Scene Informer: Occlusion Inference and Trajectory Prediction in Partially Observable Environments

Główne pojęcia

The author introduces the Scene Informer, a unified approach for predicting observed agent trajectories and inferring occlusions in partially observable settings using a transformer-based framework.

Streszczenie

The content discusses the challenges faced by autonomous vehicles in navigating complex environments with occluded regions. It introduces the Scene Informer, a novel approach that predicts both observed agent trajectories and infers occlusions in partially observable environments. The framework utilizes a transformer to aggregate input modalities and facilitate selective queries on occlusions intersecting with the AV's planned path. By estimating occupancy probabilities and likely trajectories for occlusions, as well as forecasting motion for observed agents, the Scene Informer outperforms existing methods in occupancy prediction and trajectory prediction on the Waymo Open Motion Dataset.

The paper highlights the importance of reasoning about both visible and occluded parts of the environment for safe navigation through dynamic scenarios. It emphasizes the need to consider interactions between observed and occluded agents while processing vectorized inputs from perception frameworks. The proposed Scene Informer addresses limitations in prior work by providing an end-to-end solution that integrates occlusion inference with trajectory prediction.

Furthermore, experiments conducted on the Waymo Open Motion Dataset demonstrate the superior performance of Scene Informer compared to existing methods. The framework shows increased robustness to partial observability, showcasing its ability to predict future trajectories accurately even when dealing with occluded objects. Overall, Scene Informer offers a comprehensive solution for environment prediction in partially observable settings.

Statystyki

Our method consistently outperforms all baselines in both occupied and free accuracy metrics.
In limited observability setting, FDEmin of Scene Informer is 2.37 m less than that of vanilla trajectory prediction.
The number of trainable parameters is 11.3M.
We train each model with AdamW optimizer with linear learning rate warmup over first 10k gradient steps.
Experiments were carried out on NVIDIA TITAN RTX 24GB GPU with AMD Ryzen 3960X CPU.

Cytaty

"Our approach adapts its predictions realistically based on changes in behavior of observed agents."
"Scene Informer surpasses existing methods in both occupancy prediction and trajectory forecasting."
"The framework provides robustness to partial observability scenarios."

Kluczowe wnioski z

Scene Informer

by Bernard Lang... o arxiv.org 03-12-2024

https://arxiv.org/pdf/2309.13893.pdf

Głębsze pytania

How can incorporating interactions between occlusions enhance overall planning strategies?

Incorporating interactions between occlusions can significantly enhance overall planning strategies by providing a more comprehensive understanding of the environment. By considering potential occluded agents and their movements, planners can anticipate scenarios where hidden objects might impact the planned trajectory. This foresight allows for proactive decision-making to avoid collisions or navigate around potentially hazardous situations. Additionally, integrating occlusion inference with trajectory prediction enables planners to account for uncertainties caused by partial observability, leading to more robust and adaptive plans in dynamic environments.

What are potential drawbacks or criticisms towards integrating occlusion inference with trajectory prediction?

One potential drawback of integrating occlusion inference with trajectory prediction is the increased computational complexity associated with modeling interactions between observed and unobserved agents. The need to reason about multiple possible future trajectories for both visible and occluded entities could lead to higher processing requirements and longer computation times, which may not be feasible in real-time applications such as autonomous driving. Additionally, there could be challenges in accurately predicting the behavior of occluded agents due to limited information availability, leading to uncertainties that could affect the reliability of the predictions.

How might advancements in environment prediction impact real-world applications beyond autonomous vehicles?

Advancements in environment prediction have far-reaching implications beyond autonomous vehicles across various industries and domains. In fields like robotics, improved predictive capabilities can enhance robot navigation efficiency and safety by anticipating obstacles or changes in the environment proactively. In logistics and supply chain management, accurate predictions of traffic flow or warehouse operations can optimize route planning, resource allocation, and inventory management. Environmental monitoring systems could benefit from better predictive models for disaster response planning or climate change mitigation efforts.
Overall, advancements in environment prediction technologies have the potential to revolutionize decision-making processes across diverse sectors by enabling proactive responses based on anticipated future scenarios rather than reactive measures after events unfold.