CCLSTM: Coupled Convolutional Long-Short Term Memory Network for Occupancy Flow Forecasting

• URL: http://arxiv.org/abs/2506.06128v1
• Date: Fri, 06 Jun 2025 14:38:55 GMT
• Title: CCLSTM: Coupled Convolutional Long-Short Term Memory Network for Occupancy Flow Forecasting
• Authors: Peter Lengyel,
• Abstract summary: We propose textbfCoupled Convolutional LSTM (CTM), a lightweight, end-to-end trainable architecture based solely on convolutional operations.<n>CTM achieves state-of-the-art performance on occupancy flow metrics and, as of this submission, ranks (textst) in all metrics on the 2024 Occupancy and Flow Prediction Challenge leaderboard.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Predicting future states of dynamic agents is a fundamental task in autonomous driving. An expressive representation for this purpose is Occupancy Flow Fields, which provide a scalable and unified format for modeling motion, spatial extent, and multi-modal future distributions. While recent methods have achieved strong results using this representation, they often depend on high-quality vectorized inputs, which are unavailable or difficult to generate in practice, and the use of transformer-based architectures, which are computationally intensive and costly to deploy. To address these issues, we propose \textbf{Coupled Convolutional LSTM (CCLSTM)}, a lightweight, end-to-end trainable architecture based solely on convolutional operations. Without relying on vectorized inputs or self-attention mechanisms, CCLSTM effectively captures temporal dynamics and spatial occupancy-flow correlations using a compact recurrent convolutional structure. Despite its simplicity, CCLSTM achieves state-of-the-art performance on occupancy flow metrics and, as of this submission, ranks \(1^{\text{st}}\) in all metrics on the 2024 Waymo Occupancy and Flow Prediction Challenge leaderboard.

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