Vehicular Influence Mapping for Conflict Avoidance Path Determination
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Solution Overview
Problem
Current vehicle imaging systems lack an effective method to integrate multiple sensor data for comprehensive collision avoidance and evasive steering, particularly in complex environmental conditions, leading to inadequate hazard detection and path planning.
Innovation Solution
A machine vision system utilizing two or more cameras to capture exterior images, process data, and create a 2D influence map to assess potential hazards, allowing for optimal path selection by weighing object influences based on speed, direction, and environmental factors, including legislative and ethical considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor data are integrated for comprehensive collision avoidance, then collision detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex sensor integration task into distinct functional modules: hazard detection module that identifies potential collisions, influence mapping module that weights objects by hazard level, and path planning module that generates avoidance routes. Each module processes specific aspects of the sensor data independently, improving detection accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces an influence map as an intermediary data structure that mediates between raw sensor data and path planning decisions. The influence map weights detected objects based on their hazard potential (speed, direction, distance) and provides a simplified representation that feeds into path planning, reducing the complexity of directly processing multiple raw sensor streams while maintaining comprehensive hazard assessment.
2Reliability
If real-time hazard assessment is performed, then collision avoidance effectiveness is improved, but processing time increases
Solution Approach 1:
The system performs partial hazard assessment by focusing computational resources on only the most hazardous objects identified in the influence map. Rather than fully processing all detected objects with equal detail, the system applies weighted influence values to prioritize computation on high-risk targets, maintaining effective collision avoidance while reducing overall processing time through selective detailed analysis.
3Manufacturing precision
If optimal path selection is determined through influence mapping, then maneuvering accuracy is improved, but computational load increases
Solution Approach 1:
The path planning function is segmented into influence map generation (weighting objects by hazard) and path optimization (selecting avoidance routes). This segmentation allows the system to pre-compute influence weights for multiple objects, then use these pre-computed weights to efficiently evaluate and compare different path options, improving path planning accuracy while reducing real-time computational load through staged processing.
Data Source
AI summary
A vehicular control system includes a plurality of sensors disposed at a vehicle, and a control having a data processor. Data captured by the sensors is processed at the control to determine presence of other vehicles and to determine respective speeds and directions of travel of the determined vehicles. The system determines a respective influence value for each of the determined vehicles based on a respective determined potential hazard. The system determines a plurality of potential paths of travel for the equipped vehicle to follow based on the determined respective influence values for the determined vehicles. The system selects, from the determined plurality of potential paths of travel, a path of travel for the equipped vehicle to follow that limits conflict with the determined vehicles. The system at least in part controls steering of the equipped vehicle to guide the equipped vehicle along the selected path of travel.