In the realm of control systems, model predictive control (MPC) has exhibited remarkable potential; however, its reliance on accurate models and substantial computational resources has hindered its broader application, especially within real-time nonlinear systems. This study presents an innovative control framework to enhance the practical viability of the MPC. The developed safe data-driven predictive control aims to eliminate the requirement for precise models and alleviate computational burdens in the nonlinear MPC (NMPC). This is achieved by learning both the system dynamics and the control policy, enabling efficient data-driven predictive control while ensuring system safety. The methodology involves a spatial temporal filter (STF)-based concurrent learning for system identification, a robust control barrier function (RCBF) to ensure the system safety amid model uncertainties, and a RCBF-based NMPC policy approximation. An online policy correction mechanism is also introduced to counteract performance degradation caused by the existing model uncertainties. Demonstrated through simulations on two applications, the proposed approach offers comparable performance to existing benchmarks with significantly reduced computational costs.

Data-enabled predictive control (DeePC) has garnered significant attention for its ability to achieve safe, data-driven optimal control without relying on explicit parametric models. Traditional DeePC methods use pre-collected input/output (I/O) data to construct a Hankel matrix offline and then formulate a predictive control framework online for linear, weakly nonlinear, and weakly stochastic systems. However, in systems with evolving dynamics, incorporating real-time data into the DeePC framework becomes crucial to enhance control performance. This paper proposes an online DeePC framework designed for strongly nonlinear and/or time-varying systems (i.e., systems with evolving dynamics), enabling the algorithm to update the Hankel matrix online by adding real-time informative signals. By exploiting the minimum non-zero singular value of the Hankel matrix, the developed online DeePC selectively integrates informative data and effectively captures evolving system dynamics. Additionally, a numerical singular value decomposition technique is introduced to reduce the computational complexity for updating a reduced-order Hankel matrix. Simulation results on three cases, linear time-varying system, vehicle anti-rollover control, and Li-ion battery fast charging, demonstrate the effectiveness of the proposed online reduced-order DeePC framework.

The excessively high temperature poses a significant risk to battery health, accelerating degradation and causing damage. Despite the recognized importance of battery thermal management (BTM), numerous studies in this domain often overlook the distinct timescales associated with vehicle and battery thermal dynamics. This oversight can compromise the efficacy and cost-effectiveness of BTM strategies in efficiently controlling battery temperature. This study proposes a novel predictive battery thermal and energy management (p-BTEM) strategy for connected and automated electric vehicles. The p-BTEM leverages a cloud-enabled predictive control framework to synthesize the look-ahead constant and time-varying factors, e.g., vehicle, road, and traffic information. This synthesis aims to achieve global optimization of battery temperature in the Cloud while enabling local adaptations for vehicle acceleration and compressor power on the Vehicle End. This approach ensures proactive and economical regulation of battery temperature, especially in high temperature conditions, thereby maintaining the battery within optimal temperature ranges and reducing energy consumption in dynamic traffic scenarios. To assess the effectiveness of the p-BTEM, representative route simulations are conducted utilizing real-world data. The results reveal the exceptional performance of the p-BTEM in reducing battery cooling energy when compared to two benchmark strategies, with a minimum improvement of 8.58% and 10.31%, respectively. Moreover, the sensitivity analysis is performed to elaborate on the p-BTEM under the influence of traffic, communication, and algorithmic factors.

