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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0001-8708(88)90072-2openalex-snapshot
Simulated annealing: Theory and applications
inferredcites →https://doi.org/10.1109/cvpr.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00175openalex-snapshot
Robust Physical-World Attacks on Deep Learning Visual Classification
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/iccv.2015.312openalex-snapshot
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
inferredcites →https://doi.org/10.1109/iccv.2017.153openalex-snapshot
Adversarial Examples for Semantic Segmentation and Object Detection
inferredcites →https://doi.org/10.1109/iccv.2017.300openalex-snapshot
Universal Adversarial Perturbations Against Semantic Image Segmentation
inferredcites →https://doi.org/10.1109/issre.2018.00021openalex-snapshot
DeepMutation: Mutation Testing of Deep Learning Systems
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3238147.3238202openalex-snapshot
DeepGauge: multi-granularity testing criteria for deep learning systems
inferredcites →https://doi.org/10.1177/0278364913491297openalex-snapshot
Vision meets robotics: The KITTI dataset
inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot
Adversarial Examples in the Physical World
inferredcites →https://doi.org/10.14722/ndss.2019.23415openalex-snapshot
NIC: Detecting Adversarial Samples with Neural Network Invariant Checking
inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1604.07316openalex-snapshot
End to End Learning for Self-Driving Cars
inferredcites →https://doi.org/10.48550/arxiv.1605.07277openalex-snapshot
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
inferredcites →https://doi.org/10.48550/arxiv.1611.02770openalex-snapshot
Delving into Transferable Adversarial Examples and Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.1707.03501openalex-snapshot
NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
inferredcites →https://doi.org/10.48550/arxiv.1707.05373openalex-snapshot
Houdini: Fooling Deep Structured Prediction Models
inferredcites →https://doi.org/10.48550/arxiv.1802.02295openalex-snapshot
DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4647085openalex-extracted
Criticalfuzz: A Critical Neuron Coverage-Guided Fuzz Testing Framework for Deep Neural Networks
inferred← citeshttps://doi.org/10.2139/ssrn.4862589openalex-extracted
Test & Evaluation Best Practices for Machine Learning-Enabled Systems
inferred← citeshttps://doi.org/10.1002/asmb.2765openalex-snapshot
Some statistical challenges in automated driving systems
inferred← citeshttps://doi.org/10.1002/smr.2644openalex-snapshot
MetaLiDAR: Automated metamorphic testing of LiDAR‐based autonomous driving systems
inferred← citeshttps://doi.org/10.1002/smr.70119openalex-snapshot
A Review on Testing Approaches for Autonomous Driving Systems Based on Metamorphic Testing and Fuzzing Testing
inferred← citeshttps://doi.org/10.1002/stvr.1878openalex-snapshot
MetaSem: metamorphic testing based on semantic information of autonomous driving scenes
inferred← citeshttps://doi.org/10.1007/978-3-030-71500-7_16openalex-snapshot
Understanding Local Robustness of Deep Neural Networks under Natural Variations
inferred← citeshttps://doi.org/10.1007/978-3-031-01233-4_1openalex-snapshot
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
inferred← citeshttps://doi.org/10.1007/978-3-031-27041-3_1openalex-snapshot
Preventing Adversarial Attacks on Autonomous Driving Models
inferred← citeshttps://doi.org/10.1007/978-3-031-28016-0_26openalex-snapshot
Safety-Assured Design and Adaptation of Connected and Autonomous Vehicles
inferred← citeshttps://doi.org/10.1007/978-3-031-49266-2_6openalex-snapshot
A Process for Scenario Prioritization and Selection in Simulation-Based Safety Testing of Automated Driving Systems
inferred← citeshttps://doi.org/10.1007/978-3-031-69201-7_21openalex-snapshot
A Survey: Cyber-Security in Connected & Automated Vehicles
inferred← citeshttps://doi.org/10.1007/978-3-031-96782-5_8openalex-snapshot
Compatibility of Blockchains in the Internet of Vehicles
inferred← citeshttps://doi.org/10.1007/978-3-032-19099-4_32openalex-snapshot
