Estimation of tire-road peak adhesion coefficient for intelligent electric vehicles based on camera and tire dynamics information fusion

The tire-road peak adhesion coefficient (TRPAC) describes the tire adhesion limit that a road can provide. The TRPAC is a key parameter for precise vehicle motion control and an important basis for decision-making and planning of intelligent vehicles. Considering the critical and difficult problems in the estimation of TRPAC, such as slow convergence and low accuracy, a TRPAC estimation method based on the fusion of vehicle dynamics and machine vision is proposed in this paper. Based on the observability theory of nonlinear systems, local weak observability of the dynamics-based estimator is analyzed to explain the limitation of a single dynamics-based estimator. The framework of dynamics-image-based fusion estimator is then proposed, including the fusion of data, model and decision levels. A dynamics-based fusion estimator is designed by considering the coupling relationship of longitudinal and lateral tire forces to adapt the conditions of complex excitations. Start-and-stop strategy for the dynamics-based fusion estimator is designed by setting excitation thresholds for different types of road surfaces, which are identified using vision information. Parameter self-tuning for the dynamics-based fusion estimator based on the image-based estimator is proposed to improve convergence speed and reduce oscillation. The results of the simulation and vehicle test show that the road estimation error of the proposed method is within 0.03 and the convergence time is within 0.5 s. Compared with other existing estimators, the fusion estimator achieved better accuracy, sensitivity and stability, particularly when complex excitations were present. 

Tire-Road Peak Adhesion Coefficient Estimation Method Based on Fusion of Vehicle Dynamics and Machine Vision

Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction from baseline imaging. We made a comprehensive literature search on eligible studies developing ML models for core infarcted tissue estimation on admission CT or MRI in AIS patients. Eleven studies meeting the inclusion criteria were included in the quantitative analysis. Study characteristics, model methodology, and predictive performance of the included studies were extracted. A meta-analysis was conducted on the dice similarity coefficient (DSC) score by using a random-effects model to assess the overall predictive performance. Study heterogeneity was assessed by Cochrane Q and Higgins I2 tests. The pooled DSC score of the included ML models was 0.50 (95% CI 0.39–0.61), with high heterogeneity observed across studies (I2 96.5%, p < 0.001). Sensitivity analyses using the one-study removed method showed the adjusted overall DSC score ranged from 0.47 to 0.52. Subgroup analyses indicated that the DL-based models outperformed the conventional ML classifiers with the best performance observed in DL algorithms combined with CT data. Despite the presence of heterogeneity, current ML-based approaches for final infarct prediction showed moderate but promising performance. Before well integrated into clinical stroke workflow, future investigations are suggested to train ML models on large-scale, multi-vendor data, validate on external cohorts and adopt formalized reporting standards for improving model accuracy and robustness.

/pdf/performance-of-machine-learning-for-tissue-outcome-1bxy17ld.pdf

Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

This article proposes an adaptive strong tracking Kalman filter (STKF) to enhance the estimation performance of the tire–road friction coefficient (TRFC), a critical factor in vehicle control and safety. First, a vehicle dynamics model based on a quasi-steady-state tire model is introduced to improve the accuracy of modeling the vehicle response to continuous acceleration fluctuations. Second, an adaptive STKF is developed using the vehicle dynamics model, incorporating a fading factor and an excitation factor. The fading factor, determined from the residual sequence of the observation variable, is utilized to correct the prediction error variance, thereby enhancing the tracking capability and estimation accuracy of the algorithm. The excitation factor, calculated based on real-time vehicle state, adjusts the fading factor to prevent significant prediction error variance under low excitation intensity and to avoid divergence of the estimation algorithm. Experimental results under various road conditions demonstrate the effectiveness of the proposed method in improving the convergence speed and stability of TRFC estimation. The root-mean-square error of the estimated values is improved by more than 18%, and the mean absolute error is improved by over 34% compared to the benchmark method.

Capturing Rapid Vehicle Dynamics: A Refined Adaptive Kalman Filter for Enhanced Tire–Road Friction Estimation

Segmentation of acute ischemic stroke lesions based on deep feature fusion

The type of road terrain is an important piece of environment perception information for smart vehicles. In this paper, an approach to recognize the road terrain by using road surface images is proposed. The texture features were extracted from images captured by a camera for the road terrain classification task. The support vector machine method was selected for terrain classification. The simulation experiments indicated that this approach worked very well on terrains with distinguished texture. However, this approach could not recognize the road terrain when the damaged captured image changed frequently due to the luminance or when the moving speed of the vehicle was too fast.

Image Feature Based Machine Learning Approach for Road Terrain Classification

ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps

Background Hippocampal sclerosis (HS) is the most common pathological type of temporal lobe epilepsy (TLE) and one of the important surgical markers. Currently, HS is mainly diagnosed manually by radiologists based on visual inspection of MRI, which greatly relies on MRI quality and physician experience. In clinical practice, non-thin MRI scans are often used due to the time and efficiency needed for the acquisition. However, these scans can be difficult for junior physicians to interpret accurately. Thus, the rapid and accurate diagnosis of HS using real-world MRI images in clinical settings is a challenging task. Objective Our aim was to explore the feasibility of using computer vision methods to diagnose HS on real-world clinical MRI images and to provide a reference for future clinical applications of artificial intelligence methods to aid in detecting HS. Methods We proposed a deep learning algorithm called “HS-Net” to discriminate HS using real-world clinical MRI images. First, we delineated and segmented a region of interest (ROI) around the hippocampus. Then, we utilized the fractional differential (FD) method to enhance the textures of the ROIs. Finally, we used a small-sample image classification method based on transfer learning to fine-tune the feature extraction part of a pretrained model and added two fully connected layers and an output layer. In the study, 96 TLE patients with HS confirmed by postoperative pathology and 89 healthy controls were retrospectively enrolled. All subjects were cross-validated, and models were evaluated for performance, robustness, and clinical utility. Results The HS-Net model achieved an area under the curve (AUC) of 0.894, an accuracy of 82.88%, an F1-score of 84.08% in the test cohort based on real, routine, clinical T2-weighted fluid attenuated inversion recovery (FLAIR) sequence MRI images. Additionally, the AUC, accuracy and F1 scores of our model all increased by around 3 percentage points when the inputs were augmented with the ROIs of the textures enhanced using the FD method. Conclusions Our computational model has the potential to be used for the diagnosis of HS in real clinical MRI images, which could assist physicians, particularly junior physicians, in improving the accuracy of discrimination.

/pdf/automated-detection-of-hippocampal-sclerosis-using-real-1azneldh.pdf

Automated detection of hippocampal sclerosis using real-world clinical MRI images

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Papers

Image Feature Based Machine Learning Approach for Road Terrain Classification

ISP-Net: Fusing features to predict ischemic stroke infarct core on CT perfusion maps

Automated detection of hippocampal sclerosis using real-world clinical MRI images