Drivable Area Detection Using Multi-Frame Obstacle Trajectories
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Solution Overview
Problem
Existing automatic driving systems struggle with accurate detection of drivable areas due to reliance on single-frame detection without considering dynamic changes, leading to inconsistent static boundaries and unstable trajectory predictions for dynamic objects.
Innovation Solution
A method involving the collection and analysis of N consecutive video frames to determine historical trajectories and position information of static and dynamic obstacles, followed by correction using preset rules and prediction models to establish a drivable area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If single-frame detection is used, then detection speed is improved, but detection accuracy and consistency deteriorate
Solution Approach 1:
The system performs preliminary detection on multiple consecutive video frames before final drivable area determination. By analyzing N consecutive frames and establishing historical trajectories of obstacles in advance, the system accumulates sufficient data to improve detection accuracy while maintaining real-time performance through optimized processing sequences.
Solution Approach 2:
The system continuously processes N consecutive video frames to track obstacle trajectories and update drivable area boundaries in real-time. This continuous multi-frame analysis ensures that detection accuracy is maintained throughout the driving process, with each frame contributing to the overall understanding of the drivable space while preserving temporal continuity.
2Measurement precision
If multi-frame analysis is performed, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential information from N consecutive video frames - specifically obstacle positions, trajectories, and drivable area boundaries - while discarding redundant visual data. This selective extraction approach maintains high detection accuracy by focusing on critical parameters while significantly reducing computational complexity through targeted data processing.
Solution Approach 2:
The detection process is segmented into distinct functional modules: video frame collection, obstacle detection and tracking, trajectory analysis, and drivable area determination. Each module processes specific aspects of the data independently, allowing for optimized computation in each segment and reducing overall system complexity while maintaining comprehensive multi-frame analysis capability.
3Measurement precision
If historical trajectory correction is applied, then prediction accuracy is improved, but processing time increases
Solution Approach 1:
The system changes the parameter representation of obstacle trajectories by establishing historical trajectories from N consecutive frames and applying correction algorithms. This parameter transformation approach improves prediction accuracy by using corrected trajectory parameters while optimizing processing time through efficient mathematical operations on the transformed data.
Solution Approach 2:
The system applies feedback mechanisms by using corrected historical trajectory information to continuously refine future trajectory predictions. The correction results from analyzing past frames are fed back into the prediction model, improving accuracy over time while the feedback loop is optimized to minimize additional processing time through efficient iterative refinement.
Data Source
AI summary
A method for detecting a drivable area includes: collecting N consecutive video frames of a road when a vehicle is driving, where N is a positive integer greater than 1; determining a historical trajectory of a dynamic obstacle and position information of a static obstacle included in the N consecutive video frames by analyzing the N consecutive video frames with a 3D detection algorithm; correcting the historical trajectory and the position information based on a preset rule; determining a predicted trajectory of the dynamic obstacle based on the corrected historical trajectory; and determining the drivable area of the vehicle based on the predicted trajectory and the corrected position information.