Pedestrian Detection Using Logistic Probability and Point Cloud Segmentation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting pedestrian presence due to complex sensor-to-object and inter-object geometries, making it difficult to cleanly separate pedestrians from other objects in a scene, which affects safety and practicality.
Innovation Solution
A system that processes point cloud data using a logistic function and binary Bayes filter to determine the likelihood of pedestrian presence, assigning attribute values and adjusting vehicle operations, such as applying brakes, based on the probability of pedestrian presence exceeding a false alarm threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses traditional object detection methods to identify pedestrians, then the detection process is simple and fast, but the accuracy is insufficient due to complex sensor-to-object and inter-object geometries making it difficult to separate pedestrians from other objects
Solution Approach 1:
The patent segments the detection process into multiple independent components: point cloud data acquisition, object detection with bounding boxes, pedestrian presence classification using logistic functions, and probability aggregation. This segmentation allows each component to be optimized independently, improving overall detection accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary classification layer that processes the relationship between detected objects and potential pedestrians. The logistic function acts as a mediator that calculates pedestrian presence probability based on object attributes and spatial relationships, rather than directly detecting pedestrians in complex scenes. This intermediary step resolves the contradiction by adding computational sophistication without requiring complete scene reconstruction.
2Reliability
If the autonomous vehicle applies aggressive braking when detecting potential pedestrians, then safety is improved, but false alarms increase due to uncertain sensor data and geometric complexities
Solution Approach 1:
The patent implements feedback through probability aggregation and threshold-based decision making. The system continuously updates pedestrian presence probability based on multiple object detections and their attributes, then compares against a threshold to determine braking action. This feedback loop allows the system to distinguish between high-confidence and low-confidence detections, reducing false alarms while maintaining safety for true pedestrian detections.
Solution Approach 2:
The patent changes the parameter representation from binary detection (pedestrian/not pedestrian) to continuous probability values (0-1). By using logistic functions to model pedestrian presence probability and aggregating probabilities across multiple detections, the system can adjust its response based on confidence levels. This parameter transformation enables nuanced decision-making that balances safety and false alarm reduction.
3Measurement precision
If the system processes all point cloud data to ensure complete pedestrian detection, then detection thoroughness is improved, but processing time and computational load increase significantly
Solution Approach 1:
The patent extracts only the relevant subset of point cloud data by first detecting objects and their bounding boxes, then focusing computational resources on determining pedestrian presence within those specific regions. Rather than processing the entire point cloud, the system extracts and analyzes only the portions containing detected objects, significantly reducing processing time while maintaining detection completeness through the object-centric approach.
Solution Approach 2:
The patent performs preliminary object detection and bounding box generation before conducting pedestrian presence analysis. This preliminary action filters the point cloud data into manageable object-specific regions, allowing the subsequent pedestrian classification to operate on pre-segmented data. This two-stage approach maintains thoroughness by checking all detected objects while reducing computational load through early data filtering and organization.
Data Source
AI summary
A system may receive point cloud data that includes one or more data points associated with an object that was detected by sensors of an autonomous vehicle. The system may identify a subset of the point cloud data having data points that are associated with a likelihood of a pedestrian entering a scene with the object, determine a current probability value using a logistic function that is associated with the subset of the point cloud data, determine, based at least in part on the current probability value, a probability value representing a likelihood of the pedestrian actually being present for the subset of the point cloud data, determine whether the probability value exceeds a false alarm threshold value, and in response to the probability value exceeding the false alarm threshold value, assign data points of the subset an attribute value indicative of the pedestrian being present.