Search

With more autonomous vehicles (AVs) being tested or deployed on public roads, human-driven vehicles (HVs) have to share the road with AVs. However, human drivers may not interact AVs the same way as they interact with HVs. Very few studies have investigated drivers’ behaviors when sharing the road with AVs. Based on a real-world dataset, our study explored drivers’ interactions with AVs in two types of events on highway, i.e., car-following event and car-passing event. The results show that, compared to interacting with HVs, drivers tended to keep a larger safety margin (i.e., larger gap distance and time gap) at high speed in both types of events when interacting with AVs. At the same time, drivers seemed to have difficulty anticipating AVs’ speed changes at high speed, as indicated by a larger standard deviation of the HVs’ speed and a smaller time to collision when following AVs versus following HVs.

In-vehicle infotainment systems can increase cognitive load and impair driving performance. These effects can be alleviated through interfaces that can assess cognitive load and adapt accordingly. Eye-tracking and physiological measures that are sensitive to cognitive load, such as pupil diameter, gaze dispersion, heart rate (HR), and galvanic skin response (GSR), can enable cognitive load estimation. The advancement in cost-effective and nonintrusive sensors in wearable devices provides an opportunity to enhance driver state detection by fusing eye-tracking and physiological measures. As a preliminary investigation of the added benefits of utilizing physiological data along with eye-tracking data in driver cognitive load detection, this paper explores the performance of several machine learning models in classifying three levels of cognitive load imposed on 33 drivers in a driving simulator study: no external load, lower difficulty 1-back task, and higher difficulty 2-back task. We built five machine learning models, including k-nearest neighbor, support vector machine, feedforward neural network, recurrent neural network, and random forest (RF) on (1) eye-tracking data only, (2) HR and GSR, (3) eye-tracking and HR, (4) eye-tracking and GSR, and (5) eye-tracking, HR, and GSR. Although physiological data provided 1%–15% lower classification accuracies compared with eye-tracking data, adding physiological data to eye-tracking data increased model accuracies, with an RF classifier achieving 97.8% accuracy. GSR led to a larger boost in accuracy (29.3%) over HR (17.9%), with the combination of the two factors boosting accuracy by 34.5%. Overall, utilizing both physiological and eye-tracking measures shows promise for driver state detection applications.

Objective The aim of this study is to investigate how anticipatory driving is influenced by distraction. Background The anticipation of future events in traffic can allow potential gains in recognition and response times. Anticipatory actions (i.e., control actions in preparation for potential traffic changes) have been found to be more prevalent among experienced drivers in simulator studies when driving was the sole task. Despite the prevalence of visual-manual distractions and their negative effects on road safety, their influence on anticipatory driving has not yet been investigated beyond hazard anticipation. Methods A simulator experiment was conducted with 16 experienced and 16 novice drivers. Half of the participants were provided with a self-paced visual-manual secondary task presented on a dashboard display. Results More anticipatory actions were observed among experienced drivers; experienced drivers also exhibited more efficient visual scanning behaviors as indicated by higher glance rates toward and percent times looking at cues that facilitate the anticipation of upcoming events. Regardless of experience, those with the secondary task displayed reduced anticipatory actions and paid less attention toward anticipatory cues. However, experienced drivers had lower odds of exhibiting long glances toward the secondary task compared to novices. Further, the inclusion of glance duration on anticipatory cues increased the accuracy of a model predicting anticipatory actions based on on-road glance durations. Conclusion The results provide additional evidence to existing literature supporting the role of driving experience and distraction engagement in anticipatory driving. Application These findings can guide the design of in-vehicle systems and guide training programs to support anticipatory driving.

Driver distraction is one of the leading causes of vehicle crashes. The introduction of higher levels of vehicle control automation is expected to alleviate the negative effects of distraction by delegating the driving task to automation, thus enabling drivers to engage in non-driving-related tasks more safely. However, before fully automated vehicles are realized, drivers are still expected to play a supervisory role and intervene with the driving task if necessary while potentially having more spare capacity for engaging in non-driving-related tasks. Traditional distraction mitigation perspectives need to be shifted for automated vehicles from mainly preventing the occurrence of non-driving-related tasks to dynamically coordinating time-sharing between driving and non-driving-related tasks. In this paper, we provide a revised and expanded taxonomy of driver distraction mitigation strategies, discuss how the different strategies can be used in an automated driving context, and propose directions for future research in supporting time-sharing in automated vehicles.

