IPDM: Intent-Parameterized Dynamics Mamba for Efficient Multimodal Motion Forecasting
Accurate, real-time motion forecasting is crucial for safe operation of autonomous vehicles. State-space models, such as Mamba, can model long-range dependencies with linear computational complexity, reducing sequence modeling overhead. However, Mamba-based motion forecasting methods still rely on Transformer-style decoding frameworks, which have high computational complexity and struggle to meet real-time requirements in complex traffic scenarios. To address this, this paper proposes a multimodal motion forecasting method IPDM, based on the intent-parametrized dynamics Mamba model. First, we design the decoupled intent-state parameterization module, which decomposes multimodal queries into intent and state queries, constructs specific state-space model parameters, and generates multimodal trajectories with linear computational complexity, thereby reducing computational complexity of the decoding stage. Furthermore, addressing the challenge that single-time-scale state-space models within the Mamba decoding framework struggle to balance long-term memory with instantaneous maneuvering responses, we propose a dual-scale controlled scan mechanism. This mechanism constructs short-term and long-term flows and employs intent-aware gated adaptive fusion to enhance forecasting performance while maintaining linear decoding properties. Experimental results on the Argoverse 1 and Argoverse 2 datasets demonstrate that IPDM achieves state-of-the-art performance in inference speed and parameter efficiency, outperforming most existing methods while reducing the number of parameters by over 40%. Note to Practitioners—In autonomous driving systems, vehicles must determine the likely trajectories of surrounding road users over the next few seconds within a very short timeframe to support path planning, obstacle avoidance, and safety decisions. However, in practical deployment, motion forecasting modules must not only ensure accuracy but also meet the strict constraints of onboard edge hardware regarding computational resources and inference latency. While many existing multimodal prediction methods offer good performance, their decoding processes typically involve high computational overhead, making it difficult to balance accuracy and real-time performance in complex traffic scenarios. This work is motivated by the need to address this engineering challenge. The method proposed in this paper separates the modeling of “how the vehicle might want to move” from “how the trajectory evolves over time.” By doing so, it reduces the computational burden of the decoding stage while maintaining multimodal prediction capabilities, thereby enhancing the method’s potential for deployment on resource-constrained platforms. Experimental results demonstrate that the method maintains strong prediction performance while reducing both model parameters and inference overhead. It should be noted that the method has primarily been validated on standard public datasets; for real-world deployment, further consideration must be given to the impacts of perception noise, map errors, and extreme traffic conditions. Beyond autonomous driving, this approach is expected to be applicable to delivery robots, roadside intelligent devices, and other embedded automation systems requiring rapid motion forecasting.