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This article develops a three-dimensional (3-D) impact angle control guidance law with the field-of-view (FOV) constraint. The impact error is defined as the difference between the predicted impact angle by proportional navigation guidance and the desired impact angle. The derived guidance command enables the impact angle error trajectory to follow a desired impact angle error dynamic. It can guarantee that the impact error converges to zero before the time of interception. Meanwhile, zero terminal guidance command is achieved, and the FOV constraint is always met during the guidance process. Furthermore, the trajectory of a guided missile is not affected by the variation of missile speed even under the FOV constraint, which reveals that the impact angle control performance is independent of time-varying missile speed. Numerical simulations are conducted to demonstrate the effectiveness of the proposed guidance law. [ABSTRACT FROM AUTHOR]

The hybrid brain-computer interface (hBCI) combining motor imagery (MI) and steady-state visual evoked potential (SSVEP) has been proven to have better performance than a pure MI- or SSVEP-based brain-computer interface (BCI). In most studies on hBCIs, subjects have been required to focus their attention on flickering light-emitting diodes (LEDs) or blocks while imagining body movements. However, these two classical tasks performed concurrently have a poor correlation. Therefore, it is necessary to reduce the task complexity of such a system and improve its user-friendliness. Aiming to achieve this goal, this study proposes a novel hybrid BCI that combines MI and intermodulation SSVEPs. In the proposed system, images of both hands flicker at the same frequency (i.e., 30 Hz) but at different grasp frequencies (i.e., 1 Hz for the left hand, and 1.5 Hz for the right hand), resulting in different intermodulation frequencies for encoding targets. Additionally, movement observation for subjects can help to perform the MI task better. In this study, two types of brain signals are classified independently and then fused by a scoring mechanism based on the probability distribution of relevant parameters. The online verification results showed that the average accuracies of 12 healthy subjects and 11 stroke patients were 92.40 ± 7.45% and 73.07 ± 9.07%, respectively. The average accuracies of 10 healthy subjects in the MI, SSVEP, and hybrid tasks were 84.00 ± 12.81%, 80.75 ± 8.08%, and 89.00 ± 9.94%, respectively. The high recognition accuracy verifies the feasibility and robustness of the proposed system. This study provides a novel and natural paradigm for a hybrid BCI based on MI and SSVEP. [ABSTRACT FROM AUTHOR]

In the switching process of driving authority for man-machine cooperative driving, it should be ensured that the driver can take over the vehicle safely, stably and efficiently. This paper proposes a driving authority allocation strategy based on real-time allocation domain (RAD), which mainly includes the establishment of RAD and the dynamic optimization. The RAD is a comprehensive dynamic allocation internal, whose range is determined by the corresponding allocation values of driver’s cognitive state, driver’s muscle state and environment state. The dynamic optimization is to search for the optimal driving authority in RAD at any time, and its optimization objectives and constraints all contain time-varying parameters. This work proposes an improved simulated annealing algorithm to solve this problem. Simulation results show that the proposed driving authority allocation strategy can effectively allocate the driving authority according to the current condition and driver’s state, and ensure the safety, stability and efficiency of the take-over process. [ABSTRACT FROM AUTHOR]

This article focuses on event-triggered consensus control for multiagent systems subject to sensor faults or noises. First, a descriptor state observer with a low-pass filtering characteristic being developed for each agent using output information. The convergence regions of estimation errors can be reduced by a nonsingular suppression matrix. Leader–follower event-triggered consensus protocols with continuous-time communication are designed for multiagent systems based on the estimated states. By virtue of the Jordan form of the Laplacian matrix, the stability conditions are derived by using the Lyapunov analysis. Then, new self-triggered consensus protocols are designed for the multiagent systems to remove the requirement of the continuous monitoring triggering condition and continuous communication simultaneously. The triggering interval is proved greater than 0, and the Zeno behavior is excluded for all agents. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

In this article, fixed-time cooperative guidance laws are designed for multiple missiles to simultaneously attack a maneuvering target at desired terminal angles. The 3-D guidance commands consist of tangential acceleration along line-of-sight (LOS) direction to achieve a salvo attack and normal acceleration perpendicular to the LOS direction to realize a terminal angle-constrained interception. First, the tangential acceleration is derived from a distributed cooperative protocol to guarantee the fixed-time consensus of impact times for multiple missiles. Second, based on the integral sliding manifold and fixed-time reaching law, the normal acceleration is developed for each missile to converge the LOS angle to the desired value within a fixed time. In particular, the unknown target acceleration components are estimated by fixed-time observers and compensated in the guidance commands. The fixed-time stability of the proposed cooperative guidance law is rigorously proved and the explicit estimate for the convergence time is theoretically provided. Then, the proposed methods are utilized to realize the cooperative mission in the planar guidance scenario. Finally, the effectiveness and advantages of the proposed cooperative guidance laws are demonstrated through numerical simulations with comparative studies. [ABSTRACT FROM AUTHOR]

