Preview Time Research Articles

Novel docking controller for autonomous aerial refueling with probe direct control and learning-based preview method

Abstract Autonomous aerial refueling (AAR) has always been a hot research area due to its significant application and complicated control problem. In order to improve the docking precision of AAR, a novel docking controller with probe direct control and learning-based preview method is proposed. Firstly, the controlled object is transformed from receiver barycenter to probe tactfully. Then, a suitable probe direct controller designed via the combination of reference-observer-based tracking control method and the high order sliding mode control method is proposed for the probe direct control. Furthermore, a learning-based preview method is introduced to solve the tracking lag problem. The prediction of drogue motion is considered in the reference signal. Then, a novel learning algorithm, named deep learning and reinforcement learning (DLRL), which combines deep learning (DL) and reinforcement learning (RL) spatially rather than structurally like deep reinforcement learning (DRL) is proposed to generate the preview time adaptively. And a novel preview index is proposed to adapt for it. Through the combination of probe direct controller and learning-based preview method, the proposed docking controller could improve the tracking precision largely. Effectiveness of the proposed method is demonstrated by the simulations.

Evidence for a retrieval over a sum counting theory of adults' simple addition.

Researchers have recently proposed that educated adults solve the simplest addition problems (e.g., 3 + 2) by an automatic counting procedure, challenging the long-held view that educated adults solve small additions by associative memory retrieval. We tested predications of a sum-counting model that assumes a procedure in which the 2 quantities represented by the operands are encoded and counted sequentially. Here, we presented the 2 operands sequentially (e.g., "3 +" first and then "2") and manipulated the preview time for the first operand (O1) and operator across 2 experiments (both n = 36); the O1 preview times were 1000 ms and 500 ms in Experiments 1 and 2, respectively. We measured response time (RT) from the presentation of the second operand (O2) and compared it with RT when both operands appeared simultaneously. Contrary to the sum-counting model, with sequential presentation, problems with the same O2 sizes (e.g., 3 + 2, 4 + 2) demonstrated significant RT differences across levels of O1, and the sum of the operands was a better RT predictor than O2 with both sequential and simultaneous displays. These results challenge a sum-counting model of the present data but are consistent with a memory retrieval theory. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Closed-Course Human Factors Evaluation of Marking and Marker Visibility

Raised retroreflective pavement markers (RRPMs) are commonly used to provide nighttime delineation of roadways. Although RRPMs are visible during dry conditions, they provide their greatest benefit during wet-night conditions, when typical pavement markings become flooded and lose their retroreflectivite properties. Naturally, the retroreflectivity of RRPMs degrades over time as a result of traffic, ultraviolet light, precipitation, and roadway maintenance activities. Subsequently, it is necessary to examine the relationship between driver performance and the condition of the RRPMs. To assess visibility relative to RRPM condition, study participants rode in the passenger seat of a vehicle operated by a member of the research team, traveling at approximately 15 mph, for two laps around a closed course. Throughout each lap of the course, nine treatments consisting of RRPMs or preformed pavement marking tape of various retroreflectivity levels diverged from a center line to either the right or left. Participants indicated when they could tell which direction the treatment diverged, which was recorded using a GPS unit. A generalized linear model was estimated on a dataset constructed by pairing the observed distances from various treatments with demographic information about each participant. The analysis indicates the distance at which a particular treatment would be visible, which can then be converted to preview time to assess treatment adequacy for a variety of speeds. The RRPM treatments generally provided adequate preview time for older drivers based on the extant literature; however, the preformed pavement marking tape was less adequate at higher speeds and under overhead lighting.

Preview-scheduled steering assistance control for co-piloting vehicle: a human-like methodology

ABSTRACT As a significant active safety technology towards fully autonomous driving, driver-automation co-piloting provides a new thinking for enhancing vehicle active safety and relieving driver’s workload. However, the preview pattern divergence between driver and steering assistance system (SAS) may give rise to serious steering conflicts, which reduces driver’s faith in SAS, cause him/her to turn off it, and even result in fatal traffic accidents. Focusing on mimicking the driver’s steering behaviour, this paper proposes a human-like steering assistance scheme by adaptively varying the preview time and assistance gain of the SAS controller. Driver’s preview behaviour of the road curvature is numerically identified using simulator tests along a multi-curves road, based on which a nominal preview-scheduled driver model is established. Based on the nominal driver model, a co-piloting system featuring human-like preview behaviour and a time-varying assistance penalty is proposed to formulate the steering torque-overlay dynamics of both agents in path-tracking. Next, a linear parameter varying (LPV)/H∞ controller satisfying pole placement is integrated to ensure the robustness and stability within the entire parameter space. This enables the steering assistance scheme to have a scheduled preview pattern like a human driver. Results of driving simulator experiment indicate that the proposed steering assistance scheme is able to mitigate driver-SAS conflicts and improve driver’s path-tracking performance.

