Due to the increase of signal bandwidth, automotive radar can receive multiple scatting centers from the target of interest. This is typically described by an extended target model. However, existing methods for extended target detection and tracking often exhibit subpar performance because they struggle to adapt to the hybrid-scale and time-varying texture feature exhibited by moving traffic participants. Therefore, this work presents a unified framework hybrid-scale spatio-temporal transformer for joint detection and tracking of hybrid-scale extended targets. It consists of three progressive stages: hybrid-scale embedding network, spatio-temporal transformer, and Wasserstein loss. The hybrid-scale embedding network extracts the features of hybrid-scale targets by parallel multiple convolution kernels of different scales. Then, the spatio-temporal transformer locates targets by integrating these spatial-temporal features across the time. It can allow the target’s hybrid-scale and time-varying texture feature changes incurred through the movement, while eliminating the need for cross frame target association, thus achieving robust detection and tracking. The Wasserstein loss metric is designed to assist the model in optimizing hybrid-scale target location proficiency, especially for small targets. With intensive experiments on simulated datasets and public real datasets, such as RAMP-CNN and RADDet, the proposed model achieves better detection and tracking performance than existing methods with different noise added, and shows strong robustness in various traffic scenarios.

Radar and vision fusion in autonomous driving has gained increasing attention, however, most existing feature-level fusion methods (named as feature map-based fusion) typically map sparse point clouds into dense feature maps, fuse them with visual features, and then apply the combined features to detect sparse objects. These methods face challenges due to the structural disparity between sparse point clouds and dense feature maps, and memory-redundant mapping of point cloud processing. This paper proposes a proposal-guided radar and vision fusion framework for 3D object detection in autonomous driving, where proposals are referred to as potential objects with abundant high dimensional features. Specifically, two frame radar point clouds are first accumulated by deep learning-based motion compensation with supervision. Then, proposals are generated from radar point clouds and vision images. Subsequently, radar and vision fusion is performed on the proposal level using attention mechanism. Finally, objects are detected based on the fused proposal feature. The proposed framework introduces an object-centric feature fusion methodology that skips the common process of point-to-feature map projection, thereby circumventing the inherent structural disparity between sparse point clouds and dense feature maps. Experiments on two different autonomous driving datasets (nuScenes and VoD) demonstrate that our fusion method can outperform most published radar and vision fusion networks. The source code will be released athttps://github.com/leehungu/prv

The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, existing dynamic graph storage systems suffer from read or write amplification and face the challenge of optimizing both read and write performance simultaneously. To address this challenge, we propose LSMGraph, a novel dynamic graph storage system that combines the write-friendly LSM-tree and the read-friendly CSR. It leverages the multi-level structure of LSM-trees to optimize write performance while utilizing the compact CSR structures embedded in the LSM-trees to boost read performance. LSMGraph uses a new memory structure, MemGraph, to efficiently cache graph updates and uses a multi-level index to speed up reads within the multi-level structure. Furthermore, LSMGraph incorporates a vertex-grained version control mechanism to mitigate the impact of LSM-tree compaction on read performance and ensure the correctness of concurrent read and write operations. Our evaluation shows that LSMGraph significantly outperforms state-of-the-art (graph) storage systems on both graph update and graph analytical workloads.

An efficient data structure is fundamental to meeting the growing demands in dynamic graph processing. However, the dual requirements for graph computation efficiency (with contiguous structures) and graph update efficiency (with linked list-like structures) present a conflict in the design principles of graph structures. After experimental studies of state-of-the-art dynamic graph structures, we observe that the overhead of cache misses accounts for a major portion of the graph computation time. This paper presents GastCoCo, a system with graph storage and coroutine-based prefetch co-design. By employing software prefetching via stackless coroutines and designing a prefetch-friendly data structure CBList, GastCoCo significantly alleviates the performance degradation caused by cache misses. Our results show that GastCoCo outperforms state-of-the-art graph storage systems by 1.3× - 180× in graph updates and 1.4× - 41.1× in graph computation.

This article presents a semi-automated method to extract the lane features along the curved roads from mobile laser scanning (MLS) point clouds. The proposed method consists of four steps. After data pre-processing, a road edge detection algorithm is performed to distinguish road curbs and extract road surfaces. Then, textual and directional road markings such as arrows, symbols, and words, to inform drivers in necessary cases, are detected by intensity thresholding and conditional Euclidean clustering algorithms. Furthermore, lane markings are extracted by local intensity analysis and distance thresholding methods according to road design standards, because they are more regular along the road. Finally, centerline points on lanes are estimated based on the coordinates of extracted lane markings. Our method shows strong feasibility and robustness when creating high-definition (HD) maps from MLS data, by increasing the number of blocks in the curve and the distance threshold control in curved lane centerline extraction. Quantitative evaluations show that the average recall, precision, and F1-score obtained from four datasets for road marking extraction are 93.87%, 93.76%, and 93.73%, respectively. The generated lane centerlines are evaluated by overlaying them on manually labeled reference buffers from 4 cm resolution orthoimagery. The comparative study indicates that the proposed methods can achieve higher accuracy and robustness than most state-of-the-art methods.