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Juan Zhang
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK

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Journal article
Published: 04 June 2021 in Actuators
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Integration technologies of artificial intelligence (AI) and autonomous vehicles play important roles in intelligent transportation systems (ITS). In order to achieve better logistics distribution efficiency, this paper proposes an intelligent actuator of an indoor logistics system by fusing multiple involved sensors. Firstly, an actuator based on a four-wheel differential chassis is equipped with sensors, including an RGB camera, a lidar and an indoor inertial navigation system, by which autonomous driving can be realized. Secondly, cross-floor positioning can be realized by multi-node simultaneous localization and mappings (SLAM) based on the Cartographer algorithm Thirdly the actuator can communicate with elevators and take the elevator to the designated delivery floor. Finally, a novel indoor route planning strategy is designed based on an A* algorithm and genetic algorithm (GA) and an actual building is tested as a scenario. The experimental results have shown that the actuator can model the indoor mapping and develop the optimal route effectively. At the same time, the actuator displays its superiority in detecting the dynamic obstacles and actively avoiding the collision in the indoor scenario. Through communicating with indoor elevators, the final delivery task can be completed accurately by autonomous driving.

ACS Style

Pangwei Wang; Yunfeng Wang; Xu Wang; Ying Liu; Juan Zhang. An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion. Actuators 2021, 10, 120 .

AMA Style

Pangwei Wang, Yunfeng Wang, Xu Wang, Ying Liu, Juan Zhang. An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion. Actuators. 2021; 10 (6):120.

Chicago/Turabian Style

Pangwei Wang; Yunfeng Wang; Xu Wang; Ying Liu; Juan Zhang. 2021. "An Intelligent Actuator of an Indoor Logistics System Based on Multi-Sensor Fusion." Actuators 10, no. 6: 120.

Journal article
Published: 28 April 2021 in IEEE Transactions on Intelligent Transportation Systems
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Vehicular platoon control can effectively achieve group consensus, improve vehicular running safety and increase road capacity. However, some constraints exist in practical situations due to the limitations of traffic environment in time-varying metrics (time-delay, packet-dropout or interruption) in wireless communication systems. In this work, a distributed model predictive control (MPC) algorithm is proposed for connected vehicle platoon with a focus on switching communication topologies and control strategy under abnormal communications. Firstly, the predecessor-leader following is selected as the basic communication topology, by which the switching communication topology and the desired vehicle spacing policy are established. Secondly, the platoon control algorithm of connected vehicles is established and a set of constraints is analyzed. Thirdly, the Lâ‚‚ -norm string stability criterion and the asymptotic stability criterion are considered within the proposed MPC. Finally, a co-simulation platform for connected vehicle platoon is developed based on Prescan/Matlab/V2X communication simulator. In addition, the platoon control algorithm is tested in three traffic scenarios including normal communication, leading vehicle with abnormal communication and following vehicle with abnormal communication. The experiments demonstrate that the communication topologies in different communication environments can be switched well in real time through the proposed platoon control algorithm. In addition, the string stability, the consistency of vehicle spacing, speed and acceleration are proven to be guaranteed simultaneously.

ACS Style

Pangwei Wang; Hui Deng; Juan Zhang; Li Wang; Mingfang Zhang; Yongfu Li. Model Predictive Control for Connected Vehicle Platoon Under Switching Communication Topology. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -14.

AMA Style

Pangwei Wang, Hui Deng, Juan Zhang, Li Wang, Mingfang Zhang, Yongfu Li. Model Predictive Control for Connected Vehicle Platoon Under Switching Communication Topology. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-14.

Chicago/Turabian Style

Pangwei Wang; Hui Deng; Juan Zhang; Li Wang; Mingfang Zhang; Yongfu Li. 2021. "Model Predictive Control for Connected Vehicle Platoon Under Switching Communication Topology." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-14.

Research article
Published: 15 December 2020 in Journal of Advanced Transportation
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It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin’s maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme.

ACS Style

Pangwei Wang; Yunfeng Wang; Hui Deng; Mingfang Zhang; Juan Zhang. Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles. Journal of Advanced Transportation 2020, 2020, 1 -15.

AMA Style

Pangwei Wang, Yunfeng Wang, Hui Deng, Mingfang Zhang, Juan Zhang. Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles. Journal of Advanced Transportation. 2020; 2020 ():1-15.

Chicago/Turabian Style

Pangwei Wang; Yunfeng Wang; Hui Deng; Mingfang Zhang; Juan Zhang. 2020. "Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles." Journal of Advanced Transportation 2020, no. : 1-15.