TNT: Target-driveN Trajectory Prediction (2008.08294v2)

Abstract: Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and intrinsically multimodal. Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states. This leads to our target-driven trajectory prediction (TNT) framework. TNT has three stages which are trained end-to-end. It first predicts an agent's potential target states $T$ steps into the future, by encoding its interactions with the environment and the other agents. TNT then generates trajectory state sequences conditioned on targets. A final stage estimates trajectory likelihoods and a final compact set of trajectory predictions is selected. This is in contrast to previous work which models agent intents as latent variables, and relies on test-time sampling to generate diverse trajectories. We benchmark TNT on trajectory prediction of vehicles and pedestrians, where we outperform state-of-the-art on Argoverse Forecasting, INTERACTION, Stanford Drone and an in-house Pedestrian-at-Intersection dataset.

• The paper presents the TNT framework, which departs from latent variable models by predicting discrete target locations to capture multimodal trajectory futures.
• It employs a three-stage methodology: target prediction, target-conditioned motion estimation, and trajectory scoring using a non-maximum suppression approach.
• Evaluations demonstrate enhanced accuracy with metrics like a minFDE of 1.29 meters on Argoverse, outperforming models such as DESIRE and MultiPath.

Target-driven Trajectory Prediction: Understanding TNT

Introduction

Trajectory prediction for moving agents is a critical task in fields such as autonomous driving. The paper "TNT: Target-driveN Trajectory Prediction" introduces a new framework, TNT, which departs from traditional models by emphasizing interpretable target-driven prediction over latent variable models. This approach addresses the inherent uncertainty and multimodality in predicting future states of agents in dynamic environments.

Methodology

The TNT framework comprises three stages: target prediction, target-conditioned motion estimation, and trajectory scoring and selection.

1. Target Prediction: TNT hypothesizes that predicting a discrete set of potential future locations, or targets, can effectively capture the multimodal nature of future trajectories. Targets are quantized spatial locations grounded in physical entities, allowing for more interpretable predictions than latent variables. The model then predicts a distribution over these targets using a classification approach based on spatial context.
2. Target-conditioned Motion Estimation: Once potential target states are identified, the TNT framework conditions trajectory generation on these targets. Unlike approaches that rely on sampling from probabilistic models during inference, the estimated trajectories in TNT are derived through a deterministic model that assumes minimal inherent uncertainty once a target is selected.
3. Trajectory Scoring and Selection: The final stage aims to present a concise set of probable trajectories by scoring each trajectory based on its likelihood. A non-maximum suppression-like algorithm finds a diverse set of high-likelihood predictions, ensuring efficiency and robustness in prediction results.

Numerical Results and Evaluation

TNT was evaluated on several datasets, including the Argoverse Forecasting, INTERACTION, Stanford Drone, and an in-house Pedestrian-at-Intersection dataset. The framework showcased improved performance metrics like minimum Final Displacement Error (minFDE) and minimum Average Displacement Error (minADE) compared to state-of-the-art models such as DESIRE and MultiPath. On the Argoverse dataset, for instance, TNT achieved a minFDE of 1.29 meters, outperforming other models significantly.

Implications and Future Directions

The TNT framework offers several advantages over prior techniques. Its hierarchical structure aligns with traditional path planning systems in robotics, facilitating ease of integration and interpretability. The target-based approach provides a more intuitive understanding of the likely future behavior of agents, which is vital for applications requiring transparency and interpretability, such as those in autonomous vehicles.

Further exploration could involve extending TNT's ability for longer-term predictions via iterative target and trajectory estimation, potentially incorporating reinforcement learning techniques for improved efficacy. Moreover, adopting hybrid models that integrate map representations with imagery data could enhance generalization across diverse environments.

Conclusion

Overall, the TNT framework represents a sophisticated step forward in trajectory prediction, providing a clear and effective means of modeling agent motion with an emphasis on interpretability and reduced computational complexity. Its robust performance across multiple datasets highlights its potential as a valuable tool in real-world applications where accurate motion forecasting is crucial.