Gait recognition enables proactive and personalized smart home interactions, but its long-term reliability is challenged by the non-static nature of gait. Covariates like carrying items and clothing induce a persistent domain shift that degrades traditional, static models. To solve this, we introduce FlowGait, a mmWave-based framework designed for robust, long-term adaptation. It combines self-training with continual learning, allowing the model to daily align with a user’s evolving gait by learning from readily available unlabeled data. It features a specialized transformer network for radar spectrogram analysis and a novel two-stage labeling algorithm that leverages the gait’s hierarchical nature to assign pseudo-labels to the unlabeled data accurately. Evaluated on three challenging datasets from 47 volunteers (covering 12 gait-covariates, 11 routes, and two weeks), FlowGait achieves high accuracies of 94.8 (cross-covariate), 98.6% (cross-route), and 95.5% (cross-day). Notably, for the long-term dataset, it reduced performance decay from 13.6% to just 1.4%, demonstrating its real-world robustness.

Voice has been a primary interaction mode with LLM-powered assistants. Beyond semantics, voice carries emotional cues with potential to guide empathetic system responses. Yet, robust vocal emotion sensing in noise and its use in optimizing interactions remain underexplored. In response, we present FeelWave, which achieves empathetic voice interaction through noise-robust mmWave emotion sensing and structured LLM prompts. It extracts robust vocal information from mmWave signals, applies audio-to-mmWave transfer learning for efficient emotion recognition, and employs chain-of-thought-based query optimization to enable emotion-adaptive responses. Evaluations show that FeelWave achieves 92.3% emotion recognition accuracy and remains robust in noisy environments, yielding a 62.9 percentage-point gain over audio-based models. In voice interaction studies, 74.3% of users prefer FeelWave, reporting significantly higher satisfaction than a baseline without emotion sensing (4.37 vs. 3.22). A SUS score of 88.3 confirms FeelWave’s high usability in real-world deployment. We hope this work will inspire more empathetic, user-centered AI-driven assistants.

Vehicle detection in tunnels is crucial for traffic monitoring and accident response, yet remains underexplored. In this paper, we develop mmTunnel, a millimeter-wave radar system that achieves far-range vehicle detection in tunnels. The main challenge here is coping with ghost points caused by multi-path reflections, which lead to severe localization errors and false alarms. Instead of merely removing ghost points, we propose correcting them to true vehicle positions by recovering their signal reflection paths, thus reserving more data points and improving detection performance, even in occlusion scenarios. However, recovering complex 3D reflection paths from limited 2D radar points is highly challenging. To address this problem, we develop a multi-path ray tracing algorithm that leverages the ground plane constraint and identifies the most probable reflection path based on signal path loss and spatial distance. We also introduce a curve-to-plane segmentation method to simplify tunnel surface modeling such that we can significantly reduce the computational delay and achieve real-time processing.

The fusion of LiDARs and cameras has been increasingly adopted in autonomous driving for perception tasks. The performance of such fusion-based algorithms largely depends on the accuracy of sensor calibration, which is challenging due to the difficulty of identifying common features across different data modalities. Previously, many calibration methods involved specific targets and/or manual intervention, which has proven to be cumbersome and costly. Learning-based online calibration methods have been proposed, but their performance is barely satisfactory in most cases. These methods usually suffer from issues such as sparse feature maps, unreliable cross-modality association, inaccurate calibration parameter regression, etc. In this paper, to address these issues, we propose CalibFormer, an end-to-end network for automatic LiDAR-camera calibration. We aggregate multiple layers of camera and LiDAR image features to achieve high-resolution representations. A multi-head correlation module is utilized to identify correlations between features more accurately. Lastly, we employ transformer architectures to estimate accurate calibration parameters from the correlation information. Our method achieved a mean translation error of 0.8751cm and a mean rotation error of 0.0562° on the KITTI dataset, surpassing existing state-of-the-art methods and demonstrating strong robustness, accuracy, and generalization capabilities.

The integration of complementary characteristics from camera and radar data has emerged as an effective approach in 3D object detection. However, such fusion-based methods remain unexplored for place recognition, an equally important task for autonomous systems. Given that place recognition relies on the similarity between a query scene and the corresponding candidate scene, the stationary background of a scene is expected to play a crucial role in the task. As such, current well-designed camera-radar fusion methods for 3D object detection can hardly take effect in place recognition because they mainly focus on dynamic foreground objects. In this paper, a background-attentive camera-radar fusion-based method, named CRPlace, is proposed to generate background-attentive global descriptors from multi-view images and radar point clouds for accurate place recognition. To extract stationary background features effectively, we design an adaptive module that generates the background-attentive mask by utilizing the camera BEV feature and radar dynamic points. With the guidance of a background mask, we devise a bidirectional cross-attention-based spatial fusion strategy to facilitate comprehensive spatial interaction between the background information of the camera BEV feature and the radar BEV feature. As the first camera-radar fusion-based place recognition network, CRPlace has been evaluated thoroughly on the nuScenes dataset. The results show that our algorithm outperforms a variety of baseline methods across a comprehensive set of metrics (recall@1 reaches 91.2%).

A crucial task in autonomous driving is to continuously detect nearby vehicles. Problems thus arise when a vehicle is occluded and becomes "unseeable", which may lead to accidents. In this study, we develop mmOVD, a system that can detect fully occluded vehicles by involving millimeter-wave radars to capture the ground-reflected signals passing beneath the blocking vehicle's chassis. The foremost challenge here is coping with ghost points caused by frequent multi-path reflections, which highly resemble the true points. We devise a set of features that can efficiently distinguish the ghost points by exploiting the neighbor points' spatial and velocity distributions. We also design a cumulative clustering algorithm to effectively aggregate the unstable ground-reflected radar points over consecutive frames to derive the bounding boxes of the vehicles.

Precise real-time unmanned aerial vehicle (UAV) positioning is crucial for preventing unauthorized UAVs from damaging cooperative intelligent transportation systems (C-ITSs). However, UAV positioning remains extremely challenging due to the small target size and high flexibility. Therefore, we develop a modal-oriented self-tuning fusion framework for precise UAV millimeter-wave(MMW)-vision positioning. The framework selects and extracts cross-modal features based on modality characters, and migrates the Doppler features of MMW radar data to the image features for precise pixel-level positioning. Based on the framework, a modal-oriented self-tuning fusion network is proposed to adaptively enhance UAV feature without direct supervision by exploiting the cross-modal correlations. A novel characteristic-based 3DMMW feature extraction method is presented to extract UAV Doppler motion characteristics while a self-tuning cross-modal affine transfer is proposed for UAV visual feature enhancement. Due to lack of dataset for our task, we establish a practical positioning platform and two novel datasets containing synchronized visual images and MMW radio frequency (RF) sequences in various scenarios. Experimental results confirm that our framework outperforms the benchmark methods in terms of positioning accuracy while maintaining real-time performance. Moreover, ablation studies also confirm the effectiveness of each module in the framework.