Implementing Virtual Reality Technology for Supporting Autonomous Vehicle-Pedestrian Behavioral and Interaction Research

The deployment of autonomous vehicles (AVs) has been expected to significantly reshape traffic safety on roads. However, there is still a relatively long journey to achieve high-level autonomy and the safety level of interaction between AVs and vulnerable road users (e.g., pedestrians, cyclists, or passengers) is still unclear due to very limited data and field tests. The main objective of this paper is to propose a high-fidelity human-in-the-loop simulation that is capable of supporting AV-pedestrian interactions by coupling an advanced AV simulator with virtual reality technology. The prototype of the extended simulation framework has been developed and demonstrated with experimental scenarios. The inclusion of real-world humans offers extensive opportunities for understanding the safety of AVs co-existing with vulnerable road users

Enhancing Pedestrian-Autonomous Vehicle Safety in Low Visibility Scenarios: A Comprehensive Simulation Method

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance in low visibility scenarios

Multifunctional photonic integrated circuit for diverse microwave signal generation, transmission and processing

Microwave photonics (MWP) studies the interaction between microwave and optical waves for the generation, transmission and processing of microwave signals (i.e., three key domains), taking advantages of broad bandwidth and low loss offered by modern photonics. Integrated MWP using photonic integrated circuits (PICs) can reach a compact, reliable and green implementation. Most PICs, however, are recently developed to perform one or more functions restricted inside a single domain. In this paper, as highly desired, a multifunctional PIC is proposed to cover the three key domains. The PIC is fabricated on InP platform by monolithically integrating four laser diodes and two modulators. Using the multifunctional PIC, seven fundamental functions across microwave signal generation, transmission and processing are demonstrated experimentally. Outdoor field trials for electromagnetic environment surveillance along an in-service high-speed railway are also performed. The success to such a PIC marks a key step forward for practical and massive MWP implementations.Comment: 17 page

PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds

Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated. Existing works start from the given labels and propagate them to highly-related but unlabeled points, with the guidance of data, e.g. intra-point relation. However, it suffers from (i) the inefficient exploitation of data information, and (ii) the strong reliance on labels thus is easily suppressed when given much fewer annotations. Therefore, we propose a novel framework, PointMatch, that stands on both data and label, by applying consistency regularization to sufficiently probe information from data itself and leveraging weak labels as assistance at the same time. By doing so, meaningful information can be learned from both data and label for better representation learning, which also enables the model more robust to the extent of label sparsity. Simple yet effective, the proposed PointMatch achieves the state-of-the-art performance under various weakly-supervised schemes on both ScanNet-v2 and S3DIS datasets, especially on the settings with extremely sparse labels, e.g. surpassing SQN by 21.2% and 17.2% on the 0.01% and 0.1% setting of ScanNet-v2, respectively.Comment: 8 pages, 3 figure

Pixel-level intra-domain adaptation for semantic segmentation

Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks. Despite such progress, existing works mainly focus on bridging the inter-domain gaps between the source and target domain, while only few of them noticed the intra-domain gaps within the target data. In this work, we propose a pixel-level intra-domain adaptation approach to reduce the intra-domain gaps within the target data. Compared with image-level methods, ours treats each pixel as an instance, which adapts the segmentation model at a more fine-grained level. Specifically, we first conduct the inter-domain adaptation between the source and target domain; Then, we separate the pixels in target images into the easy and hard subdomains; Finally, we propose a pixel-level adversarial training strategy to adapt a segmentation network from the easy to the hard subdomain. Moreover, we show that the segmentation accuracy can be further improved by incorporating a continuous indexing technique in the adversarial training. Experimental results show the effectiveness of our method against existing state-of-the-art approaches