Mobile Collision Prediction Using Superimposed Probability Cones

Each mobile device independently performs collision risk assessment using its own sensors and processing capabilities, without requiring centralized server processing. The device self-generates movement profiles, probability profiles, and collision risk assessments locally, enabling rapid response while distributing computational complexity across multiple devices rather than concentrating it in a central system.

The system combines data from multiple sensor types including LIDAR, cameras, and other detection devices to create comprehensive movement profiles. By merging information from different sensor modalities, the system achieves high measurement precision while distributing the functional requirements across multiple components rather than relying on a single complex system.

The system calculates probability profiles for multiple potential collision scenarios simultaneously by superimposing movement profiles of different traffic participants. This partial action approach computes only the necessary probability assessments for relevant collision risks rather than exhaustive analysis of all possible scenarios, achieving high prediction accuracy while managing computational energy consumption.