Real-time humanoid whole-body remote control framework for imitating human motion based on kinematic mapping and motion constraints

ABSTRACTIn this paper, we propose a whole-body remote control framework that enables a robot to imitate human motion efficiently. The framework is divided into kinematic mapping and quadratic programming based whole-body inverse kinematics. In the kinematic mapping, the human motion obtained through a data acquisition device is transformed into a reference motion that is suitable for the robot to follow. To address differences in the kinematic configuration and dynamic properties of the robot and human, quadratic programming is used to calculate the joint angles of the robot considering self-collision, joint limits, and dynamic stability. To address dynamic stability, we use constraints based on the divergent component of motion and zero moment point in the linear inverted pendulum model. Simulation using Choreonoid and a locomotion experiment using the HUBO2+ demonstrate the performance of the proposed framework. The proposed framework has the potential to reduce the preview time or offline task computation time found in previous approaches and hence improve the similarity of human and robot motion while maintaining stability.

Analysis of Human Remnant in Pursuit and Preview Tracking Tasks

In manual pursuit and preview tracking tasks, humans apply feedforward control to exploit available information of the target trajectory to follow. While the human’s linear, time-invariant dynamics in such tasks are well-understood and have been modeled in the quasi-linear framework, the remaining nonlinear and time-invariant control behavior, the human remnant, is typically ignored. This paper extends the current state-of-the-art theories of human remnant, which are applicable to compensatory tracking tasks only, to the more common and relevant pursuit and preview tracking tasks. Data are presented from three human-in-the-loop tracking experiments. The ratio of the remnant relative to the linear control output is quantified in the frequency domain, and remnant spectra are computed and modeled. The results show that the injected remnant is identical in compensatory, pursuit, and preview tasks, regardless of the task’s controlled element dynamics, preview time, and target trajectory bandwidth. The presented remnant data and models can be used together with already available linear, time-invariant models, to better predict characteristics of human control behavior in pursuit and preview tracking tasks, enabling the design of human assistance systems.

Path tracking control for inverse vehicle handling dynamics

A path planning problem for inverse vehicle handling dynamics is considered. The objective is to devise a path tracking controller under the condition that a vehicle travels along a prescribed path to generate an expected trajectory. Aiming at this purpose, a tyre model and optimal preview time are established firstly. And then a path tracking controller based on Linear Quadratic Regulator (LQR) method is designed and calculation for the path tracking problem is simulated with a joint simulation of Carsim/Simulink. Finally, a real-vehicle test is executed to verify the rationality of the path tracking controller. The results show that the minimum lateral position error between the generated trajectory and the prescribed path can be a good indicator of successful solving of the path tracking problem in inverse vehicle handling dynamics for LQR method. The study can help drivers easily identify safe lane-keeping trajectories and area.

Coordinated control for path following of two‐wheel independently actuated autonomous ground vehicle

This study investigates the path following control problem about distributed-drive self-driving vehicle through coordinated control of the autonomous front-wheel steering and the differential steering control, to improve the tracking performance. On the basis of the model predictive control algorithm and vehicle dynamic model, the preview time adaptive control of path following is proposed to realise path following with changing preview time in straight and turning conditions. Considering the characteristics of the distributed-drive self-driving vehicle, the differential torque control is utilised based on the reference heading angle to realise trajectory tracking under the condition of constant torque demands. To integrate the advantages of the two methods, coordinated control of trajectory tracking based on the autonomous steering and differential steering is performed by setting the weight coefficients method. MATLAB co-simulation with Carsim and road testing validation are executed, and both demonstrate that the coordinated control strategy can not only effectively improve the response speed and flexibility of steering, but also improve the reliability and accuracy of trajectory tracking.