This paper explores the influence of high cognitive load on vehicle driver's electroencephalography (EEG) signals collected from two channels (Fp1, Fp2) using a wireless consumer-grade system. Although EEG has been used in driving-related research to assess cognitive load, only a few studies focused on high load, and they used research-grade systems. Recent advancements allow for less intrusive and more affordable systems. As an exploration, we tested the feasibility of one such system to differentiate among three levels of cognitive taskload in a simulator study. Thirty-seven participants completed a baseline drive with no secondary task and two drives with a modified version of the n -back task (1-back and 2-back). The modification removed the verbal response required during task presentation to prevent EEG-signal degradation, with the 2-back task expected to impose higher load than that by the 1-back task. Another objective of this study is to validate that this modified task increased the cognitive load in the expected manner. The modified task led to significant trends from baseline to 1-back, and from 1-back to 2-back in participants’ heart rate, galvanic skin response, respiration, horizontal gaze position variability, and pupil diameter, all in line with the previous driving-related studies on cognitive load. Furthermore, the EEG system was observed to be sensitive to the modified task, with the power of alpha band decreasing significantly with increasing n -back levels (baseline versus 1-back: 0.092 Bels on Fp1, 0.179 on Fp2; 1-back versus 2-back: 0.209 on Fp1, 0.147 on Fp2). Thus, a consumer-grade EEG system has the potential to capture high levels of cognitive load experienced by drivers.

State-of-the-art vehicle automation requires drivers to visually monitor the driving environment and the automation (through interfaces and vehicle’s actions) and intervene when necessary. However, as evidenced by recent automated vehicle crashes and laboratory studies, drivers are not always able to step in when the automation fails. Research points to the increase in distraction or secondary-task engagement in the presence of automation as a potential reason. However, previous research on secondary-task engagement in automated vehicles mainly focused on experienced drivers. This issue may be amplified for novice drivers with less driving skill. In this paper, we compared secondary-task engagement behaviors of novice and experienced drivers both in manual (non-automated) and automated driving settings in a driving simulator. A self-paced visual-manual secondary task presented on an in-vehicle display was utilized. Phase 1 of the study included 32 drivers (16 novice) who drove the simulator manually. In Phase 2, another set of 32 drivers (16 novice) drove with SAE-level-2 automation. In manual driving, there were no differences between novice and experienced drivers’ rate of manual interactions with the secondary task (i.e., taps on the display). However, with automation, novice drivers had a higher manual interaction rate with the task than experienced drivers. Further, experienced drivers had shorter average glance durations toward the task than novice drivers in general, but the difference was larger with automation compared with manual driving. It appears that with automation, experienced drivers are more conservative in their secondary-task engagement behaviors compared with novice drivers.

In this paper, we report a secondary analysis of data collected from two driving simulator experiments to understand the effects of SAE-Level 2 driving automation on drivers’ glances in anticipation of traffic events. Background: Current state-of-the-art consumer vehicle automation requires drivers to monitor the road and intervene when automation fails. Limited research has investigated the effects of automation on drivers’ anticipation of upcoming traffic events. We recently reported two driving simulator studies that focused on drivers’ glance behaviors before such events; however, we did not compare the results of these two studies. Methods: In this paper, we report statistical analyses comparing the glance data from these two studies that had 32 participants each, half of whom were novices and the other half were experienced drivers. The two experiments were comparable in terms of the driving scenarios that required anticipation: the first experiment focused on driving without automation; while the second focused on driving with automation consisting of adaptive cruise control and lane keeping assistance. Further, half of the participants in each experiment were provided with a self-paced visual-manual secondary task. Results: In the no-secondary-task condition, drivers in the automation experiment spent a higher percent of time glancing at anticipatory cues that indicated an upcoming traffic event than did drivers in the no-automation experiment. In the secondary-task condition, no such difference was observed between the two experiments. Conclusion: When there is no distraction to engage in, it appears that automation can allow drivers to have increased visual attention to anticipatory cues.

