This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding ...traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is applied to solve the decision-making, which is formulated as a non-cooperative game problem. Besides, potential field is adopted in the motion planning model, which uses different potential functions to describe surrounding vehicles with different behaviors and road constraints. Then, Model Predictive Control (MPC) is utilized to predict the state and trajectory of the autonomous vehicle. Finally, the decision-making and motion planning is then integrated into a constrained multi-objective optimization problem. Three testing scenarios considering different social behaviors of surrounding vehicles are carried out to validate the performance of the proposed approach. Testing results show that the integrated approach is able to address different social interactions with other traffic participants, and make proper and safe decisions and planning for autonomous vehicles, demonstrating its feasibility and effectiveness.
Improving vehicle safety and reducing traffic accidents have always been of cardinal importance in vehicle dynamics control fields. A reasonable and comprehensive safety index that characterizes the ...vehicle's safe region is the most challenging aspect of research. With the linear dynamics model as the benchmark, this article uses the deviation of yaw rate and vehicle sideslip angle from the corresponding linear response as the lateral stability indices. Meanwhile, the maximum slip ratio of the driven wheels is selected as the longitudinal stability index. The safety indicator , a quantitative index featuring the safety degree of vehicle planar motions, is then inferred by a fuzzy inference system using the lateral and longitudinal stability indices. As such, the recurrent high-order neural network model predicts the vehicle states. Based on the predicted states, a safety indicator is then derived by using fuzzy inference system, which can assess the safety of a driver's control commands. In the case of an improper driving torque demand given by the driver, the torque correction process is immediately conducted to maintain the vehicle in a safe region. Finally, two typical scenarios-slippery curves and double lane changes in low friction roads-are simulated on the MATLAB/Simulink-CarSim cosimulation platform. The hardware-in-the-loop experiments are also conducted on a driving simulator test rig, validating the performance of the developed algorithms. The holistic stability performance of the in-wheel motor driven vehicle is thoroughly analyzed and compared using three existing methods. The simulation and experimental results validate the effectiveness and feasibility of the proposed method.
Driver workload inference is significant for the design of intelligent human-machine cooperative driving schemes since it allows the systems to alert drivers before potentially dangerous maneuvers ...and achieve a safer control transition. However, pattern variations among individual drivers and sensor artifacts pose great challenges to the existing cognitive workload recognition approaches. In this paper, we develop an attention-enabled recognition network with a decision-level fusion architecture to further improve the workload estimation performance. Specifically, the cross-attention mechanism can enhance useful feature representations learned by hyper long short-term memory (HyperLSTM) based modules from time-series multimodal information, i.e., electroencephalogram signals, eye movements, vehicle states. A novel dataset containing multiple driving scenarios is constructed to evaluate the model performance across different historical horizons and decision thresholds, and test results demonstrate the superior performance of the proposed model to other existing methods. Furthermore, robustness tests and driver-in-the-loop experiments are conducted to verify the effectiveness of the developed model in real-time workload levels inference. The code and supplementary materials are available at https://yanghh.io/Driver-Workload-Recognition .
As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great ...importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
Connected and Automated Vehicles (CAVs) rely on the correctness of position and other vehicle kinematics information to fulfill various driving tasks such as vehicle following, lane change, and ...collision avoidance. However, a malicious vehicle may send false sensor information to the other vehicles intentionally or unintentionally, which may cause traffic inconvenience or loss of human lives. Here, we take the advantage of vehicular cloud and increase the resilience of CAVs to malicious vehicles by assuming each vehicle shares its local sensor information with other vehicles to create information redundancy on the cloud side. We exploit this redundancy and propose a sensor fusion algorithm for the vehicular cloud, capable of providing robust state estimation of all vehicles under the condition that the number of malicious information is sufficiently small. Using the proposed estimator, we provide an algorithm for isolating malicious vehicles. We use numerical examples to illustrate the effectiveness of our methods.
