# ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots

arXiv: https://arxiv.org/abs/2004.10293 · HTML: https://mlsanity.com/paper/2004.10293/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning](https://mlsanity.com/paper/2408.02061/) (2024)
- [ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer](https://mlsanity.com/paper/2204.10777/) (2022)
- [Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning](https://mlsanity.com/paper/2204.10383/) (2022)
- [Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making](https://mlsanity.com/paper/2311.11058/) (2023)
- [ParkDiffusion: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction for Automated Parking using Diffusion Models](https://mlsanity.com/paper/2505.00586/) (2025)
- [TransParking: A Dual-Decoder Transformer Framework with Soft Localization for End-to-End Automatic Parking](https://mlsanity.com/paper/2503.06071/) (2025)
- [ParkFormer: A Transformer-Based Parking Policy with Goal Embedding and Pedestrian-Aware Control](https://mlsanity.com/paper/2506.16856/) (2025)
- [ParkDiffusion++: Ego Intention Conditioned Joint Multi-Agent Trajectory Prediction for Automated Parking using Diffusion Models](https://mlsanity.com/paper/2602.20923/) (2026)
- [End-to-End Visual Autonomous Parking via Control-Aided Attention](https://mlsanity.com/paper/2509.11090/) (2025)

## Abstract (arXiv)

We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space. Using the CARLA simulator, we develop a parking lot environment and collect a dataset of human parking maneuvers. We then study the impact of model complexity and feature information by comparing a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment improves long term predictions.