GPD-1: Generative Pre-training for Driving (2412.08643v1)

Abstract: Modeling the evolutions of driving scenarios is important for the evaluation and decision-making of autonomous driving systems. Most existing methods focus on one aspect of scene evolution such as map generation, motion prediction, and trajectory planning. In this paper, we propose a unified Generative Pre-training for Driving (GPD-1) model to accomplish all these tasks altogether without additional fine-tuning. We represent each scene with ego, agent, and map tokens and formulate autonomous driving as a unified token generation problem. We adopt the autoregressive transformer architecture and use a scene-level attention mask to enable intra-scene bi-directional interactions. For the ego and agent tokens, we propose a hierarchical positional tokenizer to effectively encode both 2D positions and headings. For the map tokens, we train a map vector-quantized autoencoder to efficiently compress ego-centric semantic maps into discrete tokens. We pre-train our GPD-1 on the large-scale nuPlan dataset and conduct extensive experiments to evaluate its effectiveness. With different prompts, our GPD-1 successfully generalizes to various tasks without finetuning, including scene generation, traffic simulation, closed-loop simulation, map prediction, and motion planning. Code: https://github.com/wzzheng/GPD.

• The paper proposes GPD-1, a novel autoregressive transformer architecture that utilizes hierarchical and VQ-VAE tokenization for unified autonomous driving scene modeling.
• Empirical evaluation on nuPlan shows GPD-1 effectively performs scene generation, traffic simulation, and motion planning with competitive metrics.
• GPD-1 suggests generative pre-training can unify diverse autonomous driving tasks, enhancing robustness and reducing the need for extensive task-specific tuning.

Analyzing the GPD-1 Model: A Unified Approach for Autonomous Driving

The paper "GPD-1: Generative Pre-training for Driving" introduces a novel perspective on modeling autonomous driving scenarios, tackling the inherent complexity by leveraging a generative pre-training paradigm. The GPD-1 model proposes a unified framework to address the multifaceted requirements of autonomous driving systems, encompassing tasks such as scene generation, traffic simulation, map prediction, and motion planning without necessitating further fine-tuning.

Methodology Overview

Central to the GPD-1 approach is the incorporation of a generative modeling framework into the autonomous driving domain, facilitated by a novel autoregressive transformer architecture. The model incorporates an essential autoregressive transformer with a scene-level attention mask ensuring intra-scene interactions, enhancing its ability to model both spatial and temporal dynamics essential for driving tasks. The components and innovations of this setup include:

• Tokenization Framework: The paper introduces a complex tokenization strategy using a hierarchical positional tokenizer for agent tokens, which encodes positions and headings into discrete tokens. An additional vector-quantized autoencoder (VQ-VAE) compresses the map data into manageable tokens, thus mitigating the challenges associated with continuous data prediction and augmenting the model's generalization capabilities.
• Generative Architecture: GPD-1 employs a transformer architecture to facilitate cohesive scene modeling. The structured use of tokens (ego, agent, and map) represents a unified approach to scene evolution, with learnable spatial and temporal embeddings encoding contextual information for effective sequence modeling.
• Training Paradigm: The system's training is bifurcated into stages focused on generating a robust structure, achieving a balance between comprehensive modeling of driving scenarios and task-specific precision through modest fine-tuning.

Empirical Performance Evaluation

The GPD-1 model is rigorously evaluated against the nuPlan benchmark dataset, emphasizing its adaptability and robustness across various autonomous driving tasks:

• Scene Generation: The model autonomously initializes and evolves complete driving scenes, maintaining competitive metrics of Average Displacement Error (ADE) and Final Displacement Error (FDE) over extended prediction horizons (up to 8 seconds).
• Traffic Simulation and Closed-loop Applications: By incorporating dynamic agent behavior and reaction to the ego trajectory, GPD-1 demonstrates its capability to simulate real-world driving environments effectively, sustaining low collision rates and reliable trajectory predictions.
• Motion Planning: The model shows notable performance in generating planned trajectories that align closely with realistic driving norms, confirming its competency in tackling complex decision-making scenarios inherent in autonomous driving.

Implications and Future Research Directions

The GPD-1 model posits significant implications for both theoretical advancements and practical implementations in autonomous driving systems. Its ability to unify diverse tasks into a single framework paves the way for more standardized approaches in the field. The model suggests that such generative pre-training architectures may provide foundational elements for future developments, enhancing robustness, reducing the need for extensive hand-tuning, and accommodating a broad spectrum of autonomous driving scenarios.

The extensive empirical evaluation indicates the potential for generative models in improving simulation efficiency and scalability of autonomous driving systems. Future research could explore the integration of hybrid architectures to further streamline the transition from model training to real-world adaptation, addressing residual challenges such as unseen obstacle anticipation and dynamic environmental interaction modeling.

In conclusion, the GPD-1 model presents a compelling vision for autonomous vehicle navigation by integrating a comprehensive generative framework, marking a step toward more adaptable and resilient autonomous driving technologies.