352 research outputs found
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
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