An Object-Level Entropy-Based Adversarial Attack for Image Privacy
inferred← citeshttps://doi.org/10.1007/978-981-96-3564-1_9openalex-snapshot
Fitness-Guided Point Cloud Weather Synthesis for Testing Autonomous Systems
inferred← citeshttps://doi.org/10.1007/s00371-022-02660-6openalex-snapshot
A survey on adversarial attacks and defenses for object detection and their applications in autonomous vehicles
inferred← citeshttps://doi.org/10.1007/s00521-024-10794-yopenalex-snapshot
Metamorphic testing of deep neural network-based autonomous driving systems using behavioural domain adequacy
inferred← citeshttps://doi.org/10.1007/s10462-025-11147-4openalex-snapshot
Adversarial machine learning: a review of methods, tools, and critical industry sectors
inferred← citeshttps://doi.org/10.1007/s10462-025-11373-wopenalex-snapshot
Securing (vision-based) autonomous systems: taxonomy, challenges, and defense mechanisms against adversarial threats
inferred← citeshttps://doi.org/10.1007/s10664-025-10787-yopenalex-snapshot
Empirical evaluation of simulation-based fuzz testing for autonomous driving systems
inferred← citeshttps://doi.org/10.1007/s10664-026-10920-5openalex-snapshot
Does road diversity really matter in testing automated driving systems?
inferred← citeshttps://doi.org/10.1007/s13177-025-00543-3openalex-snapshot
An Optimal Feature Selection and MRBLSTM-Based Autonomous Vehicle Detection System in Challenging Weather Conditions
inferred← citeshttps://doi.org/10.1007/s13735-024-00330-yopenalex-snapshot
RDAT: an efficient regularized decoupled adversarial training mechanism
inferred← citeshttps://doi.org/10.1007/s40747-025-02184-1openalex-snapshot
Llm-ga: A gradient-based multi-label adversarial attack by large language models
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.111808openalex-snapshot
Sampling-based test scenarios searching method for autonomous system robustness evaluation
inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107476openalex-snapshot
CriticalFuzz: A critical neuron coverage-guided fuzz testing framework for deep neural networks
inferred← citeshttps://doi.org/10.1016/j.infsof.2025.107909openalex-snapshot
MT4Image: An efficient metamorphic testing approach for image processing applications
inferred← citeshttps://doi.org/10.1016/j.infsof.2025.108010openalex-snapshot
FlowRepair: Search-based automated program repair of CPS controllers modeled in Simulink-Stateflow
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126995openalex-snapshot
Neuron importance based verification of neural networks via divide and conquer
inferred← citeshttps://doi.org/10.1016/j.sysarc.2020.101936openalex-snapshot
Efficient algorithms for task mapping on heterogeneous CPU/GPU platforms for fast completion time
inferred← citeshttps://doi.org/10.1016/j.trc.2024.104797openalex-snapshot
A qualitative AI security risk assessment of autonomous vehicles
inferred← citeshttps://doi.org/10.1109/access.2023.3327820openalex-snapshot
Distribution Aware Testing Framework for Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/aihcir61661.2023.00016openalex-snapshot
PSO-Based Black-Box Lane Detection Adversarial Attack
inferred← citeshttps://doi.org/10.1109/apsec65559.2024.00028openalex-snapshot
Enhancing the Transferability of Adversarial Attacks for End-to-End Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00137openalex-snapshot
A Majority Invariant Approach to Patch Robustness Certification for Deep Learning Models
inferred← citeshttps://doi.org/10.1109/ase56229.2023.00156openalex-snapshot
ATOM: Automated Black-Box Testing of Multi-Label Image Classification Systems
inferred← citeshttps://doi.org/10.1109/codesisss51650.2020.9244017openalex-snapshot
Heatmap-Aware Low-Cost Design to Resist Adversarial Attacks: Work-in-Progress
inferred← citeshttps://doi.org/10.1109/dsc59305.2023.00073openalex-snapshot
Survey on Fuzzing Techniques in Deep Learning Libraries
inferred← citeshttps://doi.org/10.1109/iccv.2019.00492openalex-snapshot
Physical Adversarial Textures That Fool Visual Object Tracking
inferred← citeshttps://doi.org/10.1109/icdcs60910.2024.00065openalex-snapshot
DNN Partitioning and Assignment for Distributed Inference in SGX Empowered Edge Cloud
