Summary of Scarl- a Synthetic Multi-modal Dataset For Autonomous Driving, by Avinash Nittur Ramesh et al.
SCaRL- A Synthetic Multi-Modal Dataset for Autonomous Driving
by Avinash Nittur Ramesh, Aitor Correas-Serrano, María González-Huici
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a novel multi-modal dataset called SCaRL, designed to enable the training and validation of autonomous driving solutions. The dataset combines camera, lidar, and radar sensor data, which is essential for achieving robustness and high accuracy in object detection, classification, and tracking tasks. The existing real/synthetic datasets for autonomous driving lack synchronized data collection from a complete sensor suite, making SCaRL a valuable contribution to the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a large dataset called SCaRL that provides synchronized synthetic data from multiple sensors including cameras, lidar, and radar. The dataset is based on the CARLA Simulator and includes diverse, dynamic scenarios and traffic conditions. This is the first dataset to include synthetic synchronized data from coherent Lidar and MIMO radar sensors. |
Keywords
» Artificial intelligence » Classification » Multi modal » Object detection » Synthetic data » Tracking
