{"url":"/dataset/safebench","name":"SafeBench","full_name":null,"description_markdown":"SafeBench is a benchmarking platform designed for the safety evaluation of autonomous vehicles (AVs) in safety-critical scenarios¹. It aims to provide a unified platform that integrates various types of safety-critical testing scenarios, scenario generation algorithms, and other variations such as driving routes and environments¹. The platform implements four deep reinforcement learning-based AV algorithms with four types of input to perform fair comparisons on SafeBench¹.\r\n\r\nThe creators of SafeBench have observed that machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts, especially in safety-critical domains like autonomous driving¹. Traditional AD testing requires extensive driving miles due to the high dimensionality and rarity of safety-critical scenarios in the real world. SafeBench addresses this challenge by providing a large-scale and effective testing environment that encourages the development of new testing scenario generation and safe AD algorithms¹.\r\n\r\n(1) SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous .... https://arxiv.org/abs/2206.09682.\r\n(2) GitHub - trust-ai/SafeBench: A Benchmark for Evaluating Autonomous .... https://github.com/trust-ai/SafeBench.\r\n(3) SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous .... https://arxiv.org/pdf/2206.09682v1.\r\n(4) undefined. https://doi.org/10.48550/arXiv.2206.09682.\r\n(5) undefined. https://bing.com/search?q=.","description_withheld":null,"homepage":"https://github.com/trust-ai/SafeBench","introduced_date":"2022-06-20","introduced_date_note":null,"introduced_by":{"paper":null,"title":"SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["SafeBench"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}