Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility

The evolution of autonomous vehicle motion prediction has transitioned from classical probabilistic models like Bayes filters to deep neural networks such as Transformer-based QCNet, HPNet, and Polaris, trained on large datasets like Argoverse and Waymo. Despite high accuracy, these models are black boxes, offering limited interpretability and rule compliance. Recent efforts in neuro-symbolic AI aim to embed explicit reasoning into neural architectures, but most lack real-time, scalable reasoning capabilities. The integration of natural language understanding with symbolic rule encoding remains a frontier, crucial for transparent, safe autonomous driving.

Current deep learning predictors excel in accuracy but lack explicit rule adherence, leading to safety risks and limited interpretability. Especially in tail scenarios, models often violate traffic regulations, causing unsafe predictions. The core challenge is to embed explicit, flexible, and interpretable traffic rules into neural prediction frameworks without sacrificing efficiency. Achieving this balance requires a system that can dynamically reason over uncertain environments, incorporate natural language regulations, and adapt in real-time to scene changes, which existing methods struggle to accomplish.

TraCS introduces several innovations: 1) transforming natural language traffic regulations into probabilistic first-order logic programs, enabling explicit, interpretable rule encoding; 2) extending spatial relations with StaR Maps to model uncertain environment features; 3) leveraging LLMs like GPT-3 for automated rule synthesis; 4) employing a reactive inference engine for real-time compliance landscape updates; 5) integrating a neural confidence mechanism to adapt rule influence based on scene context. These innovations collectively enable a flexible, explainable, tail-robust motion prediction system that seamlessly combines deep learning with symbolic reasoning.

• ��Input: natural language traffic regulations and scene features. •LLMs generate probabilistic logic programs encoding rules and spatial relations. •StaR Maps extend spatial relations by modeling uncertainty in position and velocity. •Reactive inference engine maintains and updates compliance landscapes dynamically, based on scene evolution. •Neural confidence rating assesses scene trustworthiness, modulating rule influence. •Fusion: a log-linear opinion pool combines deep prediction outputs with symbolic compliance scores, balancing accuracy and rule adherence. •Output: trajectories that respect traffic regulations, with improved safety and interpretability.

The evaluation used Argoverse 2, testing three backbone models (DEMO, POLARIS, QCNet). Metrics included bMinADE, bMinFDE, and tail error analysis. Experiments involved scenarios with diverse agent types, assessing the impact of TraCS with gating mechanisms. Ablation studies examined the contribution of each component, and hyperparameter tuning optimized trust thresholds. Results showed consistent performance gains, especially in tail scenarios, with reductions in maximum errors and improved compliance, validating the framework's robustness and practical utility.

TraCS improved bMinADE1 by approximately 0.02-0.03 across models, with significant error reductions in tail scenarios (>30%). It enhanced the prediction of underrepresented agents like pedestrians and cyclists, where traditional models often fail. The gating mechanism effectively suppressed unreliable predictions, leading to safer outputs. The tail-risk mitigation was particularly effective, reducing worst-case errors without degrading overall accuracy. These results demonstrate TraCS’s ability to combine deep learning with symbolic reasoning for safer, rule-compliant motion prediction.

TraCS can be integrated into autonomous vehicle systems for path planning, behavior prediction, and traffic management, ensuring rule adherence and safety. Its modular design allows deployment on existing predictors, with natural language regulations enabling easy updates. Long-term, it can facilitate regulation-aware AI, supporting smarter, safer urban mobility, and enabling autonomous systems to understand and obey complex traffic laws in real-world scenarios.

The approach depends on accurate, comprehensive regulation descriptions; ambiguous rules can impair performance. Real-time inference remains computationally intensive, especially in dense scenes. Generalization to diverse environments needs further validation, and the system's scalability in large-scale deployments is yet to be demonstrated. Future work should focus on automating regulation extraction, improving inference efficiency, and extending applicability to unstructured or novel traffic scenarios.