In the manufacturing industry, the Facility Layout Problem (FLP) focuses on determining the optimal arrangement of facilities to minimize production costs, primarily by reducing the total transportation cost between facilities. As FLP is a complex combinatorial optimization problem, evolutionary algorithms such as Genetic Algorithms (GA) are often employed, requiring repeated evaluation of transportation costs. To reduce computational time, simple distance measures like Euclidean distance, Manhattan distance, or shortest paths obtained via the A* algorithm are commonly used. However, these measures do not account for collisions between transporters and facilities, leading to unrealistic solutions. Although Multi-Agent Path Finding (MAPF) algorithms are well known for calculating collision-free shortest paths, their high computational complexity makes them impractical for repeated evaluation within evolutionary frameworks. To address this challenge, this study proposes a method to reduce computation time by partitioning the MAPF problem using a divide-and-conquer approach. We demonstrate that dividing the problem reduces the number of transporters considered in each subproblem, significantly decreasing overall computation time. Furthermore, we show that additional speedup can be achieved through parallel computation of the subproblems.

In this paper, first of all, the group walking behavior by four pedestrians are observed. In the observation, not only the motion data but also the decision making of each pedestrian are collected by using special device. Then, three behavioral indicators: deceleration, detour amount, and decision entropy, are defined and calculated. It has been found that these three indicators successfully quantify the ’smoothness’of the group walking behavior. Finally, the principal component analysis(PCA) is applied to the three dimensional indicator data. As the result, the meaning of three principal components are clearly explained. The discussion based on the PCA will be a basis for the further analysis and classification of the group walking behavior.

Smooth and safe interactions between pedestrians and Autonomous Mobile Robots (AMRs) are crucial for integrating robotic systems into shared environments. Previous studies on external Human-Machine Interfaces (eHMIs) often employed static information presentation, neglecting dynamic interaction contexts. This study investigates the effectiveness of gaze-based nudge-intuitive and unconscious communication via gaze behavior—in facilitating pedestrian role selection (leader or follower) during perpendicular crossing interactions with AMRs. Virtual reality (VR) experiments using Unity and Cybershoes are conducted to evaluate two gaze patterns generated by the AMR: ‘Leader’s gaze’, a brief gaze directed toward pedestrians in the early stages of interactions, and ‘Follower’s gaze’, a gaze keeping track of pedestrians throughout the interaction until crossing completion. The impact of gaze timing (Early/Late) and initial positional relationships between pedestrians and AMRs (initial ∆TTCP) are systematically analyzed. The results indicate that gaze nudging significantly enhances pedestrians’ subjective ratings of safety, smoothness, and understanding of the robot’s intention compared to no-gaze conditions. Leader’s gaze effectively encourages pedestrians to adopt the follower role under conditions favoring AMR priority (small or negative ∆TTCP), whereas Follower’s gaze promotes pedestrians to adopt the leader role under conditions naturally favoring pedestrian priority (larger ∆TTCP). Additionally, the effectiveness of gaze nudging strongly depends on interaction timing, with early-stage gaze presentations exhibiting greater influence. These findings confirm the potential of gaze-based nudging as a non-intrusive, context-sensitive strategy for pedestrian-AMR interactions, emphasizing the importance of precisely timed gaze presentations for facilitating pedestrians’ natural and intuitive role selection.

This paper presents a novel Stochastic Optimal Control (SOC) method based on Model Predictive Path Integral control (MPPI), named Stein Variational Guided MPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal action distributions. While MPPI can find a Gaussian-approximated optimal action distribution in closed form, i.e., without iterative solution updates, it struggles with the mul-timodality of the optimal distributions. This is due to the less representative nature of the Gaussian. To overcome this limitation, our method aims to identify a target mode of the optimal distribution and guide the solution to converge to fit it. In the proposed method, the target mode is roughly estimated using a modified Stein Variational Gradient Descent (SVGD) method and embedded into the MPPI algorithm to find a closed-form "mode-seeking" solution that covers only the target mode, thus preserving the fast convergence property of MPPI. Our simulation and real-world experimental results demonstrate that SVG-MPPI outperforms both the original MPPI and other state-of-the-art sampling-based SOC algorithms in terms of path-tracking and obstacle-avoidance capabilities. https://github.com/kohonda/proj-svg_mppi

