Toward autonomous vehicles: A survey on cooperative vehicle-infrastructure system.

Cooperative vehicle-infrastructure system (CVIS) is an important part of the intelligent transport system (ITS). Autonomous vehicles have the potential to improve safety, efficiency, and energy saving through CVIS. Although a few CVIS studies have been conducted in the transportation field recently, a comprehensive analysis of CVIS is necessary, especially about how CVIS is applied in autonomous vehicles. In this paper, we overview the relevant architectures and components of CVIS. After that, state-of-the-art research and applications of CVIS in autonomous vehicles are reviewed from the perspective of improving vehicle safety, efficiency, and energy saving, including scenarios such as straight road segments, intersections, ramps, etc. In addition, the datasets and simulators used in CVIS-related studies are summarized. Finally, challenges and future directions are discussed to promote the development of CVIS and provide inspiration and reference for researchers in the field of ITS.

SMIFormer: Learning Spatial Feature Representation for 3D Object Detection from 4D Imaging Radar via Multi-View Interactive Transformers.

4D millimeter wave (mmWave) imaging radar is a new type of vehicle sensor technology that is critical to autonomous driving systems due to its lower cost and robustness in complex weather. However, the sparseness and noise of point clouds are still the main problems restricting the practical application of 4D imaging radar. In this paper, we introduce SMIFormer, a multi-view feature fusion network framework based on 4D radar single-modal input. SMIFormer decouples the 3D point cloud scene into 3 independent but interrelated perspectives, including bird's-eye view (BEV), front view (FV), and side view (SV), thereby better modeling the entire 3D scene and overcoming the shortcomings of insufficient feature representation capabilities under single-view built from extremely sparse point clouds. For multi-view features, we proposed multi-view feature interaction (MVI) to exploit the inner relationship between different views by integrating features from intra-view interaction and cross-view interaction. We evaluated the proposed SMIFormer on the View-of-Delft (VoD) dataset. The mAP of our method reached 48.77 and 71.13 in the fully annotated area and the driving corridor area, respectively. This shows that 4D radar has great development potential in the field of 3D object detection.

A comprehensive study of speed prediction in transportation system: From vehicle to traffic.

In the intelligent transportation system (ITS), speed prediction plays a significant role in supporting vehicle routing and traffic guidance. Recently, a considerable amount of research has been devoted to a single-level (e.g., traffic or vehicle) prediction. However, a systematic review of speed prediction in and between different levels is still missing. In this article, existing research is comprehensively analyzed and divided into three levels, i.e. macro traffic, micro vehicles, and meso lane. In addition, this article summarizes the influencing factors and reviews the prediction methods based on how those methods utilize the available information to meet the challenges of the prediction at different levels. This is followed by a summary of evaluation metrics, public datasets, and open-source codes. Finally, future directions in this field are discussed to inspire and guide readers. This article aims to draw a complete picture of speed prediction and promote the development of ITS.

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper.

How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge-Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

Multi-sensor Fusion Road Friction Coefficient Estimation During Steering with Lyapunov Method.

The road friction coefficient is a key parameter for autonomous vehicles and vehicle dynamic control. With the development of autonomous vehicles, increasingly, more environmental perception sensors are being installed on vehicles, which means that more information can be used to estimate the road friction coefficient. In this paper, a nonlinear observer aided by vehicle lateral displacement information for estimating the road friction coefficient is proposed. First, the tire brush model is modified to describe the tire characteristics more precisely in high friction conditions using tire test data. Then, on the basis of vehicle dynamics and a kinematic model, a nonlinear observer is designed, and the self-aligning torque of the wheel, lateral acceleration, and vehicle lateral displacement are used to estimate the road friction coefficient during steering. Finally, slalom tests and DLC (Double Line Change) tests in high friction conditions are conducted to verify the proposed estimation algorithm. Test results showed that the proposed method performs well during steering and the estimated road friction coefficient converges to the reference value rapidly.

Reinforcement Learning-Based End-to-End Parking for Automatic Parking System.

According to the existing mainstream automatic parking system (APS), a parking path is first planned based on the parking slot detected by the sensors. Subsequently, the path tracking module guides the vehicle to track the planned parking path. However, since the vehicle is non-linear dynamic, path tracking error inevitably occurs, leading to inclination and deviation of the parking. Accordingly, in this paper, a reinforcement learning-based end-to-end parking algorithm is proposed to achieve automatic parking. The vehicle can continuously learn and accumulate experience from numerous parking attempts and then learn the command of the optimal steering wheel angle at different parking slots. Based on this end-to-end parking, errors caused by path tracking can be avoided. Moreover, to ensure that the parking slot can be obtained continuously in the process of learning, a parking slot tracking algorithm is proposed based on the combination of vision and vehicle chassis information. Furthermore, given that the learning network output is hard to converge, and it is easy to fall into local optimum during the parking process, several reinforcement learning training methods in terms of parking conditions are developed. Lastly, by the real vehicle test, it is proved that using the proposed method can achieve a better parking attitude than using the path planning and path tracking-based method.

IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics.

The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.

Stability of High-Performance Pt-Based Catalysts for Oxygen Reduction Reactions.

Due to their environmental sustainability and high efficiency, proton-exchange-membrane fuel cells (PEMFCs) are expected to be an essential type of energy source for electric vehicles, energy generation, and the space industry in the coming decades. Here, the recent developments regarding shape-controlled nanostructure catalysts are reviewed, with a focus on the stability of high-performance Pt-based catalysts and related mechanisms. The catalysts, which possess great activity, are still far from meeting the requirements of their applications, due to stability issues, especially in membrane electrode assemblies (MEAs). Thus, solutions toward the comprehensive performance of Pt-based catalysts are discussed here. The research trends and related theories that can promote the application of Pt-based catalysts are also provided.

Immunomodulatory effect of a formula developed from American ginseng and Chinese jujube extracts in mice.

BACKGROUND: American ginseng (Panax quinquefolius L.) and Chinese jujube (Zizyphus jujuba Mill.) are commonly used in traditional Chinese medicine to enhance immune function. OBJECTIVE: The present study aimed to develop one Chinese prescription, Shenzao Cha (SZC), consisting of American ginseng and Chinese jujube, and systematically investigate its immunomodulation in healthy ICR mice. METHODS: Normal ICR mice received intragastric administration of SZC (1.3, 2.6, and 5.2 g raw material/kg body weight) once daily for four weeks, while a control group received the same amount of sterile water. RESULTS: SZC significantly increased the spleen and thymus indices and T-lymphocyte proliferation, while the T-lymphocyte proliferation in the 5.2 g/kg group was 1.4-fold higher than that in the control. Further, 1.3 g/kg SZC could markedly improve hemolytic activity by 25.2%, and 2.6 g/kg SZC increased the NK cell activity by 78.6% relative to the control. In addition, the activities of antioxidant enzymes (superoxide dismutase, catalase, and glutathione peroxidase), that participated in modulating oxidative stress, were significantly increased in the liver, spleen, thymus, and serum, while the contents of malondialdehyde were dramatically decreased. CONCLUSIONS: SZC exhibited potent immunomodulatory effects on innate and adaptive immunity in healthy ICR mice, as well as potential antioxidant activity for prevention of oxidative stress, which was suggested to partly contribute to the immune enhancement.

Inhibitory effects of sweet cherry anthocyanins on the obesity development in C57BL/6 mice.

In the present study, purified sweet cherry anthocyanins (CACN) were evaluated to determine their inhibitory effects on adipocyte differentiation of 3T3-L1 cells and their anti-obesity properties in male C57BL/6 mice fed with high-fat diet (HFD). CACN prevented HFD-induced obesity in C57BL/6 mice. In vivo experiment revealed that 40 and 200 mg/kg of CACN in food reduced the body weight by 5.2% and 11.2%, respectively. CACN supplementation could also reduce the size of adipocytes, leptin secretion, serum glucose, triglyceride, total cholesterol, LDL-cholesterol and liver triglycerides. Furthermore, CACN could effectively reduce the expression levels of IL-6 and TNFα genes, markedly increase the SOD and GPx activity. Our results indicated that CACN slowed down the development of HFD-induced obesity in male C57BL/6 mice.

Blueberry and mulberry juice prevent obesity development in C57BL/6 mice.

OBJECTIVES: To establish whether blueberry (Vaccinium ashei) and mulberry (Morus australis Poir) juice, anthocyanin rich fruit juice, may help counteract obesity. DESIGN: And Methods: Four-week-old C57BL/6 mice were fed a high-fat diet (HFD) with or without blueberry and mulberry juice for 12 weeks. Body weight, serum and hepatic lipids, liver and adipose tissues morphology, insulin and leptin were assessed. RESULTS: Mice fed HFD exhibited increased body weight, insulin resistance, serum and hepatic lipids. In comparison, blueberry and mulberry juice inhibited body weight gain, decreased the serum cholesterol, reduced the resistance to insulin, attenuated lipid accumulation and decreased the leptin secretin. CONCLUSION: These results indicate that blueberry and mulberry juice may help counteract obesity.

Honeysuckle anthocyanin supplementation prevents diet-induced obesity in C57BL/6 mice.

This study investigated the anti-obesity effects of honeysuckle anthocyanins (HA) in a high fat diet-induced mouse model. The mice were initially fed with a low-fat diet (LFD) or high-fat diet (HFD) for 8 weeks. After that, the HFD-fed mice were divided into five groups, with 12 mice in each group, including a HFD group, a HFD plus Orlistat group, and three HFD plus HA (at a dose of 50, 100, or 200 mg kg(-1)) groups, for another 8-week experiment. HA at 100 or 200 mg kg(-1) can suppress body weight gain, reduce serum and liver lipid profiles, ameliorate impaired hepatic function, and significantly increase serum adiponectin concentration while decreasing serum insulin and leptin levels. These results suggest that the anti-obesity effect of HA might be through the blockage of lipid accumulation.