S2R-Bench: A Sim-to-Real Evaluation Benchmark for Autonomous Driving

TL;DR

S2R-Bench is a novel benchmark that evaluates autonomous driving perception systems' robustness by comparing simulated and real-world sensor data across diverse conditions, addressing a critical gap in safety assessment.

Contribution

The paper introduces the first real-world corruption robustness benchmark for autonomous driving perception, incorporating diverse environmental and sensor anomalies.

Findings

Demonstrates the gap between simulated and real-world perception robustness.

Provides a comprehensive dataset for evaluating perception under various real-world conditions.

Highlights the importance of real-world data for developing reliable autonomous driving systems.

Abstract

Safety is a long-standing and the final pursuit in the development of autonomous driving systems, with a significant portion of safety challenge arising from perception. How to effectively evaluate the safety as well as the reliability of perception algorithms is becoming an emerging issue. Despite its critical importance, existing perception methods exhibit a limitation in their robustness, primarily due to the use of benchmarks are entierly simulated, which fail to align predicted results with actual outcomes, particularly under extreme weather conditions and sensor anomalies that are prevalent in real-world scenarios. To fill this gap, in this study, we propose a Sim-to-Real Evaluation Benchmark for Autonomous Driving (S2R-Bench). We collect diverse sensor anomaly data under various road conditions to evaluate the robustness of autonomous driving perception methods in a comprehensive and realistic manner. This is the first corruption robustness benchmark based on real-world scenarios, encompassing various road conditions, weather conditions, lighting intensities, and time periods. By comparing real-world data with simulated data, we demonstrate the reliability and practical significance of the collected data for real-world applications. We hope that this dataset will advance future research and contribute to the development of more robust perception models for autonomous driving. This dataset is released on https://github.com/adept-thu/S2R-Bench.