Four-wheel independent drive and steering vehicle (4WIDS Vehicle, Swerve Drive Robot) has the ability to move in any direction by its eight degrees of freedom (DoF) control inputs. Although the high maneuverability enables efficient navigation in narrow spaces, obtaining the optimal command is challenging due to the high dimension of the solution space. This paper presents a navigation architecture using the Model Predictive Path Integral (MPPI) control algorithm to avoid collisions with obstacles of any shape and reach a goal point. The key idea to make the problem easier is to explore the optimal control input in a reasonably reduced dimension that is adequate for navigation. Through evaluation in simulation, we found that the selecting sampling space of MPPI greatly affects navigation performance. In addition, our proposed controller which switches multiple sampling spaces according to the real-time situation can achieve balanced behavior between efficiency and safety.Source code is available at https://github.com/MizuhoAOKI/mppi_swerve_drive_ros.

This study presents a new framework for vehicle motion planning and control based on the automatic generation of model predictive controllers (MPCs) named MPC Builder. In this framework, several components necessary for MPC, such as prediction models, constraints, and cost functions, are prepared in advance. The MPC Builder then generates various MPCs online in a unified manner according to traffic situations. This scheme enabled us to represent various driving tasks with less design effort than typical switched MPC systems. The proposed framework was implemented considering the continuation/generalized minimum residual (C/GMRES) method optimization solver, which can reduce computational costs. Finally, numerical experiments on multiple driving scenarios were presented.

Model predictive control (MPC) has been widely used for controlling multi-input multi-output (MIMO) systems. MIMO systems might consist of dynamics with different response speeds. Therefore, different horizons and prediction rates should be applied according to the response speed of each dynamic. However, multi-horizon and multi-rate prediction leads to mismatches of prediction points and results in prediction difficulties. In addition, multiple control rates should also be considered due to hardware constraints. This paper presents a multi-horizon and multi-rate MPC (MM-MPC) with zero-order hold interpolation for dealing with the mismatches of prediction points. Furthermore, by running MM-MPCs at different rates, a multi-control-rate system is constructed without ignoring the dynamic interaction between the dynamics. The presented methods were demonstrated through simulations. The results show that the presented MM-MPC can reach a better overall performance compared to conventional unified MPCs. In addition, the multi-control-rate system consisting of MM-MPCs with multiple execution rates reduces the average computation time without deteriorating the performance.

A novel collision-free motion planner was proposed for tractor-trailer mobile robots (TTMRs) in a narrow environment with consideration of the polygonal shape of the TTMR and obstacles. The motion planner was designed as an iterative nonlinear optimization problem with a receding horizon similar to the model predictive control. Collision-free constraints with a configuration of the TTMRs were derived from the Farkas’ lemma with simplification, which were the hard constraints in the optimization problem. As such, the proposed method guarantees collision avoidance in its motion planning. The presented modified Farkas’ lemma stabled the fluctuated calculation time during the optimization. Numerical simulations confirmed the validity of the proposed method. A thorough evaluation of the dynamic environment with a moving obstacle was also carried out.

This study compares and evaluates the effect of the choice of the vehicle's prediction model on the performance in designing a path-tracking controller for vehicles using Model Predictive Control (MPC). The Kinematic Ackermann Model (KAM), the Kinematic Bicycle Model (KBM), and the Dynamic Bicycle Model (DBM) are well known as nonlinear prediction models. The stability and tracking performance of these models are evaluated using simulations, and a newly proposed DBM improved in Low-speed range (DBM-L) is also compared. As a result of the simulation, the proposed DBM-L was able to run in the widest 0 to 120km/h speed range among the models tested, and it was able to achieve the stop-and-go behavior that was not possible with the conventional DBM. In the future, if we can solve the problem that the tracking accuracy of the DBM-L is slightly decreased in the extremely low and high speed ranges, a vehicle prediction model that can be used in all speed ranges is expected to be realized.

