- The paper presents a self-supervised approach that uses an entropic optimal transport solver to generate pseudo motion labels from LiDAR point clouds.
- It employs cluster consistency and forward-backward regularization losses to improve prediction accuracy and reduce noise in pseudo labels.
- Empirical results on the nuScenes dataset show significant error reductions across static, slow, and fast speeds, highlighting practical improvements for autonomous driving.
Self-Supervised Motion Prediction Using LiDAR Point Clouds
The development of autonomous driving systems necessitates an understanding of dynamic environments, particularly through motion prediction in LiDAR point clouds. This paper introduces a novel approach for class-agnostic motion prediction using a self-supervised methodology that relies solely on point cloud data, addressing the limitations of previous methods such as PillarMotion that require image and point cloud pairs.
Methodology
The proposed approach leverages an optimal transport solver to generate coarse correspondences between point clouds across different timestamps. This is complemented by the introduction of self-supervised loss mechanisms. Specifically, the paper presents three key contributions:
- Pseudo Label Generation: The use of entropic optimal transport solves the correspondence problem by finding soft assignments between points, facilitating the generation of pseudo motion labels.
- Cluster and Consistency Losses: To improve prediction accuracy within rigid instances, a cluster consistency loss is applied, ensuring points grouped in the same cluster exhibit consistent motion. Moreover, forward and backward regularization losses are implemented to mitigate the influence of noise and low-quality pseudo labels, which are common challenges in such datasets.
- Motion and State Estimation: The approach integrates a moving statement mask to distinguish between static and dynamic points, further refining the motion predictions by reducing training bias from erroneously labeled static points.
Empirical Results
The proposed method was evaluated on the nuScenes dataset, where it demonstrated superior performance compared to state-of-the-art methods, including the self-supervised PillarMotion and certain fully supervised approaches. Notably, the method achieved error reductions of 44.9%, 38.5%, and 11.3% at static, slow, and fast speed levels, respectively.
Implications and Future Directions
This research provides valuable insights into self-supervised learning for motion prediction without reliance on additional modalities or pre-trained models. The use of optimal transport and novel loss structures presents a scalable solution that decreases the dependency on labeled data, making it a cost-effective option for real-world applications.
Future investigations could explore extending this framework to more complex scenes and integrating additional sensory data when available. Furthermore, enhancing pseudo label quality, particularly for fast-moving objects, remains a vital area for improvement. As autonomous systems evolve, refining self-supervised mechanisms to achieve high reliability in diverse environments will be critical.
This approach lays the groundwork for more sophisticated and data-efficient motion prediction models, crucial for advancing autonomous driving technologies.