Driver drowsiness detection is a critical service in road safety to significantly reduce vehicle accidents. However, existing solutions often prioritize detection accuracy while neglecting speed. This paper proposes a novel mechanism, named ALERT, based on Electroencephalography (EEG) signals for real-time and accurate drowsiness detection. ALERT consists of four phases: periodic driver monitoring, feature extraction, feature selection and drowsy driving detection. Specifically, a periodic collection model is introduced to monitor driver behavior, followed by extracting features from time, frequency, and time-frequency domains. To optimize performance, a Sigmoid-based Grasshopper Optimization Algorithm (SGOA) is proposed to select the optimal feature set with minimal redundancy. Finally, various classifiers are evaluated to identify the best performance. Simulation results on real EEG signals demonstrate that the system achieves an accuracy of up to 96
Accurate state of charge (SOC) estimation helps to maintain the battery running in a reliable and efficient way. The moving horizon estimation (MHE) algorithm is an efficient way to estimate SOC in the battery system with physical constraints and noises, but its huge computational burden hinders its online application. To reduce the burden, this paper proposes an explicit MHE method to offload the computationally complex optimization solution in the MHE. First, the complex and constrained optimization problem is solved offline by multi-parametric quadratic programming (mp-QP). Then, the explicit solution of optimal estimation is derived by piecewise affine (PWA) function, which is suitable for online estimation. The proposed explicit MHE method significantly reduces the online computational resources and enables real-time optimal SOC estimation. Finally, experiments and simulations validate the effectiveness of the explicit MHE method in ensuring estimation accuracy and saving computational cost. Results show the proposed method can achieve a max absolute SOC estimation error of 1.78
Rigorous safety evaluation of automated vehicles is crucial before widespread deployment. The rarity of safety-critical scenarios limits the efficiency of scenario-based safety evaluation. Accelerated evaluation methods using importance sampling offer promising solutions, though existing approaches struggle with local optima and inefficient search when constructing the importance sampling distribution in large parameter spaces. In this paper, a prior knowledge-guided genetic algorithm for importance sampling distribution (PKGA-ISD) is proposed to address these challenges. The PKGA-ISD extracts scenario risk-related prior knowledge through random forest-based variable importance quantification and statistical analysis, which leads to refined parameter search ranges. By constraining the population initialization within these refined ranges, high-quality initial populations focusing on promising parameter regions are created, enabling more effective exploration of large parameter spaces and faster convergence to the optimal distribution. Simulation validation in cut-in scenarios demonstrates that PKGA-ISD achieves improved evaluation efficiency with reduced test sample requirements while maintaining unbiased collision rate estimation. These results confirm the potential of integrating prior knowledge into importance sampling distribution construction for accelerated safety evaluation, facilitating the development and deployment of automated vehicles.
