Static Obstacle Detection via Multi-Frame Location Consistency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in distinguishing between static and dynamic objects, leading to false positive detections and improper reactions, especially when dynamic obstacles initially appear static and then move.
Innovation Solution
The method involves comparing sensor data frames to determine if an object's location and presence are consistent across multiple frames within a predetermined time window, using a threshold number of detections and time threshold to differentiate between static and dynamic objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fusion based practices combine data over time to track objects, then object tracking capability is improved, but the system cannot effectively distinguish between static and dynamic objects when a dynamic obstacle first appears static and then moves
Solution Approach 1:
The system dynamically adjusts its detection approach by initially treating objects as static and then re-evaluating them as dynamic if movement is detected. This allows the system to maintain reliable tracking while accurately distinguishing between static and dynamic objects, resolving the contradiction by making the detection methodology adaptive rather than fixed.
Solution Approach 2:
The system performs preliminary detection of objects as static entities before confirming their dynamic nature. By establishing a baseline detection and then monitoring for movement, the system improves both tracking reliability and classification accuracy, as objects are not immediately dismissed as false positives but rather tracked with appropriate classification.
2Measurement precision
If the system uses multiple frame comparisons with threshold detections, then false positive detections are reduced, but computational intensity increases
Solution Approach 1:
The system applies partial action by using a threshold-based approach where only detections meeting the threshold criteria undergo full multi-frame verification. This reduces computational intensity by avoiding exhaustive analysis of every detection while maintaining high detection accuracy through selective rigorous verification of promising candidates.
3Reliability
If the system tracks every detected object, then object presence is monitored, but the system cannot differentiate between static obstacles and dynamic objects that may move into the vehicle's path
Solution Approach 1:
The system segments object tracking into distinct categories: static objects and dynamic objects. By separating the tracking methodology based on detected movement characteristics, the system maintains reliable monitoring of all objects while preserving mobility information through classification, allowing different response strategies for static versus dynamic entities.
Data Source
AI summary
A vehicle is provided that may distinguish between dynamic obstacles and static obstacles. Given a detector for a class of static obstacles or objects, the vehicle may receive sensor data indicative of an environment of the vehicle. When a possible object is detected in a single frame, a location of the object and a time of observation of the object may be compared to previous observations. Based on the object being observed a threshold number of times, in substantially the same location, and within some window of time, the vehicle may accurately detect the presence of the object and reduce any false detections.