Accurate prediction of cyclist trajectories is essential for safe and reliable autonomous driving and intelligent transportation systems (ITSs) in complex traffic scenarios. To address the challenges posed by cyclists’ diverse intentions and non-linear motion patterns, we propose PhysiCycle, a novel multi-task learning framework that jointly predicts future trajectories and turning intentions. This framework integrates interpretable physical modeling with deep learning to enhance both prediction accuracy and behavioral consistency. Our model integrates a dual-path encoder to extract temporal motion cues and behavioral features, an intention classifier module, and a physically consistent decoder with bicycle kinematics consistency constraints. Experimental results on a real-world cyclist action dataset demonstrate that our method significantly outperforms baseline models in both intention classification and trajectory accuracy, achieving strong physical plausibility and generalization performance.

A game-based cooperative steering control (GCSC) approach is introduced to facilitate effective collaboration between human drivers and automation, incorporating the neuromuscular delay inherent in human responses. In this framework, a dynamic coordination between driver and automation goals is achieved through the establishment of a game equilibrium in instances of driver and automation conflict. In response to the challenges posed by frequent modifications in driving weights and their subsequent burden on human drivers, this article proposes a strategy that integrates fixed initial weights with dynamic adjustments to driver–automation driving weights. Moreover, a comprehensive evaluation method including subjective and objective evaluation indexes is proposed. Different drivers are invited to perform virtual driving experiments, and the experimental results are analyzed by the proposed evaluation method. It is concluded that the driver’s driving weight should be kept at a high level during cooperative steering control when the driver’s intention cannot be perfectly obtained, and the determination of the driving weight should also consider the driver’s driving skills.

The embedding of high-level traffic semantics has elevated the precision of vehicle trajectory prediction tasks to a new level. However, owing to the absence of feature-level integration, the information from high-definition maps is underutilized. To this end, a map search-based vehicle trajectory prediction method conditioned on lane segments is proposed in this article. The map is discretized into a graph, where nodes represent lane centerline segments. On this basis, the agent-to-agent, agent-to-map, and map-to-map modules are designed to depict heterogeneous interaction patterns involving vehicles and pedestrians. In addition, a goal node querying mechanism is introduced, which integrates vehicle motion, interaction, and traffic flow states and serves as prior information for trajectory prediction. Finally, a feasible path selection strategy is proposed, generating traffic rule-related prediction trajectories point by point, fully utilizing map information. The experimental results on the nuScenes dataset indicate that the proposed method achieves state-of-the-art prediction accuracy compared with advanced methods.

For personalized human-machine cooperative (HMC) control, traditional model-driven approaches, which rely on predefined driver-vehicle-road (DVR) models, often struggle to adapt to individual driver differences. To address this, a data-learning shared control strategy based on game output regulation and adaptive dynamic programming (ADP) is presented. Firstly, considering the differences in driver’s characteristics, vehicle-road dynamics and human-machine interaction, an uncertain DVR system is established. Subsequently, robust output regulation (ROR) is utilized to handle road curvature perturbations and ensure closed-loop system stability. Subsequently, a dynamic game framework between the front-wheel steering system (AFS) and the active rear-wheel steering system (ARS) is further developed to ensure both vehicle stability and path-tracking accuracy in complex environments. Finally, the AFS-ARS optimal control strategies are iteratively learned and updated by ADP, using online DVR system data, without requiring prior knowledge of specific drivers or vehicles. Through driver-in-the-loop experiments, it is demonstrated that the presented method exhibits good adaptability to different drivers.

This paper introduces a novel nonlinear game-based driver-automation cooperative steering control method to mitigate collision caused by the driver’s limited experience on low adhesion road conditions. First, we utilize a model predictive control (MPC) driver model to capture the characteristics of driver experience deficit in low adhesion road conditions, considering the driver’s neuromuscular delay as the system time lag. Then, a dynamic driving weighting strategy is proposed to adjust the driving weights, taking into account both driver-automation handling conflicts and road risks. Next, in order to account for the nonlinear tire dynamics encountered on low adhesion road surfaces, the problem of driver-automation cooperative steering control is mathematically framed as a nonlinear game. The utilization of the piecewise affine(PWA) theory enables the linearization of the nonlinear game optimization problem, facilitating the derivation of an optimal control strategy for ensuring vehicle stability on low adhesion road conditions. Finally, the proposed method is rigorously validated through simulations and driver-in-the-loop tests, comparing its performance against an existing driver-automation cooperative steering control approach. The experimental results substantiate the effectiveness of the proposed method in mitigating the driver’s steering workload and leveraging tire forces optimally to enhance vehicle stability.

