiography (ECG), and pupillometry. In addition, various machine learning methods were used to evaluate the recognition performance of different feature combinations. The results showed that the random forest method, trained using spectral power, pupil diameter, and heart rate variability, achieved the highest recognition accuracy of 83.13% for the three mental workload states. This study provides valuable reference information for multimodal recognition of mental workload states. [ABSTRACT FROM AUTHOR]

• We quantify the cognitive load under varying non-driving-related tasks (NDRTs) in automated driving by physiological state of eye movement. • Dempster-Shafer (D-S) Evidence Theory is used to associate the workload of different typical scenarios, NDRTs and the safety of takeover. • Takeover safety is predicted by Convolutional Neural Networks (CNN) based on cognitive load. • The effect of window size of the input data for takeover safety prediction is especially considered. Automated vehicles alleviate the need for driver attention and control. However, a takeover request (TOR) to the driver remains essential for emergencies and safety–critical scenarios beyond automation's capability. Thus, accessing drivers' safety performance in various TOR scenarios is crucial for conditionally automated driving (SAE L3). However, TOR safety performance is seldom examined concerning drivers' cognitive load, despite its presumed relevance to TOR scenarios and human–machine interaction. Moreover, the adequacy of the time window preceding a TOR, critical for TOR safety prediction, remains inadequately explored. This study aims to assess safety performance across diverse TOR scenarios in Level 3 conditional automation, incorporating drivers' cognitive load, and predict TOR safety by considering the time window's impact. A driving simulator experiment gathered eye movement and driving behavior data from 37 recruited participants. Participants were instructed to take control of the vehicle from automated driving within a time budget (TB) of 3 s or 7 s in obstacle avoidance (OA) or lane keeping (LK) scenarios while engaging in non-driving-related tasks (NDRTs). Participants' subjective cognitive load in the different TOR scenarios was scaled using NASA-TLX. Furthermore, safe TOR performance was predicted utilizing Convolutional Neural Networks (CNNs) across different time window sizes preceding a TOR. The results indicate that: 1) cognitive loads in takeover scenarios ranked from highest to lowest as TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; and the cognitive loads of the NDRTs ranked from highest to lowest as mistake finding, texting, chatting, monitoring; 2) the takeover safety performance in the four scenarios from lowest to highest was TB = 3s_OA, TB = 3s_LK, TB = 7s_OA, TB = 7s_LK; likewise, the takeover safety performance during the four NDRTs ranked from lowest to highest as mistake finding, monitoring, texting, chatting; 3) the time window size before the TORs significantly affected the prediction performance of the model. A 30-second window was recommended as optimal for predicting takeover safety using the CNN model, achieving an average F1 score of 0.8120 and 81.98 % accuracy. This study's findings enhance our comprehension of driving behavior characteristics during TOR and offer valuable insights for detecting driver states in conditionally automated driving contexts. [ABSTRACT FROM AUTHOR]

• HRV indices predict multidimensional driving-related fatigue. • Lower RMSSD associated with greater fatigue in young drivers. • Higher LF/HF ratio linked to higher fatigue levels. • Findings support HRV metrics as fatigue biomarkers. • Applied research needed to validate real-world use. Driver fatigue significantly impairs performance and is a major risk factor for road crashes. However, fatigue is difficult to objectively measure and quantify. This study aimed to elucidate associations between heart rate variability (HRV) metrics and multidimensional fatigue construct encompassing objective and subjective measures in young drivers with self-reported short sleep. Eighty-four young adults underwent assessments during simulated driving. HRV indices RMSSD and LF/HF ratio were derived from an electrocardiogram, while relative Theta power, oculography drowsiness, lane position variability, and Karolinska Sleepiness Score measured fatigue. RMSSD negatively correlated with Theta power, and LF/HF ratio positively correlated with position variability. Exploratory factor analysis extracted three factors, with RMSSD and LF/HF loading onto one. Structural equation modelling tested the prediction of a latent fatigue construct from HRV. Two models demonstrated an acceptable fit. RMSSD negatively predicted fatigue, explaining 6.4 % of the variance, while the LF/HF ratio positively predicted fatigue, accounting for 3.7 % of the variance. In summary, HRV, particularly RMSSD, showed significant relationships with multidimensional fatigue. Lower RMSSD and higher LF/HF ratio are associated with greater fatigue levels. This study demonstrates that HRV indices exhibit significant relationships within a multidimensional model of fatigue incorporating objective performance, ocular, and subjective measures. The findings provide initial evidence towards using HRV for monitoring complex manifestations of fatigue, rather than single dimensions. Further applied research should investigate translating these findings to naturalistic settings and validating HRV thresholds predictive of on-road impairment. [ABSTRACT FROM AUTHOR]