The Driver Experience Under Extreme Lane Change Conditions

The objective of this paper is to examine the driver’s mental workload experience during extreme nonlinear high slip driving conditions, i.e. with the tyres being close to saturation. This is done for the ISO Double Lane Change manoeuvre, based on extensive test data from two professional test drivers, for different speeds, and with varying tyres. Optimal double lane change closed loop performance is defined and determined and compared to the actual test performance, showing and explaining differences in this performance. A path-tracking driver model has been applied to examine the driver model parameters steering gain and preview time. It is shown that these parameters, which can easily be derived from the closed loop vehicle handling data, vary with mental workload as experienced by these test drivers. This correlation has been demonstrated in previous research for normal driving conditions, where workload, in this paper determined through RSME (Rating Scale Mental Effort) scores, is affected by traffic conditions, where fatigue, experience and learning effects play a significant role. Hence, this paper shows that this, i.e. using the driver model as a virtual sensor to estimate mental workload, can be extended to high slip conditions.Driver model parameters vary in time, and are not independent. Different combinations of preview time and steering gain lead to the same closed loop performance. We have extended the application of the driver model, the relationship between the driver model parameters and closed loop stability to high slip conditions, before we applied it to severe, i.e. high slip lane change performance.

Effects of speech rate, preview time of visual context, and participant instructions reveal strong limits on prediction in language processing

There is a consensus among language researchers that people can predict upcoming language. But do people always predict when comprehending language? Notions that “brains … are essentially prediction machines” certainly suggest so. In three eye-tracking experiments we tested this view. Participants listened to simple Dutch sentences (‘Look at the displayed bicycle’) while viewing four objects (a target, e.g. a bicycle, and three unrelated distractors). We used the identical visual stimuli and the same spoken sentences but varied speech rates, preview time, and participant instructions. Target nouns were preceded by definite gender-marked determiners, which allowed participants to predict the target object because only the targets but not the distractors agreed in gender with the determiner. In Experiment 1, participants had four seconds preview and sentences were presented either in a slow or a normal speech rate. Participants predicted the targets as soon as they heard the determiner in both conditions. Experiment 2 was identical except that participants were given only a one second preview. Participants predicted the targets only in the slow speech condition. Experiment 3 was identical to Experiment 2 except that participants were explicitly told to predict. This led only to a small prediction effect in the normal speech condition. Thus, a normal speech rate only afforded prediction if participants had an extensive preview. Even the explicit instruction to predict the target resulted in only a small anticipation effect with a normal speech rate and a short preview. These findings are problematic for theoretical proposals that assume that prediction pervades cognition.

Effects of Preview Time in Manual Tracking Tasks

In manual control tasks, preview of the target trajectory ahead is often limited by poor lighting, objects, or display edges. This paper investigates the effects of limited preview, or preview time, in manual tracking tasks with single- and double-integrator controlled element dynamics. A quasi-linear human controller model is used to predict the human behavior adaptations offline, by finding the model parameters that yield optimal performance at each preview time. These predictions are then verified by fitting the same model to measurements from a human-in-the-loop experiment, where subjects performed a tracking task with eight different preview time settings between 0 and 2 s. Results show that the tracking performance improves and the model's “look-ahead” time parameters increase with increasing preview time. Beyond a certain preview time, approximately 0.6 s and 1.15 s in single- and double-integrator tasks, respectively, additional preview evokes no further adaptations. The offline model predictions closely match the experimental results, which thereby promises to facilitate similar quantitative insights in other tasks with restricted preview.

Preview Distance Index for the Analysis of Powered Wheelchair Driving

Background This article deals with powered wheelchair (PW) simulators. In any application of simulation (learning, medical prescription, experimentation of new features, etc.) it is necessary to define driving performance criteria. The aim of this work is to validate a new performance index for PW driving analysis.Methods The validation was carried out in two stages. First, we modelled the human-machine system consisting of the pilot and the PW with the OPCM model (Optimal Preview Control Model) and we deduced the importance of the preview time Tp or its equivalent, the preview distance Dp. Experiments on a panel of 15 healthy subjects were carried out on the 3D simulator ViEW (Virtual Electrical Wheelchair) associated with an eye-tracker system and allowed to validate this modelling. In the second stage, the healthy panel was separated into two skill-level groups (accustomed/novice).Results The experimental results showed that the parameter Dp is representative of the driving quality: accustomed drivers get greater values of Dp than the novice users during the driving. To illustrate this assertion, we conclude by a case study conducted on five subjects with motor disabilities.Conclusion This work showed that Dp allows to differentiate accustomed users from the novice ones using a test on healthy subjects. A second test on subjects with disabilities showed that this indicator can be used for analysing their abilities to drive the PW.