As typical applications of cyber-physical systems (CPSs), connected and automated vehicles (CAVs) are able to measure the surroundings and share local information with the other vehicles by using ...multimodal sensors and wireless networks. CAVs are expected to increase safety, efficiency, and capacity of our transportation systems. However, the increasing usage of sensors has also increased the vulnerability of CAVs to sensor faults and adversarial attacks. Anomalous sensor values resulting from malicious cyberattacks or faulty sensors may cause severe consequences or even fatalities. In this article, we increase the resilience of CAVs to faults and attacks by using multiple sensors for measuring the same physical variable to create redundancy. We exploit this redundancy and propose a sensor fusion algorithm for providing a robust estimate of the correct sensor information with bounded errors independent of the attack signals, and for attack detection and isolation. The proposed sensor fusion framework is applicable to a large class of security-critical CPSs. To minimize the performance degradation resulting from the usage of the estimation for control, we provide an <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> controller for cooperative adaptive cruise control-equipped CAVs. The designed controller is capable of stabilizing the closed-loop dynamics of each vehicle in the platoon while reducing the joint effect of estimation errors and communication channel noise on the tracking performance and string behavior of the vehicle platoon. Numerical examples are presented to illustrate the effectiveness of our methods.
Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains ...under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent.
Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active ...safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technology, which can be realized through torque vectoring. To further advance the lateral stabilization performance of the IMDV, in this article a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method. First, the new RHONN model is developed to represent vehicle's nonlinear dynamic behaviors. Different from the conventional physics-based modelling method, the RHONN model forms high-order polynomials by neuron states to feature nonlinear dynamics. Based on the RHONN model, the steady-state responses of vehicle's yaw rate and sideslip angle are iteratively optimized and set as the control objectives for low-level controller, aiming to improve the system robustness. Besides, a nonlinear model predictive controller is designed based on the RHONN, which is expected to improve the prediction accuracy during the receding horizon control. Further, a constrained optimization problem is formulated to derive the required yaw moment for vehicle lateral dynamics stabilization. Finally, the performance of the developed RHONN-based nonlinear MPC is validated on an IMDV in the CarSim/Simulink simulation environment. The validation results show that the developed approach outperforms the conventional method, and further improves the stable margin of the system. It is able to enhance the lateral stabilization performance of the IMDV under various driving scenarios, demonstrating the feasibility and effectiveness of the proposed approach.
The learning-based methods for single image super- resolution (SISR) can reconstruct realistic details, but they suffer severe performance degradation for low-light images because of their ignorance ...of negative effects of illumination, and even produce overexposure for unevenly illuminated images. In this paper, we pioneer an anti-illumination approach toward SISR named Light-guided and Cross-fusion U-Net (LCUN), which can simultaneously improve the texture details and lighting of low-resolution images. In our design, we develop a U-Net for SISR (SRU) to reconstruct super- resolution (SR) images from coarse to fine, effectively suppressing noise and absorbing illuminance information. In particular, the proposed Intensity Estimation Unit (IEU) generates the light intensity map and innovatively guides SRU to adaptively brighten inconsistent illumination. Further, aiming at efficiently utilizing key features and avoiding light interference, an Advanced Fusion Block (AFB) is developed to cross-fuse low-resolution features, reconstructed features and illuminance features in pairs. Moreover, SRU introduces a gate mechanism to dynamically adjust its composition, overcoming the limitations of fixed-scale SR. LCUN is compared with the retrained SISR methods and the combined SISR methods on low-light and uneven-light images. Extensive experiments demonstrate that LCUN advances the state-of-the-arts SISR methods in terms of objective metrics and visual effects, and it can reconstruct relatively clear textures and cope with complex lighting.
To address the safety and efficiency issues of vehicles at multi-lane merging zones, a cooperative decision-making framework is designed for connected automated vehicles (CAVs) using a coalitional ...game approach. Firstly, a motion prediction module is established based on the simplified single-track vehicle model for enhancing the accuracy and reliability of the decision-making algorithm. Then, the cost function and constraints of the decision making are designed considering multiple performance indexes, i.e. the safety, comfort and efficiency. Besides, in order to realize human-like and personalized smart mobility, different driving characteristics are considered and embedded in the modeling process. Furthermore, four typical coalition models are defined for CAVS at the scenario of a multi-lane merging zone. Then, the coalitional game approach is formulated with model predictive control (MPC) to deal with decision making of CAVs at the defined scenario. Finally, testings are carried out in two cases considering different driving characteristics to evaluate the performance of the developed approach. The testing results show that the proposed coalitional game based method is able to make reasonable decisions and adapt to different driving characteristics for CAVs at the multi-lane merging zone. It guarantees the safety and efficiency of CAVs at the complex dynamic traffic condition, and simultaneously accommodates the objectives of individual vehicles, demonstrating the feasibility and effectiveness of the proposed approach.
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