This work proposes an active collision avoidance between autonomous driving vehicle and pedestrian with motion uncertainty under urban road. A candidate trajectory planning method considering spatial and time sequences is proposed, which combines the polynomial path planning and the velocity planning with variable safety velocity. Then, a pedestrian-vehicle interaction model is constructed, which takes the pedestrian’s uncertain motion as a superposition of the Markov process without interference and the motion caused by the vehicle, and predicts the pedestrian’s motion probabilistically. On these bases, the optimal trajectory is evaluated from the candidate trajectories by safety, stability, and efficiency, as well as different driving styles. The proposed collision avoidance strategy is verified in conventional and emergency simulation scenarios. Simulation results show that it can effectively plan a safe, stable and efficient trajectory under normal and emergency conditions. [ABSTRACT FROM AUTHOR]

In order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time headway (TH) and the probabilistic lane distribution of the surrounding vehicles. With the extracted traffic state, the reinforcement learning (RL) is adopted to learn the optimal state-action pair for specific scenario. Analogously, by mapping millions of traffic scenarios, huge amounts of state-action pairs can be stored in the experience pool. Then the imitation learning (IL) is further employed to memorize the experience pool by deep neural networks. The learning result shows that the accuracy of the decision network can reach 94.17%. Besides, for some imperfect decisions of the network, the rule-based method is taken to rectify by judging the long-term reward. Finally, the IDF is simulated in G25 highway and has promising results, which can always drive the vehicle to the state with high efficiency while ensuring safety. [ABSTRACT FROM AUTHOR]

Uncertain cut-in maneuver of vehicles from adjacent lanes makes it difficult for vehicle's automatic speed control strategy to make judgments and effective control decisions. In this paper, an intelligent speed control strategy for uncertain cut-in scenarios is established based on a basic autonomous driving system. This strategy judges cut-in maneuver from surrounding vehicles and outputs adaptive control action under current environment according to Q value of state-action pair based on a Q network. In addition, according to the analysis of cut-in scenarios, the Q network is trained based on a novel reinforcement learning method named as experience screening deep Q-learning network (ES-DQN). The proposed ES-DQN is an extension of double deep Q-learning network (DDQN) algorithm, and includes two parts: experience screening and policy learning. Based on the experience screened from the experience screening part, the proposed learning method can train an intelligent speed control strategy which has stronger adaptability and control effect in uncertain cut-in scenarios. According to simulation results, the proposed intelligent speed control strategy trained by ES-DQN has better performance under uncertain cut-in scenarios than DDQN method and traditional ACC strategy. Meanwhile, by adjusting weight value in reward function, the system can realize different control target. [ABSTRACT FROM AUTHOR]

In order to improve the fault-tolerant performance of wheel angle sensor of steer-by-wire (SBW) vehicle, a fault diagnosis and fault-tolerant compensation (FDFTC) strategy for wheel angle sensor of SBW vehicle via extended Kalman filter (EKF) is proposed in this paper. Firstly, the steering motor model, rack and pinion model, vehicle dynamics model and variable transmission ratio model are established. Then, based on the analytical redundancy method, the EKF algorithm is adopted to obtain the wheel angle estimation signal. On this basis, a sensor FDFTC strategy with improved majority voting scheme is proposed, which is composed of fault diagnosis module and fault isolation compensation module. Finally, combined with Matlab/Simulink and Carsim, the estimation results are analyzed under sinusoidal condition and double lane change condition, and the performance of the proposed FDFTC strategy is further verified under the sensor stuck fault condition, deviation fault condition and noise interference condition. The results indicate that the proposed FDFTC strategy can not only accurately diagnose the fault signal, but also reconstruct the correct wheel angle signal, which is of great significance to improve the safety and reliability of SBW vehicle. [ABSTRACT FROM AUTHOR]

