# Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving arXiv: https://arxiv.org/abs/2505.13872 ยท HTML: https://mlsanity.com/paper/2505.13872/ ## Key facts - Cited by 2 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 36 of its 51 references that are in the map. - First posted on arXiv 20 May 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 36) - [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014) - [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017) - [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018) - [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022) - [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022) - [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023) - [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017) - [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022) - [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014) - [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024) - [Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes](https://mlsanity.com/paper/2305.10430/) (2023) - [Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving](https://mlsanity.com/paper/2305.06242/) (2023) - [PnPNet: End-to-End Perception and Prediction with Tracking in the Loop](https://mlsanity.com/paper/2005.14711/) (2020) - [Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://mlsanity.com/paper/2007.03639/) (2020) - [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021) - [CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving](https://mlsanity.com/paper/2203.07724/) (2022) - [Adversarial Texture for Fooling Person Detectors in the Physical World](https://mlsanity.com/paper/2203.03373/) (2022) - [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024) - [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022) - [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022) - [CarLLaVA: Vision language models for camera-only closed-loop driving](https://mlsanity.com/paper/2406.10165/) (2024) - [AdvDO: Realistic Adversarial Attacks for Trajectory Prediction](https://mlsanity.com/paper/2209.08744/) (2022) - [Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving](https://mlsanity.com/paper/2405.15324/) (2024) - [Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving](https://mlsanity.com/paper/2405.17426/) (2024) - [Towards Robust Physical-world Backdoor Attacks on Lane Detection](https://mlsanity.com/paper/2405.05553/) (2024) - [SoK: On the Semantic AI Security in Autonomous Driving](https://mlsanity.com/paper/2203.05314/) (2022) - [LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions](https://mlsanity.com/paper/2406.00934/) (2024) - [MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2402.11677/) (2024) ## Built on by (most cited first, 2 of 2) - [NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment](https://mlsanity.com/paper/2608.04776/) (2026) - [A Degradation-Tolerance Benchmark for Camera-Only End-to-End Driving](https://mlsanity.com/paper/2608.29005/) (2026) ## Abstract (arXiv) Autonomous Driving (AD) systems demand the high levels of safety assurance. Despite significant advancements in AD demonstrated on open-source benchmarks like Longest6 and Bench2Drive, existing datasets still lack regulatory-compliant scenario libraries for closed-loop testing to comprehensively evaluate the functional safety of AD. Meanwhile, real-world AD accidents are underrepresented in current driving datasets. This scarcity leads to inadequate evaluation of AD performance, posing risks to safety validation and practical deployment. To address these challenges, we propose Safety2Drive, a safety-critical scenario library designed to evaluate AD systems. Safety2Drive offers three key contributions. (1) Safety2Drive comprehensively covers the test items required by standard regulations and contains 70 AD function test items. (2) Safety2Drive supports the safety-critical scenario generalization. It has the ability to inject safety threats such as natural environment corruptions and adversarial attacks cross camera and LiDAR sensors. (3) Safety2Drive supports multi-dimensional evaluation. In addition to the evaluation of AD systems, it also supports the evaluation of various perception tasks, such as object detection and lane detection. Safety2Drive provides a paradigm from scenario construction to validation, establishing a standardized test framework for the safe deployment of AD.