Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders (2308.09882v1)

Abstract: This study explores the application of self-supervised learning (SSL) to the task of motion forecasting, an area that has not yet been extensively investigated despite the widespread success of SSL in computer vision and natural language processing. To address this gap, we introduce Forecast-MAE, an extension of the mask autoencoders framework that is specifically designed for self-supervised learning of the motion forecasting task. Our approach includes a novel masking strategy that leverages the strong interconnections between agents' trajectories and road networks, involving complementary masking of agents' future or history trajectories and random masking of lane segments. Our experiments on the challenging Argoverse 2 motion forecasting benchmark show that Forecast-MAE, which utilizes standard Transformer blocks with minimal inductive bias, achieves competitive performance compared to state-of-the-art methods that rely on supervised learning and sophisticated designs. Moreover, it outperforms the previous self-supervised learning method by a significant margin. Code is available at https://github.com/jchengai/forecast-mae.

• The paper presents Forecast-MAE as the first masked autoencoding framework for self-supervised motion forecasting, significantly outperforming scratch-trained models.
• It employs a novel complementary and random masking strategy to effectively learn bidirectional motion connections and cross-modal relationships.
• Empirical results on the Argoverse 2 benchmark demonstrate that Forecast-MAE achieves superior ADE and FDE metrics, underscoring its potential for autonomous driving applications.

Forecast-MAE: Advancing Self-Supervised Motion Forecasting

This paper, entitled "Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders," introduces an innovative approach to the motion forecasting task within self-supervised learning frameworks. Despite the proven efficacy of SSL in domains like computer vision and NLP, its application to motion forecasting remains largely unexplored. The authors present Forecast-MAE, which leverages the masked autoencoder framework to address this gap. The method incorporates a novel masking strategy that involves complementary masking of agents' future or history trajectories and random masking of lane segments.

Core Contributions and Findings

The authors conducted experiments on the Argoverse 2 motion forecasting benchmark and demonstrated that Forecast-MAE achieves competitive performance against state-of-the-art supervised methods while outperforming existing self-supervised approaches. Key contributions include:

1. Masked Autoencoding for Motion Forecasting: Forecast-MAE is the first masked autoencoding framework proposed for self-supervised learning in motion forecasting, significantly enhancing performance with pre-training compared to training from scratch.
2. Innovative Masking Scheme: The proposed complementary and random masking strategy enables effective learning of bidirectional motion connections and cross-modal relationships within a single reconstruction task. This method shows particular strength in generating accurate predictions of the most likely future trajectories.
3. Empirical Advancements: The approach outperformed traditional SSL methods such as SSL-Lanes by a notable margin, which aligns with the authors' claim that carefully designed pretext tasks can indeed enhance performance.
4. Minimal Inductive Bias: Using standard Transformer blocks, the approach eschews complex, hand-crafted model structures, relying on the ability of the masking strategies to guide the learning process toward effective motion forecasting.

Numerical Results and Comparison

In detailed evaluations on the challenging Argoverse 2 dataset, Forecast-MAE was shown to provide strong numerical performance improvements over baseline approaches trained from scratch and SSL-Lanes. For instance, Forecast-MAE achieved better minima ADE$_1$ and FDE$_1$ metrics, surpassing even ensemble models in some aspects. More impressively, it demonstrated that self-supervised pre-training could learn more generalizable features, as evidenced by its favorable performance even when trained and tested on different data distributions.

Implications and Future Directions

The implications of this research extend both theoretically and practically. Theoretically, Forecast-MAE provides a viable pathway for SSL in domains traditionally dominated by supervised learning, like motion forecasting for autonomous driving. Practically, its performance in predicting the most likely trajectories suggests potential applications in real-world autonomous systems, contributing to safer and more robust vehicle navigation.

Future developments may focus on further scaling the approach and exploring its transfer learning or few-shot learning capabilities, given the relatively smaller size of public motion forecasting datasets compared to other domains. The scalability of the approach with increased data and model capacity could potentially amplify its applicability in large-scale, real-world situations.

Additionally, incorporating inductive biases such as local attention mechanisms or relative positional encoding, alongside novel data augmentation strategies, may further enhance Forecast-MAE in terms of both computational efficiency and prediction accuracy. These potential innovations represent promising avenues for subsequent research endeavors in the field.

In conclusion, Forecast-MAE offers a compelling case for the viability of self-supervised learning in motion forecasting and sets a foundation for future explorations into simpler, efficient, and more capable motion prediction models.