Advanced driver assistance systems should not only make the driving experience safer and more comfortable, it should also have a positive effect on driving behaviors. In this paper, an instructor-like assistance system for collision avoidance is developed and realized on an actual vehicle. The proposed system is activated only if the driver is not operating the vehicle properly when facing a collision risk. The vehicle control is shared by the driver and the assistance system. It is controlled by servomotors. In order to fulfill this requirement, a constraint satisfaction problem (CSP) is proposed and solved based on safe driving constraints and predictive vehicle states. Vehicle motion is predicted by a combination of a dynamics model and a potential field model that reflects the driver's risk feeling to an obstacle. Improved driving behavior is verified and evaluated quantitatively based on driving simulator data. By comparing the driving data before and after using the assistance system, it is found that distance is increased and speed is reduced when passing an obstacle. As a result, driving behavior becomes safer for collision avoidance due to the system's instruction. Furthermore, an experiment with an actual vehicle also demonstrates the practicability of the control system and shows the influence of different safe driving constraints.

This study models the decision-making characteristics of a driver regarding whether he accepts a merging car at a highway junction. Then, the application of the modeling to the design of merging behavior control is proposed. First, the driving behavior on the main lane at a highway junction is observed using a driving simulator, particularly focusing on the driver's state of decision (SOD), which represents the acceptance for merging a car coming from the merging lane. Second, the driver's SOD is modeled using a logistic regression model and the prediction performance of the identified model is verified. Finally, the speed controller of the merging car is designed to maximize the acceptance from the cars on the main lane. The key idea here is to minimize the entropy of the SOD of the driver on the main lane by optimizing the speed of a merging vehicle. This problem is quantitatively formulated using an identified decision-making model and addressed by applying a randomized approach to the optimization. This enables the automated vehicle to realize a considerate merging behavior at a highway junction. Numerical experiments are performed to demonstrate the usefulness of the proposed design scheme.

In this research, an innovative framework that taking advantage of unsupervised clustering and variable selection method is proposed for the modeling of car-following behavior, suitable for incorporating explainable microscopic traffic models into understanding driver behavior. The proposed framework retains the advantages of both conventional and data-driven method. The experimental result presented in this paper shows that the unsupervised clustering method helps identify driver behaviors naturally in an intelligible way, while variable selection has shown a good property of identifying the true model of driving task while efficiently reducing model complexity. Especially, the proposed framework is demonstrated using real-world data collected from a sequence of instrumented install on a driving vehicle in Sakae, downtown area of Nagoya city, Japan. Gazis-Herman-Rothery (GHR) models, one of the most extensively used non-linear car-following models is calibrated against the same data and used as a reference benchmark.

This paper presents the application of Graphics Processing Unit (GPU) to improve the control performance of sampling based predictive control algorithms. As an example problem, obstacle avoidance situation with parked cars in a street is modeled as a non-linear model predictive control problem. Car dynamics and non-linear constraints are considered to achieve collision avoidance. The control input must be optimized in every control step in real-time considering the non-linear constraints. Sampling based approach is used to solve this problem and one of the major limitations to this approach is the computational cost involved. In this paper, the sampling-based optimization algorithm was adapted to utilize the parallel computing capabilities of GPU using CUDA. The generated input sequence and the computational speeds were compared with a CPU based program for the same case. The proposed method is implemented in a simulation experiment with car dynamics simulator to verify its performance in terms of path tracking. Finally, a general relationship between sample size and GPU acceleration of its calculation speed is also discussed.

This paper proposes a model predictive controller to perform the lane merging task. We consider a hierarchical control structure which consists of an inner control loop and an outer one. The inner loop is an adaptive cruise controller which gives the acceleration command to the autonomous vehicle to follow the designated speed and keep a relative distance with the preceding vehicle while satisfying constraints. The outer loop is to determine the vehicle in the main lane that the autonomous one should follow to minimize the entropy in the decision making of the human drivers in the main lane. It is verified in the simulation that the proposed controller can complete the lane merging task under uncertainties and modeling errors.

This paper presents a new control method for merging task at highway junction by using the model predictive control in which the decision entropy of the drivers on the main lane is explicitly considered as the cost function. Authors have already proposed the evaluation measure for the acceptance of the drivers on the main lane (supposed to be manual cars) to the merging car approaching from the merging lane (supposed to be automated car). In addition, the decision entropy of the driver on the main lane has been formally defined by using the stochastic model of the decision making. Based on this previous study, a new control method for the merging task of the automated car is addressed. The control problem is formulated so as to find the optimal speed of the merging car which minimizes the decision entropy of the drivers on the main lane. The proposed control strategy achieves the harmonized merging task in a sense that the drivers on the main lane can easily decide whether to accept or reject the cut-in of the merging car. The model predictive control is formulated as a nonlinear optimization problem, and solved by using the randomized approach. Finally, the validity of the proposed method is verified through some simulation studies.

