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

Abstract

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.

Water Surface Targets Detection Based on the Fusion of Vision and LiDAR

“…In the present day, vision detection [ 3 ], LiDAR detection [ 4 ], and multi-sensor fusion detection [ 5 ] are the main methods used for target detection on the water surface. The mainstay of vision detection is the convolutional neural network that is used to train the target sample to obtain the model and feature information that can be used for subsequent detection.…”

Advanced Sensing and Safety Control for Connected and Automated Vehicles

“…This Special Issue on Sensors aims to report on some of the recent research efforts on this increasingly important topic. The 11 accepted papers in this Issue cover vehicle position estimation [ 1 ], 3D Object Detection [ 2 ], pedestrian state sense [ 3 ], trajectory prediction [ 4 ], criticality assessment [ 5 ], active fault-tolerant control for actuator failure [ 6 ], decision-making in the scenario of highway driving out of the ramp [ 7 ] and uncertain interactive traffic scenarios [ 8 ], RRT-based path planning algorithm [ 9 ], C/GMRES motion planning algorithm [ 10 ], and lane-keeping controller design [ 11 ]. In this introduction, a brief description of the content of each contribution forming the Special Issue is provided.…”

“…In [ 3 ], a novel pedestrian status-detecting approach, based on multi-sensor fusion, is developed for automated patrol vehicles. First, the 2D and 3D pedestrian data are acquired through YOLO V4 and Euclidean clustering algorithms, respectively.…”

“…In summary, there is a huge potential for the application of CAV in areas of traffic perception, decision-making, and driving safety improvement. This Special Issue contributes to this by proposing solutions to the general problems of state estimation [ 1 ], objective detection [ 2 , 3 ], trajectory prediction [ 4 ], driving safety improvement [ 5 , 6 ], decision-making [ 7 , 8 ], path planning [ 9 , 10 ] and vehicle motion control [ 11 ], which largely represent the theoretical challenges and practical interest of this research topic. Finally, we wish to thank the authors, reviewers, and journal staff for their commitment and effort, without which we could not have completed this Special Issue on time.…”

Advanced Sensing and Safety Control for Connected and Automated Vehicles

“…This Special Issue on Sensors aims to report on some of the recent research efforts on this increasingly important topic. The 11 accepted papers in this Issue cover vehicle position estimation [1], 3D Object Detection [2], pedestrian state sense [3], trajectory prediction [4], criticality assessment [5], active fault-tolerant control for actuator failure [6], decision-making in the scenario of highway driving out of the ramp [7] and uncertain interactive traffic scenarios [8], RRT-based path planning algorithm [9], C/GMRES motion planning algorithm [10], and lane-keeping controller design [11]. In this introduction, a brief description of the content of each contribution forming the Special Issue is provided.…”