Intersections, especially those without traffic signals, are prone to frequent traffic accidents and disorder. The regulation of vehicle speed is an effective measure that can be taken in order to mitigate accidents and alleviate disorder at unsignalized intersections. This study aims to assess the effectiveness of four different temporary low-cost speed-calming measures: traffic crash barrels, lane markings, intersection markings, and a combination of the three. In this study, we used a driving simulator to design four speed-calming static measures that are typical of intersections; we then statistically analyzed the speed and acceleration data (recorded by the driving simulator) and the driver's visual data (recorded by an eye tracker). The simulation results demonstrated that different speed-calming measures have varied effects on the attention and visual fixations of drivers; however, they are all effective in reducing the average driving speed at the intersection. The traffic-channelizing lane demonstrated superior performance compared to the other three measures in terms of its cumulative impact on deceleration, driving comfort, and safety. The experimental findings presented herein broaden the range of currently accessible temporary speed-limiting strategies and provide valuable insight into intersections that require low-cost speed management solutions. [ABSTRACT FROM AUTHOR]

Objective: We examined the effectiveness of a second exposure to ACCEL, a novel driving training program, on latent hazard anticipation (HA) performance several months after their first exposure. Background: Past research has demonstrated that PC-based driver training programs can improve latent HA performance in young novice drivers, but these improvements are below the ceiling level. Method: Twenty-five participants were randomly assigned to either the Placebo group, the ACCEL-1 group, or the ACCEL-2 group. Following the completion of the assigned training program, participants drove a series of eighteen scenarios incorporating latent hazards in a high-fidelity driving simulator with their eyes tracked. Participants returned two to six months following the first session and completed either the placebo program (ACCEL-1 and Placebo groups), or a second dose of training program (ACCEL-2 group), again followed by simulated evaluation drives. Results: The ACCEL-2 group showed improved HA performance compared to the ACCEL-1 and Placebo groups in the second evaluation. Conclusion: ACCEL enhances young novice drivers' latent HA performance. The effectiveness of ACCEL is retained up to 6 months, and a second dose further improves HA performance. Application: Policy makers should consider requiring such training before the completion of graduate driver license programs. Young novice drivers that do not show successful latent HA performance could be required to complete additional training before being allowed to drive without restrictions. [ABSTRACT FROM AUTHOR]

This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA's Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world. [ABSTRACT FROM AUTHOR]

This driving simulation study is aimed at evaluating the effects on the longitudinal and transversal behaviour of drivers attributable to layout changes in an existing long tunnel caused by the introduction of jet fans along the tunnel walls. Furthermore, the effects of two different levels of light intensity capable of revealing or masking the presence of jet fans, thus manipulating the vision of participants, were also evaluated. Forty drivers were involved in this multi–level mixed-factorial experiment, in which twenty car and twenty truck drivers drove on three different layouts of a two–way two-lane long tunnel. Two different pairwise analyses were carried out to assess the effects of these two within-subject factors. The presence of oncoming traffic was also considered as a factor in the study.The presence of jet fans led to lower speeds and affected the transversal behaviour of car and truck drivers in different ways. In the presence of the jet fans trucks reduced the lateral distance to the tunnel wall, while car trajectories were always closer to it. All drivers showed better lateral control when facing the jet fans and when meeting oncoming vehicles. When the light intensity was reduced, truck drivers adopted lower speeds, while with higher light intensity they drove closer to the tunnel wall, thus reducing the risk of collision with any oncoming traffic.

