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1. Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks

   Cao, Yulong; Wang, Ningfei; Xiao, Chaowei; Yang, Dawei; Fang, Jin; Yang, Ruigang; Chen, Qi Alfred; Liu, Mingyan; Li, Bo (January 2021, IEEE Symposium on Security and Privacy (S&P))

   null (Ed.)

   In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite various prior studies on its security issues, all of them only consider attacks on cameraor LiDAR-based AD perception alone. However, production AD systems today predominantly adopt a Multi-Sensor Fusion (MSF) based design, which in principle can be more robust against these attacks under the assumption that not all fusion sources are (or can be) attacked at the same time. In this paper, we present the first study of security issues of MSF-based perception in AD systems. We directly challenge the basic MSF design assumption above by exploring the possibility of attacking all fusion sources simultaneously. This allows us for the first time to understand how much security guarantee MSF can fundamentally provide as a general defense strategy for AD perception. We formulate the attack as an optimization problem to generate a physically-realizable, adversarial 3D-printed object that misleads an AD system to fail in detecting it and thus crash into it. To systematically generate such a physical-world attack, we propose a novel attack pipeline that addresses two main design challenges: (1) non-differentiable target camera and LiDAR sensing systems, and (2) non-differentiable cell-level aggregated features popularly used in LiDAR-based AD perception. We evaluate our attack on MSF algorithms included in representative open-source industry-grade AD systems in real-world driving scenarios. Our results show that the attack achieves over 90% success rate across different object types and MSF algorithms. Our attack is also found stealthy, robust to victim positions, transferable across MSF algorithms, and physical-world realizable after being 3D-printed and captured by LiDAR and camera devices. To concretely assess the end-to-end safety impact, we further perform simulation evaluation and show that it can cause a 100% vehicle collision rate for an industry-grade AD system. We also evaluate and discuss defense strategies. 

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2. Calibration method for a multi-focus microscopic 3D imaging system

   https://doi.org/10.1364/OL.498283  

   Chen, Liming; Xiang, Wang; Zhang, Song (January 2023, Optics Letters)

   This Letter presents a novel, to the best of our knowledge, method to calibrate multi-focus microscopic structured-light three-dimensional (3D) imaging systems with an electrically adjustable camera focal length. We first leverage the conventional method to calibrate the system with a reference focal lengthf₀. Then we calibrate the system with other discrete focal lengthsf_iby determining virtual features on a reconstructed white plane usingf₀. Finally, we fit the polynomial function model using the discrete calibration results forf_i. Experimental results demonstrate that our proposed method can calibrate the system consistently and accurately. 

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3. MiHazeFree3D: 3D Bounding Box Prediction for Vehicles and Pedestrians in Fog and Low-Light Conditions

   https://doi.org/10.1145/3786764  

   Regmi, Hem; Tavasoli, Reza; Sur, Sanjib; Nelakuditi, Srihari (February 2026, ACM Transactions on Internet of Things)

   We presentMiHazeFree3D, a system that leverages millimeter-wave (mmWave) radar signals to predict 3D bounding boxes of vehicles and pedestrians in real-world traffic scenarios. While current 3D object detection methods rely primarily on cameras and LiDARs, their performance degrades significantly in rain, fog, or poor lighting conditions. Our system exploits mmWave radar’s ability to operate reliably in these challenging conditions, offering a complement to existing sensors without increasing computational costs. The key challenge in using mmWave for 3D detection lies in handling motion-induced errors and the specular reflection of mmWave signals. To address these issues, we developed a deep learning architecture with multiple feature fusion layers and trained it on diverse real-world scenarios. We evaluatedMiHazeFree3Dusing data collected from mmWave radars mounted on the dashboard of an ego-vehicle driving through urban environments. Our results show thatMiHazeFree3Ddetects and bounds both vehicles and pedestrians in tested conditions, including fog and low-light scenarios, highlighting the potential of mmWave radar for 3D object detection in autonomous driving systems. 

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4. PECAN: Personalizing Robot Behaviors through a Learned Canonical Space

   https://doi.org/10.1145/3737894  

   Nemlekar, Heramb; Sanchez, Robert Ramirez; Losey, Dylan P (December 2025, ACM Transactions on Human-Robot Interaction)

   Robots should personalize how they perform tasks to match the needs of individual human users. Today’s robots achieve this personalization by asking for the human’s feedback in the task space. For example, an autonomous car might show the human two different ways to decelerate at stoplights, and ask the human which of these motions they prefer. This current approach to personalization isindirect: Based on the behaviors the human selects (e.g., decelerating slowly), the robot tries to infer their underlying preference (e.g., defensive driving). By contrast, our article develops a learning and interface-based approach that enables humans todirectlyindicate their desired style. We do this by learning an abstract, low-dimensional, and continuous canonical space from human demonstration data. Each point in the canonical space corresponds to a different style (e.g., defensive or aggressive driving), and users can directly personalize the robot’s behavior by simply clicking on a point. Given the human’s selection, the robot then decodes this canonical style across each task in the dataset—e.g., if the human selects a defensive style, the autonomous car personalizes its behavior to drive defensively when decelerating, passing other cars, or merging onto highways. We refer to our resulting approach as PECAN:Personalizing Robot Behaviors through a LearnedCanonical Space. Our simulations and user studies suggest that humans prefer using PECAN to directly personalize robot behavior (particularly when those users become familiar with PECAN), and that users find the learned canonical space to be intuitive and consistent. See videos here:https://youtu.be/wRJpyr23PKI. 

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5. Enhanced spectral-domain optical coherence tomography (SD-OCT) using in situ ultrasonic virtual tunable optical waveguides

   https://doi.org/10.1364/OE.462500  

   Karimi, Yasin; Yang, Hang; Liu, Junze; Park, B. hyle; Chamanzar, Maysamreza (January 2022, Optics Express)

   A conventional optical lens can enhance lateral resolution in optical coherence tomography (OCT) by focusing the input light onto the sample. However, the typical Gaussian beam profile of such a lens will impose a tradeoff between the depth of focus (DOF) and the lateral resolution. The lateral resolution is often compromised to achieve amm-scale DOF. We have experimentally shown that using a cascade system of an ultrasonic virtual tunable optical waveguide (UVTOW) and a short focal-length lens can provide a large DOF without severely compromising the lateral resolution compared to an external lens with the same effective focal length. In addition, leveraging the reconfigurability of UVTOW, we show that the focal length of the cascade system can be tuned without the need for mechanical translation of the optical lens. We compare the performance of the cascade system with a conventional optical lens to demonstrate enhanced DOF without compromising the lateral resolution as well as reconfigurability of UVTOW for OCT imaging. 

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