Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent (2311.18307v1)

Abstract: Adept traffic models are critical to both planning and closed-loop simulation for autonomous vehicles (AV), and key design objectives include accuracy, diverse multimodal behaviors, interpretability, and downstream compatibility. Recently, with the advent of LLMs, an additional desirable feature for traffic models is LLM compatibility. We present Categorical Traffic Transformer (CTT), a traffic model that outputs both continuous trajectory predictions and tokenized categorical predictions (lane modes, homotopies, etc.). The most outstanding feature of CTT is its fully interpretable latent space, which enables direct supervision of the latent variable from the ground truth during training and avoids mode collapse completely. As a result, CTT can generate diverse behaviors conditioned on different latent modes with semantic meanings while beating SOTA on prediction accuracy. In addition, CTT's ability to input and output tokens enables integration with LLMs for common-sense reasoning and zero-shot generalization.

Overview of the Categorical Traffic Transformer

The Categorical Traffic Transformer (CTT) presents a novel framework for behavior prediction in autonomous vehicles (AVs) by addressing significant gaps in existing traffic models, specifically those related to interpretability, diversity, and integration with LLMs. Traditional traffic models often rely on noninterpretable latent spaces, leading to challenges like mode collapse and limited downstream applicability. CTT's approach of utilizing an interpretable tokenized latent space allows for a more robust and diverse prediction of traffic behaviors, improving both accuracy and practical utility in AV systems.

Core Contributions and Methodology

1. Interpretable Latent Space:
   • CTT introduces a fully interpretable latent space by leveraging categorical representations. The latent space comprises two key components: agent-to-lane (a2l) modes and agent-to-agent (a2a) interaction modes. This setup allows for direct supervision of the latent variables during training, which significantly mitigates the issue of mode collapse commonly encountered in end-to-end learning approaches.
   • The a2l modes classify the positional relationships of agents relative to lanes, while a2a modes classify interaction types between agents using homotopy-based methods. This structured representation enhances the model's ability to generate predictions that align well with human-understandable semantics.
2. Integration with LLMs:
   • By outputting tokenized categorical predictions, CTT can seamlessly interface with LLMs. This feature leverages the cognitive reasoning capabilities of LLMs to provide common-sense insights and zero-shot generalization, extending the remit of traffic models beyond traditional trajectory predictions.
   • A practical example is given where CTT works in conjunction with GPT-4, illustrating how CTT's detailed semantic predictions can guide LLMs to refine and validate high-level driving decisions, ultimately enhancing AV decision-making processes.
3. Flexible Transformer and Graph Neural Network (GNN) Architecture:
   • CTT employs a flexible architecture that combines transformers with GNNs to handle tokenized edges and ensure consistency under coordinate transformations. This configuration allows for efficient multi-axis attention and equivariant processing, crucial for the complex spatial-temporal nature of traffic scenarios.
4. Evaluation on Public Datasets:
   • CTT was evaluated on multiple public datasets, including nuScenes, nuPlan, and Waymo Open Dataset (WOMD), showing superior performance in prediction accuracy and scene consistency metrics compared to state-of-the-art models like AgentFormer and scene-centric transformers.
   • In particular, CTT demonstrated significant improvements in prediction accuracy (minADE and minFDE) and lower collision rates, underscoring its practical efficacy in real-world scenarios.

Implications and Future Directions

The theoretical and practical implications of CTT are extensive:

• Theoretical Implications:
  • The structured latent space with direct supervision reveals a pathway to more robust and interpretable multimodal behavior prediction. This directly addresses limitations in current AV planning systems where opaque latent representations often lead to suboptimal downstream planning.
  • The integration capacity with LLMs opens avenues for incorporating broader cognitive reasoning into AV systems, potentially transforming how autonomous systems conceptualize and react to complex road environments.
• Practical Implications:
  • Improved prediction diversity and accuracy enhance the reliability of AV systems in dynamic traffic environments. The strong numerical results on public datasets suggest that real-world deployment of CTT could lead to increased safety and efficiency.
  • The detailed semantic understanding provided by a2l and a2a modes can significantly streamline the communication between AV components and external systems or frameworks, facilitating smarter, more context-aware interactions.

Speculative Future Developments

Looking forward, there are several promising directions for extending the work on CTT:

• Enhanced LLM Integration:
  • Developing more sophisticated pipelines for seamless integration of traffic models with LLMs, particularly focusing on real-time decision support and behavior planning, could yield substantial improvements in AV performance.
• Adaptive and Context-Aware Models:
  • Building on the structured latent space concept, future models might adaptively adjust mode granularity based on situational context, enhancing adaptability and expressiveness without significantly increasing computational complexity.
• Domain-Specific Refinements:
  • Further exploring domain-specific custom edge functions and embeddings tailored to unique traffic environments (e.g., urban vs. highway settings) could improve model robustness and accuracy across varied scenarios.
• Real-World Trials and Applications:
  • Extending validation through extensive real-world trials, possibly in collaboration with AV manufacturers, would provide invaluable insights and validation, ensuring the model's practical applicability in diverse and dynamic real-world conditions.

In conclusion, CTT marks a significant advancement in traffic modeling for autonomous vehicles, addressing critical challenges through interpretable, diverse, and integratable behavior prediction mechanisms. Its robust performance metrics and potential for future enhancement position it as a pivotal development in the ongoing evolution of autonomous driving technologies.