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time. This is a particular issue along curves with limited available sight, where speed management is necessary to avoid unsafe situations (e.g., driving off the road or invading the other traffic lane). To solve this issue, we proposed a novel intelligent speed adaptation (ISA) system for visibility, called V-ISA (intelligent speed adaptation for visibility). It estimates the real-time safe speed limits based on the prevailing sight conditions. V-ISA comes with three variants with specific feedback modalities (1) visual and (2) auditory information, and (3) direct intervention to assume control over the vehicle speed.Here, we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances, using a driving simulator. We also considered curve road geometry (curve direction: rightward vs. leftward). Sixty active drivers were recruited for the study. While half of them (experimental group) tested the three V-ISA variants (and a V-ISA off condition), the other half always drove with the V-ISA off (validation group). We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.All V-ISA variants were efficient at reducing speeds at entrance points, with no discernible negative impact on driver lateral behaviour. On rightward curves, the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations. Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and, consequently, establishes safer driving conditions.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis being the default method for processing signals from Doppler radar owing to their inherent noise. However, conventional analysis approaches often struggle to detect weak signals buried within the sidelobes of other signals. Some data analysis techniques for Doppler radar rely on machine learning, but they struggle to generate clear time-frequency diagrams, complicating heartbeat detection. In this study, we employed non-harmonic analysis (NHA) as a frequency analysis method to mitigate sidelobe interference and implemented semantic segmentation for precise heartbeat detection. To validate the proposed approach, we conducted heartbeat detection tests both in stationary, low-noise conditions and in a noisy driving simulation environment. The results indicated that the NHA method successfully analyzed heartbeat harmonics, suggesting its potential for detecting heartbeat components through machine learning. To validate these findings, we determined the detection accuracy by comparing true and false positive rates, allowing us to quantify the detectability of heartbeats under both resting and driving simulation conditions.

Virtual reality (VR) has recently been adopted for driving simulations to enhance their realism and thus improve the validity of the simulation results. However, given that perceived realism is a subjective factor that varies by individual, understanding and predicting perceived realism in VR driving simulations are prerequisites for enhancing their validity. Studies on VR have investigated how individual factors such as psychophysiological metrics are associated with perceived realism. However, how these psychophysiological metrics are associated with perceived realism in VR driving simulations has not yet been investigated. To address this problem, this study investigated the relationship between perceived realism and psychophysiological metrics, including individual characteristics (sex, age), personality traits (psychopathy, Machiavellianism, sensation seeking, impulsivity), heart rate changes during the event, and risky decision-making during the event, across three driving simulations. The results indicated that psychopathy, Machiavellianism, heart rate changes during the event, and risky decision-making during the event were significantly correlated with the perceived realism of VR driving simulations. In addition, we tested three types of machine learning models to find the appropriate ones for predicting perceived realism, showing that the tree-based algorithm had the highest prediction accuracy.

Mobile robotic systems offer valuable test platforms due to their shared features with autonomous vehicles, including features such as sensor technologies, navigation algorithms, and control systems. However, constraints in laboratory environments or technical resources, along with the need for extensive testing, often necessitate the use of virtual test laboratories. While line-following is a widely preferred application in mobile robotics, research on this topic within virtual laboratories is limited. This study pioneers the use of a car-like robotic vehicle in conducting line-following tests within a virtual laboratory environment. To facilitate these tests, a virtual simulator was developed to meet the requirements of realistic simulations. This simulator includes simulated elements, such as roads and environmental features, along with virtual sensors designed to collect and process dynamic motion data. An exceptional aspect of this study is its ability to collect consistent dynamic travel data by sampling realistic sensor information within a virtual environment. The developed line-following algorithm employs a controller to minimise lateral deviation while the robotic vehicle follows a road line during its movement. The study conducted virtual driving tests using two different manoeuvre modes on four distinct road segments, exploring how the manoeuvring style influences the driving quality. It was demonstrated that in the low manoeuvre mode, the ride is more comfortable, but exhibits a greater route deviation due to reduced oscillation, while the high manoeuvre mode exhibits the opposite behaviour.

