TL;DR
ParkingScenes is a structured, multimodal dataset for end-to-end autonomous parking in simulation, enabling improved learning and benchmarking of parking policies.
Contribution
It introduces a high-quality, structured parking dataset with multimodal data and a collection framework, filling a key gap in autonomous parking research.
Findings
Models trained on ParkingScenes outperform those trained on unstructured data.
Structured supervision significantly improves parking policy accuracy.
The dataset supports scalable, reproducible autonomous parking research.
Abstract
Autonomous parking remains a critical yet challenging task in intelligent driving systems, particularly within constrained urban environments where maneuvering space is limited and precise control is essential. While recent advances in end-to-end learning have shown great promise, the lack of high-quality, structured datasets tailored for parking scenarios remains a significant bottleneck.To address this gap, we present ParkingScenes, a comprehensive multimodal dataset specifically designed for end-to-end autonomous parking in simulated scenes. Built on the CARLA simulator, ParkingScenes features structured parking trajectories generated by a Hybrid A* planner and a Model Predictive Controller (MPC), providing accurate and reproducible supervision signals. The dataset includes 16 reverse-in and 6 parallel parking scenarios, each executed under two pedestrian conditions (present and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
