Improve the Generalization of Autonomous Driving Diffusion with Additional Trajectories
- 講者藍立呈 先生 (Computer Science, University of California, Los Angeles)
邀請人：吳廸融 - 時間2024-05-03 (Fri.) 14:30 ~ 16:30
- 地點資訊所舊館108室
摘要
In recent advancements within the domain of autonomous driving, the diffusion model has emerged as a promising approach due to its proficiency in handling multi-solution problems, which are characteristic of autonomous navigation scenarios. This paper explores the use of diffusion models to generate diverse sets of trajectories for autonomous vehicles. Autonomous driving inherently requires a solution space with multiple viable trajectories to enhance the decision-making flexibility of the navigation planner, which selects the optimal path based on real-time environmental data and objectives.
Previous studies have successfully utilized diffusion models for trajectory generation, capitalizing on their ability to explore a broad range of solutions effectively. However, while these models are innovative, the diversity of the resulting trajectories often remains constrained by the model’s training and inherent design limitations. Traditionally, diverse trajectories have been generated through various conventional methods outside the scope of machine learning.
In this research, we propose a novel integration method where trajectories generated by traditional techniques are used as additional training data for diffusion models. We hypothesize and subsequently demonstrate that incorporating these traditionally-generated trajectories can significantly enhance the diversity of the trajectory sets produced by diffusion models. Through comprehensive experiments and validations, we show that our enhanced diffusion model not only exhibits improved performance in generating viable trajectory options but also shows superior generalization in diverse operational scenarios.
Ultimately, this work not only underscores the importance of trajectory diversity for effective autonomous navigation but also sets a precedent for using hybrid data-enhancement techniques to refine the capabilities of machine learning models in real-world applications. The implications of this are profound, paving the way for more robust and adaptable autonomous driving technologies.
Previous studies have successfully utilized diffusion models for trajectory generation, capitalizing on their ability to explore a broad range of solutions effectively. However, while these models are innovative, the diversity of the resulting trajectories often remains constrained by the model’s training and inherent design limitations. Traditionally, diverse trajectories have been generated through various conventional methods outside the scope of machine learning.
In this research, we propose a novel integration method where trajectories generated by traditional techniques are used as additional training data for diffusion models. We hypothesize and subsequently demonstrate that incorporating these traditionally-generated trajectories can significantly enhance the diversity of the trajectory sets produced by diffusion models. Through comprehensive experiments and validations, we show that our enhanced diffusion model not only exhibits improved performance in generating viable trajectory options but also shows superior generalization in diverse operational scenarios.
Ultimately, this work not only underscores the importance of trajectory diversity for effective autonomous navigation but also sets a precedent for using hybrid data-enhancement techniques to refine the capabilities of machine learning models in real-world applications. The implications of this are profound, paving the way for more robust and adaptable autonomous driving technologies.
BIO
https://lan-lc.github.io/