# ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer

arXiv: https://arxiv.org/abs/2204.10777 · HTML: https://mlsanity.com/paper/2204.10777/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://mlsanity.com/paper/1811.02146/) (2018)
- [End-to-end Contextual Perception and Prediction with Interaction Transformer](https://mlsanity.com/paper/2008.05927/) (2020)
- [On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions](https://mlsanity.com/paper/2012.03390/) (2020)
- [ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots](https://mlsanity.com/paper/2004.10293/) (2020)
- [Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information](https://mlsanity.com/paper/2106.00559/) (2021)

## Built on by (most cited first, 14 of 14)

- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning](https://mlsanity.com/paper/2408.02061/) (2024)
- [Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance](https://mlsanity.com/paper/2309.08382/) (2023)
- [HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios](https://mlsanity.com/paper/2405.20579/) (2024)
- [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)
- [E2E Parking Dataset: An Open Benchmark for End-to-End Autonomous Parking](https://mlsanity.com/paper/2504.10812/) (2025)
- [A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking](https://mlsanity.com/paper/2510.14000/) (2025)
- [RoadWeaver: Large-Scale Lane-Level HD Map Generation from Scratch for Autonomous Driving Simulation](https://mlsanity.com/paper/2608.11580/) (2026)
- [ParkingScenes: A Structured Dataset for End-to-End Autonomous Parking in Simulation Scenes](https://mlsanity.com/paper/2604.22835/) (2026)
- [Timed Rule-Based Supervision of an End-to-End Autonomous Parking Policy](https://mlsanity.com/paper/2609.31773/) (2026)

## Abstract (arXiv)

The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper. Using models designed with CNN and Transformer networks, we extract temporal-spatial and contextual information from trajectory history and local bird's eye view (BEV) semantic images, and generate predictions about intent distribution and future trajectory sequences. Our methods outperform existing models in accuracy, while allowing an arbitrary number of modes, encoding complex multi-agent scenarios, and adapting to different parking maps. To train and evaluate our method, we present the first public 4K video dataset of human driving in parking lots with accurate annotation, high frame rate, and rich traffic scenarios.