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229 results on '"Guo, Xuexun"'

4. A Two-Stage Cooperative Adaptive Cruise Control for Connected Automated Vehicles in Multislope Roads considering Communication Delay and Actuator Delay

Author

Kuang, Jianjie, Tan, Gangfeng, Guo, Xuexun, Pei, Xiaofei, and Peng, Dengzhi

Subjects
Actuators -- Usage, Traffic congestion -- Usage -- Technology application, Technology application, Transportation industry
Abstract

Connected and automated vehicle platoons (CAVPs) are considered an effective way to alleviate traffic congestion, reduce the incidence of traffic accidents, and improve vehicle economy in the intelligent transportation system (ITS). Vehicles in the CAVPs can communicate with each other through V2X technology, which could optimize the economy of the platoon. Cooperative adaptive cruise control (CACC) can make effective use of the characteristics of CAVPs and contribute to resource conservation, ecological driving, and traffic system development. In this paper, a two-stage CACC method is proposed for CAVPs to reduce fuel consumption in the multislope road section. In the first stage, the optimal velocity profiles for the leader based on dynamic programming (DP) are planned according to the road information and the fuel consumption model. In the second stage, a vehicle longitudinal third-order differential dynamics model is utilized to build the platoon time-delay system considering communication delay and actuator delay. A feedback controller is developed for each vehicle considering the internal stability and the string stability of the CAVPs. Results show that the proposed method can save 5.33% of fuel consumption compared to the constant speed cooperative adaptive cruise control (CS-CACC) method and has a better tracking performance compared to the model predictive control (MPC) method. The CACC method proposed in this paper can provide a theoretical basis and data support for building an ecological CACC strategy for CAVPs., Author(s): Jianjie Kuang [1]; Gangfeng Tan (corresponding author) [1,2]; Xuexun Guo [1,2]; Xiaofei Pei [1,2]; Dengzhi Peng [1,3] 1. Introduction The rapid development of automobiles has brought convenience to people's [...]

Published
2024
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7. Spatiotemporal Trajectory Planning for Autonomous Vehicle Based on Reachable Set and Iterative LQR

Author

Liu, Yiping, Pei, Xiaofei, Zhou, Honglong, and Guo, Xuexun

Abstract

Traditional decoupling trajectory planning strategy deals with complex urban road scenes with many shortcomings, such as stereotyped behavior and poor trajectory quality. Aiming at these problems, the paper proposes a combined spatiotemporal trajectory planning and control method based on reachable set and optimization technique. Firstly, to address the shortcomings of existing reachable set methods in terms of unreasonable risk characterization and lack of risk consideration in the spatiotemporal corridor generation process, a risk assessment model that takes into account the uncertainty of the predicted location distribution and a reachable set spatiotemporal corridor generation strategy that integrates the risk field are proposed. Secondly, a trajectory optimization strategy of rolling iterative optimization within the spatiotemporal corridor is designed by using the iterative linear quadratic regulator (ILQR) and taking into account the dynamics of the vehicle's transverse-longitudinal coupling. The optimization strategy ensures the dynamics of the trajectory is feasible and realizes the strong coupling of planning and control. Finally, a safety check module is designed to coordinate the time-consuming corridor generation and the real-time application of the algorithm. To verify the performance of the proposed method, the long-time simulation and real-vehicle comparison tests of complex stochastic interaction scenes are fully carried out. The test results show that the proposed method can effectively realize the coordination between driving efficiency, comfort, and safety in high-density complex traffic flow.

Published
2024
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25. Research on Recognition of Working Area and Road Garbage for Road Sweeper Based on Mask R-CNN Neural Network

Author

Liu Teng, Guo Xuexun, and Xiaofei Pei

Subjects
Set (abstract data type), Artificial neural network, Computer science, Road surface, Real-time computing, Process (computing), Feature (machine learning), Image segmentation, Garbage, Convolutional neural network
Abstract

In order to reduce the energy waste of the road sweeper and simplify the operation process of the driver, a set of intelligent cleaning device for road sweeper is designed, which can automatically identify road garbage and adjust the cleaning mechanism to the required power. This device is composed by a monocular camera, an industrial computer, a vehicle DC power, and a cleaning mechanism. In terms of algorithms, two Mask R-CNN neural network models are used to detect road garbage. First, the road surface information is obtained by the first model to obtain the workable area of the road sweeper, which can reduce the influence of factors such as vehicles and pedestrians. Secondly, the road surface information in the workable area is divided into two types, road-specific information and garbage, the garbage detection and marking are completed after the second model is tested. Finally, the garbage coverage rate is used as a feature to adjust the power of the cleaning device. The result of testing and analysis of this algorithms shows that the real-time performance and recognition accuracy can achieve the expected results.

Published
2021

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