Electrical vehicles (EV) have been considered to be an effective way to combat global climate change. To extend the driving range of EV, this paper studies the active battery cell balancing control based on linear parametric varying model predictive control (MPC). Specifically, an equivalent circuit model is used to predict cell terminal voltage, and three different MPC-based battery cell balancing control strategies are proposed to dynamically transport electricity from cell to cell to reduce the imbalance. In particular, for the first control strategy, MPC is set up to be a tracking controller with the primary control objective of forcing all cells’ terminal voltage to follow the same trajectory generated by a nominal cell model; for the second control strategy, MPC maximizes the lowest cell voltage, so that the battery operating range can be extended; for the third and last strategy, MPC minimizes the maximum variation among cell terminal voltages. To assess the effectiveness of the proposed battery cell balancing control strategies, simulations are performed on all three MPC formulations, using both steady-state and transient conditions. Numerical results show that the proposed battery cell balancing control can achieve a driving range extension of 9% for dynamic driving cycle and 7% for steady-state condition, based on our simulation setup. Compared to the existing work, our approaches do not require the over-restrictive assumption that the trip duration is known in advance, while at the same time achieve similar driving range extension. Furthermore, it is also shown that different driving condition favors different cell balancing control strategy, indicating a need for a hybrid approach. Finally, real time implementability is demonstrated via throughput analysis.Note to Practitioners—Improving the efficiency of electric vehicles is of paramount importance to combat the global climate challenge. This paper contributes by proposing effective cell level balancing control methodologies to extend the driving range of electric vehicles to improve their energy efficiency and public acceptance. The control methods, which are based on model predictive control, are analytically derived with details for embedded implementation. Simulation results demonstrate the effectiveness of the proposed methodologies, with future work to investigate the applicability of nonlinear model predictive control with large number of cells.

Optimal control schemes have achieved remarkable performance in numerous engineering applications. However, they typically require high computational cost, which has limited their use in real-world engineering systems. To address this challenge, Neighboring Extremal (NE) has been developed to adapt a pre-computed nominal control solution to perturbations from the nominal trajectory. The resulting control law is a time-varying feedback gain that can be pre-computed along with the original optimal control problem, and it takes negligible online computation. However, existing NE frameworks only deal with state perturbations while in modern applications, optimal controllers frequently incorporate preview information. Therefore, a new NE framework is needed to adapt to such preview perturbations. In this work, an extended NE (ENE) framework is developed to systematically adapt the nominal control to both state and preview perturbations. We show that the derived ENE law is two time-varying feedback gains on the state and preview perturbations. We also develop schemes to handle nominal non-optimal solutions and large perturbations to retain optimal performance and constraint satisfaction. Case study on nonlinear model predictive control is presented due to its popularity but it can be easily extended to other optimal control schemes. Promising simulation results on the cart inverted pendulum problem demonstrate the efficacy of the ENE algorithm. Note to Practitioners—Due to the vast success in predictive control and advancement in sensing, modern control applications have frequently been incorporating preview information in the control design. For example, the road profile preview obtained from vehicle crowdsourcing is exploited for simultaneous suspension control and energy harvesting, demonstrating a significant performance enhancement using the preview information despite noises in the preview (Hajidavalloo et al., 2022). Another example is thermal management for cabin and battery of hybrid electric vehicles, where traffic preview is employed in hierarchical model predictive control to improve energy efficiency (Amini et al., 2019). In Laks et al. (2011), light detection and ranging systems are used to provide wind disturbance preview to enhance the controls of turbine blades. In Yazdandoost et al. (2022), virtual water preview is employed using integrated water resources management modelling to optimize agricultural patterns and control level of water in lakes. In this work, we develop an extended neighboring extremal framework that can adapt a nominal control law to state and preview perturbations simultaneously. This setup is widely applicable as in many applications, a nominal preview is available while the preview signal can also be measured or estimated online.

In this work, we consider the problem of Koopman modeling and data-driven predictive control for a class of uncertain nonlinear systems subject to time delays. A robust deep learning-based approach–deep recurrent Koopman operator is proposed. Without requiring the knowledge of system uncertainties or information on the time delays, the proposed deep recurrent Koopman operator method is able to learn the dynamics of the nonlinear systems autonomously. A robust predictive control framework is established based on the deep Koopman operator. Conditions on the stability of the closed-loop system are presented. The proposed approach is applied to a chemical process example. The results confirm the superiority of the proposed framework as compared to baselines.