Automatic Summarization of Lecture Slides for Enhanced Student PreviewTechnical Report and User Study

This paper is an extension of research originally reported in [1] . Here, we propose a novel method for summarizing lecture slides to enhance students’ preview efficiency and understanding of the content. Students are often asked to prepare for a class by reading lecture materials. However, because the attention span of students is limited, this is not always beneficial. We surveyed 326 students regarding the preview of lecture materials, revealing a preference for summarized materials to preview. Therefore, we developed an automatic summarization method for condensing original lecture materials into a summarized set. Our proposed approach utilizes image and text processing to extract important pages from lecture materials, optimizing selection of pages in accordance with a specified preview time. We applied the proposed summarization method to a set of lecture slides. In an experiment with 372 students, we compared the effectiveness of the summarized slides and the original materials in terms of quiz scores, preview achievement ratio, and time spent previewing. We found that students who previewed the summarized slides achieved better scores on pre-lecture quizzes, even though they spent less time previewing the material.

Contextual Constraint and Preview Time Modulate the Semantic Preview Effect: Evidence from Chinese Sentence Reading.

Word recognition in sentence reading is influenced by information from both preview and context. Recently, semantic preview effect (SPE) was observed being modulated by the constraint of context, indicating that context might accelerate the processing of semantically related preview words. Besides, SPE was found to depend on preview time, which suggests SPE may change with different processing stages of preview words. Therefore it raises the question of whether preview-time-dependent SPE would be modulated by contextual constraint. In the current study, we investigated the impact of contextual constraint on SPE in Chinese reading, but also examined its dependency on preview time. The preview word and the target word were identical, semantically related, or unrelated to the target word. The results showed a significant three-way interaction: The SPE depended on contextual constraint and preview time. In separate analyses for low and high contextual constraint of target words, the SPE significantly decreased with an increase in preview duration when the target word was of low constraint in the sentence. The effect was numerically in the same direction, but weaker and statistically non-significant when the target word was highly constrained in the sentence. The results indicate that word processing in sentences is a dynamic process of integrating information from both preview (bottom up) and context (top down).

Output-feedback robust control for vehicle path tracking considering different human drivers’ characteristics

A dynamic output-feedback, robust, shared controller considering different drivers’ characteristics is proposed to assist human drivers for path tracking. The uncertain, diverse parameters for describing different drivers’ characteristics including delay time, preview time, and steering proportional gain are considered and handled by a polytope. The regional pole placement is applied in the proposed dynamic output-feedback controller to improve the stability and performance of the driver-vehicle system. A method to convert the multi-objective H∞ robust control into the single objective control is also introduced. Simulation results indicate that performance of path following for the driver-vehicle systems with different human drivers are improved with the proposed controller. Meanwhile, physical workloads of the inexperienced drivers are significantly reduced. Simulation results also show that the delay time of the driver-vehicle system can be adjusted by the pole placement, and the preview time of the driver can also be reduced to some extent with the proposed controller. Robustness of the controller is preserved against parameter variations and disturbance.

A Gain-Scheduling Driver Assistance Trajectory-Following Algorithm Considering Different Driver Steering Characteristics

In this paper, a gain-scheduling, robust, and shared controller is proposed to assist drivers in tracking vehicle reference trajectory. In the controller, the driver steering parameters such as delay time, preview time, and steering gain are assumed to be varying with respect to the different characteristics of drivers, vehicle states, and driving scenarios. Meanwhile, the modeling errors and uncertainties in the tire cornering stiffness are also considered in the driver–vehicle system model and the controller design. A global objective function, considering the tracking error, the driver's physical and mental workloads, and the control effort, is designed to optimize the overall performance of the driver–vehicle system. Constraint on eigenvalue placement is added to the controller design to improve the performance of the closed-loop driver–vehicle system. Simulation results under different maneuvers show that the controller can significantly improve the system performance and reduce the driver's workloads. The controller can reduce the delay time of the driver–vehicle system in emergency maneuvers, particularly for inexperienced drivers.