Benefiting from the advancement of intelligent transportation systems (ITSs), intelligent connected vehicles (ICVs) are ushering in a once-in-a-generation development opportunity. Considering the widespread presence of heterogeneous vehicles with disturbances and uncertain dynamics in actual platoon scenarios as well as the multimodel switching produced by unavoidable interruptions in the communication process, this paper proposes a data–mechanism adaptive switched predictive (DASP) control strategy. The characteristics of the mechanism model are mapped based on state data to more accurately describe the system’s dynamic characteristics and improve the interpretability of variables. The introduction of Givens rotations and switching criteria enables online adaptive switching of the controller. A robustness analysis of heterogeneous platoon switching control under bounded disturbance is presented, and sufficient conditions for$\mathcal {L}_{2}$string stability are provided. Finally, CarSim simulations and real-time bench experiments are reported to demonstrate the effectiveness of the DASP algorithm for heterogeneous multivehicle regulation with communication interruptions.

In ice and snow-rutted conditions, it is challenging to design a vehicle stability controller to simultaneously resolve the conflict between the accuracy of the system model and the easy implementation of the controller. To this end, the application of a data-driven control method for vehicle stability control represents a novel, feasible opportunity. This article introduces Givens rotation and forgetting factors to efficiently update the subspace prediction equation with online data. An online data-driven predictive control (ODPC) method is proposed on this basis. To address the problem that persistently excited (PE) condition will cause fluctuations in the steady-state response of ODPC, a data-driven switched predictive control strategy (DSPCS) employing attenuated excitation (AE) signals and hysteresis comparisons based on posterior prediction errors is proposed. In addition, an implementation method involving the Laguerre function (LF) parameterization of the control input is proposed to improve the computational efficiency further. Numerical simulation results show that both the ODPC method and the DSPCS can effectively track given yaw rate and sideslip angle reference under the influence of ruts. Furthermore, the DSPCS can effectively reduce steady-state response fluctuations. In addition, the LF parameterization is superior regarding computational time.

As a critical component of the Internet of Things, connected automated vehicles (CAVs) are progressively gaining attention for their benefits in terms of increased safety and reduced traffic congestion. In this article, a novel distributed data-driven model-predictive control (DDMPC) approach including feedforward for disturbance is proposed for cruise control of a hybrid platoon with a combination of human-operated and autonomous vehicles. By employing a predictor constructed from input/output data, predictive controllers are obtained without depending on the characteristic information of the system. A robustness analysis is performed with a combination of the input-to-state stability (ISS) theory with the sampled-data systems theory, and the$\mathcal {L}_{2}$-norm string stability is ensured by strict mathematical proof. In addition, we also discuss the asymptotic stability when the controller switches. CarSim simulation and bench experiment results verify that the DDMPC for connected vehicles can be robust to velocity disturbances and achieve satisfactory performance in ensuring string stability.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.

The roundabout is a typical changeable, interactive scenario in which automated vehicles should make adaptive and safe decisions. In this article, an optimization embedded reinforcement learning (OERL) is proposed to achieve adaptive decision-making under the roundabout. The promotion is the modified actor of the Actor-Critic framework, which embeds the model-based optimization method in reinforcement learning to explore continuous behaviors in action space directly. Therefore, the proposed method can determine the macroscale behavior (change lane or not) and medium-scale behaviors of desired acceleration and action time simultaneously with high sample efficiency. When scenarios change, medium-scale behaviors can be adjusted timely by the embedded direct search method, promoting the adaptability of decision-making. More notably, the modified actor matches human drivers' behaviors, macroscale behavior captures the human mind's jump, and medium-scale behaviors are preferentially adjusted through driving skills. To enable the agent adapts to different types of the roundabout, task representation is designed to restructure the policy network. In experiments, the algorithm efficiency and the learned driving strategy are compared with decision-making containing macroscale behavior and constant medium-scale behaviors of the desired acceleration and action time. To investigate the adaptability, the performance under an untrained type of roundabout and two more dangerous situations are simulated to verify that the proposed method changes the decisions with changeable scenarios accordingly. The results show that the proposed method has high algorithm efficiency and better system performance.

