2020/04/21 by Xu Shen, Ivo Batkovic, Shen, Xu +9 · 3 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Robotics (cs.RO) #Smart Parking Systems Research #Systems and Control (eess.SY) #Video Surveillance and Tracking Methods #cs.AI #cs.LG #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.10293
* Indicates equal contribution. Accepted at IEEE Intelligent Vehicles Symposium (IV) 2020
arxiv created 2020/04/21 · openalex publication_date 2020/04/21 · arxiv updated 2020/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.