Future Object Localization With Joint Uncertainty Prediction
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Solution Overview
Problem
Current vehicle control and navigation systems face challenges in predicting future motion of agents in dynamic environments, particularly due to the oversight of noise and uncertainty in data-driven models, which limits their practicality for autonomous driving and advanced driving assistance systems.
Innovation Solution
A system that utilizes vehicle sensors and a processor with a data receiving module, motion prediction module, and object localization module to generate joint uncertainty distributions and sample kinematic predictions, accounting for both host and proximate vehicle data to display predicted trajectories, incorporating aleatoric and epistemic uncertainty modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data-driven models are used for motion prediction, then prediction capability is improved, but uncertainty and noise are overlooked leading to reduced reliability
Solution Approach 1:
The patent transforms the prediction output from deterministic values to probability distributions by changing the parameter representation. Instead of predicting single future positions, the system predicts mean and variance parameters that characterize uncertainty, thereby maintaining productivity while improving reliability through explicit uncertainty quantification
Solution Approach 2:
The patent introduces an uncertainty modeling layer as an intermediary between data-driven prediction and decision-making. This intermediary component processes raw predictions through uncertainty-aware transformations, allowing the system to leverage data-driven capabilities while systematically accounting for reliability concerns through learned uncertainty parameters
2Device complexity
If single-modal future forecasting is performed, then computational simplicity is improved, but epistemic uncertainty is overlooked leading to reduced robustness
Solution Approach 1:
The patent segments the uncertainty into two distinct components: aleatoric uncertainty (noise inherent in the dataset) and epistemic uncertainty (uncertainty from limited observations). By segmenting the uncertainty modeling, the system maintains computational simplicity while improving robustness through targeted handling of different uncertainty types
Solution Approach 2:
The patent transforms the static single-modal prediction into a dynamic multi-modal distribution that adapts to different uncertainty conditions. The model dynamically adjusts prediction characteristics based on observed uncertainty levels, maintaining simplicity in normal conditions while automatically becoming more robust when noise or data scarcity is detected
3Measurement precision
If joint uncertainty distribution is generated for host and proximate vehicles, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent merges the uncertainty modeling of host and proximate vehicles into a single joint uncertainty distribution. By combining individual uncertainty models into a unified joint model, the system achieves improved prediction accuracy through correlated uncertainty handling while avoiding the computational complexity of separate independent processing pipelines
Data Source
AI summary
In one embodiment, a system includes one or more vehicle sensors for capturing host data and a processor having modules. The data receiving module identifies one or more proximate vehicles within the environment based on one or more of the host data and proximate data received from the one or more proximate vehicles. The motion prediction module generates a first joint uncertainty distribution based on an initial joint uncertainty model and a host model distribution. The motion prediction module also samples host kinematic predictions based on the first joint uncertainty distribution and the host data. The object localization module generates a second joint uncertainty distribution based on the initial joint uncertainty model and an object prediction model distribution. The object localization module also samples proximate kinematic predictions based on the second joint uncertainty distribution and the proximate data.