High salinity wastewater and oil contaminated wastewater are two main types of production wastewater discharge in oil field. In this paper, an approach for these two types of wastewater recognition is presented. We analyzed the spectral properties of high salinity wastewater, oil contaminated wastewater and clean water at the range of 450-900nm based on field measured spectral data and multitemporal high resolution GF-2 data. A recognition method was developed to extract the wastewater with high salinity and high HC concentration based on GF-2 data. Results show that remote sensing can be of great help in production wastewater monitoring by using multi-temporal remote sensing image with high resolution in oil field.

Effectively monitoring the location and land use of oil production facilities and production emissions in the oil region is of great significance to the HSE management of oilfields. In the study, we construct a location-based Petroleum Remote Sensing dataset (PetroRS dataset), which consist of 10 thousand labelled high-resolution images in two classes of oil production-related objects. After two distinct forms of data augmentation, the dataset is enlarged 9 times. We applied Faster R-CNN, a deep learning method, to the PetroRS dataset to set up a preliminary result as the baseline. On this basis, we use the model to train the augmented dataset and improve the model by optimized anchor based on scale and aspect-ratio statistics. The results show: (1) Faster R-CNN model could detect two classes of oil production-related object automatically and simultaneously with the accuracy of 76% and 32%, respectively; (2) The model training with augmented dataset gives better result, more than 5% accuracy increments, compared to the baseline; (3) The improved model with optimized anchor returns a better result, more than 10% accuracy increments, compared to the baseline. We believe deep learning could provide a new practical and applicable idea in terms of applying remote sensing technology in the petroleum industry.

Connected automated vehicle (CAV) platoon control is becoming increasingly prevalent because of its unique advantages in reducing fuel consumption and improving traffic efficiency. A novel control framework for CAV platoon control is designed in this article. First, a model predictive control (MPC)-based method is proposed to obtain the optimal velocity of the whole platoon, in which both reducing fuel consumption and improving transport efficiency are taken into account in the optimization process. Then, a distributed adaptive triple-step nonlinear control strategy is investigated from the perspective of multiagent system control. The adaptive performance of the control strategy can guarantee the string stability of the CAV platoon under the premise of the existence of dynamic uncertainties. Various simulation conditions with heterogeneous dynamic disturbances are designed to validate the proposed control strategy, and the results show that the proposed control strategy can be robust to dynamic disturbances while ensuring the string stability of the CAV platoon.

The rapid development of active safety control systems has paved the way for the discussion of lateral stability in critical running conditions. To improve the lateral stability of a vehicle in critical running conditions, a nonlinear model predictive control (NMPC) strategy that integrates active front steering and additional yaw moment is proposed in this manuscript. The reference yaw rate in critical running conditions is obtained by employing Lyapunov's second method. In addition, this method adopts a varied sideslip angle to describe a stability region using a phase plane approach, which is used as the vehicle stability constraints. To verify the effectiveness of the presented lateral NMPC stability controller, off-line simulations under various running conditions are carried out using the high-precision vehicle simulation veDYNA software. It is shown that the N-MPC controller presents a feasible performance enhancement in tracking the reference yaw rate and keeping the vehicle stable in critical running conditions.

An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time application. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corresponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique. Also, hardware-in-the-loop experiment is built to validate the real-time characteristic of the proposed strategy.

