Multi-modal Integrated Prediction and Decision-making with Adaptive Interaction Modality Explorations (2408.13742v2)

Abstract: Navigating dense and dynamic environments poses a significant challenge for autonomous driving systems, owing to the intricate nature of multimodal interaction, wherein the actions of various traffic participants and the autonomous vehicle are complex and implicitly coupled. In this paper, we propose a novel framework, Multi-modal Integrated predictioN and Decision-making (MIND), which addresses the challenges by efficiently generating joint predictions and decisions covering multiple distinctive interaction modalities. Specifically, MIND leverages learning-based scenario predictions to obtain integrated predictions and decisions with social-consistent interaction modality and utilizes a modality-aware dynamic branching mechanism to generate scenario trees that efficiently capture the evolutions of distinctive interaction modalities with low variation of interaction uncertainty along the planning horizon. The scenario trees are seamlessly utilized by the contingency planning under interaction uncertainty to obtain clear and considerate maneuvers accounting for multi-modal evolutions. Comprehensive experimental results in the closed-loop simulation based on the real-world driving dataset showcase superior performance to other strong baselines under various driving contexts.

• The paper presents the MIND framework, achieving integrated multi-modal predictions and decisions for improved autonomous navigation.
• It employs modality-aware dynamic branching to generate scenario trees that minimize uncertainty in complex traffic interactions.
• Experimental validation on real-world datasets shows that MIND outperforms existing baselines in diverse driving scenarios.

The paper "Multi-modal Integrated Prediction and Decision-making with Adaptive Interaction Modality Explorations" introduces a novel framework called MIND, designed to enhance autonomous driving systems' ability to navigate dense and dynamic environments. The complexity of such environments arises from the intricate multimodal interactions between various traffic participants and the autonomous vehicle itself.

MIND addresses the challenge of generating efficient joint predictions and decisions by focusing on multiple distinctive interaction modalities. The framework adopts a learning-based approach for scenario predictions, allowing it to produce integrated predictions and decisions that maintain social consistency across interaction modalities.

Key features of the MIND framework include:

1. Modality-aware Dynamic Branching: This mechanism is crucial for generating scenario trees that effectively capture the evolution of different interaction modalities. By doing so, it minimizes the variation of interaction uncertainty across the planning horizon, which is vital for accurate decision-making.
2. Contingency Planning: The framework uses the scenario trees to support contingency planning, enabling it to execute clear and considerate maneuvers. This aspect is fundamental for addressing interaction uncertainties and adapting effectively to various interactions.
3. Experimental Validation: The paper demonstrates MIND's superiority through comprehensive experiments in closed-loop simulation environments. These simulations are based on real-world driving datasets, and the results indicate that MIND significantly outperforms existing strong baselines across diverse driving scenarios.

Overall, MIND presents a sophisticated solution for the challenges of autonomous navigation by integrating prediction and decision-making processes tailored to handling multimodal interactions in traffic.