This article deals with sensor fault tolerant control design and large delay compensation for a class of linear time-invariant systems. Based on predictor-based state transformation and predictive descriptor observer design, both the state and output feedback control laws are constructed. Within the framework of Lyapunov–Krasovskii functionals, a set of stability conditions are identified. The closed-loop systems is globally asymptotically stable at the origin under the state feedback and is ultimately bounded under the output feedback. Finally, the proposed design is validated through numerical simulations and then implemented on a quadrotor trajectory tracking test. Both simulation and experimental results demonstrate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of fault detection results, this paper proposes a novel fault detection strategy of vehicle wheel angle signal via long short-term memory network (LSTM) and improved sequential probability ratio test (SPRT). Firstly, a signal estimation method based on data-driven modeling is presented, which fuses the vehicle current status information and adopts the LSTM based on deep learning to estimate the vehicle wheel angle signal. Then, the signal residual sequence is obtained by comparing the estimated wheel angle signal with the measured wheel angle signal. Based on this, the improved SPRT method based on mathematical statistics is used to analyze the signal residual sequence, so as to detect the fault signal timely and accurately. Finally, the accuracy of the estimation results is analyzed under sinusoidal condition, double-lane change condition and sinusoidal sweep frequency condition, and the effectiveness of the fault detection strategy proposed in this paper is further verified under the stuck fault condition and drift fault condition. The results indicate the effectiveness of the proposed fault detection strategy, which is of great significance to improve the safety and reliability of the vehicle. [ABSTRACT FROM AUTHOR]

Recently, inferring lane change intention has received considerable attention. Due to the high nonlinearity and complexity of traffic contexts, traditional methods cannot satisfy the requirements of long-term prediction tasks and lack the ability of capturing nonlinear temporal dependencies. This paper proposes an intention inference model based on Recurrent Neural Networks (RNN), to tackle time series prediction problems. Considering dynamic interaction among surrounding vehicles, our model takes the sequence motion information of surrounding vehicles as inputs and calculates the congestion of different lanes, integrated with vehicle states of the object vehicle. To illustrate the availability of the proposed RNN intention inference model, a motion planning controller considering intention was developed. A Nonlinear Model Predictive Control (NMPC) was established to planning a safe, sub-optimal path for autonomous driving vehicle under collision avoidance constraints. The experiments on the proposed model were conducted, based on two RNN structure Long-Short Term Memory (LSTM) and Generalized Recurrent Unit (GRU), by Tensorflow with NGSIM data. The motion planning controller is modeled and simulated by Carsim with Simulink for some typical scenarios. Subsequently, experimental results demonstrate that RNN achieves best performance, inferring intention with 96% accuracy, compared with other approaches. [ABSTRACT FROM AUTHOR]

In this article, a guidance law with impact angle constraint is developed using the state-dependent Riccati equation (SDRE) technique. First, the nonlinear guidance dynamics consisting of line-of-sight (LOS) angle rate and error is formulated under a planar engagement scenario. After the state-dependent coefficient parameterization, a nonlinear regulator problem is formed to concurrently zero LOS angle rate and error. Second, a preliminary guidance law is directly developed under the framework of the SDRE technique. By applying the Lyapunov stability analysis, a nonlinear suboptimal guidance law is obtained through further parameter modifications. Then, the proposed guidance law is extended to attack maneuvering targets. Finally, the effectiveness of the proposed guidance law is demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]

Random network delay will introduce uncertainty into trajectory tracking model of the autonomous vehicle, which seriously deteriorates the vehicle's control system stability and trajectory tracking accuracy. In this paper, considering steering angle oscillation caused by random network delay, trajectory tracking system robustness and stability is analyzed and a linear uncertain time-delay system is established. Comprehensively considering control system accuracy, robustness, and computational efficiency in the rolling optimization of Model Predictive Control (MPC), Adaptive Model Predictive Control for Uncertain model (UM-AMPC) algorithm is proposed to predict control variables for the next sampling time and alleviate the target angle discontinuity. This is achieved by operating target angle signal and augmented state variables, which are received by the lower nodes during the period from the current sampling time to network delay upper bound. The hardware-in-the-loop simulation results show that the proposed algorithm can effectively guarantee system stability and tracking accuracy of the autonomous vehicle under random network delay. [ABSTRACT FROM AUTHOR]

In order to decide a safe and reliable trajectory for autonomous driving vehicles, the threat of surrounding vehicles need to be assessed quantitatively and consider the potential risk. This paper proposes a novel integrated threat assessment algorithm for the decision-making system. First, the motion of the surrounding vehicle is predicted probabilistic based on the interact multiple model (IMM) to consider the potential threat. Then, we build an integrated threat assessment function to assess the threat in each state quantitatively and objectively, which synthesizes the existing time-to-collision (TTC), time-headway (TH), and the original proposed time-to-front (TTF). Based on this, the decision-making system is established according to the Markov decision process (MDP) and the feedback value of each decision sequence is calculated by the integrated threat assessment function, thus the safest trajectory for the current moment can be determined by optimal search. Finally, the decision-making system is verified in the overtaking and cut-in scenario by Carsim and Simulink co-simulation. The results show that the proposed threat assessment algorithm for the decision-making system can help autonomous vehicles decide a safe trajectory in real-time and maintain good maneuverability. [ABSTRACT FROM AUTHOR]