The rapid development of intelligent vehicles has paved the way for active chassis lateral stability, which is a novel issue and critical to vehicle stability and handling performance. To obtain active chassis lateral stability for intelligent vehicles, a nonlinear model predictive control (NMPC) method integrating active front steering and an additional yaw moment is proposed. It adopts the tire sideslip angle to express vehicle lateral stability, and addresses the actuator and security constraints and the nonlinear properties of the tire-road force effectively. Moreover, the hardware implementation, based on the field programmable gate array (FPGA), is presented to satisfy miniaturization and to discuss the computational efficiency of the proposed NMPC method. To verify the effectiveness of the presented NMPC method, offline simulations comparing the NMPC method with the direct yaw moment control (DYC) method under various running conditions and a real-time implementation experiment are carried out. The results indicate that the proposed NMPC method controls better than the DYC-based method. In addition, the presented NMPC method exhibits good robustness when the longitudinal velocity and tire-road friction coefficient vary within a suitable range. Moreover, the computational time of the proposed NMPC controller, implemented using the FPGA, is only 4.994 ms during one sampling period, which can satisfy the real-time requirement of active chassis lateral stability control.

This paper proposes an extensive overview of the tire-road interaction estimation issue as it relates to automated driving from the prospectives of sensor configuration, tire modeling, and estimation approaches. The tire-road interactions needed for estimation are first determined and classified. Then, the sensor configuration schemes of different types of tire-road interactions are presented and analyzed. The following introduces various types of tire models and provides the limitations and advantages of different estimation approaches based on categorizing and summarizing those techniques. Moreover, some interesting perspectives for future research are listed based on the extensive experience of the authors.

Different application conditions applied for different models used in satellite-based terrestrial latent heat estimation. Therefore, great uncertainties exist in large-scale application of such methods. BMA fusion algorithm, which has combined three commonly used models (including Penman Monteith LE algorithm, Priestly-Taylor LE algorithm and Semi-empirical Penman LE algorithm), is then carried out in this study. It can effectively reduce the uncertainty and improve the accuracy of terrestrial latent heat estimation comparing with single algorithm itself after testing with 190 eddy covariance tower site data (Fluxnet site data). The error of mean square root (RMSE) has decreased by 5W/m2and the value of average correlation coefficient (R2) has increased by 0.05 for most of observation points in this test. The fusion model has applied in China to carry out a monthly-based latent heat estimation and mapping for data achieved from 1989 to 2006. The estimation result, after analyzed quantitatively, returns sound precision and stability, which can make up the shortage of current latent heat products. Meanwhile, the spatial distribution analysis shows that: latent heat spatial distribution is the combined contribution of temperature, precipitation and vegetation together. The temporal distribution of latent heat has obvious seasonal characteristic, which is low in winter and high in summer. The latent heat value is declined by 0.07 W/m2per year for past 18 years.

The driving model switched between an intelligent vehicle and a human driver is a hot discussing issue for intelligent driving system, and it relates to the safety of the intelligent vehicle and traffic efficiency of transportation system. It presents a hazard-evaluation-based driver-automation switched shared steering control approach for intelligent vehicles in this manuscript. The switched operation between human driver and autopilot system is carried out when the hazard situation is tested by the autopilot controller. The driver's operation and the deviation from the road center line are employed to carry out the hazard evaluation. The autopilot controller is designed using the constrained model predictive control (MPC) approach to keep the intelligent vehicle run in the safe area that is between the road boundary. In order to verify the control performance of the proposed algorithm, simulation verification under hazard situation of the proposed approach are carried out and compared with the non-switching method. The results show that the intelligent vehicle can keep safe in the hazard situation.

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.

As a typical example of cyber-physical systems, intelligent vehicles are receiving increasing attention, and the obstacle avoidance problem for such vehicles has become a hot topic of discussion. This paper presents a simultaneous trajectory planning and tracking controller for use under cruise conditions based on a model predictive control (MPC) approach to address obstacle avoidance for an intelligent vehicle. The reference trajectory is parameterized as a cubic function in time and is determined by the lateral position and velocity of the intelligent vehicle and the velocity and yaw angle of the obstacle vehicle at the start point of the lane change maneuver. Then, the control sequence for the vehicle is incorporated into the expression for the reference trajectory that is used in the MPC optimization problem by treating the lateral velocity of the intelligent vehicle at the end point of the lane change as an intermediate variable. In this way, trajectory planning and tracking are both captured in a single MPC optimization problem. To evaluate the effectiveness of the proposed simultaneous trajectory planning and tracking approach, joint veDYNA-Simulink simulations were conducted in the unconstrained and constrained cases under leftward and rightward lane change conditions. The results illustrate that the proposed MPC-based simultaneous trajectory planning and tracking approach achieves acceptable obstacle avoidance performance for an intelligent vehicle.

