A Driver-Automation Shared Control for Forward Collision Avoidance While Automation Failure

The aim of this study was to compare continuous versus bandwidth haptic steering guidance in terms of lane-keeping behavior, aftereffects, and satisfaction. An important human factors question is whether operators should be supported continuously or only when tolerance limits are exceeded. We aimed to clarify this issue for haptic steering guidance by investigating costs and benefits of both approaches in a driving simulator. Thirty-two participants drove five trials, each with a different level of haptic support: no guidance (Manual); guidance outside a 0.5-m bandwidth (Band1); a hysteresis version of Band1, which guided back to the lane center once triggered (Band2); continuous guidance (Cont); and Cont with double feedback gain (ContS). Participants performed a reaction time task while driving. Toward the end of each trial, the guidance was unexpectedly disabled to investigate aftereffects. All four guidance systems prevented large lateral errors (>0.7 m). Cont and especially ContS yielded smaller lateral errors and higher time to line crossing than Manual, Band1, and Band2. Cont and ContS yielded short-lasting aftereffects, whereas Band1 and Band2 did not. Cont yielded higher self-reported satisfaction and faster reaction times than Band1. Continuous and bandwidth guidance both prevent large driver errors. Continuous guidance yields improved performance and satisfaction over bandwidth guidance at the cost of aftereffects and variability in driver torque (indicating human-automation conflicts). The presented results are useful for designers of haptic guidance systems and support critical thinking about the costs and benefits of automation support systems.

It is becoming a common scene to observe the usages of level 2 automated driving in our daily life. To ensure driving safety while using the level 2 automated driving systems in various traffic conditions, it is an essential issue to clarify the influences of the level 2 automated driving on driver behaviors. Previous studies normally focused on drivers’ reactions to emergency events while using level 2 automated driving. However, it is still unclear that how will drivers interact with the level 2 automated driving systems during the periods when no emergency event occurs. Therefore, a driving simulator experiment was performed, and the differences in driver behaviors, especially eye-gaze behaviors, under level 2 automated driving and manual driving were analyzed. Meanwhile, to simulate non-driving related tasks that may occur in real driving environment, a visual task, Surrogate Reference Task (SuRT), was applied in the experiment. It was observed that the percentage of gaze that fell within the road center area and speedometer significantly decreased, and the gaze time to the left and right mirrors, and the SuRT display significantly increased during level 2 automated driving. Meanwhile, the eyelid closure time were significant longer and the subjective evaluation scores of attention to front and surroundings were significant lower while applying level 2 automated driving. The results indicated that drivers’ attention levels, especially for the front areas, might be significantly reduced during level 2 automated driving, compared to that of manual driving.

After you?! – Use of external human-machine interfaces in road bottleneck scenarios

Shared steering control has been developed to reduce driver workload while keeping the driver in the control loop. A driver could integrate visual sensory information from the road ahead and haptic sensory information from the steering wheel to achieve better driving performance. Previous studies suggest that, compared with adaptive automation authority, fixed automation authority is not always appropriate with respect to human factors. This paper focuses on designing an adaptive shared steering control system via sEMG (surface electromyography) measurement from the forearm of the driver, and evaluates the effect of the system on driver behavior during a double lane change task. The shared steering control was achieved through a haptic guidance system which provided active assistance torque on the steering wheel. Ten subjects participated in a high-fidelity driving simulator experiment. Two types of adaptive algorithms were investigated: haptic guidance decreases when driver grip strength increases (HG-Decrease), and haptic guidance increases when driver grip strength increases (HG-Increase). These two algorithms were compared to manual driving and two levels of fixed authority haptic guidance, for a total of five experimental conditions. Evaluation of the driving systems was based on two sets of dependent variables: objective measures of driver behavior and subjective measures of driver workload. The results indicate that the adaptive authority of HG-Decrease yielded lower driver workload and reduced the lane departure risk compared to manual driving and fixed authority haptic guidance.

