Obstacle Classification via Observer Confirmation Density
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Solution Overview
Problem
Existing driver assistance systems face challenges in accurately classifying surrounding objects as obstacles or non-obstacles, particularly distinguishing between vehicles and non-threatening structures like bridges or signs, leading to potential erroneous interventions.
Innovation Solution
A method utilizing environment detection sensors, such as radar, lidar, or camera sensors, to track objects and classify them based on movement patterns, confirmation density, shape, and size, with multiple observers confirming classifications to differentiate between obstacles and non-obstacles by analyzing the average maximum confirmation density and shape recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If environment detection sensors are used to detect stationary and moving objects, then the detection capability is improved, but the risk of erroneous classification increases
Solution Approach 1:
The system uses feedback loops where classification results are continuously refined based on multiple observer inputs and confirmation densities. The classification is adjusted based on whether objects are confirmed as obstacles or non-obstacles by multiple observers, creating a self-correcting mechanism that improves reliability while maintaining detection precision.
Solution Approach 2:
Multiple observers act as intermediaries between the raw sensor data and the final classification decision. These observers include stationary object observers, moving object observers, and confirmation density observers that mediate the classification process by evaluating different aspects of object characteristics and cross-validating results.
2Reliability
If multiple observers are used to classify objects, then the classification reliability is improved, but the system complexity increases
Solution Approach 1:
The classification system is segmented into multiple specialized observers, each responsible for specific aspects of object detection. This includes stationary object observers for detecting non-moving objects, moving object observers for tracking moving objects, and confirmation density observers for evaluating classification confidence. Each observer operates independently with specialized algorithms, making the overall complex system manageable through modular design.
Solution Approach 2:
The multiple observers serve universal functions by collectively performing detection, tracking, classification, and validation tasks. Rather than having separate dedicated systems for each function, the observers are multi-functional components that can adapt their analysis based on object characteristics, reducing overall system complexity while maintaining high reliability.
3Reliability
If confirmation density evaluation is performed, then false obstacle recognition is reduced, but the processing time increases
Solution Approach 1:
The system applies partial confirmation density evaluation by focusing computational resources on objects that require classification verification. Rather than performing exhaustive analysis on all detected objects, the system selectively applies confirmation density calculations to objects near decision boundaries or with ambiguous characteristics, reducing processing time while maintaining high reliability for critical classifications.
Solution Approach 2:
The system dynamically adjusts confirmation density thresholds based on contextual factors such as object size, speed, and location. By changing the parameter requirements for confirmation based on the specific object being evaluated, the system reduces processing time for obvious cases while maintaining high reliability for ambiguous cases that require more thorough analysis.
Data Source
AI summary
The invention relates to a method for classifying objects into obstacles and non-obstacles for a vehicle. The vehicle comprises a vicinity detection sensor which detects stationary and moving objects in a scene in front of a vehicle, and, if necessary, tracks the progression of the movements of the objects. In said method, one or more observers are used, wherein an observer classifies an object according to predefined characteristics and contributes to an overall classification result in case several observers are used. An observer detects the progression of the movements of vehicles in a vicinity of at least one stationary object and classifies the stationary object in accordance therewith.