MAVEN-T: Multi-Agent enVironment-aware Enhanced Neural Trajectory predictor with Reinforcement Learning
Summary: arXiv:2604.10169v1 Announce Type: new
Abstract: Trajectory prediction remains a critical yet challenging component in autonomous driving systems, requiring sophisticated reasoning capabilities while meeting strict real-time deployment constraints.
In the rapidly advancing field of autonomous driving, trajectory prediction is integral to ensuring safety and efficiency. However, it presents unique challenges that demand advanced reasoning abilities, especially in dynamic environments with multiple agents. Traditional methods often struggle to maintain the complexity of decision-making required in these scenarios. Recognizing this gap, researchers have proposed the MAVEN-T framework, which utilizes a teacher-student model to enhance trajectory prediction capabilities.
Key Features of MAVEN-T
- Teacher-Student Framework: MAVEN-T employs a dual architecture, where a powerful teacher model guides a more efficient student model.
- Hybrid Attention Mechanisms: The teacher model utilizes advanced hybrid attention mechanisms to maximize its representational capacity, allowing it to capture complex patterns in data.
- Efficient Architectures: The student model is designed for optimal deployment, balancing performance with resource constraints.
- Multi-Granular Distillation: Knowledge transfer occurs through a process of multi-granular distillation, which adapts to the student’s performance, progressively increasing the complexity of tasks as the student improves.
- Reinforcement Learning Integration: MAVEN-T incorporates reinforcement learning to surpass the limitations of traditional imitation-based methods, enabling the student to refine its understanding and decision-making through direct interactions with its environment.
Performance and Results
The effectiveness of MAVEN-T has been validated through extensive experiments on established datasets, including NGSIM and highD. The results are promising:
- Achieved a remarkable 6.2x parameter compression, significantly reducing the model’s complexity.
- Demonstrated a 3.7x speedup in inference, making it suitable for real-time applications.
- Maintained state-of-the-art accuracy in trajectory prediction, setting a new benchmark for future research.
Conclusion
The introduction of MAVEN-T represents a significant advancement in the field of trajectory prediction for autonomous driving. By leveraging the strengths of both teacher and student models, and incorporating reinforcement learning, MAVEN-T not only addresses the limitations of existing methods but also paves the way for deploying sophisticated decision-making models in resource-constrained environments. As the autonomous driving industry continues to evolve, frameworks like MAVEN-T will play a crucial role in enhancing safety and efficiency on our roads.