2 citations · 3 across the 13 of their papers we have counts for
17 papers
Lang3DSeg: Annotation-Free Open-Vocabulary 3D Segmentation with Point Transformers
Cigdem Kokenoz, Amir Salarpour, Alkim Domeke +5
Accurate 3D semantic perception is critical for safe autonomous navigation. However, supervised LiDAR segmentation remains tied to closed taxonomies and to the cost of point-wise m…
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…
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis
David Fernandez, Pedram MohajerAnsari, Amir Salarpour +1
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decisi…
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…
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…
Enhancing 3D Point Cloud Classification with ModelNet-R and Point-SkipNet
Mohammad Saeid, Amir Salarpour, Pedram MohajerAnsari
The classification of 3D point clouds is crucial for applications such as autonomous driving, robotics, and augmented reality. However, the commonly used ModelNet40 dataset suffers…
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…
Point-LN: A Lightweight Framework for Efficient Point Cloud Classification Using Non-Parametric Positional Encoding
Marzieh Mohammadi, Amir Salarpour, Pedram MohajerAnsari
We introduce Point-LN, a novel lightweight framework engineered for efficient 3D point cloud classification. Point-LN integrates essential non-parametric components-such as Farthes…
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI
Muhammet Anil Yagiz, Pedram MohajerAnsari, Mert D. Pese +1
In the evolving landscape of autonomous vehicles, ensuring robust in-vehicle network (IVN) security is paramount. This paper introduces an advanced intrusion detection system (IDS)…
Achieving the Safety and Security of the End-to-End AV Pipeline
Noah T. Curran, Minkyoung Cho, Ryan Feng +6
In the current landscape of autonomous vehicle (AV) safety and security research, there are multiple isolated problems being tackled by the community at large. Due to the lack of c…
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…