In the process of driving in an underground interchange, drivers are faced with many challenges, such as being in a closed space, visual changes alternating between light and dark conditions, complex road conditions in the confluence section, and dense signage, which directly affect the safety and comfort of drivers in an underground interchange. Thus, driving simulation, building information modeling (BIM), and data mining were used to analyze the impact of underground interchange safety facilities on driving safety and comfort. Acceleration disturbance and steering wheel comfort loss values were used to assist the comfort analysis. The CART algorithm, classification decision trees, and neural networks were used for data mining, which uses a dichotomous recursive partitioning technique where multiple layers of neurons are superimposed to fit and replace very complex nonlinear mapping relationships. Ten different scenarios were designed for comparison. Multiple linear regression combined with ANOVA was used to calculate the significance of the control variables for each scenario on the evaluation index. The results show that appropriately reducing the length of the deceleration section can improve driving comfort, setting reasonable reminder signs at the merge junction can improve driving safety, and an appropriate wall color can reduce speed oscillation. This study indicates that the placement of traffic safety facilities significantly influences the safety and comfort of driving in underground interchanges. This study may provide support for the optimization of the design of underground interchange construction and internal traffic safety facilities. [ABSTRACT FROM AUTHOR]

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time. This is a particular issue along curves with limited available sight, where speed management is necessary to avoid unsafe situations (e.g., driving off the road or invading the other traffic lane). To solve this issue, we proposed a novel intelligent speed adaptation (ISA) system for visibility, called V-ISA (intelligent speed adaptation for visibility). It estimates the real-time safe speed limits based on the prevailing sight conditions. V-ISA comes with three variants with specific feedback modalities (1) visual and (2) auditory information, and (3) direct intervention to assume control over the vehicle speed. Here, we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances, using a driving simulator. We also considered curve road geometry (curve direction: rightward vs. leftward). Sixty active drivers were recruited for the study. While half of them (experimental group) tested the three V-ISA variants (and a V-ISA off condition), the other half always drove with the V-ISA off (validation group). We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour. All V-ISA variants were efficient at reducing speeds at entrance points, with no discernible negative impact on driver lateral behaviour. On rightward curves, the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations. Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and, consequently, establishes safer driving conditions. [ABSTRACT FROM AUTHOR]

''Using eye-tracking technology, this study examined hazard detection at night. Using a 2 (younger versus older) x 2 (simulator versus on road) repeated-measures mixed design, 16 older adults and 17 younger adults drove their own vehicle and on a driving simulator under nighttime conditions wearing eye tracking technology. Both driving conditions had three roadway hazards of pedestrians looking at their cell phone while posed to cross the roadway. Pupil glances were recorded using outcome measures of total fixation duration, number of fixations, and time to first fixation. Results showed older adults detected hazards similarly to younger adults, especially during on-road performance. Night hazard detection was similar across driving conditions except for time to first fixation, which was faster on-road for both age groups. Results support potential use of driving simulators as a proxy for on-road with night driving needed for research and practice. [ABSTRACT FROM AUTHOR]

This study aimed to establish inter-rater reliability among three raters while training new driver rehabilitation specialists to correctly identify driving errors on a DriveSafety 250 driving simulator. Five participants completed adaptation, residential and suburban, and city and highway scenarios. Intraclass correlation coefficients indicated scores between.623–.877 (p =.003–.122) for the total driving errors recorded in the two scenario drives with rater agreement initially ranging between 7–8%. When analyzing the data for types of driving errors, the intraclass correlation coefficients ranged from.556–.973 (p <.05) and rater agreement between 15–100%. Through proper training and strategy development, raters reached 100% consensus on all aspects of inter-rater reliability while assessing driving errors. [ABSTRACT FROM AUTHOR]