Objective This paper investigates the effectiveness of in-vehicle displays in supporting drivers’ anticipation of traffic conflicts in automated vehicles (AVs). Background Providing takeover requests (TORs) along with information on automation capability (AC) has been found effective in supporting AV drivers’ reactions to traffic conflicts. However, it is unclear what type of information can support drivers in anticipating traffic conflicts, so they can intervene (pre-event action) or prepare to intervene (pre-event preparation) proactively to avert them. Method In a driving simulator study with 24 experienced and 24 novice drivers, we evaluated the effectiveness of two in-vehicle displays in supporting anticipatory driving in AVs with adaptive cruise control and lane keeping assistance: TORAC (TOR + AC information) and STTORAC displays (surrounding traffic (ST) information + TOR + AC information). Both displays were evaluated against a baseline display that only showed whether the automation was engaged. Results Compared to the baseline display, STTORAC led to more anticipatory driving behaviors (pre-event action or pre-event preparation) while TORAC led to less, along with decreased attention to environmental cues that indicated an upcoming event. STTORAC led to the highest level of driving safety, as indicated by minimum gap time for scenarios that required driver intervention, followed by TORAC, and then the baseline display. Conclusions Providing surrounding traffic information to drivers of AVs, in addition to TORs and automation capability information, can support their anticipation of potential traffic conflicts. Without the surrounding traffic information, drivers can over-rely on displays that provide TORs and automation capability information.

The anticipation of future events in traffic can allow potential gains in recognition and response times. Anticipatory actions (i.e., actions in preparation for a potential upcoming conflict) have been found to be more prevalent among experienced drivers in a driving simulator study where driving was the sole task. The influence of secondary tasks on anticipatory driving has not yet been investigated, despite the prevalence and negative effects of distraction widely documented in the literature. A driving simulator experiment was conducted with 16 experienced and 16 novice drivers to address this gap with half of the participants provided with a self-paced visual-manual secondary task. More anticipatory actions were observed among experienced drivers in general compared to novices; experienced drivers also exhibited more efficient visual scanning behaviors. Secondary task engagement reduced anticipatory actions for both experienced and novice drivers.

Objective To understand the influence of driving experience and distraction on drivers’ anticipation of upcoming traffic events in automated vehicles. Background In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. Methods A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Results Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. Conclusion In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Application Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.

This article provides an interdisciplinary perspective on driver monitoring systems by discussing state-of-the-art signal processing solutions in the context of road safety issues identified in human factors research. Recently, the human factors community has made significant progress in understanding driver behaviors and assessed the efficacy of various interventions for unsafe driving practices. In parallel, the signal processing community has had significant advancements in developing signal acquisition and processing methods for driver monitoring systems. This article aims to bridge these efforts and help initiate new collaborations across the two fields. Toward this end, we discuss how vehicle measures, facial/body expressions, and physiological signals can assist in improving driving safety through adaptive interactions with the driver, based on the driver's state and driving environment. Moreover, by highlighting the current human factors research in road safety, we provide insights for building feedback and mitigation technologies, which can act both in real time and postdrive. We provide insights into areas with great potential to improve driver monitoring systems, which have not yet been extensively studied in the literature, such as affect recognition and data fusion. Finally, a high-level discussion is given on the challenges and possible future directions for driver monitoring systems.

A model-based vehicle stability controller is proposed in this paper. Since a model-based controller highly depends on the model accuracy, the lateral forces of the tyres and the instantaneous cornering stiffnesses of the predictive vehicle model are estimated online in order to deal with the model mismatch. The model-based controller calculates a desired yaw moment according to the prevailing errors in the the yaw rate and the side-slip angles of the vehicle. Then, by using linearization, an optimal braking force allocation algorithm is proposed to allocate the yaw moments to the target slip ratios of each wheel. Simulations are carried out with a full-vehicle model in CarSim software. The effectiveness and the robustness of the proposed control algorithm are evaluated through simulation cases of open-loop and closed-loop manoeuvres.

To enhance the trafficability of semi-track air-cushion vehicle (STACV), a prototype is manufactured and a heirarchy controller is designed. The control objectives are, 1) to minimize the total power consumption; 2) to follow the driver’s commands and maintain the body attitude; 3) to assign and realize the generalized forces. The controller is proposed by using carefully designed controller configuration, neural network, Model Predictive Control (MPC) method, hybrid generalized extremal optimization (HGEO), etc. Finally, the effectiveness of the proposed controller is verified by experiments under two typical operation conditions.