SearcharxivSearch

arXiv subjects

Zitian Peng

Publications and source records attributed to Zitian Peng.

2 recordsLinked to original sources

The Evaluation for Usability Methods of Unmanned Surface Vehicles: Are Current Usability Methods Viable for Unmanned Surface Vehicles? Insights from a Multiple Case Study Approach to Human-Robot Interaction

Unmanned Surface Vehicles (USVs) are increasingly utilised for diverse applications, ranging from environmental monitoring to security patrols. While USV technology is progressing, it remains clear that full autonomy is not achievable in all scenarios, and remote human intervention is still crucial, particularly in dynamic or complex environments. This continued reliance on human intervention highlights a range of Human-Robot Interaction (HRI) challenges that remain unresolved. Compared to the extensive body of HRI research in domains such as unmanned aerial vehicles and autonomous vehicles, HRI considerations specific to USVs remain significantly underexplored. Addressing this gap, our study investigates real-world usability challenges in USV operation through in-depth interviews with 9 engineers and users, supported by field observations. We focus especially on the difficulties beginner operators encounter and their coping strategies. Our findings reveal existing usability issues, mental models, and adaptation strategies of beginners that inform future user-centered design of USV systems, contributing new insights to the emerging field of maritime HRI. Based on these findings, we argue that current USV systems are poorly suited for beginner operation in dynamic inland and offshore environments, where operators must make timely decisions under uncertainty, manage complex spatial awareness, and adapt to changing environmental conditions. Furthermore, we identify key operational patterns in three representative use cases-harmful algal bloom detection, underwater concealed pipe inspection and post-construction hydrographic survey, and summarise key interaction constraints that should inform future maritime HRI design efforts.

cs.HC

Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review

With the rapid advancements of sensor technology and deep learning, autonomous driving systems are providing safe and efficient access to intelligent vehicles as well as intelligent transportation. Among these equipped sensors, the radar sensor plays a crucial role in providing robust perception information in diverse environmental conditions. This review focuses on exploring different radar data representations utilized in autonomous driving systems. Firstly, we introduce the capabilities and limitations of the radar sensor by examining the working principles of radar perception and signal processing of radar measurements. Then, we delve into the generation process of five radar representations, including the ADC signal, radar tensor, point cloud, grid map, and micro-Doppler signature. For each radar representation, we examine the related datasets, methods, advantages and limitations. Furthermore, we discuss the challenges faced in these data representations and propose potential research directions. Above all, this comprehensive review offers an in-depth insight into how these representations enhance autonomous system capabilities, providing guidance for radar perception researchers. To facilitate retrieval and comparison of different data representations, datasets and methods, we provide an interactive website at https://radar-camera-fusion.github.io/radar.

cs.CV