Preceding vehicles typically dominate the movement of following vehicles in traffic systems, thereby significantly influencing the efficacy of eco-driving control that concentrates on vehicle speed optimization. To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this study proposes an overtaking-enabled eco-approach control (OEAC) strategy. It combines driving lane planning and speed optimization for connected and automated vehicles to relax the first-in-first-out queuing policy at the signalized intersection, minimizing the host vehicle’s energy consumption and travel delay. The OEAC adopts a two-stage receding horizon control framework to derive optimal driving trajectories for adapting to dynamic traffic conditions. In the first stage, the driving lane optimization problem is formulated as a Markov decision process and solved using dynamic programming, which takes into account the uncertain disturbance from preceding vehicles. In the second stage, the vehicle’s speed trajectory with the minimal driving cost is optimized rapidly using Pontryagin’s minimum principle to obtain the closed-form analytical optimal solution. Extensive simulations are conducted to evaluate the effectiveness of the OEAC. The results show that the OEAC is excellent in driving cost reduction over constant speed and regular eco-approach and departure strategies in various traffic scenarios, with an average improvement of 20.91% and 5.62%, respectively.

Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models. However, employing the data-driven strategy involves the collection and sharing of privacy-sensitive vehicle information, which is vulnerable to privacy leakage and might further lead to malicious activities. In this paper, we develop a privacy-preserving data-enabled predictive control scheme for CAVs in a mixed traffic environment, where human-driven vehicles (HDVs) and CAVs coexist. We tackle external eavesdroppers and honest-but-curious central unit eavesdroppers who wiretap the communication channel of the mixed traffic system and intend to infer the CAVs’ state and input information. An affine masking-based privacy protection method is designed to conceal the true state and input signals, and an extended form of the data-enabled predictive leading cruise control under different data matrix structures is derived to achieve privacy-preserving optimal control for CAVs. Numerical simulations demonstrate that the proposed scheme can protect the privacy of CAVs against attackers without affecting control performance or incurring heavy computations.

Federated learning (FL) learns a model jointly from a set of participating devices without sharing each other’s privately held data. The characteristics of non-i.i.d. data across the network, low device participation, high communication costs, and the mandate that data remain private bring challenges in understanding the convergence of FL algorithms, particularly regarding how convergence scales with the number of participating devices. In this paper, we focus on Federated Averaging (FedAvg), one of the most popular and effective FL algorithms in use today, as well as its Nesterov accelerated variant, and conduct a systematic study of how their convergence scale with the number of participating devices under non-i.i.d. data and partial participation in convex settings. We provide a unified analysis that establishes convergence guarantees for FedAvg under strongly convex, convex, and overparameterized strongly convex problems. We show that FedAvg enjoys linear speedup in each case, although with different convergence rates and communication efficiencies. For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings. Empirical studies of the algorithms in various settings have supported our theoretical results.

On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the mixed-traffic highway on-ramp merging problem as a multi-agent reinforcement learning (MARL) problem, where the AVs (on both merge lane and through lane) collaboratively learn a policy to adapt to HDVs to maximize the traffic throughput. We develop an efficient and scalable MARL framework that can be used in dynamic traffic where the communication topology could be time-varying. Parameter sharing and local rewards are exploited to foster inter-agent cooperation while achieving great scalability. An action masking scheme is employed to improve learning efficiency by filtering out invalid/unsafe actions at each step. In addition, a novel priority-based safety supervisor is developed to significantly reduce collision rate and greatly expedite the training process. A gym-like simulation environment is developed and open-sourced with three different levels of traffic densities. We exploit curriculum learning to efficiently learn harder tasks from trained models under simpler settings. Comprehensive experimental results show the proposed MARL framework consistently outperforms several state-of-the-art benchmarks.