As age increases, the cognitive perception, decision-making ability, and operational response ability of elderly drivers all decline, leading to changes in their traffic risk perception and stress response ability. In order to study the stress response characteristics of elderly drivers under risk scenarios, a virtual experimental scenario of road intersection risk was designed and constructed, including pedestrian to motor vehicle, non motor vehicle to motor vehicle, and motor vehicle to motor vehicle conflict. Fifteen middle-aged, young, and elderly drivers were recruited for driving simulation experiments, and data on driver behavior and vehicle operation during the driving process were collected. Relevant indicators were selected for analysis of the experiment. The results indicate that elderly drivers generally have longer reaction time and brake pedal travel compared to middle-aged and young drivers in various risk scenarios; In high-risk driving scenarios, the longitudinal acceleration fluctuation of elderly drivers is greater than that of young and middle-aged drivers, with 10 elderly drivers having a collision accident, accounting for 33. 3% of all participants. The research results can provide some theoretical basis for traffic safety policy makers to formulate measures to improve the driving safety of elderly drivers. [ABSTRACT FROM AUTHOR]

As one of Tibet's most vital traffic safety devices, guide signs are usually Tibetan-Chinese bilingual. Therefore, drivers need effective recognition of Tibetan-Chinese bilingual traffic signs for safe driving. This study aims to determine two design parameters of the Tibetan-Chinese bilingual guide signs: Tibetan character height (hT) and aspect (height to width) ratio. Two highway facilities were selected for the simulation experiment: a general highway intersection and a high-grade highway exit ramp. To incorporate drivers' visual recognition characteristics, 20 Tibetan drivers and 20 Han drivers were invited to wear head-mounted eye trackers for a dynamic driving simulation experiment of visual identification of guide signs. Four layout designs were used to analyze Tibetan and Han drivers' visual recognition characteristics. The regression model for hT was first developed based on the visibility theory of guide signs. Then, the character-height design values for the Tibetan-Chinese bilingual guide signs were established for different speed limits. The results of this study should help to optimize the design of traffic signs in Tibet and improve highway traffic safety. [ABSTRACT FROM AUTHOR]

Driving a car requires a complex combination of various cognitive functions (e.g., visual perception, motor control, decision making, and others), and deficits in any of these processes may compromise driving safety. Amongst these, executive functions such as inhibitory control, task switching, and decision-making are important, as they enable drivers to process information from their surroundings and respond appropriately to changing road conditions. Although previous research has focused on laboratory measures of individual executive functions, it remains unclear whether performance on such laboratory tests readily translates to actual on-the-road driving performance, especially since drivers' skill levels can vary widely, based on their driving frequency. To this end, we divided 30 participants into two categories based on their driving frequency (i.e., daily commuter vs. weekend only drivers), and we used three well-known executive functioning tasks (the stop signal task, Iowa gambling task or IGT, and a task-switching test) to see whether scores on these tasks predicted such driving performances and behaviors such as braking time, lane-keeping, speed limit violations, and inter-vehicle distance (e.g., in a driving simulator). Participants went through a follow-lead-car scenario in the driving simulator for 20 minutes and then completed the three executive tasks. We found that stop signal reaction time (SSRT) best predicted driving performance, and remained predictive against driver distraction, as well as variabilities in driving frequency. The IGT predicted speed limit violations in high-frequency drivers, whereas task-switching cost predicted lane keeping performance in low-frequency drivers. Together, these results highlight the importance of driving frequency when considering correlates between executive functions and driving performance and behavior. They also imply that executive tasks better predict driving performance in low-frequency (or inexperienced) drivers, while driver temperament (i.e., impulsiveness as indicated by IGT) better predicted driving performance in high-frequency (or experienced) drivers. [ABSTRACT FROM AUTHOR]