A new approach to evaluate personalized energy consumption is presented in this paper. The method consists of identifying driver-vehicle dynamics using the probability weighted autoregressive model, which is one of the multi-mode ARX models, and then of reproducing the driver-vehicle behavior in a vehicle-following task. The energy consumption of the vehicle is estimated from the velocity profile calculated by using the driver-vehicle model. In this paper, driving simulator and real-world driving data were recorded to identify the driver-vehicle model in various situations. As a result, real-world energy consumption could be reproduced in a variety of situations with an average error of 1.9% and a standard deviation within 1.5%. Several promising applications of the energy consumption evaluation are introduced in this paper, such as an online energy consumption prediction, a powertrain choice-assistance system for car buyers, and a solution to estimate the macroscopic energy consumption of aggregated vehicles in a traffic flow.

This paper presents a control system for autonomous driving based on MPC in which driving style can be easily modified by changing control parameters. Each of the motion controls for the longitudinal and the lateral direction are formulated as the model predictive control problem. Finally the experimental verification by using driving simulator and a real electric vehicle is performed by implementing MPC on each platform, and it is confirmed that the proposed system can produce a large variety of driving characteristics. The implemented MPC package will also be beneficial to the developers and researchers in various fields other than control engineering field.

This paper proposes a new method to analyze driver behavior. Analysis of the behavior is done through the observation of the time-evolution of parameters of simple driver models. The behavior analysis is decomposed in two steps. First the driver model have to be selected or designed to represent the average behavior of a large sample of drivers. Then personal driver's behavior evolution can be analyzed over the time. To be able to identify time-varying non-linear hybrid model parameters, an iterative metaheuristic method based on particle optimization and moving average filtering has been created. This method enables to identify parameters of any model type while filtering the parameter time-variation based on the possible parameter dynamics. This methods also enables to interpolate parameters values while model output values are occluded. Demonstration of the identification algorithm efficiency with Gipps car-following driver model is done based on theoretical examples, and time-evolution of parameter are identified from real-world measured data.

This paper presents cooperative adaptive cruising control of multiple cars in automated/un-automated mixed traffic. In order to take account of un-automated cars, the vehicle maneuver is expressed as a PrARX model that is a continuous approximation of hybrid dynamical system. The PrARX model describes the driver's logical decision making as well as continuous maneuver in a uniform manner. The acceleration inputs of automated vehicles are computed in model predictive control framework where the state equation includes a platoon of automated and un-automated cars coupled with PrARX driver models. For computing assisting outputs in real time, a fast computation method for nonlinear model predictive control based on the continuation technique is employed. Simulation studies of the proposed CACC system indicates that explicit prediction of un-automated cars improves the overall stability of the platoon.

This paper presents an autonomous lane tracking that reflects different driving characteristics using model predictive control (MPC). We consider that human driver minimizes a cost function depending on his/her skill, experience, and preference on driving. The cost function of MPC can be used to model personal driving characteristics. To identify the parameters of cost function, at first, we analyze the difference of driving characteristics between skilled and un-skilled drivers through experiments of driving behavior on a driving simulator (DS). Next, we introduce "meta-performance indices" that can evaluate human driving data of experiment and results of autonomous driving. These parameters are expected to express the driving characteristics as a group, i.e., not tuned to personal driver. Finally, the validity of the proposed system is verified.

We investigate a possible method for detecting a driver's negative adaptation to an automated driving system by analyzing consistency of driver decision making and driver gaze behavior during automated driving. We focus on an automated driving system equivalent to Level 2 automation per the NHTSA's definition. At this level of automation, drivers must be ready to take control of the vehicle in critical situations by monitoring the driving environment and vehicle behavior. Since drivers are not required to operate the pedals or steering wheel during automated driving, a driver's negative adaptation to an automated system needs to be detected from behavior other than vehicle operation. In this study, we focus on driver gaze behavior. We conduct a simulator study to compare the gaze behavior of fifteen drivers during conventional and automated driving. We also analyze the consistency of driver decision making when changing lanes during conventional and automated driving. Experimental results show that drivers who pay less attention to the road ahead during automated driving tend to be less sensitive to risk factors in the surrounding environment and also tend to make inconsistent lane change decisions during automated driving.