Cooperative on-ramp merging control for connected and automated vehicles (CAVs) can effectively improve traffic throughput and vehicle fuel efficiency at highway on-ramp merging bottlenecks. However, in the mixed traffic scenario where CAVs and human-driven vehicles (HDVs) coexist, the uncertain maneuvers of human drivers pose a major challenge to merging control in terms of safety and flexibility. To this end, this paper proposes a hierarchical cooperative on-ramp merging control strategy for CAVs to optimize flexible trajectories with safety guarantees in mixed traffic. First, the on-ramp merging control problem for CAVs is considered in the case of a three-vehicle coordination, resulting in an optimal control problem (OCP) coordinating on-ramp and main-lane CAVs for efficient operation while satisfying multiple safety-critical constraints. Second, a two-level hierarchical control architecture is developed to solve the OCP with mixed state-control constraints. The upper-level planner solves an unconstrained OCP with Pontryagin’s Minimum Principle to calculate an expected merging position, which is embedded in the variable time headway of safe merging constraints in the lower-level controller. Then, the controller converts the nonlinear OCP with safety-critical constraints to a quadratic programing (QP) problem by exploiting Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). By solving the QP efficiently, the time and energy efficient trajectory for each CAV is obtained. In addition, a receding horizon control framework is employed, which enables CAVs to determine flexible merging opportunity and tackle the disturbances caused by HDVs. Finally, comprehensive simulation results show that the proposed cooperative on-ramp merging strategy has potential in enabling merging flexibility, improving traffic efficiency and energy economy in real time.

Feature selection plays a critical role in data-driven fault diagnosis. Existing works often perform feature selection on a single overcomplete feature pool that is collected from various feature extraction approaches, such as signal processing and deep learning. However, simple concatenation of these “multiview” features may neglect the inherent properties and cross-feature correlations, often leading to ineffectiveness in feature selection for fault diagnosis. Therefore, a multiview feature selection with information complementarity and consensus (MFSICC) is proposed in this article to efficiently select relevant features for fault diagnosis. In particular, both complementary and consensus properties that are shared among the multiview features are incorporated based on a structured sparsity learning model. Besides, an iterative algorithm is proposed to solve the MFSICC problem with theoretically approved convergence. Experiments on bearing and gearbox fault diagnosis with real-world datasets validate the effectiveness of MFSICC. Important model parameters and empirical convergence are also discussed to facilitate practical use.

Cloud computing is an emerging paradigm to enable computation and data-intensive automotive systems for improved safety and drivability. In this article, we propose a hierarchical, decentralized, and auction-based resource allocation model for cloud-enabled automotive vehicles. In this model, cloud-enabled vehicles bid for resources at a high level, inducing a multiplayer game; at a low level, each vehicle performs an onboard resource optimization to allocate its obtained resources to its cloud-based applications. The Nash equilibrium of the induced game is defined, and we show the existence and uniqueness of the equilibrium. A constrained optimization problem is solved for onboard resource allocation. A distributed update mechanism is considered: asynchronized update where only a subset of vehicles updates their bid at each iteration. This mechanism shares desired features of requiring little communication and being secure. Convergence to Nash equilibrium is proved for the proposed update mechanism. Furthermore, the robustness to stochastic task arrival rate is characterized in terms of total variance distance. Numerical simulations are presented to demonstrate the efficacy of the proposed framework.

Due to labor shortage and rising labor cost for the apple industry, there is an urgent need for the development of robotic systems to efficiently and autonomously harvest apples. In this paper, we present a system overview and algorithm design of our recently developed robotic apple harvester prototype. Our robotic system is enabled by the close integration of several core modules, including visual perception, planning, and control. This paper covers the main methods and advancements in deep learning-based multi-view fruit detection and localization, unified picking and dropping planning, and dexterous manipulation control. Indoor and field experiments were conducted to evaluate the performance of the developed system, which achieved an average picking rate of 3.6 seconds per apple. This is a significant improvement over other reported apple harvesting robots with a picking rate in the range of 7–10 seconds per apple. The current prototype shows promising performance towards further development of efficient and automated apple harvesting technology. Finally, limitations of the current system and future work are discussed.

