Damping multi-model adaptive switching controller design for electronic air suspension system

This paper presents the design and verification of a damping multi-model adaptive switching controller for electronic air suspension (EAS) system. In order to improve the convergence rate of identification algorithm of conventional adaptive controller, multiple local linear full-car vehicle models of EAS system with fixed parameters are established according to the actual damping control process of EAS for different vehicle driving conditions and an adaptive model whose initial value of parameters can be re-assigned is introduced to enhance the system control precision. The model switching control strategy based on minimum error is used to select the best matching model online and the optimum damping force is regulated by adaptive control algorithm, thus constituting the damping multi-model adaptive control for EAS. Simulation results show that the control method proposed in this paper can improve the damping regulating performance of EAS effectively in wide range driving conditions, especially for the case of sudden change in driving conditions

V2X-AHD:Vehicle-to-Everything Cooperation Perception via Asymmetric Heterogenous Distillation Network

Object detection is the central issue of intelligent traffic systems, and recent advancements in single-vehicle lidar-based 3D detection indicate that it can provide accurate position information for intelligent agents to make decisions and plan. Compared with single-vehicle perception, multi-view vehicle-road cooperation perception has fundamental advantages, such as the elimination of blind spots and a broader range of perception, and has become a research hotspot. However, the current perception of cooperation focuses on improving the complexity of fusion while ignoring the fundamental problems caused by the absence of single-view outlines. We propose a multi-view vehicle-road cooperation perception system, vehicle-to-everything cooperative perception (V2X-AHD), in order to enhance the identification capability, particularly for predicting the vehicle's shape. At first, we propose an asymmetric heterogeneous distillation network fed with different training data to improve the accuracy of contour recognition, with multi-view teacher features transferring to single-view student features. While the point cloud data are sparse, we propose Spara Pillar, a spare convolutional-based plug-in feature extraction backbone, to reduce the number of parameters and improve and enhance feature extraction capabilities. Moreover, we leverage the multi-head self-attention (MSA) to fuse the single-view feature, and the lightweight design makes the fusion feature a smooth expression. The results of applying our algorithm to the massive open dataset V2Xset demonstrate that our method achieves the state-of-the-art result. The V2X-AHD can effectively improve the accuracy of 3D object detection and reduce the number of network parameters, according to this study, which serves as a benchmark for cooperative perception. The code for this article is available at https://github.com/feeling0414-lab/V2X-AHD

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers

Can the green bond market enter a new era under the fluctuation of oil price?

This paper investigates how oil price (OP) influences the prospects of green bonds by utilising the quantile-onquantile (QQ) method and researching the interactions between OP and green bond index (GBI) from 2011:M1 to 2021:M11. We find that impacts from OP on the GBI are positive in the short run. The positive effects indicate that high OP can promote the development of the green bond market, indicating that green bonds can be considered an asset to avoid OP shocks. However, in the medium and long term, there is a negative impact due to the oversupply of the oil market and the increase in green energy industry profits. These results are identical to the supply and demand-based correlation model of green bonds and oil price, which underlines a specific effect of OP on GBI. The GBI effect on OP is consistently positive across all quantiles. It indicates that green bonds cannot be considered efficient measures to alleviate the oil crisis due to the instability of the Middle East COVID-19 and the small scale of green bonds. The issuers of green bonds can make decisions based on OP. Understanding the relationship between OP and GBI is also beneficial for investors

Learning Sequence Descriptor based on Spatiotemporal Attention for Visual Place Recognition

Sequence-based visual place recognition (sVPR) aims to match frame sequences with frames stored in a reference map for localization. Existing methods include sequence matching and sequence descriptor-based retrieval. The former is based on the assumption of constant velocity, which is difficult to hold in real scenarios and does not get rid of the intrinsic single frame descriptor mismatch. The latter solves this problem by extracting a descriptor for the whole sequence, but current sequence descriptors are only constructed by feature aggregation of multi-frames, with no temporal information interaction. In this paper, we propose a sequential descriptor extraction method to fuse spatiotemporal information effectively and generate discriminative descriptors. Specifically, similar features on the same frame focu on each other and learn space structure, and the same local regions of different frames learn local feature changes over time. And we use sliding windows to control the temporal self-attention range and adpot relative position encoding to construct the positional relationships between different features, which allows our descriptor to capture the inherent dynamics in the frame sequence and local feature motion