The impact of weather conditions and driver characteristics on road safety on rural roads

Shared control has gained considerable attention in the automated vehicle field in recent years, both from a theoretical point of view and also with multiple applications. The development of shared control systems was discussed in a previous review, which presented a taxonomy focused on control algorithms. However, it is still necessary to understand how these systems should be assessed in terms of system performance, driver behavior, cooperation, and road safety. This paper aims to review and classify evaluation methods used in recent studies with real drivers. Results of the present review showed that shared control continues to be of interest to researchers of automated vehicles. The methodology for system evaluation has evolved, with more participants, better testing platforms, and a greater number of comparison baselines. To guide the path toward implementing shared control features in commercial vehicles, this review aims to help researchers to perform relevant evaluation studies in future developments.

Autonomous Emergency Braking systems have been already introduced to the markets to realize road traffic zero fatalities, and humans positively evaluated the functions of such assistance system. However, such assistance system reaches its limit when there are unexpected moving obstacles appearing suddenly from poor visibility area, such as a corner of intersection, and a space behind a parked vehicle. This paper focuses on a parked vehicle overtaking scenario that an occluded pedestrian suddenly might intend to cross a road, and a risk predictive shared deceleration control system which can prepare and avoid for a hidden collision risk is proposed. Under a driving simulator experiment, we investigated its functionality and effectiveness of the proposed early intervention support, and we confirmed that the proposed assistance system can be effective for guiding the drivers to trace the desired velocity.

ABSTRACTConsidering that unexpected events are a major contributory factor of road accidents the main objective of this article is to investigate the effect of several parameters including overall driving performance, distraction sources, driver characteristics, as well as road and traffic environment on accident probability at unexpected incidents. For this purpose, a driving simulator experiment was carried out, in which 95 participants from all age groups were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in different road and traffic conditions. Then, in the framework of the statistical analysis, driving performance is estimated as a new unobserved (latent) variable based on several individual driving simulator parameters while a structural equation model is developed investigating which factors lead to increased accident probability at unexpected incidents. Regarding driver distraction, results indicate that cell phone use has a negative effect on accident risk confirming the initial hypothesis that when talking on the cell phone drivers find it difficult to handle an unexpected incident and as a result are more likely to commit an accident. Overall, a risky driving profile is developed, completing the puzzle of the effect of driver distraction on driver behavior and road safety.

Fatigue is one of the causes of falling asleep at the wheel, which can result in fatal accidents. Thus, it is necessary to have practical fatigue detection solutions for drivers. In literature, electroencephalography (EEG) along with the surrogate measure of reaction time (RT) has been used to develop fatigue detection algorithms. However, these solutions are often based upon wet multi-channel EEG electrodes which are not feasible or practical for drivers. Using dry electrodes and headband like designs would be better. Hence, this study aims to investigate the correlation of EEG log bandpower against RT via a Muse headband which has dry frontal EEG electrodes. 31 subjects underwent an hour-long driving simulation experiment with car deviation events. Based on the video and EEG data, 5 `Sleepy' and 5 `Alert' subjects are identified and analyzed. A differential signal between Fp1 and Fp2 is computed so as to remove the effects of eye blinks, and is analyzed for correlation with RT. Significant positive correlation is found for log delta (1-4 Hz) bandpower, and significant negative correlations for log theta (4-8 Hz) and alpha (8-12 Hz) bandpowers, but the positive correlation of log beta (12-30 Hz) bandpower with RT is not significant. This is a good first step towards building a practical fatigue detection solution for drivers in the future.

A cascade steering shared controller with dual-level dynamic authority

Effect of verbal messages with reminders to communicate driving situations to alter driver behavior in conditional driving automation

Effects of directional auditory and visual warnings at intersections on reaction times and speed reduction times