inferred← citeshttps://doi.org/10.1109/ichora69329.2026.11537168openalex-snapshot
Attack Potential-Based Cybersecurity Risk Assessment of AV Sensing Systems
inferred← citeshttps://doi.org/10.1109/icra48506.2021.9561240openalex-snapshot
World-in-the-Loop Simulation for Autonomous Systems Validation
inferred← citeshttps://doi.org/10.1109/icse43902.2021.00043openalex-snapshot
AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair System
inferred← citeshttps://doi.org/10.1109/icse43902.2021.00045openalex-snapshot
Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
inferred← citeshttps://doi.org/10.1109/icse43902.2021.00068openalex-snapshot
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
inferred← citeshttps://doi.org/10.1109/icse43902.2021.00119openalex-snapshot
Understanding Bounding Functions in Safety-Critical UAV Software
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00017openalex-snapshot
JITfuzz: Coverage-guided Fuzzing for JVM Just-in-Time Compilers
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00045openalex-snapshot
Evaluating and Improving Hybrid Fuzzing
inferred← citeshttps://doi.org/10.1109/icse48619.2023.00216openalex-snapshot
Doppelgänger Test Generation for Revealing Bugs in Autonomous Driving Software
inferred← citeshttps://doi.org/10.1109/icst62969.2025.10988980openalex-snapshot
Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems
inferred← citeshttps://doi.org/10.1109/issre55969.2022.00046openalex-snapshot
Towards the Robustness of Multiple Object Tracking Systems
inferred← citeshttps://doi.org/10.1109/issre59848.2023.00041openalex-snapshot
Fuzzing with Sequence Diversity Inference for Sequential Decision-making Model Testing
inferred← citeshttps://doi.org/10.1109/itsc45102.2020.9294426openalex-snapshot
Feasibility and Suppression of Adversarial Patch Attacks on End-to-End Vehicle Control
inferred← citeshttps://doi.org/10.1109/itsc55140.2022.9922532openalex-snapshot
Sensing Intrusion Detection for Automatic Driving System based on Scene Semantic Centroid
inferred← citeshttps://doi.org/10.1109/iv48863.2021.9575549openalex-snapshot
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
inferred← citeshttps://doi.org/10.1109/iv64158.2025.11097717openalex-snapshot
Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors
inferred← citeshttps://doi.org/10.1109/jiot.2021.3099164openalex-snapshot
Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles
inferred← citeshttps://doi.org/10.1109/mce.2024.3442451openalex-snapshot
Physical Adversarial Example in Car Plate Recognition Systems Based on SA Algorithm
inferred← citeshttps://doi.org/10.1109/mecom67453.2025.11439584openalex-snapshot
Disrupting and Securing LiDAR Perception: A Study on Laser Traps and Countermeasures
inferred← citeshttps://doi.org/10.1109/qrs-c51114.2020.00054openalex-snapshot
Test Data Augmentation for Image Recognition Software
inferred← citeshttps://doi.org/10.1109/saner56733.2023.00087openalex-snapshot
Environment-aware Testing for DNN-based Smart-home WiFi Sensing Systems
inferred← citeshttps://doi.org/10.1109/smc58881.2025.11342958openalex-snapshot
Testing Autonomous Driving System via Object-Level Replacement
inferred← citeshttps://doi.org/10.1109/socc66126.2025.11235443openalex-snapshot
Adversarial and Backdoor Threats in Autonomous-Vehicle and Embodied-AI Systems
inferred← citeshttps://doi.org/10.1109/taffc.2025.3547218openalex-snapshot
Testing Correctness, Fairness, and Robustness of Speech Emotion Recognition Models
inferred← citeshttps://doi.org/10.1109/tii.2021.3071405openalex-snapshot
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
inferred← citeshttps://doi.org/10.1109/tits.2021.3103441openalex-snapshot
Overriding Autonomous Driving Systems Using Adaptive Adversarial Billboards
inferred← citeshttps://doi.org/10.1109/tiv.2023.3319689openalex-snapshot
A Survey on Self-Evolving Autonomous Driving: A Perspective on Data Closed-Loop Technology
inferred← citeshttps://doi.org/10.1109/tiv.2024.3459071openalex-snapshot
The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review
inferred← citeshttps://doi.org/10.1109/tse.2019.2962027openalex-snapshot
Machine Learning Testing: Survey, Landscapes and Horizons
