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A Trend-Guided Testing Framework Balancing Risk and Coverage to Accelerate Autonomous Vehicle Safety Validation

https://doi.org/10.2139/ssrn.6795468
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How would autonomous vehicles behave in real-world crash scenarios?
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TrajVAE: A Variational AutoEncoder model for trajectory generation
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Dense reinforcement learning for safety validation of autonomous vehicles
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High-risk powered two-wheelers scenarios generation for autonomous vehicle testing using WGAN
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Identification of Test Cases for Automated Driving Systems Using Bayesian Optimization
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MARL-Based High-Risk Multivehicle Scenario Generation for Autonomous Vehicle Safety Testing
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Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques
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Accelerated Evaluation of Automated Vehicles in Car-Following Maneuvers
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Hazardous Scenario Enhanced Generation for Automated Vehicle Testing Based on Optimization Searching Method
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Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios
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Adaptive Design of Experiments for Safety Evaluation of Automated Vehicles
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SceGene: Bio-Inspired Traffic Scenario Generation for Autonomous Driving Testing
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Safety Performance Boundary Identification of Highly Automated Vehicles: A Surrogate Model-Based Gradient Descent Searching Approach
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Online Adaptive Generation of Critical Boundary Scenarios for Evaluation of Autonomous Vehicles
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A Critical Scenario Search Method for Intelligent Vehicle Testing Based on the Social Cognitive Optimization Algorithm
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Accelerated Risk Assessment for Highly Automated Vehicles: Surrogate-Based Monte Carlo Method
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Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey
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A Hazardous Lane-Changing Scenario Search Approach Based on Adaptive Random Testing for Automated Vehicles
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Interactive Critical Scenario Generation for Autonomous Vehicles Testing Based on In-Depth Crash Data Using Reinforcement Learning
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Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles
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scenoRITA: Generating Diverse, Fully Mutable, Test Scenarios for Autonomous Vehicle Planning
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EpiTESTER: Testing Autonomous Vehicles With Epigenetic Algorithm and Attention Mechanism
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LLMScenario: Large Language Model Driven Scenario Generation
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MOSAT: finding safety violations of autonomous driving systems using multi-objective genetic algorithm
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Instance Space Analysis for Algorithm Testing: Methodology and Software Tools
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BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems
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Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios
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