Road information such as road profile has been widely used in intelligent vehicle systems to improve road safety, ride comfort, and fuel economy. However, practical challenges, such as vehicle heterogeneity, parameter uncertainty, and measurement reliability, make it extremely difficult for a single vehicle to accurately and reliably estimate such information. To overcome these limitations, we propose a new learning-based collaborative estimation approach by fusing information from a fleet of networked vehicles. However, information exchange among these vehicles necessary for collaborative estimation may disclose sensitive information such as individual vehicle’s identity, which poses serious privacy threats. To address this issue, we propose a unified privacy-preserving collaborative estimation framework which allows connected vehicles to iteratively refine estimation results through exploiting sequential measurements made by multiple vehicles traversing the same road segment. The collaborative estimation approach systematically incorporates privacy-protection schemes into the estimation design and exploits estimation dynamics to obscure exchanged information. Different from patching conventional privacy mechanisms like differential privacy that will compromise algorithmic accuracy or homomorphic encryption that will incur heavy communication/computation overhead, the dynamics enabled privacy protection does not sacrifice accuracy or significantly increase communication/computation overhead. Numerical simulations confirm the effectiveness of our proposed approach.

There is an increasing popularity in exploiting modern vehicles as mobile sensors to obtain important road information such as potholes, black ice and road profile. Availability of such information has been identified as a key enabler for next-generation vehicles with enhanced safety, efficiency, and comfort. However, existing road information discovery approaches have been predominately performed in a single-vehicle setting, which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations, this paper presents a novel cloud-assisted collaborative estimation framework that can utilize multiple heterogeneous vehicles to iteratively enhance estimation performance. Specifically, each vehicle combines its onboard measurements with a cloud-based Gaussian process (GP), crowdsourced from prior participating vehicles as “pseudo-measurements”, into a local estimator to refine the estimation. The resultant local onboard estimation is then sent back to the cloud to update the GP, where we utilize a noisy input GP (NIGP) method to explicitly handle uncertain GPS measurements. We employ the proposed framework to the application of collaborative road profile estimation. Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.

Lane-change decision models with enhanced human-likeness are increasingly important as they are integral in traffic simulations for training autonomous driving algorithms. This work proposes a computational model of driver lane-change decision-making by integrating relevant human features in perception, reasoning, emotion, and decision (PRED). The PRED model describes how drivers make lane-change decisions under collision risk. Here risk is represented by probabilities and outcomes of the possible consequences. The PRED model formulates drivers’ risk perception and risk propensity in its modules: the perception module is modeled with Bayesian inference; the reasoning module is modeled with Newtonian simulation; the emotion and decision module is modeled with the extended regret theory. The PRED model was fitted and tested with an empirical dataset from a naturalistic driving database. The prediction performance of the PRED model ranks higher than the selected benchmarks and is close to the state-of-the-art machine learning models. Moreover, the explicit modeling of risk propensity sheds light on an important question in transportation: what causes human drivers’ risk-taking behaviors? The results support the rationale that downplaying crash consequences is the main contributor.

In this article, the eye-in-hand visual trajectory tracking control problem of wheeled mobile robots (WMRs) is considered. Different from the conventional vision-based approaches, the monocular camera is not required to be mounted at the center of WMR, and thus the derived visual model is subject to not only the nonholonomic constraint but also the unknown camera extrinsic parameters. To ensure the WMR can track a desired trajectory effectively, a combined observation/control strategy is proposed. First, a concurrent learning observer is designed to identify the camera extrinsic parameters with measurable visual signals. Then, with the aid of the estimated parameters, a nonlinear controller is presented to achieve the tracking task. The closed-loop system stability is analyzed with Lyapunov methods, showing that both the estimation and tracking errors are asymptotically convergent to zero. Simulation and experimental results are provided to validate the developed approach.

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, the centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. The multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now, the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Most existing studies in MARL focus on designing efficient communication and coordination among traditional Q-learning agents. This paper presents, for the first time, a fully scalable and decentralized MARL algorithm for the state-of-the-art deep RL agent, advantage actor critic (A2C), within the context of ATSC. In particular, two methods are proposed to stabilize the learning procedure, by improving the observability and reducing the learning difficulty of each local agent. The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic network of Monaco city, under simulated peak-hour traffic dynamics. The results demonstrate its optimality, robustness, and sample efficiency over the other state-of-the-art decentralized MARL algorithms.