inferred← citeshttps://doi.org/10.1109/tse.2020.2979701openalex-snapshot
Reinforcement-Learning-Guided Source Code Summarization Using Hierarchical Attention
inferred← citeshttps://doi.org/10.1109/tse.2022.3195640openalex-snapshot
Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles
inferred← citeshttps://doi.org/10.1109/tse.2022.3202311openalex-snapshot
Mind the Gap! A Study on the Transferability of Virtual Versus Physical-World Testing of Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1109/tse.2023.3301443openalex-snapshot
DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles
inferred← citeshttps://doi.org/10.1109/tse.2023.3309610openalex-snapshot
scenoRITA: Generating Diverse, Fully Mutable, Test Scenarios for Autonomous Vehicle Planning
inferred← citeshttps://doi.org/10.1109/tse.2024.3370807openalex-snapshot
Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing
inferred← citeshttps://doi.org/10.1109/tse.2024.3449429openalex-snapshot
EpiTESTER: Testing Autonomous Vehicles With Epigenetic Algorithm and Attention Mechanism
inferred← citeshttps://doi.org/10.1109/tse.2025.3650399openalex-snapshot
Drivence: Realistic Driving Sequence Synthesis for Testing Multi-Sensor Fusion Perception Systems
inferred← citeshttps://doi.org/10.1145/3324884.3416545openalex-snapshot
Problems and opportunities in training deep learning software systems
inferred← citeshttps://doi.org/10.1145/3324884.3416584openalex-snapshot
Metamorphic object insertion for testing object detection systems
inferred← citeshttps://doi.org/10.1145/3324884.3416621openalex-snapshot
Multiple-boundary clustering and prioritization to promote neural network retraining
inferred← citeshttps://doi.org/10.1145/3368089.3409671openalex-snapshot
Correlations between deep neural network model coverage criteria and model quality
inferred← citeshttps://doi.org/10.1145/3368089.3417063openalex-snapshot
A first look at the integration of machine learning models in complex autonomous driving systems: a case study on Apollo
inferred← citeshttps://doi.org/10.1145/3394112openalex-snapshot
Practical Accuracy Estimation for Efficient Deep Neural Network Testing
inferred← citeshttps://doi.org/10.1145/3442412openalex-snapshot
Architecting Internet of Things Systems with Blockchain
inferred← citeshttps://doi.org/10.1145/3460319.3464802openalex-snapshot
Automatic test suite generation for key-points detection DNNs using many-objective search (experience paper)
inferred← citeshttps://doi.org/10.1145/3460319.3464822openalex-snapshot
Attack as defense: characterizing adversarial examples using robustness
inferred← citeshttps://doi.org/10.1145/3468891.3468897openalex-snapshot
Rare event failure test case generation in Learning-Enabled-Controllers
inferred← citeshttps://doi.org/10.1145/3470493openalex-snapshot
Research Progress and Challenges on Application-Driven Adversarial Examples: A Survey
inferred← citeshttps://doi.org/10.1145/3487043openalex-snapshot
Software Engineering for AI-Based Systems: A Survey
inferred← citeshttps://doi.org/10.1145/3508035openalex-snapshot
Metamorphic Testing of Deep Learning Compilers
inferred← citeshttps://doi.org/10.1145/3510003.3510089openalex-snapshot
Evaluating and improving neural program-smoothing-based fuzzing
inferred← citeshttps://doi.org/10.1145/3510003.3510174openalex-snapshot
One fuzzing strategy to rule them all
inferred← citeshttps://doi.org/10.1145/3510003.3510212openalex-snapshot
Semantic image fuzzing of AI perception systems
inferred← citeshttps://doi.org/10.1145/3527317openalex-snapshot
Coverage-guided tensor compiler fuzzing with joint IR-pass mutation
inferred← citeshttps://doi.org/10.1145/3533767.3534390openalex-snapshot
An extensive study on pre-trained models for program understanding and generation
inferred← citeshttps://doi.org/10.1145/3533767.3534397openalex-snapshot
LiRTest: augmenting LiDAR point clouds for automated testing of autonomous driving systems
inferred← citeshttps://doi.org/10.1145/3540250.3549085openalex-snapshot
Fuzzing deep-learning libraries via automated relational API inference
inferred← citeshttps://doi.org/10.1145/3540250.3549111openalex-snapshot
Testing of autonomous driving systems: where are we and where should we go?
