A Training Dynamics-Aware Contrastive Learning Framework for Improving Long-Tail Trajectory Prediction in Autonomous Driving

Khái niệm cốt lõi

A training dynamics-aware contrastive learning framework that leverages rich contextual information and clustering based on model learning behaviors to improve accuracy and scene compliance on long-tail trajectory prediction samples.

Tóm tắt

The paper proposes a training dynamics-aware contrastive learning framework, TrACT, to address the long-tail issue in trajectory prediction for autonomous driving.
The key components are:

Constructing a dataset map by analyzing the training dynamics of each sample, including the final prediction error and the variance of errors across training epochs. This allows segmenting the dataset into four clusters of varying difficulty levels: easy, hard, confusing, and trained.

Computing prototypes by averaging the feature embeddings of samples within each cluster. These prototypes are then used in a prototypical contrastive learning framework to better organize the feature space and distinguish between samples of different difficulty levels.

Evaluations on the nuScenes and ETH-UCY datasets show that TrACT achieves state-of-the-art performance on the top 1-5% most challenging samples, improving accuracy by up to 22.48% on KDE-NLL and 14.24% on minFDE compared to prior methods. TrACT also generates more scene-compliant trajectories, reducing off-road rates by up to 13.11%.

Additional experiments demonstrate the benefit of the dataset map in reducing training bias towards easy samples, leading to more balanced performance on the full dataset without explicit contrastive learning.

Thống kê

The top 1% most challenging samples on nuScenes have a minFDE of 4.43 for the baseline Traj++ EWTA model, which is reduced to 2.65 with TrACT.
On the top 5% challenging samples on nuScenes, the KDE-NLL metric is improved from 7.21 for the baseline to 4.62 with TrACT.
On the ETH-UCY dataset, TrACT reduces the minFDE on the top 1% challenging samples from 2.54 for the baseline to 2.00.

Trích dẫn

"TrACT exhibits a significant improvement across most challenging subsets on both [scene compliance] metrics by up to 13.11% and 22.22% on HOR and SOR, respectively."
"By removing a portion of the easy samples, we reduce the overall data bias, as the easy samples are more frequent in the dataset. Hence, the model would focus more on challenging scenarios, and as a result, achieve a more balanced performance without the use of an explicit contrastive objective."

Thông tin chi tiết chính được chắt lọc từ

TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction

by Junrui Zhang... lúc arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12538.pdf

Yêu cầu sâu hơn

How can the training dynamics information be further leveraged to guide the model architecture design or the training process for improved long-tail performance

To further leverage training dynamics information for improved long-tail performance, the model architecture design and training process can be adapted in several ways. Firstly, incorporating dynamic weighting mechanisms based on the training dynamics information can help prioritize challenging samples during training. By assigning higher weights to samples with fluctuating errors or slow convergence, the model can focus more on learning from these challenging scenarios. Additionally, adaptive learning rate schedules that adjust based on the training dynamics can help the model navigate through difficult samples more effectively. For example, decreasing the learning rate for samples with high variance in errors can allow the model to converge more steadily on these challenging instances. Moreover, introducing curriculum learning strategies that gradually expose the model to increasingly difficult samples based on their training dynamics can help the model learn more robustly across the long-tail distribution.

What other contextual information beyond map layout and agent interactions could be incorporated to better characterize the challenging scenarios

Beyond map layout and agent interactions, other contextual information that could be incorporated to better characterize challenging scenarios includes environmental conditions, such as weather patterns, lighting conditions, and road surface conditions. These factors can significantly impact the behavior of agents in the scene and influence trajectory predictions. Additionally, incorporating semantic information about the scene, such as the presence of traffic signs, signals, and road markings, can provide valuable context for understanding agent behaviors. Social cues, such as gestures, eye contact, and verbal communication between agents, can also be crucial contextual information for predicting complex interactions in challenging scenarios. By integrating a broader range of contextual features into the model, it can gain a more comprehensive understanding of the scene dynamics and make more informed predictions in long-tail scenarios.

Can the proposed dataset mapping and clustering approach be applied to other perception tasks beyond trajectory prediction to address long-tail issues

The proposed dataset mapping and clustering approach can indeed be applied to other perception tasks beyond trajectory prediction to address long-tail issues. Tasks such as object detection, semantic segmentation, and action recognition often face similar challenges with imbalanced datasets and long-tail distributions. By leveraging training dynamics information to cluster samples based on their difficulty levels, models in these tasks can focus on learning from challenging instances and improve performance on rare classes or scenarios. For instance, in object detection, the model can benefit from prioritizing training on challenging object classes that are underrepresented in the dataset. By adapting the dataset mapping and clustering approach to different perception tasks, models can achieve more balanced performance across the entire distribution of classes or scenarios, leading to more robust and accurate predictions.