RuLiF: Rule-Guided Lightweight Framework for Multiagent Trajectory Forecasting in Traffic Systems

Abstract

Accurate multiagent trajectory forecasting is paramount for the safety of autonomous driving systems, yet existing methods frequently struggle to balance high predictive fidelity with the computational efficiency required for real-time deployment. This study proposes a rule-guided lightweight framework (RuLiF), a novel approach designed to address this trade-off by explicitly integrating traffic-rule priors into the representation learning process. The methodology centers on a rule-guided symmetric fusion transformer that employs a bidirectional attention mechanism to unify dynamic agent interactions and static map topologies. This fusion is dynamically modulated by an 18-dimensional rule feature vector that strictly encodes kinematic states, collision risks, and lane constraints. To guarantee kinematic feasibility, the framework utilizes a Bézier-parameterized decoder that generates smooth continuous curves, complemented by a training-time constraint rectification strategy. This strategy applies projected gradient descent to the top-three confident modes during training to enforce safety norms—such as yielding protocols and safe following distances—without incurring any additional inference latency. Extensive experiments on two large-scale motion-forecasting benchmarks validate the effectiveness of RuLiF under both short- and long-horizon prediction settings. On the short-horizon benchmark, RuLiF achieves a minimum final displacement error of 0.95 m and a miss rate of 0.08 with only 1.9 million parameters. On the more challenging long-horizon benchmark, the model obtains a minimum average displacement error of 0.78 m, a minimum final displacement error of 1.45 m, and a miss rate of 0.20 while using 2.6 million parameters and only 34% of the floating-point operations (FLOPs) required by the most computationally intensive compared model. Furthermore, the rectification module significantly enhances social compliance, reducing the time-to-collision critical rate to 1.8% and lane violations to a mere 0.9%, demonstrating that RuLiF successfully harmonizes geometric reasoning with behavioral compliance for resource-constrained onboard applications.