Object Uncertainty Heatmaps for Autonomous Vehicle Collision Prediction
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Solution Overview
Problem
Autonomous vehicles face computational challenges in safely navigating through environments due to the high cost of maintaining and updating uncertainty associated with predicted object behavior, especially in crowded areas where millions of operations per second are required for collision avoidance.
Innovation Solution
The system employs an uncertainty model represented as a heatmap, fusing data from multiple objects into a single model, using symmetric and rotationally invariant disks to simplify collision checks, and enlarging objects to reduce complexity, allowing for faster and more efficient operational decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system maintains and updates uncertainty associated with each detected object individually, then collision detection accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent combines multiple individual object uncertainty models into a single aggregated uncertainty model. Instead of maintaining separate uncertainty representations for each detected object, the system merges them into one unified model that captures the collective uncertainty of all objects in the environment. This dramatically reduces computational complexity while preserving the essential uncertainty information needed for safe navigation.
Solution Approach 2:
The aggregated uncertainty model serves multiple functions simultaneously: it represents uncertainty for collision detection, defines drivable areas, and provides safety margins for multiple objects. This single universal model replaces what would otherwise require multiple separate processing streams, improving efficiency without sacrificing detection accuracy.
2Measurement precision
If the system processes each object separately with full uncertainty calculations, then prediction accuracy is improved, but processing speed decreases
Solution Approach 1:
The system merges individual object predictions and their associated uncertainties into a single aggregated model. This combining process maintains the predictive accuracy needed for safety-critical applications while enabling parallel processing and reducing the overall computational burden, thereby improving processing speed.
3Reliability
If the system uses detailed object representations for collision checks, then detection accuracy is improved, but computational cost increases
Solution Approach 1:
The patent merges detailed individual object representations into a consolidated uncertainty model that retains the necessary detection accuracy. By aggregating the uncertainty information from multiple objects into a single model, the system reduces the computational cost of collision checks while maintaining reliable detection capabilities.
Data Source
AI summary
Techniques for representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.