The ultimate goal of self-driving technologies is to offer a safe and human-like driving experience. As one of the most important enabling functionalities, trajectory planning has been extensively studied from the perspective of safety. However, human-like trajectory planning on curved roads has rarely been studied. In this paper, we characterize and model human driving using extensive experimental driving collected on an urban curved road with 30 participants (10 experienced and 20 novice drivers) and five vehicles of different types. Differential global positioning system (GPS) is used to measure vehicle positions in high precision. We study factors that affect the driving trajectory, including vehicle speed, road curvature, and sight distance. We find that the human drivers typically do not follow lane centerline and the human-driven trajectories are very different from planners like rapidly exploring random tree (RRT). To generate human-like driving trajectory, we develop a data-driven trajectory model using general regression neural network (GRNN). The model was validated in various cases with promising performance.

Microscopic traffic models describe how cars interact with their neighbors in an uninterrupted traffic flow and are frequently used for reference in advanced vehicle control design. In this paper, we propose a novel mechanical system-inspired microscopic traffic model using a mass-spring-damper-clutch system. This model naturally captures the ego vehicle's resistance to large relative speed and deviation from a (driver- and speed-dependent) desired relative distance when following the lead vehicle. Compared with the existing car-following (CF) models, this model offers physically interpretable insights into the underlying CF dynamics and is able to characterize the impact of the ego vehicle on the lead vehicle, which is neglected in the existing CF models. Thanks to the nonlinear wave propagation analysis techniques for mechanical systems, the proposed model, therefore, has great scalability so that multiple mass-spring-damper-clutch systems can be chained to study the macroscopic traffic flow. We investigate the stability of the proposed model on the system parameters and the time delay using the spectral element method. We also develop a parallel recursive least square with inverse QR decomposition (PRLS-IQR) algorithm to identify the model parameters online. These real-time estimated parameters can be used to predict the driving trajectory that can be incorporated into advanced vehicle longitudinal control systems for improved safety and fuel efficiency. The PRLS-IQR is computationally efficient and numerically stable, and therefore, it is suitable for online implementation. The traffic model and the parameter identification algorithm are validated on both the simulations and naturalistic driving data from multiple drivers. Promising performance is demonstrated.

We investigate the problem of decentralized fault prognosis in the context of discrete-event systems. In this problem, the system is monitored by a set of local agents; each of them sends its local information to a coordinator in order to issue a fault alarm before the occurrence of fault. Two new decentralized protocols are proposed by exploiting the state-estimate of each local agent. For each protocol, a necessary and sufficient condition for its correctness is proposed; they are termed as positive state-estimate-prognosability and negative state-estimate prognosability. Verification algorithms for the necessary and sufficient conditions are also provided. We show that the proposed new protocols are incomparable with any of the existing protocols in the literature. Therefore, they provide new opportunities for correctly predicting the fault when all existing protocols fail.

This paper investigates the problem of driver distraction detection using driving performance indicators from onboard kinematic measurements. First, naturalistic driving data from the integrated vehicle-based safety system program are processed, and cabin camera data are manually inspected to determine the driver's state (i.e., distracted or attentive). Second, existing driving performance metrics, such as steering entropy, steering wheel reversal rate, and lane offset variance, are reviewed against the processed naturalistic driving data. Furthermore, a nonlinear autoregressive exogenous (NARX) driving model is developed to predict vehicle speed based on the range (distance headway), range rate, and speed history. For each driver, the NARX model is then trained on the attentive driving data. We show that the prediction error is correlated with driver distraction. Finally, two features, steering entropy and mean absolute speed prediction error from the NARX model are selected, and a support vector machine is trained to detect driving distraction. Prediction performances are reported.

The objective of drift counteraction optimal control (DCOC) problem is to compute an optimal control law that maximizes the expected time of violating specified system constraints. In this paper, we reformulate the DCOC problem as a reinforcement learning (RL) one, removing the requirements of disturbance measurements and prior knowledge of the disturbance evolution. The optimal control policy for the DCOC is then trained with RL algorithms. As an example, we treat the problem of adaptive cruise control, where the objective is to maintain desired distance headway and time headway from the lead vehicle, while the acceleration and speed of the host vehicle are constrained based on safety, comfort, and fuel economy considerations. An informed approximate Q-learning algorithm is developed with efficient training, fast convergence, and good performance. The control performance is compared with a heuristic driver model in simulation and superior performance is demonstrated.

