6 citations · 7 across the 13 of their papers we have counts for
17 papers
GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models
Pedram MohajerAnsari, Amir Salarpour, Run Wang +1
Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data that differ from the pretraining…
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles
Pedram MohajerAnsari, Amir Salarpour, Mert D. Pesé
Traffic sign recognition (TSR) models based on deep neural networks achieve strong clean-data performance but remain vulnerable to physically realizable adversarial attacks, includ…
Adversarial Prompts for Acceptance Collapse in Speculative Decoding
Run Wang, Chaoyi Zhou, Xi Liu +7
Lossless acceleration schemes, such as speculative decoding, promise significant inference speedups by relying on dynamic token-level alignment between a draft and a target model.…
Budget-Aware Adaptive Adversarial Patches for Black-Box Object Detection
Pedram MohajerAnsari, Amir Salarpour, David Fernandez +1
Adversarial patches pose a practical threat to modern object detectors. Prior work shows vulnerability, but three gaps limit actionable insight: (i) few \emph{score-based black-box…
Sat3R: Satellite DSM Reconstruction via RPC-Aware Depth Fine-tuning
Qiaoyi Yang, Chaoyi Zhou, Xi Liu +9
Accurate Digital Surface Model (DSM) reconstruction from satellite imagery is critical for applications such as disaster response, urban planning, and large-scale geographic mappin…
FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views
Chaoyi Zhou, Run Wang, Feng Luo +4
Recent advances in vision foundation models have revolutionized geometry reconstruction and semantic understanding. Yet, most of the existing approaches treat these capabilities in…
Comparative Analysis of Patch Attack on VLM-Based Autonomous Driving Architectures
David Fernandez, Pedram MohajerAnsari, Amir Salarpour +3
Vision-language models are emerging for autonomous driving, yet their robustness to physical adversarial attacks remains unexplored. This paper presents a systematic framework for…
SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognition
Mohammad Saeid, Amir Salarpour, Pedram MohajerAnsari +1
We present SLNet, a lightweight backbone for 3D point cloud recognition designed to achieve strong performance without the computational cost of many recent attention, graph, and d…
NPNet: A Non-Parametric Network with Adaptive Gaussian-Fourier Positional Encoding for 3D Classification and Segmentation
Mohammad Saeid, Amir Salarpour, Pedram MohajerAnsari +1
We present NPNet, a fully non-parametric approach for 3D point-cloud classification and part segmentation. NPNet contains no learned weights; instead, it builds point features usin…
FlexMap: Robust HD Map Construction under Flexible Camera Configurations
Run Wang, Chaoyi Zhou, Amir Salarpour +5
High-definition (HD) maps provide essential semantic information about road structures for autonomous driving, but existing HD map construction methods typically require calibrated…
David vs. Goliath: A comparative study of different-sized LLMs for code generation in the domain of automotive scenario generation
Philipp Bauerfeind, Amir Salarpour, David Fernandez +3
Scenario simulation is central to testing autonomous driving systems. Scenic, a domain-specific language (DSL) for CARLA, enables precise and reproducible scenarios, but NL-to-Scen…
DisPatch: Disarming Adversarial Patches in Object Detection with Diffusion Models
Jin Ma, Mohammed Aldeen, Christopher Salas +4
Object detection is fundamental to various real-world applications, such as security monitoring and surveillance video analysis. Despite their advancements, state-of-the-art object…
Anomalous Decision Discovery using Inverse Reinforcement Learning
Ashish Bastola, Mert D. Pesé, Long Cheng +2
Anomaly detection plays a critical role in Autonomous Vehicles (AVs) by identifying unusual behaviors through perception systems that could compromise safety and lead to hazardous…
Toward Inherently Robust VLMs Against Visual Perception Attacks
Pedram MohajerAnsari, Amir Salarpour, Michael Kühr +6
Autonomous vehicles rely on deep neural networks (DNNs) for traffic sign recognition, lane centering, and vehicle detection, yet these models are vulnerable to attacks that induce…
Contextualizing Security and Privacy of Software-Defined Vehicles: A Literature Review and Industry Perspectives
Marco De Vincenzi, Mert D. Pesé, Chiara Bodei +6
The growing reliance on software in road vehicles has led to the emergence of Software-Defined Vehicles (SDV). This work analyzes SDV security and privacy through a systematic lite…
Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehicles
Pedram MohajerAnsari, Amir Salarpour, Jan de Voor +6
We present a novel physical-world attack on autonomous vehicle (AV) lane detection systems that leverages negative shadows -- bright, lane-like patterns projected by passively redi…
SoK: Security of the Image Processing Pipeline for Camera-based Sensing in Autonomous Vehicles
Michael Kühr, Mohammad Hamad, Pedram MohajerAnsari +2
Cameras capture images that are essential for many safety-critical tasks. To process these images, a complex pipeline with multiple layers is used. Security attacks on this pipelin…