Driver behavior while using Level 2 vehicle automation: a hybrid naturalistic study

Vehicle automation is becoming more prevalent. Understanding how drivers use this technology and its safety implications is crucial. In a 6-8 week naturalistic study, we leveraged a hybrid naturalistic driving research design to evaluate driver behavior with Level 2 vehicle automation, incorporating unique naturalistic and experimental control conditions. Our investigation covered four main areas: automation usage, system warnings, driving demand, and driver arousal, as well as secondary task engagement. While on the interstate, drivers were advised to engage Level 2 automation whenever they deemed it safe, and they complied by using it over 70% of the time. Interestingly, the frequency of system warnings increased with prolonged use, suggesting an evolving relationship between drivers and the automation features. Our data also revealed that drivers were discerning in their use of automation, opting for manual control under high driving demand conditions. Contrary to common safety concerns, our data indicated no significant rise in driver fatigue or fidgeting when using automation, compared to a control condition. Additionally, observed patterns of engagement in secondary tasks like radio listening and text messaging challenge existing assumptions about automation leading to dangerous driver distraction. Overall, our findings provide new insights into the conditions under which drivers opt to use automation and reveal a nuanced behavioral profile that emerges when automation is in use.

Fractional-order models identification and control within a supervisory control framework for efficient nutrients removal in biological wastewater treatment plants

Wastewater treatment plants (WWTPs) are highly non-linear processes that must be optimized to meet rigorous environmental water regulations. In this context, efficiency and costs are equally important terms. The ASM3bioP framework is employed in this study to enable simultaneous nitrogen and phosphorus removal using an activated sludge process model with seven-reactor configurations. The activated sludge process is the most complicated and energy-intensive phase of a WWTP. To control dissolved oxygen in aerobic reactors and nitrate levels in anoxic reactors, two robust PI controllers - a classical PI and a non-integer (fractional)-order PI - with both integer-order and fractional-order models are designed. The controllers are created and simulated with the use of a mathematical model that has been developed based on the input data. The lower level fractional controller with a fractional-order model improves both the effluent quality (EQI) and operational cost (OCI) indices significantly. For such biological WWTP, a hierarchical fuzzy logic controller is designed to adjust the dissolved oxygen in the seventh reactor (DO₇) to control ammonia. The implemented supervisory layer control strategy improves effluent quality EQI while increasing OCI marginally.

Scan-based eye tracking measures are predictive of workload transition performance

Given there is no unifying theory or design guidance for workload transitions, this work investigated how visual attention allocation patterns could inform both topics, by understanding if scan-based eye tracking metrics could predict workload transition performance trends in a context-relevant domain. The eye movements of sixty Naval flight students were tracked as workload transitioned at a slow, medium, and fast pace in an unmanned aerial vehicle testbed. Four scan-based metrics were significant predictors across the different growth curve models of response time and accuracy. Stationary gaze entropy (a measure of how dispersed visual attention transitions are across tasks) was predictive across all three transition rates. The other three predictive scan-based metrics captured different aspects of visual attention, including its spread, directness, and duration. The findings specify several missing details in both theory and design guidance, which is unprecedented, and serves as a basis of future workload transition research.

Long-term nitrite-oxidizing bacteria suppression in a continuous activated sludge system exposed to frequent changes in pH and oxygen set-points

Research has proven the adaptation of nitrite-oxidizing bacteria to unfavorable environmental conditions, and this work presents a novel concept to prevent nitrite oxidation during partial nitrification in wastewater. The approach is based on the real-time updating of mathematical models of the process to search for optimal set-points of pH and oxygen concentration in a continuous activated sludge reactor with a high sludge age (20.3 days). A heuristic optimization technique by 13 optimum set-points simultaneously maximized the degree of ammonia oxidation (α) and nitrite accumulation (β), achieving an (α + β) = 190% per day. The activated sludge reactor was conducted for 780 days under three control schemes: open-loop control, fuzzy model supervisory control and phenomenological supervisory control. The phenomenological supervisory control system achieved the best results, simultaneously reaching 95% ammonium oxidation and 90% nitrite accumulation. The Haldane kinetics were analyzed using steady-state concentrations of all nitrogen species, concluding that the simultaneous maximization of α + β led to selecting set-points at the extreme values of the following ranges: pH = 7.5-8.5 and DO = 0.8-1.0 mg O₂/L, which enabled the inhibition of one nitrifier species. At the same time, the other one was relieved from inhibition. The 16sRNA assays indicated that the nitrite-oxidizing bacteria presence (genera Nitrobacter and Nitrospira) shifted from 32% to less than 8% after 280 days of continuous operation with optimal pH and oxygen set-points.

