Ling Zheng

Scenario-based validation requires efficient screening of large test libraries, but existing complexity metrics often combine traffic hazard and automated-driving-system workload into a single construct. This study proposes a two-dimensional framework that separates scenario hazardousness from system processing difficulty. Hazardousness combines normalized acceleration and time-to-collision measures. Processing difficulty aggregates perception, decision-making, and execution demands, with weights obtained from a triangular fuzzy analytic hierarchy process. The framework was compared with four baseline metrics using 2000 Latin-hypercube-sampled cut-in scenarios and was explored in 22 closed-field scenarios tested with three production Level 2 vehicles. The proposed metric produced a broad and less concentrated distribution of cases across the normalized score range. However, total complexity was not significantly correlated with any vehicle score in the 22-case dataset; therefore, the field experiment supports only preliminary discriminant analysis, not predictive validity. A 10% hierarchical-weight perturbation analysis showed that the three largest global weights remained the same set in 82.3% of 10,000 simulations. The model is currently limited by empirically assigned perception constants, a small field sample, few ultra-high-complexity cases, and omission of road curvature, crosswinds, tire condition, and actuator delay. The additive aggregation assumes that interactions among criteria can be neglected for the intended comparison; this assumption has not been validated. The framework is best viewed as an interpretable scenario-screening tool requiring further calibration and validation.

With the rapid development of intelligent automobiles, the chassis serves as an essential carrier of intelligence and a necessary condition for achieving high-level autonomous driving. Its electronic and electrical architecture is evolving toward centralized development, which is also significantly increasing the complexity of system functions. Meanwhile, with the integration of more sensors and an increase in data volume, stricter requirements have been placed on software scalability, portability, and maintainability. This paper presents a system software design and implementation approach for the chassis domain controller by integrating the AUTOSAR standard with model-based design (MBD). The developed software is subsequently deployed on a domain controller hardware platform based on the Renesas u2a16 chip for integrated testing. The software algorithm development, model-in-the-loop (MIL) testing, hardware-in-the-loop (HIL) testing, and real vehicle calibration processes are described in detail, focusing on the roll stability control software component in the chassis domain controller. A detailed definition of the toolchain for each development stage is also provided. The feasibility and effectiveness of the proposed chassis domain controller software system development process, based on the combination of the AUTOSAR standard and model-based design, are validated through test results. This method effectively achieves software–hardware decoupling and enhances software scalability, module reusability, and reliability, which is of great significance for improving the efficiency and iteration of chassis domain controller development.

A critical challenge in vehicle state estimation is managing unknown and non-Gaussian noise characteristics resulting from sensor degradation, measurement abnormalities, model inaccuracies, and external interference. However, existing research has problems with filter divergence and insufficient accuracy and robustness. To this end, we propose a robust adaptive filtering estimator, the Variational Bayesian Maximum Correntropy Square-Root Cubature Kalman Filter (VBMCSCKF), for the joint estimation of vehicle sideslip angle and tire-road longitudinal/lateral forces. The proposed VBMCSCKF utilizes onboard inertial measurement unit (IMU) data to adaptively estimate noise covariance matrices via the Variational Bayesian (VB) method, and introduces the Maximum Correntropy Criterion (MCC) for further correction to enhance its accuracy and robustness under unknown and abnormal noise. These features are embedded within the Square-Root Cubature Kalman Filter (SCKF) framework, and the estimator for tire force and sideslip angle is developed based on a zero-first derivative and vehicle model. Numerical simulations and Monte Carlo simulation of a double-lane change maneuver under varying speeds and road adhesion conditions demonstrate impressive estimation results for tire-road force and the vehicle sideslip angle. It effectively adapts to time-varying IMU noise and resists abnormal IMU measurements. Compared to MCSCKF and VBSCKF, the performence of VBMCSCKF improves by 44.0% and 28.5%, respectively. Notably, the increase in computation time for VBMCSCKF compared to the standard SCKF is almost negligible.

Road roughness significantly impacts vehicle safety and dynamic responses. For nonlinear suspension systems, the nonlinear characteristics often make it challenging for estimators to identify the actual road roughness accurately. This paper proposes a hybrid road roughness identification algorithm based on nonlinear auto-regressive with exogenous inputs (NARX) and a process noise adaptive square root cubature Kalman filter (PASCKF) to address this issue. Driven by vehicle acceleration data, an NARX-based road roughness identification system is constructed to mitigate the model uncertainties. Furthermore, a hybrid strategy is proposed. On the one hand, the accurate road roughness estimated by the NARX is converted into process noise covariance, enhancing the estimator’s accuracy and convergence rate. Another switching strategy is proposed to optimize the non-convergence issues of the PASCKF. Finally, simulation and actual vehicle experiment data demonstrate that this approach offers superior identification accuracy and adaptability compared to the standalone SCKF algorithm.

Humans are frequently driving with different emotions, but how driving affects human’s brain information processing mechanism in different emotional states is still unknown. In this study, we investigate the effects of driving on brain functional connectivity networks across different emotions based on electroencephalography (EEG) signals. We utilize phase lag index (PLI) to measure the degree of phase synchronization among electrode channels, and visualize the functional connectivity networks. We also employe graph theory to analyze the functional connectivity networks. Our findings indicate that the strength of functional connectivity among brain regions under different emotions significantly increased after involving in driving, with more efficient and rapid information transmission in the functional connectivity networks. This enhancement is particularly evident in the frontal and parietal lobes, with 507 enhanced connections related with the frontal lobe and 300 related with the parietal lobe, accounting for 41.94% and 24.81% of the total enhanced connections, respectively. These results offer valuable insights into the understanding of how human drives, holding potential for the development of interventions and technologies to improve emotional regulation and driving safety technologies.

3D scene perception technology enhances the safety and decision-making ability of autonomous vehicles by accurately acquiring and analyzing stereo information from the environment. Compared to traditional object detection methods, semantic occupancy perception offers greater flexibility in describing 3D scenes with arbitrary shapes and various categories. However, existing methods for semantic occupancy perception face challenges such as poor generalization of depth estimation and inaccurate alignment and fusion of multimodal features. In this paper, a novel multimodal semantic occupancy prediction method, AFOcc, is proposed. AFOcc addresses these challenges by adopting a fusion technique based on feature alignment and an attention mechanism. The method extracts multi-scale features from image and LiDAR data, encodes LiDAR voxel features using sparse convolution, and projects them onto 2D image features for precise alignment. The projection of point clouds onto images is achieved through a feature alignment module. Finally, a learnable fusion module adaptively adjusts the weights of different modal features to enhance the fusion effect. Extensive experiments on the nuScenes-Occupancy dataset demonstrate that AFOcc significantly outperforms state-of-the-art methods in terms of the mIoU metric. Notably, in the bicycle and motorcycle categories, an IoU improvement of more than 40% is achieved. These results illustrate the superior perception and robustness capabilities of AFOcc in complex scenes.