Generalized Trajectory Scoring for End-to-end Multimodal Planning (2506.06664v1)

Abstract: End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory scorer capable of selecting the optimal trajectory from these candidates. While recent trajectory scorers focus on scoring either large sets of static trajectories or small sets of dynamically generated ones, both approaches face significant limitations in generalization. Static vocabularies provide effective coarse discretization but struggle to make fine-grained adaptation, while dynamic proposals offer detailed precision but fail to capture broader trajectory distributions. To overcome these challenges, we propose GTRS (Generalized Trajectory Scoring), a unified framework for end-to-end multi-modal planning that combines coarse and fine-grained trajectory evaluation. GTRS consists of three complementary innovations: (1) a diffusion-based trajectory generator that produces diverse fine-grained proposals; (2) a vocabulary generalization technique that trains a scorer on super-dense trajectory sets with dropout regularization, enabling its robust inference on smaller subsets; and (3) a sensor augmentation strategy that enhances out-of-domain generalization while incorporating refinement training for critical trajectory discrimination. As the winning solution of the Navsim v2 Challenge, GTRS demonstrates superior performance even with sub-optimal sensor inputs, approaching privileged methods that rely on ground-truth perception. Code will be available at https://github.com/NVlabs/GTRS.

• The paper presents the GTRS framework that integrates diffusion-based trajectory generation, robust vocabulary generalization, and sensor augmentation for improved multimodal planning.
• It reports significant performance improvements in autonomous driving metrics, including a higher extended PDM Score (EPDMS) on the Navhard benchmark.
• It bridges static and dynamic trajectory scoring methods, enhancing system adaptability and safety in complex driving environments.

Generalized Trajectory Scoring for End-to-End Multimodal Planning: A Critical Analysis

The paper "Generalized Trajectory Scoring for End-to-End Multimodal Planning" presents an innovative approach to autonomous driving by addressing significant limitations in current trajectory scoring methods. The research introduces GTRS (Generalized Trajectory Scoring), a framework designed to enhance multimodal planning through effective trajectory evaluation mechanisms. This essay unpacks the core contributions and implications of the paper, focusing on the methodological advancements and results that underscore its significance in the field of autonomous vehicle trajectory planning.

Methodological Innovations

GTRS is predicated on integrating both static and dynamic trajectory evaluation techniques to augment trajectory scoring capabilities. The paper delineates three distinct components:

1. Diffusion-based Trajectory Generation: This aspect leverages a diffusion policy to generate diverse, fine-grained trajectory proposals, crucial for navigation in complex and safety-critical driving environments. By utilizing BEV features, the diffusion model enhances adaptability and precision in dynamic scenario assessments.
2. Trajectory Vocabulary Generalization: Here, the paper proposes training on super-dense trajectory sets (16,384 trajectories) accompanied by dropout regularization. This allows for robust inference across smaller trajectory subsets. The key innovation lies in deliberately mismatching training and inference vocabularies, thereby fostering the model’s ability to generalize effectively.
3. Sensor Augmentation Strategy: Out-of-domain generalization is improved through strategic sensor augmentation and refinement training. By applying rotation perturbations to sensor inputs, the model's robustness to environmental changes is enhanced, which is pivotal in real-world applications.

These methodologies are systematically integrated during inference to allow GTRS to score both dynamically generated and static trajectory proposals effectively.

Experimental Results and Implications

The experimental results, particularly in the Navhard benchmark, signify GTRS's efficacy. The system outperformed existing models by substantial margins, showing improvement in metrics such as the extended PDM Score (EPDMS). Notably, GTRS-Dense and GTRS-Aug demonstrated significant enhancements in trajectory evaluation, even under sub-optimal sensor conditions. The research highlights GTRS-E, the ensemble model, approaching performance levels of privileged planners relying on ground-truth perception data.

These findings suggest potential applications in real-world autonomous driving scenarios where models must adapt to various environmental and sensor limitations without heavy reliance on ground-truth data. Moreover, the integration of diffusion models aligns trajectory planning closer to human-like navigation capabilities, emphasizing responsive adaptation over static decision-making.

Theoretical and Practical Implications

Theoretically, GTRS contributes a nuanced understanding of trajectory scoring by challenging the boundaries between static and dynamic proposals. The research argues that effective autonomous systems must encapsulate versatile scoring strategies for truly robust navigation capabilities. Practically, the implementation of GTRS could redefine safety and efficiency standards in autonomous vehicles, particularly in urban and unpredictable driving environments.

Future Directions

While the GTRS framework sets a new benchmark in trajectory scoring, future work could explore expanded sensor augmentation strategies and further intricacies in trajectory vocabulary generalization. Incorporating real-world testing and continuous learning paradigms could also bolster the model's adaptability and deployment readiness.

In conclusion, the "Generalized Trajectory Scoring for End-to-End Multimodal Planning" paper offers significant advancements in autonomous trajectory planning, showcasing a blend of methodological rigor and practical relevance. As researchers and practitioners continue to innovate in this field, GTRS serves as a pivotal reference point for future exploration and development in dynamic trajectory scoring methodologies.