Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation

TL;DR

This paper introduces a multi-guided diffusion model enhanced with Direct Preference Optimization for generating realistic, diverse, and controllable traffic scenarios that adhere closely to traffic priors and rules.

Contribution

It proposes a novel multi-guided diffusion framework with a new training strategy and fine-tuning via DPO to improve traffic scenario realism and adherence to priors.

Findings

Achieves better balance of realism and diversity in generated scenarios.

Effectively incorporates multiple guides without deviating from traffic priors.

Provides a strong baseline on the nuScenes dataset.

Abstract

Diffusion-based models are recognized for their effectiveness in using real-world driving data to generate realistic and diverse traffic scenarios. These models employ guided sampling to incorporate specific traffic preferences and enhance scenario realism. However, guiding the sampling process to conform to traffic rules and preferences can result in deviations from real-world traffic priors and potentially leading to unrealistic behaviors. To address this challenge, we introduce a multi-guided diffusion model that utilizes a novel training strategy to closely adhere to traffic priors, even when employing various combinations of guides. This model adopts a multi-task learning framework, enabling a single diffusion model to process various guide inputs. For increased guided sampling precision, our model is fine-tuned using the Direct Preference Optimization (DPO) algorithm. This algorithm optimizes preferences based on guide scores, effectively navigating the complexities and challenges associated with the expensive and often non-differentiable gradient calculations during the guided sampling fine-tuning process. Evaluated using the nuScenes dataset our model provides a strong baseline for balancing realism, diversity and controllability in the traffic scenario generation.