With the development of automobile electrification and intelligence, the demand for electro-mechanical braking (EMB) systems is increasing rapidly. This paper reviews the development status of the EMB actuator on the basis of extensive patent and literature research. By analyzing the basic structure of the EMB actuator, this paper decomposes the actuator into five modules: service brake module, parking brake module, brake clearance compensation module, quick-return module and sensor module. On the basis of basic structure, the estimation algorithm for indirect clamping force control and the direct clamping force control algorithm of the actuator are summarized. In addition, the requirements of the EMB system for intelligent vehicles and its typical architecture are analyzed, and the preliminary application of the EMB system in intelligent driving is summarized.

Four-dimensional (4D) radar–visual odometry (4DRVO) integrates complementary information from 4D radar and cameras, making it an attractive solution for achieving accurate and robust pose estimation. However, 4DRVO may exhibit significant tracking errors owing to three main factors: 1) sparsity of 4D radar point clouds; 2) inaccurate data association and insufficient feature interaction between the 4D radar and camera; and 3) disturbances caused by dynamic objects in the environment, affecting odometry estimation. In this paper, we present 4DRVO-Net, which is a method for 4D radar–visual odometry. This method leverages the feature pyramid, pose warping, and cost volume (PWC) network architecture to progressively estimate and refine poses. Specifically, we propose a multi-scale feature extraction network called Radar-PointNet++ that fully considers rich 4D radar point information, enabling fine-grained learning for sparse 4D radar point clouds. To effectively integrate the two modalities, we design an adaptive 4D radar–camera fusion module (A-RCFM) that automatically selects image features based on 4D radar point features, facilitating multi-scale cross-modal feature interaction and adaptive multi-modal feature fusion. In addition, we introduce a velocity-guided point-confidence estimation module to measure local motion patterns, reduce the influence of dynamic objects and outliers, and provide continuous updates during pose refinement. We demonstrate the excellent performance of our method and the effectiveness of each module design on both the VoD and in-house datasets. Our method outperforms all learning-based and geometry-based methods for most sequences in the VoD dataset. Furthermore, it has exhibited promising performance that closely approaches that of the 64-beam LiDAR odometry results of A-LOAM without mapping optimization.

Autonomous driving vehicles can effectively improve traffic conditions and promote the development of intelligent transportation systems. An autonomous vehicle can be divided into four parts: environment perception, motion prediction, motion planning, and motion control, among which the motion prediction module plays an essential role in the sustainability of autonomous driving vehicles. Vehicle motion prediction improves autonomous vehicles’ understanding of the surrounding dynamic environment, which reduces the uncertainty in the decision-making system and facilitates the implementation of an active braking system for autonomous vehicles. Currently, deep learning-based methods have become prevalent in this field as they can efficiently process complex scene information and achieve long-term prediction. These methods often follow a similar paradigm: encoding scene input to obtain the context feature, then decoding the context feature to output predictions. Recent research has proposed innovative improvement designs to enhance the primary paradigm. Thus, we review recent works based on their improvement designs and summarize them based on three criteria: scene input representation, context refinement, and prediction rationality improvement. Although most works focus on trajectory prediction, this paper also discusses new occupancy flow prediction methods. Additionally, this paper outlines commonly used datasets, evaluation metrics, and potential research directions.

Odometry is a crucial technology for the autonomous positioning of intelligent vehicles. While estimating the odometry from LiDAR and cameras has progressed recently, it remains to be seen how to estimate the odometry from a 4D radar, an emerging sensor with unique advantages over cameras and LiDAR. In this study, a deep-learning-based 4D radar odometry method, named 4DRO-Net, is proposed. The method employs a coarse-to-fine hierarchical optimization technique based on a sliding window to estimate and refine an autonomous vehicle's pose in an iterative manner. A feature-extraction network for 4D radar point clouds is proposed to achieve efficient learning of sparse point clouds. An initial pose-generation module is constructed to obtain the initial pose, which is used to warp the first point cloud and bridge the distance to the second point cloud. A velocity-aware attention cost volume module is then developed to correlate the warped first point cloud with the second point cloud to obtain point-motion information. The velocity information of the radar points is used to learn the attention weights to increase the robustness of the motion information estimation. Motion information is used to regress the corrected pose, which is then used to refine the initial pose to obtain a more accurate final pose. The superior performance and effectiveness of our 4D radar odometry method are demonstrated on both the View-of-Delft and an in-house dataset.

With the development of automobile electrification and intelligence, new requirements have been put forward for automotive braking technologies. Under this background, the One-box EHB (Electro-Hydraulic Braking system) brake-by-wire technology has emerged, which combines the electric booster and wheel-cylinder control module into one box and can realize vehicle stability and comfort functions such as service brake, pedal feel simulation, brake decoupling, failure backup, active braking, and wheel-cylinder pressure control. This article reviews the current research of key technologies of One-box EHB, including system architecture design and applications under high-level autonomous driving, master cylinder pressure control algorithm design, wheel-cylinder pressure control algorithm design, and electro-hydraulic composite braking control algorithm design. Finally, this article summarizes the current research status of One-box EHB key technologies and puts forward suggestions for future research directions.