The accurate information of vehicle states that could not be obtained directly by onboard sensors is virtual important for vehicle active safety systems. This paper presents the nonlinear full-order observer which is used to estimate the longitudinal velocity, lateral velocity and road grade estimation with autonomous driving. Firstly, this paper established a simplified vehicle dynamics model for the Hongqi HQ430 which could characterize the performance of autonomous driving vehicle on highway, and the estimator was designed. Secondly, in order to verify the effectiveness of the proposed nonlinear full-order observer, we do some simulation experiments, simulation experiments were carried out under different running conditions, the simulation results showed that the estimation method have certain validity and accuracy.

In this paper, an approach for oil contaminated wastewater recognition is presented. By analyzing the relationship between thermal and multispectral characteristics of clean water and oil contaminated wastewater. The Oil Contaminated Wastewater Index (OCWI) was then developed to extract the oil contaminated wastewater. Results show that remote sensing can be of great help in tracking the oil contaminated wastewater with visible high hydrocarbon concentration in gobi area.

Path tracking issues of autonomous ground vehicles (AGVs) have attracted more attention in recent years with the intelligent and electrified development of vehicles. In order to make AGVs path tracking problem more flexible, regional path tracking problem is discussed in this manuscript based on model predictive control (MPC) method, where the front wheel steering angle is regarded as the control variable. The feasible region for AGVs running is determined first according to the detected road boundaries. In the following, AGVs running in this region is considered using kinematic model. Then, in order to make the actual trajectory of AGVs keep in the region and satisfy the safety requirements, MPC method is employed to design path tracking controller considering the vehicle dynamics, the actuator and state constraints. In order to verify the effectiveness of the proposed algorithm, simulations under various test conditions are carried out using a high fidelity vehicle simulator veDYNA, where the Hongqi vehicle HQ430 parameters are matched. The results obtained from the simulation illustrate that the proposed algorithm obtains good performance in dealing with the regional path tracking problem.

Great advances in simulation-based vehicle system design and development of various driver assistance systems have enhanced the research on improved modeling of driver steering skills. However, little effort has been made on developing driver steering skill models while capturing the uncertainties or statistical properties of the vehicle-road system. In this paper, a stochastic model predictive control (SMPC) approach is proposed to model the driver steering skill, which effectively incorporates the random variations in the road friction and roughness, a multipoint preview approach, and a piecewise affine (PWA) model structure that are developed to mimic the driver's perception of the desired path and the nonlinear internal vehicle dynamics. The SMPC method is then used to generate a steering command by minimization of a cost function, including the lateral path error and ease of driver control. In the analyses, first, the experimental data of Hongqi HQ430 are used to validate the driver steering skill controller. Then, the parametric studies of control performance during a nonlinear steering maneuver are provided. Finally, further discussions about the driver's adaption and the indication on vehicle dynamics tuning are given. The proposed switching-based SMPC driver steering control framework offers a new approach for driver behavior modeling.

Simulation-based vehicle system design and development of various active chassis control systems necessitate an enhanced understanding of driver-vehicle systems, in particular improved modeling of driver driving control characteristics. A number of research efforts have been made in developing driver models in the past few decades. However, little effort has been attempted in modeling driver steering control behavior capturing vehicle-road system parameter uncertainties. In this paper, a novel driver steering control model based on stochastic model predictive control (SMPC) is proposed to effectively incorporate the variations in the vehicle-road system parameters. The proposed SMPC-based driver steering control framework consists of three modules, namely perception, decision and execution, where a multi-point driver preview approach is employed. An internal vehicle dynamics model with the parameter uncertainty in road friction coefficient is formulated to represent the driver's knowledge and adaptation about the variations in road conditions. The SMPC method is then used to minimize a cost function that is a weighted combination of lateral path error and ease of driver control. Simulation analysis about the variant parameters and comparison with an MPC-based driver model demonstrate the effectiveness and robustness of the proposed SMPC-based driver steering control model.