This paper addresses a distributed estimator design problem for linear systems deployed over sensor networks within a multiple communication channels (MCCs) framework. A practical scenario is taken into account such that the channel used for communication can be switched and the switching is governed by a Markov chain. With the existence of communicational imperfections and external disturbances, an estimation algorithm is proposed such that the developed distributed estimators are able to give accurate state estimates against the channel switching phenomenon. The distributed estimation framework is applied to a chemical process to illustrate the effectiveness of the proposed methodology and the superiority of the MCCs framework featured by channel switching.

This paper investigates a comfort-based route planner that considers both travel time and ride comfort. We first present a framework of simultaneous road profile estimation and anomaly detection with commonly available vehicle sensors. A jump-diffusion process-based state estimator is developed and used along with a multi-input observer for road profile estimation. The estimation framework is evaluated in an experimental test vehicle and promising performance is demonstrated. Second, three objective comfort metrics are developed based on factors such as travel time, road roughness, road anomaly, and intersection. A comfort-based route planning problem is then formulated with these metrics and an extended Dijkstra's algorithm is exploited to solve the problem. A cloud-based implementation of our comfort-based route planning approach is proposed to facilitate information access and fast computation. Finally, a real-world case study, comfort-based route planning from Ford Research and Innovation Center, Michigan to Ford Rouge Factory Tour, Michigan, is presented to illustrate the efficacy of the proposed route planning framework.

This paper considers a new clustering algorithm for processing time-evolving road anomaly reports. Two cluster categories, main and outlier, are defined to deal with outliers as well as to capture the evolving nature of road anomalies. The Mahalanobis distance is exploited to quantify the similarity between a new report and the existing clusters. The clusters are maintained online and the Woodbury matrix inverse lemma is used for their recursive updates. The proposed clustering algorithm can localize isolated anomalies and compress information for densely distributed anomalies. A simulation is presented to demonstrate the efficacy of the proposed algorithm.

This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the "best" route can be adjusted to favor time versus safety metrics.

We investigate the problem of reliable decentralized fault prognosis of partially-observed discrete-event systems. In this problem, n local prognosers are deployed to send their local prognostic decisions to a coordinator that calculates the final prognostic decision. However, only k (1≤ k ≤ n) local prognostic decisions are guaranteed to be available to the coordinator due to possible failures or communication losses of at most n - k local prognosers. We propose the notion of k-reliable decentralized prognoser in order to address this reliability issue. A necessary and sufficient condition for the existence of a k-reliable decentralized prognoser, which predicts faults prior to their occurrences, is presented. This condition is termed as k-reliable coprognosability. A polynomial-time algorithm for the verification of k-reliable coprognosability is presented. We also demonstrate how to compute the k-reliable reactive bound prior to any occurrence of faults.

This study is mainly concerned with fuel efficiency modeling and prediction for common automobiles based on an informative vehicle database. The historical database is processed and the mutual information index (MII) is employed to identify a set of characteristics that significantly affect fuel efficiency. Five different machine learning techniques are exploited to build fuel efficiency prediction models. Among these techniques, quantile regression, which is a natural extension of classical least square estimation, is shown to have better performance for fuel efficiency prediction compared to other adopted techniques. It is also demonstrated that with the selected attributes based on MII, the prediction performance is almost ideal when exploiting the complete dataset.

This paper proposes a novel multi-objective route planning approach within the framework of a Vehicle-to-Cloud-to-Vehicle (V2C2V) architecture. Time and road risk index (RRI) are both considered as metrics. To evaluate road segment risk, an accident database from the Highway Safety Information System (HSIS) is processed to build a comprehensive road risk assessment model. Route planning is formulated as a multi-objective network flow problem and further reduced to a Mixed Integer Programming (MIP) problem. A real-world case study, route planning through the city of Columbus, Ohio, is presented. The Vehicle-to-Cloud-to-Vehicle (V2C2V) based implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources.