# Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions arXiv: https://arxiv.org/abs/2512.11887 ยท HTML: https://mlsanity.com/paper/2512.11887/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving. - Builds on 56 of its 104 references that are in the map. - First posted on arXiv 9 Dec 2025. ## Placed in - End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/) ## Builds on (most cited first, 30 of 56) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020) - [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021) - [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://mlsanity.com/paper/1904.08980/) (2019) - [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022) - [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017) - [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023) - [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://mlsanity.com/paper/2206.08129/) (2022) - [Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving](https://mlsanity.com/paper/2310.01957/) (2023) - [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021) - [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023) - [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022) - [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023) - [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023) - [A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective](https://mlsanity.com/paper/2202.02215/) (2022) - [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021) - [CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving](https://mlsanity.com/paper/2203.07724/) (2022) - [Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures](https://mlsanity.com/paper/2006.16974/) (2020) - [Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents](https://mlsanity.com/paper/2402.05746/) (2024) - [Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation](https://mlsanity.com/paper/2312.04265/) (2023) - [Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models](https://mlsanity.com/paper/2310.17642/) (2023) - [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024) - [Simulation-based reinforcement learning for real-world autonomous driving](https://mlsanity.com/paper/1911.12905/) (2019) - [Multi-task Learning with Attention for End-to-end Autonomous Driving](https://mlsanity.com/paper/2104.10753/) (2021) - [Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding](https://mlsanity.com/paper/2005.09202/) (2020) - [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023) ## Built on by (most cited first, 1 of 1) - [GSRAIN: Physically Calibrated High-/Low-Frequency Rainfall Synthesis for 3D Gaussian Driving Scenes](https://mlsanity.com/paper/2608.02177/) (2026) ## Abstract (arXiv) Autonomous driving systems (ADSs) promise improved transportation efficiency and safety, yet ensuring their reliability in complex real-world environments remains a critical challenge. Effective testing is essential to validate ADS performance and reduce deployment risks. This study investigates current ADS testing practices for both modular and end-to-end systems, identifies key demands from industry practitioners and academic researchers, and analyzes the gaps between existing research and real-world requirements. We review major testing techniques and further consider emerging factors such as Vehicle-to-Everything (V2X) communication and foundation models, including large language models and vision foundation models, to understand their roles in enhancing ADS testing. We conducted a large-scale survey with 100 participants from both industry and academia. Survey questions were refined through expert discussions, followed by quantitative and qualitative analyses to reveal key trends, challenges, and unmet needs. Our results show that existing ADS testing techniques struggle to comprehensively evaluate real-world performance, particularly regarding corner case diversity, the simulation to reality gap, the lack of systematic testing criteria, exposure to potential attacks, practical challenges in V2X deployment, and the high computational cost of foundation model-based testing. By further analyzing participant responses together with 105 representative studies, we summarize the current research landscape and highlight major limitations. This study consolidates critical research gaps in ADS testing and outlines key future research directions, including comprehensive testing criteria, cross-model collaboration in V2X systems, cross-modality adaptation for foundation model-based testing, and scalable validation frameworks for large-scale ADS evaluation.