Cooperative Probabilistic Trajectory Forecasting under Occlusion
(2312.03296)Abstract
Perception and planning under occlusion is essential for safety-critical tasks. Occlusion-aware planning often requires communicating the information of the occluded object to the ego agent for safe navigation. However, communicating rich sensor information under adverse conditions during communication loss and limited bandwidth may not be always feasible. Further, in GPS denied environments and indoor navigation, localizing and sharing of occluded objects can be challenging. To overcome this, relative pose estimation between connected agents sharing a common field of view can be a computationally effective way of communicating information about surrounding objects. In this paper, we design an end-to-end network that cooperatively estimates the current states of occluded pedestrian in the reference frame of ego agent and then predicts the trajectory with safety guarantees. Experimentally, we show that the uncertainty-aware trajectory prediction of occluded pedestrian by the ego agent is almost similar to the ground truth trajectory assuming no occlusion. The current research holds promise for uncertainty-aware navigation among multiple connected agents under occlusion.
Overview
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The paper presents a method for forecasting the trajectory of occluded objects using relative pose estimation and probabilistic techniques.
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Current technology challenges include sensor occlusions, leading to potential forecasting inaccuracies.
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Communication for cooperative perception between agents is bandwidth-intensive, necessitating an efficient data-sharing method.
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The proposed architecture combines existing feature detection with probabilistic forecasting, using Bayesian inference for uncertainty quantification.
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Experiments validate the robustness of the methodology, with future work suggesting the incorporation of dynamic sensor data.
Introduction to Cooperative Probabilistic Trajectory Forecasting
When navigating environments where vision can be obstructed, reliable perception and forecasting of other agents' movements become crucial for safety-critical tasks. Traditional approaches rely on continuous detection, but obstacles can often obscure objects, complicating detection and forecasting efforts. Advancements in communication among connected agents offer new solutions for such occlusions. This paper explores an innovative method to address these challenges by combining relative pose estimation with probabilistic trajectory forecasting.
Embedded Challenges in Trajectory Prediction
Current State of Technology
Accurate object detection and intention forecasting have evolved with end-to-end pipelines converting raw sensor data into predictions. But occlusions still pose a problem, leading to potential inaccuracies and safety concerns.
Cooperative Perception and Communication Limits
While connected vehicles and infrastructure can share detailed sensor data, the process can be bandwidth-intensive and induce latency. A more efficient strategy involves sharing condensed yet crucial information among agents to maintain awareness without overburdening communication channels.
Methodology for Relative Pose Estimation
Feature Detection and Matching
For relative pose estimation between connected agents, key visual features must first be detected and matched across different perspectives. Efficient methods such as the ORB descriptor facilitate this process, which is followed by calculating the relative orientation through transformations derived from these feature correspondences.
Issues with Existing Approaches
Previous research has addressed cooperative perception and orientation estimation. Still, there is limited exploration of the application of these techniques to occlusion-aware prediction.
Advancements in Trajectory Forecasting
Conventional vs. Probabilistic Prediction
While standard models such as Kalman Filters have been used for predictions, occlusions introduce uncertainties that these models are not equipped to handle. The shift towards deep probabilistic learning methods enables models to provide distributions over future states rather than deterministic point estimates, thus enhancing robustness and safety.
Proposed Solution
The paper outlines a novel end-to-end architecture that integrates relative pose recovery with probabilistic trajectory forecasting, aimed at predicting an object's future states under occlusion. A novel aspect is the use of approximate Bayesian inference methods, such as Monte Carlo dropout, allowing for uncertainty quantification in the predicted states.
Achievements and Future Directions
Robustness to Occlusion and Practical Implications
The experiments show the strength of the proposed methodology across multiple scenarios, including partial and intermittent occlusions. Such capabilities hold significant potential for improving navigation and planning in cluttered environments involving multiple connected agents.
Limitations and Future Work
One limitation mentioned is the reliance on static sensors for the experiments. Future research could include integrating dynamic relative pose estimation into the proposed framework, which would broaden the applicability of these findings to more dynamic environments and contribute to the advancement of cooperative perception.
In conclusion, this research offers a forward-thinking approach to cooperative probabilistic trajectory forecasting under occlusion. The method provides a pathway towards more reliable and safe navigation in scenarios where direct perception is hindered, presenting a significant step forward in the domain of autonomous systems and connected vehicles.
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