The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi–Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values.

Object detection in 3-D space is a fundamental technology in the autonomous driving system. Among the published 3-D object detection methods, the single-modal methods based on point clouds have been widely studied. One problem exposed by these methods is that point clouds lack color and texture features. The limitation in conveying semantic information often leads to failures in detection. In contrast, the multimodal methods based on the image and point clouds fusion may solve this problem, but relevant research is not sufficient. In this work, a single-stage multiview multimodal 3-D object detector (MVMM) is proposed, which can naturally and efficiently extract semantic and geometric information from the image and point clouds. Specifically, the data-level fusion approach of point clouds coloring is used for combining information from the camera and LIDAR. Next, an encoder–decoder backbone is devised to extract features from colored points in the range view. Then, colored points are concatenated with the range view features, voxelized, and fed into the point view bridge for down-sampling. Finally, the down-sampled feature map is used by the bird's eye view backbone and the detection head for generating 3-D results based on predefined anchors. According to extensive experiments on the KITTI dataset, MVMM achieves competitive performance while runs at 27 FPS on the 1080 Ti GPU. Particularly, MVMM performs extremely well in difficult scenes (e.g., heavy occlusion and truncation) due to the understanding of fused information.

Accurate knowledge of the vehicle states is the foundation of vehicle motion control. However, in real implementations, sensory signals are always corrupted by delays and noises. Network induced time-varying delays and measurement noises can be a hazard in the active safety of over-actuated electric vehicles (EVs). In this paper, a brain-inspired proprioceptive system based on state-of-the-art deep learning and data fusion technique is proposed to solve this problem in autonomous four-wheel actuated EVs. A deep recurrent neural network (RNN) is trained by the noisy and delayed measurement signals to make accurate predictions of the vehicle motion states. Then unscented Kalman predictor, which is the adaption of unscented Kalman filter in time-varying-delay situations, combines the predictions of the RNN and corrupted sensory signals to provide better perceptions of the locomotion. Simulations with a high-fidelity, CarSim, full-vehicle model are carried out to show the effectiveness of our RNN framework and the entire proprioceptive system.

The realization of a novel human gesture recognition algorithm is essential to enable the effective collision avoidance of autonomous vehicles. Compared to visible spectrum cameras, the use of infrared imaging can enable more robust human gesture recognition in a complex environment. However, gesture recognition in infrared images has not been extensively investigated. In this work, we propose a model to detect human gestures, based on the improved YOLO-V3 network involving a saliency map as the second input channel to enhance the reuse of features and improve the network performance. Three DenseNet blocks are added before the residual components in the YOLO-V3 network to enhance the convolutional feature propagation. The saliency maps are obtained by multiscale superpixel segmentation, superpixel block clustering and cellular automata saliency detection. The obtained five scale saliency maps are fused using a Bayesian based fusion algorithm, and the final saliency image is generated. The infrared images composed of 4 main gesture classes are collected, each of which contains several approximated gestures in morphological terms. The training and testing datasets are generated, including original and augmented infrared images with a resolution of 640 × 480. The experimental results show that the proposed approach can enable real time human gesture detection for autonomous vehicles, with an average detection accuracy of 86.2%.