arXiv subjects

Yihao Zhu

Publications and source records attributed to Yihao Zhu.

4 recordsLinked to original sources

arXiv2026

The data heat island effect: quantifying the impact of AI data centers in a warming world

The strong and continuous increase of AI-based services leads to the steady proliferation of AI data centres worldwide with the unavoidable escalation of their power consumption. It is unknown how this energy demand for computational purposes will impact the surrounding environment. Here, we focus our attention on the heat dissipation of AI hyperscalers. Taking advantage of land surface temperature measurements acquired by remote sensing platforms over the last decades, we are able to obtain a robust assessment of the temperature increase recorded in the areas surrounding AI data centres globally. We estimate that the land surface temperature increases by 2°C on average after the start of operations of an AI data centre, inducing local microclimate zones, which we call the data heat island effect. We assess the impact on the communities, quantifying that more than 340 million people could be affected by this temperature increase. Our results show that the data heat island effect could have a remarkable influence on communities and regional welfare in the future, hence becoming part of the conversation around environmentally sustainable AI worldwide.

cs.CY↗

arXiv2026

M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark

We introduce M$^3$CAD, a comprehensive benchmark designed to advance research in generic cooperative autonomous driving. M$^3$CAD comprises 204 sequences with 30,000 frames. Each sequence includes data from multiple vehicles and different types of sensors, e.g., LiDAR point clouds, RGB images, and GPS/IMU, supporting a variety of autonomous driving tasks, including object detection and tracking, mapping, motion forecasting, occupancy prediction, and path planning. This rich multimodal setup enables M$^3$CAD to support both single-vehicle and multi-vehicle cooperative autonomous driving research. To the best of our knowledge, M$^3$CAD is the most complete benchmark specifically designed for cooperative, multi-task autonomous driving research. To test its effectiveness, we use M$^3$CAD to evaluate both state-of-the-art single-vehicle and cooperative driving solutions, setting baseline performance results. Since most existing cooperative perception methods focus on merging features but often ignore network bandwidth requirements, we propose a new multi-level fusion approach which adaptively balances communication efficiency and perception accuracy based on the current network conditions. We release M$^3$CAD, along with the baseline models and evaluation results, to support the development of robust cooperative autonomous driving systems. All resources will be made publicly available on https://github.com/zhumorui/M3CAD

cs.RO↗

arXiv2024

HEAD: A Bandwidth-Efficient Cooperative Perception Approach for Heterogeneous Connected and Autonomous Vehicles

In cooperative perception studies, there is often a trade-off between communication bandwidth and perception performance. While current feature fusion solutions are known for their excellent object detection performance, transmitting the entire sets of intermediate feature maps requires substantial bandwidth. Furthermore, these fusion approaches are typically limited to vehicles that use identical detection models. Our goal is to develop a solution that supports cooperative perception across vehicles equipped with different modalities of sensors. This method aims to deliver improved perception performance compared to late fusion techniques, while achieving precision similar to the state-of-art intermediate fusion, but requires an order of magnitude less bandwidth. We propose HEAD, a method that fuses features from the classification and regression heads in 3D object detection networks. Our method is compatible with heterogeneous detection networks such as LiDAR PointPillars, SECOND, VoxelNet, and camera Bird's-eye View (BEV) Encoder. Given the naturally smaller feature size in the detection heads, we design a self-attention mechanism to fuse the classification head and a complementary feature fusion layer to fuse the regression head. Our experiments, comprehensively evaluated on the V2V4Real and OPV2V datasets, demonstrate that HEAD is a fusion method that effectively balances communication bandwidth and perception performance.

cs.CV↗

arXiv2022

Modeling the Noticeability of User-Avatar Movement Inconsistency for Sense of Body Ownership Intervention

An avatar mirroring the user's movement is commonly adopted in Virtual Reality(VR). Maintaining the user-avatar movement consistency provides the user a sense of body ownership and thus an immersive experience. However, breaking this consistency can enable new interaction functionalities, such as pseudo haptic feedback or input augmentation, at the expense of immersion. We propose to quantify the probability of users noticing the movement inconsistency while the inconsistency amplitude is being enlarged, which aims to guide the intervention of the users' sense of body ownership in VR. We applied angular offsets to the avatar's shoulder and elbow joints and recorded whether the user identified the inconsistency through a series of three user studies and built a statistical model based on the results. Results show that the noticeability of movement inconsistency increases roughly quadratically with the enlargement of offsets and the offsets at two joints negatively affect the probability distributions of each other. Leveraging the model, we implemented a technique that amplifies the user's arm movements with unnoticeable offsets and then evaluated implementations with different parameters(offset strength, offset distribution). Results show that the technique with medium-level and balanced-distributed offsets achieves the best overall performance. Finally, we demonstrated our model's extendability in interventions in the sense of body ownership with three VR applications including stroke rehabilitation, action game and widget arrangement.

cs.HC↗