This paper presents a novel personalized driver assistance system(PDAS) based on the model predictive control(MPC) together with a continuous/discrete hybrid dynamical system model of the driving behavior. First of all, the driving behavior is identified as the piecewise ARX model. Then, it is explicitly embedded in the optimization problem for finding the optimal assisting output. Since the driving behavior includes some binary variables, the optimization problem is formulated as the mixed integer programming. Some adaptation mechanism to accommodate to the change of the situation is particularly discussed. Finally, the proposed scheme is tested by using the real vehicle wherein the real-time assisting control based on MPC is implemented.

This research develops a metric for the evaluation of an automobile driver's distraction based on a mathematical driving behavior model. Driving data was collected in a driving simulator. The primary task was to maintain a constant following distance behind a lead vehicle. The secondary task, which brings about the distraction, is to operate the in-car touch panel. A PrARX model is used to describe the vehicle-following behavior. In the PrARX model, the weighting parameter represents the driver's logical decision making and the auto-regressive exogenous models characterize the driver's continuous-time motion control behavior. By calculating the entropy of the PrARX model, the driver's distraction, which is considered a degradation of decision-making ability, is assessed in a quantitative manner.

This paper proposes a probability-weighted autoregressive exogenous (PrARX) model wherein the multiple ARX models are composed of the probabilistic weighting functions. This model can represent both the motion-control and decision-making aspects of the driving behavior. As the probabilistic weighting function, a “softmax” function is introduced. Then, the parameter estimation problem for the proposed model is formulated as a single optimization problem. The “soft” partition defined by the PrARX model can represent the decision-making characteristics of the driver with vagueness. This vagueness can be quantified by introducing the “decision entropy.” In addition, it can be easily extended to the online estimation scheme due to its small computational cost. Finally, the proposed model is applied to the modeling of the vehicle-following task, and the usefulness of the model is verified and discussed.

This paper presents a new hierarchical segmentation of the observed driving behavioral data based on the multiple levels of abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed.

To realize the harmonious cooperation with the operator, the man-machine cooperative system must be designed so as to accommodate with the characteristics of the operator's skill. One of the important considerations in the skill analysis is to investigate the switching mechanism underlying the skill dynamics. On the other hand, the combination of the feedforward and feedback schemes has been proved to work successfully in the modeling of human skill. In this paper, a new stochastic switched skill model for the sliding task, wherein a minimum jerk motion and feedback schemes are embedded in the different discrete states, is proposed. Then, the parameter estimation algorithm for the proposed switched skill model is derived. Finally, some advantages and applications of the proposed model are discussed.

Recently, the demand for a man-machine cooperative system, where the machine assists the human operator, is rapidly growing in the industrial fields. To meet this demand, the human model is required to design the suitable assist controller in the man-machine cooperative system. This paper presents a new human behavior model based on a piece-wise affine model which is a class of hybrid dynamical system, and apply it to a sliding task. Since the human behavior is considered to consist of several primitive motions expressed by continuous dynamics and a decision-making expressed by the discrete switch, it seems to be natural to introduce the hybrid system modeling. Particularly, the decision strategy for the number of discrete modes is addressed by using a hierarchical clustering technique, and the measured data are classified into several modes. Then, each primitive motion in each mode is identified based on the affine model. Finally, the switching conditions among modes are identified by applying support vector machine to the classified data. The obtained piece-wise affine model can quantitatively represent both primitive motions and decision-making in the human behavior

This paper presents a new mathematical model for the human behavior called stochastic switched linear dynamical (SS-LD) model. The SS-LD model can be regarded as a natural extension of the conventional hidden Markov model (HMM), where different linear dynamical model is allocated to each discrete state of the HMM to represent continuous dynamics. Then, the parameter identification technique for SS-LD model is developed based on the EM algorithm. Finally, we apply the proposed behavior model to a typical man-machine cooperative system, and the usefulness of the proposed model is verified through some experiments