Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Purpose This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline. Design/methodology/approach Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms. Findings Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles. Originality/value To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

DBWGIE-MR: A density-based clustering algorithm by using the weighted grid and information entropy based on MapReduce

The main target of this paper is to design a density-based clustering algorithm using the weighted grid and information entropy based on MapReduce, noted as DBWGIE-MR, to deal with the problems of unreasonable division of data gridding, low accuracy of clustering results and low efficiency of parallelization in big data clustering algorithm based on density. This algorithm is implemented in three stages: data partitioning, local clustering, and global clustering. For each stage, we propose several strategies to improve the algorithm. In the first stage, based on the spatial distribution of data points, we propose an adaptive division strategy (ADG) to divide the grid adaptively. In the second stage, we design a weighted grid construction strategy (NE) which can strengthen the relevance between grids to improve the accuracy of clustering. Meanwhile, based on the weighted grid and information entropy, we design a density calculation strategy (WGIE) to calculate the density of the grid. And last, to improve the parallel efficiency, core clusters computing algorithm based on MapReduce (COMCORE-MR) are proposed to parallel compute the core clusters of the clustering algorithm. In the third stage, based on disjoint-set, we propose a core cluster merging algorithm (MECORE) to speed-up ratio the convergence of merged local clusters. Furthermore, based on MapReduce, a core clusters parallel merging algorithm (MECORE-MR) is proposed to get the clustering algorithm results faster, which improves the core clusters merging efficiency of the density-based clustering algorithm. We conduct the experiments on four synthetic clusters. Compared with H-DBSCAN, DBSCAN-MR and MR-VDBSCAN, the experimental results show that the DBWGIE-MR algorithm has higher stability and accuracy, and it takes less time in parallel clustering.

Large Area Inspection Using 3D Point Cloud Data in a Disaster Response Robot

Large area inspection using a robot is critical in a disastrous situation; especially when humans are inhabiting the catastrophic environment. Unlike natural environments, such environments lack details. Thus, creating 3D maps and identifying objects has became a challenge. This research suggests a 3D Point Cloud Data (PCD) merging algorithm for the less textured environment, aiming World Robot Summit Standard Disaster Robotics Challenge 2021 (WRS). Spider2020, a robotic system designed by the Robot Engineering Laboratory, University of Aizu, was used in this research. Detecting QR codes in a wall and merging PCD, and generating a wall map are the two main tasks in the competition. The Zxing library was used to detect and decode QR codes, and the results were quite accurate. Since the 3D mapping environment has fewer textures, decoded QR code locations are used as the PCD mapping markers. The position of the PCD file was taken from the location given by the robotic arm in Spider2020. The accuracy of merging PCD was improved by including the position of PCD files in the merging algorithm. The robotic system can be used for Large area Inspections in a disastrous situation.