This research addresses the issue of an individual's ability to drive and especially of those individuals that are questionably demented (dementia is suspected) or are in a state of very mild dementia and are therefore the most difficult to identify. A methodology has been developed for categorization of drivers by considering three driving performance indices/parameters simultaneously. This novel approach precluded the previous technique whereby only a single driving performance index (an omnibus approach without the ability to discriminate between normal driving behavior and risky driving habits primarily due to cognitive decline) is considered. Driving performance of 28 young and 28 old drivers was gauged by 24 driving performance parameters through two designed drives on driving simulator. Normal Mixture Model Cluster Analysis was used in the performance-based categorization of drivers. It was found that out of a total of 56 drivers, 8 turned out to be "poor drivers". Results from neuropsychological/cognitive tests showed on average lower cognitive performance for the "poor drivers" group. This methodology will preclude the need for measurement of driving skills through driving instructors.

The decision-making behavior of drivers during the driving process is influenced by various factors, including road conditions, traffic situations, weather conditions, and so on. However, our understanding and quantification of the driving environment are still very limited, which not only increases the risk of driving but also hinders the deployment of autonomous vehicles. To address this issue, this study attempts to transform drivers' visual perception into machine vision perception. Specifically, the study provides a detailed decomposition of the elements constituting weather and proposes three environmental quantification indicators: visibility brightness, visibility clarity, and visibility obstruction rate. These indicators help us to describe and quantify the driving environment more accurately. Based on these indicators, a visual-based environmental quantification method is further proposed to better understand and interpret the driving environment. Additionally, based on drivers' visual perception, this study extensively analyzes the impact of environmental factors on driver behavior. A cognitive assessment model is established to evaluate drivers' cognitive abilities in different environments. The effectiveness and accuracy of the model are validated through driver simulation experiments, thereby establishing a communication bridge between the driving environment and driver behavior. This research achievement enables us to better understand the decision-making behavior of drivers in specific environments and provides some references for the development of intelligent driving technology. [ABSTRACT FROM AUTHOR]

The bridge is the key node of expressway operation. The special structural characteristics of the bridge deck and pavement connection lead to the existence of a critical point of icing, so there is a greater operational risk. In order to study the safety state of vehicle operation under the critical conditions of the road bridge connection, it is of great significance to improve the level of expressway traffic safety. Aiming at the problem of expressway operation risk behavior under the critical state of bridge icing and subgrade section not icing, the friction coefficient of dry pavement, light snow pavement and thin ice pavement under the critical state was studied based on the meteorological environment simulation experiment system. Based on the friction coefficient of different road conditions and combined with the driving simulation experiment system, the driving behavior data of average speed, longitudinal acceleration, lateral acceleration and steering wheel angle were collected. The results show that the characteristics of driving behavior change at the road bridge junction in three different critical states are significantly different. The driving risk value under the road surface state in the road bridge junction 3 is calculated by the efficiency coefficient method. It is concluded that the critical state at the road bridge junction has a higher risk level. [ABSTRACT FROM AUTHOR]

Mobile robotic systems offer valuable test platforms due to their shared features with autonomous vehicles, including features such as sensor technologies, navigation algorithms, and control systems. However, constraints in laboratory environments or technical resources, along with the need for extensive testing, often necessitate the use of virtual test laboratories. While line-following is a widely preferred application in mobile robotics, research on this topic within virtual laboratories is limited. This study pioneers the use of a car-like robotic vehicle in conducting line-following tests within a virtual laboratory environment. To facilitate these tests, a virtual simulator was developed to meet the requirements of realistic simulations. This simulator includes simulated elements, such as roads and environmental features, along with virtual sensors designed to collect and process dynamic motion data. An exceptional aspect of this study is its ability to collect consistent dynamic travel data by sampling realistic sensor information within a virtual environment. The developed linefollowing algorithm employs a controller to minimise lateral deviation while the robotic vehicle follows a road line during its movement. The study conducted virtual driving tests using two different manoeuvre modes on four distinct road segments, exploring how the manoeuvring style influences the driving quality. It was demonstrated that in the low manoeuvre mode, the ride is more comfortable, but exhibits a greater route deviation due to reduced oscillation, while the high manoeuvre mode exhibits the opposite behaviour. [ABSTRACT FROM AUTHOR]