inferred← citeshttps://doi.org/10.1145/3540250.3549152openalex-snapshot
Scenario-based test reduction and prioritization for multi-module autonomous driving systems
inferred← citeshttps://doi.org/10.1145/3551349.3556920openalex-snapshot
Toward Improving the Robustness of Deep Learning Models via Model Transformation
inferred← citeshttps://doi.org/10.1145/3551349.3559528openalex-snapshot
ADEPT: A Testing Platform for Simulated Autonomous Driving
inferred← citeshttps://doi.org/10.1145/3551349.3561158openalex-snapshot
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
inferred← citeshttps://doi.org/10.1145/3564821openalex-snapshot
Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms
inferred← citeshttps://doi.org/10.1145/3579642openalex-snapshot
A Survey on Automated Driving System Testing: Landscapes and Trends
inferred← citeshttps://doi.org/10.1145/3591870openalex-snapshot
PatchCensor: Patch Robustness Certification for Transformers via Exhaustive Testing
inferred← citeshttps://doi.org/10.1145/3611643.3613079openalex-snapshot
Deeper Notions of Correctness in Image-Based DNNs: Lifting Properties from Pixel to Entities
inferred← citeshttps://doi.org/10.1145/3611643.3616277openalex-snapshot
SJFuzz: Seed and Mutator Scheduling for JVM Fuzzing
inferred← citeshttps://doi.org/10.1145/3611643.3616294openalex-snapshot
Enhancing Coverage-Guided Fuzzing via Phantom Program
inferred← citeshttps://doi.org/10.1145/3630011openalex-snapshot
A Post-training Framework for Improving the Performance of Deep Learning Models via Model Transformation
inferred← citeshttps://doi.org/10.1145/3635709openalex-snapshot
Causality-driven Testing of Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1145/3640335openalex-snapshot
Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features
inferred← citeshttps://doi.org/10.1145/3643768openalex-snapshot
How Does Simulation-Based Testing for Self-Driving Cars Match Human Perception?
inferred← citeshttps://doi.org/10.1145/3643832.3661854openalex-snapshot
Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehicles
inferred← citeshttps://doi.org/10.1145/3644032.3644457openalex-snapshot
PAFOT: A Position-Based Approach for Finding Optimal Tests of Autonomous Vehicles
inferred← citeshttps://doi.org/10.1145/3650212.3680312openalex-snapshot
Test Selection for Deep Neural Networks using Meta-Models with Uncertainty Metrics
inferred← citeshttps://doi.org/10.1145/3650212.3680376openalex-snapshot
Enhancing Multi-agent System Testing with Diversity-Guided Exploration and Adaptive Critical State Exploitation
inferred← citeshttps://doi.org/10.1145/3674805.3686673openalex-snapshot
Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems
inferred← citeshttps://doi.org/10.1145/3679006.3685070openalex-snapshot
Metamorphic Testing of Image Processing Applications: A General Framework and Optimization Strategies
inferred← citeshttps://doi.org/10.1145/3680464openalex-snapshot
Context-Aware Fuzzing for Robustness Enhancement of Deep Learning Models
inferred← citeshttps://doi.org/10.1145/3688838openalex-snapshot
History-Driven Fuzzing for Deep Learning Libraries
inferred← citeshttps://doi.org/10.1145/3691620.3695067openalex-snapshot
Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1145/3691625openalex-snapshot
Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review
inferred← citeshttps://doi.org/10.1145/3702989openalex-snapshot
Bias behind the Wheel: Fairness Testing of Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1145/3706598.3714301openalex-snapshot
Explanations Help: Leveraging Human Capabilities to Detect Cyberattacks on Automated Vehicles
inferred← citeshttps://doi.org/10.1145/3709355openalex-snapshot
A Roadmap for Software Testing in Open-Collaborative and AI-Powered Era
inferred← citeshttps://doi.org/10.1145/3715014.3722083openalex-snapshot
LiDARMarker: Machine-friendly Road Markers for Smart Driving Systems
inferred← citeshttps://doi.org/10.1145/3727875openalex-snapshot
Autonomous Driving System Testing via Diversity-Oriented Driving Scenario Exploration
inferred← citeshttps://doi.org/10.1145/3744242openalex-snapshot
SimADFuzz: Simulation-Feedback Fuzz Testing for Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1145/3744916.3764528openalex-snapshot
A Comprehensive Study of Deep Learning Model Fixing Approaches
inferred← citeshttps://doi.org/10.1145/3755881.3755915openalex-snapshot
Lightweight Probabilistic Coverage Metrics for Efficient Testing of Deep Neural Networks
inferred← citeshttps://doi.org/10.1145/3798262openalex-snapshot
Prunario: Testing Autonomous Driving Systems by Pruning Likely Redundant Scenarios
inferred← citeshttps://doi.org/10.1186/s40163-025-00245-xopenalex-snapshot
A scoping study: crime and connected and autonomous vehicles
inferred← citeshttps://doi.org/10.14722/autosec.2021.23034openalex-snapshot
WIP: End-to-End Analysis of Adversarial Attacks to Automated Lane Centering Systems
inferred← citeshttps://doi.org/10.2139/ssrn.7299992openalex-snapshot
StaRFuzz: Stability-Aware Fuzzing for Deep Neural Networks via Multi-Granularity Internal Responses
inferred← citeshttps://doi.org/10.2478/acss-2021-0012openalex-snapshot
Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review
inferred← citeshttps://doi.org/10.26226/morressier.604907f41a80aac83ca25d0fopenalex-snapshot
Understanding Local Robustness of Deep Neural Networks under Natural Variations
inferred← citeshttps://doi.org/10.3389/fcomp.2024.1349206openalex-snapshot
On the adversarial robustness of aerial detection
inferred← citeshttps://doi.org/10.48550/arxiv.2003.03238openalex-snapshot
TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search
inferred← citeshttps://doi.org/10.48550/arxiv.2009.06701openalex-snapshot
Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack
inferred← citeshttps://doi.org/10.48550/arxiv.2101.04930openalex-snapshot
An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications
inferred← citeshttps://doi.org/10.48550/arxiv.2102.02923openalex-snapshot
PredCoin: Defense against Query-based Hard-label Attack
inferred← citeshttps://doi.org/10.48550/arxiv.2107.02488openalex-snapshot
On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving
inferred← citeshttps://doi.org/10.48550/arxiv.2112.00964openalex-snapshot
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation
inferred← citeshttps://doi.org/10.48550/arxiv.2401.14736openalex-snapshot
How does Simulation-based Testing for Self-driving Cars match Human Perception?
inferred← citeshttps://doi.org/10.7467/ksae.2026.34.7.785openalex-snapshot
Enhancing the TARA Framework for Physical AI Security: Application to ADS
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