SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface Conditions
Autonomous vehicles rely on camera, LiDAR, and radar sensors to navigate the environment. Adverse weather conditions like snow, rain, and fog are known to be problematic for both camera and LiDAR-based perception systems. Currently, it is difficult to evaluate the performance of these methods due to the lack of publicly available datasets containing multimodal labeled data. To address this limitation, we propose the SemanticSpray++ dataset, which provides labels for camera, LiDAR, and radar data of highway-like scenarios in wet surface conditions. In particular, we provide 2D bounding boxes for the camera image, 3D bounding boxes for the LiDAR point cloud, and semantic labels for the radar targets. By labeling all three sensor modalities, the SemanticSpray++ dataset offers a comprehensive test bed for analyzing the performance of different perception methods when vehicles travel on wet surface conditions. Together with comprehensive label statistics, we also evaluate multiple baseline methods across different tasks and analyze their performances. The dataset will be available at https://semantic-spray-dataset.github.io .
Code (1)
Tasks
Autonomous DrivingAutonomous VehiclesNavigateRobust Object DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multimodal Backdoor Attack on VLMs for Autonomous Driving via Graffiti and Cross-Lingual Triggers
Visual language model (VLM) is rapidly being integrated into safety-critical systems such as autonomous driving, making it an important attack surface for potential backdoor attacks. Existing backdoor attacks mainly rely…
Autonomous DrivingA Survey on Multimodal Large Language Models for Autonomous Driving
With the emergence of Large Language Models (LLMs) and Vision Foundation Models (VFMs), multimodal AI systems benefiting from large models have the potential to equally perceive the real world, make decisions, and contro…
Autonomous DrivingaiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception
Autonomous driving is a popular research area within the computer vision research community. Since autonomous vehicles are highly safety-critical, ensuring robustness is essential for real-world deployment. While several…
3D Object DetectionAutonomous DrivingAutonomous VehiclesMultimodal Deep Learning+2Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems
In recent years, the rise of autonomous driving technologies has highlighted the critical importance of reliable software for ensuring safety and performance. This paper proposes a novel approach for just-in-time softwar…
Autonomous DrivingAUTOPILOT VQA: Benchmarking Vision-Language Models for Incident-Centric Dashcam Understanding
Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visua…
Visual Question AnsweringTrajectory PredictionScene UnderstandingAutonomous Driving