IntroductionThe overall aim of this study is to compare day-time driving with night-time driving looking at line crossings during self-reported sleepiness and long blinks. The hypothesis is that high levels of self-reported sleepiness (KSS 9) and long blink duration (>0.15 s) will be less associated with critical events during the day-time compared to night-time.MethodThe study is based on data from a driving simulator experiment with 16 participants driving 150 km on a typical Swedish motorway scenario twice: once during daytime and once during night time. In total data from 6 segments of 4 km each equally distributed along the drive was averaged and included in the analysis. A Mixed Model Anova was used to test the effects on KSS, Blink Duration and Line Crossings with factors for Session (Day/Night) and Road segment (1–6), and participant as random. In addition, a logistic regression was used to identify when there is a risk for line crossings. Finally, the proportion of line crossings in relation to high KSS values and long blink durations was tested with Fisher’s exact test.ResultsThe results show no differences in the percentage of Line Crossings to the left during high levels of Karolinska Sleepiness Scale during daytime (33%) compare to night-time (40%). However, there was a significant difference between day and night time line crossings while the driver had long duration blinks (4% during daytime and 35% during night-time). Despite these results the most promising predictor of line crossings in each segment of 4 km/h was KSS with an Odds Ratio of 5.4 with a reference value at Karolinska Sleepiness Scale level 5.ConclusionIn conclusion, the results do not support the hypothesis that high levels of KSS will result in more frequent line crossings at night time compared to day time. However, the result supports the hypothesis that long blink durations are associated with more line crossings when they appear during night time than during daytime.

In this article we review and assess human-centered level of automation (LOA), an alternate approach to traditional, technology-centered design of automation in dynamic-control systems. The objective of human-controlled LOA is to improve human-machine performance by taking into account both operator and technological capabilities. Automation literature has shown that traditional automation can lead to problems in operator situation awareness (SA) due to the out-of-the (control) loop performance problem, which may lead to a negative impact on overall systems performance. Herein we address a standing paucity of research into LOA to deal with these problems. Various schemes of generic control system function allocations were developed to establish a LOA taxonomy. The functions allocated to a human operator, a computer, or both, included monitoring system variables, generating process plans, selecting an “optimal” plan and implementing the plan. Five different function allocation schemes, or LOAs, were empirically investigated as to their usefulness for enhancing telerobot system performance and operator SA, as well as reducing workload. Human participants participated in experimental trials involving a high fidelity, interactive simulation of a telerobot performing nuclear materials handling at the various LOAs. Automation failures were attributed to various simulated system deficiencies necessitating operator detection and correction to return to functioning at an automated mode. Operator performance at each LOA, and during the failure periods, was evaluated. Operator SA was measured using the Situation Awareness Global Assessment Technique, and perceived workload was measured using the NASA-Task Load Index. Results demonstrated improvements in human-machine system performance at higher LOAs (levels involving greater computer control of system functions) along with lower operator subjective workload. However, under the same conditions, operator SA was reduced for certain types of system problems and reaction time to, and performance during, automation failures was substantially lower. Performance during automation failure was best when participants had been functioning at lower, intermediate LOAs (levels involving greater human control of system functions). © 2000 John Wiley & Sons, Inc.

When a conditionally automated vehicle controlled by the machine faces situations beyond the capability of the machine, the human driver is requested to take over the vehicle. This study aims to assess the short-term effects of three factors on the takeover performance: (1) traffic conditions (complex and simple); (2) modality of takeover request (auditory and auditory + visual); (3) lead time of takeover request (TORlt, 5 s and 7 s). The scenario is the obstacle ahead. Indicators include: (1) Take Over Reaction Time (TOrt); (2) approximate entropy (ApEn), operating order of steering wheel Angle and pedal torque; (3) the choice of target lane and speed of lane-changing; (4) mean and standard deviation of acceleration and velocity; (5) quantifiable lateral cross-border risk and longitudinal collision risk. A driving simulation experiment is conducted to collect data for analysis. The effects of the three factors on takeover performance are analyzed by analysis of variance (ANOVA) and non-parametric tests. The results show that when the traffic conditions are complex, drivers have a larger ApEn of the steering wheel angle and brake pedal torque, and a smaller ApEn of acceleration pedal torque. In the 5 s TORlt case, drivers have a smaller ApEn of brake pedal torque the interaction between TORlt, traffic conditions, and modality of TOR affects ApEn of accelerator pedal torque. 5 s TORlt/complex traffic condition makes the scene more urgent, which is easy to cause driver to make sudden and simultaneous turning and sudden braking dangerous behavior meanwhile. Compared with other combinations of modality and TORlt, the combination of 7 s and auditory + visual significantly reduces the lateral cross-border risk and longitudinal collision risk.