Rate and grade transition control using PI controller based supervisory and regulatory layers: Diacetone alcohol process case study

Product rate and grade transitions are two significant operations undertaken by process plants to operate in uncertain environments. Rate transitions, typically dictated by economic considerations, are often implemented in open loop via a throughput manipulator (TPM). The absence of feedback of product rate to manipulate the TPM results in an offset during rate/grade transitions as well as in presence of feed-side disturbances. In this work, we explore use of a simple PI controller based supervisory control layer that guides the plantwide regulatory layer to achieve product rate targets during grade transition or in the presence of feed grade disturbances. While such a layer cannot incorporate optimal operation and constraint handling explicitly as in model predictive control, it obviates the need for advanced process control elements that medium-to-small scale industries can ill-afford. The PI based supervisory-regulatory control structure is illustrated using simulated case studies on a prototypical process for diacetone alcohol production comprising of a reactor followed by two distillation columns and a recycle.

Does automation trust evolve from a leap of faith? An analysis using a reprogrammed pasteurizer simulation task

Trust is a critical factor that drives successful human-automation interaction in a myriad of modern professional environments. One seminal work on human-automation trust is Muir and Moray (1996) showing that human-machine trust evolves from faith, then dependability, and finally predictability in a simulated supervisory control task. However, our recent work failed to replicate the finding of the original study, calling for further replication efforts. Experiment 1 aimed to fully replicate Muir and Moray (1996) where participants performed a simulated pasteurizer task. Experiment 2 attempted to replicate Experiment 1 using participants who major in Engineering as used in the original study. Both experiments showed that dependability was the best initial predictor of trust, building later to predictability and faith. Two experiments consistently failed to support both the hypothesis proposed by Muir and Moray (1996), that trust develops from predictability to dependability to faith, and their original findings that trust develops initially from faith. The results of the current experiments challenge this widely cited view of how human-machine trust develops. Modern automation designers should be aware that dependability might control initial trust development for general users and incorporate dependability information into their designs.

Assessing the impact of autonomy and overconfidence in UAV first-person view training

With the rapid rise in unmanned aerial vehicles (UAVs) for military and civil first-person applications like infrastructure inspection, there is an increased need for skilled UAV operators. However, research on effective training of UAV pilots has not kept pace with the demand. How much autonomy should be onboard, how much training, and how much control humans should have are still points of debate. To help fill this gap, this paper examines how different training programs and levels of control autonomy affect training outcomes for people operating a UAV in inspection tasks with high onboard autonomy. Results revealed a cost-benefit trade space in that those top performers with both lower-level teleoperation and higher-level supervisory control training could achieve the best performance, but with higher variability, as compare to those who received just supervisory control training. Another important finding was that those trainees who were overconfident were more likely to spend too much time micro-controlling the UAV, and also 15 times more likely to crash. Given that commercial UAV licensing is expected to significantly increase in the next few years, these results suggest more work is needed to determine how to mitigate overconfidence bias both through training and design.

Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19

The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.

Supervisory predictive control for wheel slip prevention and tracking of desired speed profile in electric trains

This article presents a supervisory model predictive control system to track the desired speed profile and simultaneously prevent the wheels from slipping in acceleration mode of electrical trains. The proposed control strategy employs field-oriented control (FOC) to control the angular speed of the wheel. Model predictive control (MPC) is used to control the longitudinal velocity of the train to track the desired speed profile and prevent the wheels from slipping by generating the desired angular velocity for the FOC. Since, it is not possible to control the longitudinal velocity and slip ratio independently, a fuzzy supervisor system is proposed to control the train dynamics at the appropriate operating point. A method is presented to estimate train longitudinal velocity and the adhesion coefficient between the wheels and rail surface. These components are vital to implement the proposed method in a real train control system. The closed loop stability of the control system has been studied. Simulations were run under different friction coefficients corresponding to real train parameters to verify the effectiveness of the proposed re-adhesion control system. The simulation results have been compared with the results of other researches to show the feasibility and validity of the presented approach.

Most permissive liveness-enforcing Petri net supervisors for discrete event systems via linear monitors

This paper proposes a deadlock prevention method to design a maximally permissive liveness-enforcing pure Petri net supervisor for a discrete event system, if such a supervisor exists; otherwise, it obtains the most permissive one in the sense that no other pure liveness-enforcing supervisors via linear monitors can be more permissive than it. This paper exploits an iterative method. At each iteration, a first-met bad marking (FBM) is singled out and an integer linear programming problem (ILPP) is configured. If a feasible solution can be found for the ILPP, then a place invariant (PI) is designed to prohibit the FBM from being reached while no legal marking is forbidden. If the ILPP has no solution, we collect all these FBMs that cannot be optimally controlled. For each of such FBMs, another ILPP is designed to find the least number of legal markings whose reachability conditions contradict the current considered FBM and enumerate all the optimal solutions of this ILPP. Based on it, we develop a 0-1 linear programming problem to find the maximal number of legal markings after removing all the contradictory legal markings. Then, the new sets of legal markings and FBMs are obtained, and we return to the iteration stage to redesign a PI to control each FBM if the ILPP has a feasible solution. Repeat the above process until no FBM can be reached. Finally, a most permissive pure liveness-enforcing supervisor via linear monitors is derived. Two Petri net models are used to illustrate the proposed method.