• The driver's PRT was measured according to the visual fixation characteristics (using an eye-tracking device) and vehicle running data. • The PRT was measured in the interchange exit ramps. • These findings should serve as a valuable reference for refining the design of plateau expressway exit ramps and providing a theoretical basis for improving road safety in the plateau areas. The low-pressure (and low-oxygen) environment in plateau regions significantly impacts the driver's perception-reaction time (PRT) and, in turn, road safety. However, drivers faced increased workloads at interchange exit areas while the Tibet region experienced a significant improvement in high-grade highway construction. Therefore, this study aimed to quantitatively analyze the influence of the plateau environment on PRT. Using a UC/win-road simulator, 6 scenarios of expressway exit ramps were constructed. Then, a total of 50 participants (35 males, 15 females) in Nanjing (altitude of 50 m) and 50 participants (36 males, 14 females) in Lhasa (altitude of 3650 m) were recruited for the simulation experiments. The driver's PRT was measured according to the visual fixation characteristics (using an eye-tracking device) and vehicle running data. The no-difference in PRT for various scenarios was validated using the one-way-ANOVA test. Then, the Spearman bivariate correlation and generalized linear regression model were used to analyze the influence of driver's age, driving experience, gender, and acclimation period on PRT. In addition, the difference in PRT of different groups of plain and plateau drivers was further analyzed using the Mann-Whiteney U and Kruskal-Wallis tests. Although driving experience and gender affected the PRT of the plain and plateau drivers, the effect was greater in the plain areas. The low-pressure environment at high altitude had a more significant impact on young novice male drivers. These findings serve as valuable references for refining the design of expressway exit ramps and improving road safety in the plateau areas. [ABSTRACT FROM AUTHOR]

A high incidence of traffic accidents is often observed in freeway exit ramp areas. Slowing down, wandering, and changing lanes suddenly and continually in a short interval near the exit ramp are important reasons for accidents. Helping drivers start changing lanes sooner and more efficiently in freeway exit ramp areas is a feasible solution to vehicle interweaving. This paper aims to optimize the current guiding sign system and improve drivers' lane-changing behavior before the exit ramp. Three guiding sign optimization measures (sign symbols, ground signs and voice prompts) had been considered before five guiding sign plans were made for driving simulation experiments: original sign (OS) plan, new type sign (NTS) plan, ground guiding sign (GOS) plan, voice prompt (VOS) plan, and voice-ground sign (VGOS) plan. The decisions, reactions, and operation processes of 43 Chinese drivers were compared to confirm the optimal guiding sign plan. The results showed that updating sign symbols, adding ground signs and voice prompts all contributed to the drivers' shorter response time, earlier arrival at the lane-changing location, higher average speed and greater longitudinal distance of lane-changing. These findings can help freeway designers optimize the guiding sign system for freeway exit ramps. [ABSTRACT FROM AUTHOR]

Extra-long tunnels have adverse effects on driving performance since drivers will be affected by the enclosed, dimly lit, and monotonous environment for a longer time. In the context of the increasing number of extra-long tunnels, this study aims to investigate the effects of extra-long freeway tunnels with a length of 20 km on driving performance using driving simulation tests. Factors including the tunnel environment, driving time, traffic flow, and gender were considered as influencing factors. Performance indicators including the brake reaction time, standard deviation of driving speed, and standard deviation of lateral position were collected. A total of 60 drivers were recruited for the driving simulation study, and the relationships between the driving performance indicators and influencing factors were analyzed by establishing linear mixed models with random intercept. The results demonstrate that driving performance significantly degraded after 5 – 6 min of driving in the extra-long tunnel although drivers were more cautious and presented a better performance when just entering the tunnel. Moderately higher traffic flow could improve driving performance in the extra-long tunnel but not on the open road. Meanwhile, female drivers had better lateral and longitudinal stability in the extra-long tunnel, while no significant difference was witnessed in reaction ability and driving performance on the open road. [ABSTRACT FROM AUTHOR]

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