Dynamic Field of View Visibility Distance Calculation
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Solution Overview
Problem
Autonomous vehicles face challenges in determining visibility distances due to sensor limitations and occlusions, particularly in complex traffic scenarios, which can lead to blind spots and inaccurate decision-making during maneuvers like overtaking.
Innovation Solution
A method and system that determine a dynamic Field of View (FoV) by initializing sensor data, creating map polygons, and intersecting them with the FoV to identify visible areas and calculate visibility distances, effectively accounting for occlusions and road geometry to enhance decision-making safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are placed at certain positions with calculated yaw angles to overlap Fields of View, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor Fields of View into a unified dynamic FoV model that integrates detection data from multiple sensors. This merging approach maintains high detection reliability through overlapping coverage while reducing system complexity by creating a consolidated visibility representation rather than managing separate sensor systems.
Solution Approach 2:
The dynamic FoV model serves multiple functions: it consolidates data from multiple sensors, identifies visible and occluded areas, calculates visibility distances, and supports decision-making for maneuvers. This multi-functionality reduces the need for separate systems while maintaining comprehensive detection capabilities.
2Measurement precision
If high performance sensors are used to detect objects at high distances, then detection precision is improved, but cost increases
Solution Approach 1:
The patent implements a dynamic FoV model that adapts to changing environmental conditions, vehicle maneuvers, and occlusions. This dynamic approach maintains high detection precision by continuously updating visibility information based on current sensor data and scene understanding, rather than relying solely on static high-performance sensors.
Solution Approach 2:
The system changes parameters such as FoV boundaries, visibility distances, and detection thresholds based on dynamic conditions including occlusions, road geometry, and vehicle state. This allows the system to maintain high precision across varying conditions without requiring maximum-performance sensors in all scenarios.
3Loss of information
If sensors are used to detect objects in surroundings, then visibility information is obtained, but blind spots due to occlusions remain
Solution Approach 1:
The patent performs preliminary identification of occlusions and blind spots by analyzing the dynamic FoV and comparing it with the theoretical FoV. This preliminary action allows the system to anticipate areas where sensors cannot detect objects, enabling proactive decision-making that compensates for these limitations before maneuvers are executed.
Solution Approach 2:
The dynamic FoV model acts as an intermediary between raw sensor data and decision-making systems. It processes sensor information, identifies occluded areas, calculates visibility distances, and presents a comprehensive visibility picture that accounts for blind spots, thereby bridging the gap between limited sensor coverage and reliable decision-making.
4Measurement precision
If more high technology devices are added to the object detection system, then data precision is improved, but sufficiency for complex maneuver understanding may still be insufficient
Solution Approach 1:
The patent adds the dimension of spatial visibility analysis by creating a dynamic FoV model that maps visible and occluded areas in 2D/3D space. This dimensional approach transforms precise sensor data into a comprehensive spatial understanding of the environment, enabling reliable scenario assessment for complex maneuvers by visualizing what areas are actually observable.
Solution Approach 2:
The dynamic FoV model serves as an intermediary that synthesizes precise sensor data with environmental context including road geometry, occlusions, and vehicle state. This intermediary processing transforms raw high-precision data into meaningful scenario understanding that supports reliable decision-making for complex maneuvers.
Data Source
AI summary
A method and a system determine visibility distances based on a dynamic Field of View (FoV) of a subject vehicle. A vehicle incorporates the system. Map polygons are created, each of which determines edges of a road in a map of the surroundings of the subject vehicle. Further, visible areas are determined in the map by intersecting the map polygons with the dynamic FoV. Based on the visible areas, a visibility distance for the road is determined.