Model Predictive Control for Connected Vehicle Platoon Under Switching Communication Topology

Cheng

et al. 2022

Sensors

Self Cite

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.

Section: Related Workmentioning

confidence: 99%

Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion

Cheng

et al. 2022

Sensors

Self Cite

IET Intelligent Trans Sys

“…Next, the control input is calculated by solving the underlying optimisation and then is applied to the system until the next sampling time when the whole procedure is repeated. The continuous state-space model ( 59) is converted to a discrete-time system as [52] x…”

Section: Mpc Designmentioning

confidence: 99%

Connected and automated vehicle platoon formation control via differential games

Jond

Yildiz

2022

In this study, the connected and automated vehicles platooning problem is resolved under a differential game framework. Three information topologies are considered here. Firstly, the Predecessor-following topology is utilised where the vehicles control the distance with respect to the merely nearest predecessor via a sensor link-based information flow. Secondly, the Two-predecessor-following topology is exploited where each vehicle controls the distance with respect to the two nearest predecessors. In this topology, the second predecessor is communicated via a Vehicle-to-vehicle link. The individual trajectories of connected and automated vehicles under the Nash equilibrium are derived in closed-form for these two information topologies. Finally, general information topology is examined and the differential game is formulated in this context. In all these options, Pontryagin's principle is employed to investigate the existence and uniqueness of the Nash equilibrium and obtain its corresponding trajectories. In the general topology, we suppose numerical computation of eigenvalues and eigenvectors. Finally, the stability behaviour of the platoon for the Predecessor-following, Two-predecessor-following and general topologies are investigated. All these approaches represent promising and powerful analytical representations of the connected and automated vehicle platoons under the differential games. Simulation experiments have verified the efficiency of the proposed models and their solutions as well as their better results in comparison with the Model Predictive Control.

“…At an urban intersection, the traffic state usually refers to the driving state of all vehicles, which has the characteristics of dynamic, periodicity, randomness, etc. With the development of V2X communication technologies, the traffic state is generally perceived by the fusion of multi-source traffic sensors, then the information can be fed back to the users of other subsystems such as traffic management system (TMS) [24]. Finally, the perceived traffic information can be used to solve traffic and ICVs problems for ITS.…”

Section: Traffic Perception System Based On V2x Communicationmentioning

confidence: 99%

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

et al. 2021

Self Cite

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.