Petri Net controller synthesis based on decomposed manufacturing models

Utilizing of supervisory control theory on the real systems in many modeling tools such as Petri Net (PN) becomes challenging in recent years due to the significant states in the automata models or uncontrollable events. The uncontrollable events initiate the forbidden states which might be removed by employing some linear constraints. Although there are many methods which have been proposed to reduce these constraints, enforcing them to a large-scale system is very difficult and complicated. This paper proposes a new method for controller synthesis based on PN modeling. In this approach, the original PN model is broken down into some smaller models in which the computational cost reduces significantly. Using this method, it is easy to reduce and enforce the constraints to a Petri net model. The appropriate results of our proposed method on the PN models denote worthy controller synthesis for the large scale systems.

Effects of platooning on signal-detection performance, workload, and stress: A driving simulator study

Platooning, whereby automated vehicles travel closely together in a group, is attractive in terms of safety and efficiency. However, concerns exist about the psychological state of the platooning driver, who is exempted from direct control, yet remains responsible for monitoring the outside environment to detect potential threats. By means of a driving simulator experiment, we investigated the effects on recorded and self-reported measures of workload and stress for three task-instruction conditions: (1) No Task, in which participants had to monitor the road, (2) Voluntary Task, in which participants could do whatever they wanted, and (3) Detection Task, in which participants had to detect red cars. Twenty-two participants performed three 40-min runs in a constant-speed platoon, one condition per run in counterbalanced order. Contrary to some classic literature suggesting that humans are poor monitors, in the Detection Task condition participants attained a high mean detection rate (94.7%) and a low mean false alarm rate (0.8%). Results of the Dundee Stress State Questionnaire indicated that automated platooning was less distressing in the Voluntary Task than in the Detection Task and No Task conditions. In terms of heart rate variability, the Voluntary Task condition yielded a lower power in the low-frequency range relative to the high-frequency range (LF/HF ratio) than the Detection Task condition. Moreover, a strong time-on-task effect was found, whereby the mean heart rate dropped from the first to the third run. In conclusion, participants are able to remain attentive for a prolonged platooning drive, and the type of monitoring task has effects on the driver's psychological state.

Monitor design with multiple self-loops for maximally permissive supervisors

In this paper, we improve the previous work by considering that a control place can have multiple self-loops. Then, two integer linear programming problems (ILPPs) are formulated. Based on the first ILPP, an iterative deadlock control policy is developed, where a control place is computed at each iteration to implement as many marking/transition separation instances (MTSIs) as possible. The second ILPP can find a set of control places to implement all MTSIs and the objective function is used to minimize the number of control places. It is a non-iterative deadlock control strategy since we need to solve the ILPP only once. Both ILPPs can make all legal markings reachable in the controlled system, i.e., the obtained supervisor is behaviorally optimal. Finally, we provide examples to illustrate the proposed approaches.

Supervisory-level interruption recovery in time-critical control tasks

This paper investigates the effectiveness of providing interruption recovery assistance in the form of an interactive visual timeline of historical events on a peripheral display in support of team supervision in time-critical settings. As interruptions can have detrimental effects on task performance, particularly in time-critical work environments, there is growing interest in the design of tools to assist people in resuming their pre-interruption activity. A user study was conducted to evaluate the use of an interactive event timeline that provides assistance to human supervisors in time-critical settings. The study was conducted in an experimental platform that emulated a team of operators and a mission commander performing a time-critical unmanned aerial vehicle (UAV) mission. The study results showed that providing interruption assistance enabled people to recover from interruptions faster and more accurately. These results have implications for interface design that could be adopted in similar time-critical environments such as air-traffic control, process control, and first responders.

Safety analysis of discrete event systems using a simplified Petri net controller

This paper deals with the problem of forbidden states in discrete event systems based on Petri net models. So, a method is presented to prevent the system from entering these states by constructing a small number of generalized mutual exclusion constraints. This goal is achieved by solving three types of Integer Linear Programming problems. The problems are designed to verify the constraints that some of them are related to verifying authorized states and the others are related to avoiding forbidden states. The obtained constraints can be enforced on the system using a small number of control places. Moreover, the number of arcs related to these places is small, and the controller after connecting them is maximally permissive.