ITSC 2025 Paper Abstract

Paper VP-VP.64

Fu, Jun (Chinese Academy of Sciences Institute of Automation), Tian, Bin (Chinese Academy of Sciences Institute of Automation), chen, haonan (Institute of Automation), meng, shi (Institue of automation, chinese academy of sciences;School of Ar), Yao, Tingting (Institute of Automation of ，Chinese Academy of Sciences)

ParkFormer: A Transformer-Based Parking Policy with Goal Embedding and Pedestrian-Aware Control

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Keywords Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Multi-vehicle Coordination for Autonomous Fleets in Urban Environments, Energy-efficient Motion Control for Autonomous Vehicles

Abstract

Autonomous parking plays a vital role in intelligent vehicle systems, particularly in constrained urban environments where high-precision control is required. While traditional rule-based parking systems struggle with environmental uncertainties and lack adaptability in crowded or dynamic scenes, human drivers demonstrate the ability to park intuitively without explicit modeling. Inspired by this observation, we propose a Transformer-based end-to-end framework for autonomous parking that learns from expert demonstrations. The network takes as input surround-view camera images, goal-point representations, ego vehicle motion, and pedestrian trajectories. It outputs discrete control sequences including throttle, braking, steering, and gear selection. A novel cross-attention module integrates BEV features with target points, and a GRU-based pedestrian predictor enhances safety by modeling dynamic obstacles. We validate our method on the CARLA 0.9.14 simulator in both vertical and parallel parking scenarios. Experiments show our model achieves a high success rate of 96.57%, with average positional and orientation errors of 0.21 meters and 0.41 degrees, respectively. The ablation studies further demonstrate the effectiveness of key modules such as pedestrian prediction and goal-point attention fusion. The code and dataset will be released at: url {https://github.com/little-snail-f/ParkFormer}.