Design of an improved predictive LTR for rollover warning systems

Rollover warning system predicts whether the vehicle has a risk of rollover in a period of time based on the current driving state of the vehicle. Most previous studies on rollover evaluation index have only used vehicle’s current states and often ignored the judgment of the overall tendency of the motion. In the calculation of the rollover early warning algorithm, it is assumed that the vehicle input was known. Based on the above reasons, this paper introduces the derivative of LTR into the rollover index, using an improved predictive LTR (IPLTR) as the rollover index, and realized the real-time calculation through an 8-DOF nonlinear vehicle model. The vehicle rollover prediction system is achieved through the preview driver model and the rollover prediction index calculation model. To achieve a proper preview time for the driver model, a comprehensive evaluation formula that considers the trajectory deviation, the steering wheel rotation speed and lateral acceleration is used. Finally, an IPLTR is developed, which utilizes a driver model to predict the steering angle and several other sensor signals available from the electronic stability control system as inputs. The new IPLTR index can provide a time-advanced measure of rollover propensity and its effectiveness is verified by virtual tests and experimental test data. Simulation results by double lane change (DLC) and “Highway Merge” road maneuvers show that the proposed rollover warning algorithm is rather satisfying. Experimental test by pylon course slalom maneuver also proves its effectiveness and has more advantages of such a system in rollover prevention.

An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle

ABSTRACTAutomated driving has received a broad of attentions from the academia and industry, since it is effective to greatly reduce the severity of potential traffic accidents and achieve the ultimate automobile safety and comfort. This paper presents an optimal model-based trajectory following architecture for highly automated vehicle in its driving tasks such as automated guidance or lane keeping, which includes a velocity-planning module, a steering controller and a velocity-tracking controller. The velocity-planning module considering the optimal time-consuming and passenger comforts simultaneously could generate a smooth velocity profile. The robust sliding mode control (SMC) steering controller with adaptive preview time strategy could not only track the target path well, but also avoid a big lateral acceleration occurred in its path-tracking progress due to a fuzzy-adaptive preview time mechanism introduced. In addition, an SMC controller with input–output linearisation method for velocity tracking is built and validated. Simulation results show this trajectory following architecture are effective and feasible for high automated driving vehicle, comparing with the Driver-in-the-Loop simulations performed by an experienced driver and novice driver, respectively. The simulation results demonstrate that the present trajectory following architecture could plan a satisfying longitudinal speed profile, track the target path well and safely when dealing with different road geometry structure, it ensures a good time efficiency and driving comfort simultaneously.

Spatial attention is necessary for object-based attention: Evidence from temporal-order judgments

Attentional selection is a dynamic process that relies on multiple types of representations. That object representations contribute to attentional selection has been known for decades; however, most evidence for this contribution has been gleaned from studies that have relied on various forms of spatial cueing (some endogenous and some exogenous). It has thus remained unclear whether object-based attentional selection is a direct result of spatial cuing, or whether it still emerges without any spatial marker. Here we used a novel method-the temporal-order judgment (TOJ)-to examine whether object-based guidance emerges in the absence of spatial cuing. Participants were presented with two rectangles oriented either horizontally or vertically. Following a 150-ms preview time, two target stimuli were presented on the same or on different objects, and participants were asked to report which of the two stimuli had appeared first. The targets consisted of stimuli that formed a percept of a "hole" or a "hill." First, we demonstrated that the "hill" target was indeed processed faster, as evidenced by a positive perceived simultaneity (PSS) measure. We then demonstrated that if two targets appeared with equal probabilities on the same and on different objects, the PSS values, although positive, were not modulated by the objects. In a subsequent set of experiments, we showed that objects can modulate attentional allocation-however, only when they are biased by a spatial (endogenous) cue. In other words, in the absence of a spatial cue or bias, object representations do not guide attentional selection. In addition to providing new constraints for theories of object-based attentional guidance, these experiments introduce a novel paradigm for measuring object-based attentional effects.

Optimisation of active suspension control inputs for improved vehicle ride performance

ABSTRACTA collocation-type control variable optimisation method is used in the paper to analyse to which extent the fully active suspension (FAS) can improve the vehicle ride comfort while preserving the wheel holding ability. The method is first applied for a cosine-shaped bump road disturbance of different heights, and for both quarter-car and full 10 degree-of-freedom vehicle models. A nonlinear anti-wheel hop constraint is considered, and the influence of bump preview time period is analysed. The analysis is then extended to the case of square- or cosine-shaped pothole with different lengths, and the quarter-car model. In this case, the cost function is extended with FAS energy consumption and wheel damage resilience costs. The FAS action is found to be such to provide a wheel hop over the pothole, in order to avoid or minimise the damage at the pothole trailing edge. In the case of long pothole, when the FAS cannot provide the wheel hop, the wheel is travelling over the pothole bottom and then hops over the pothole trailing edge. The numerical optimisation results are accompanied by a simplified algebraic analysis.