Lane Course Estimation via Vehicle Trajectory Analysis
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Solution Overview
Problem
Existing lane detection methods in vehicles are limited by the visibility of lane markings and rely heavily on camera or LIDAR systems, which struggle in poor weather conditions or without markings, and can be erroneous due to movements of vehicles ahead or incorrect trajectory tracking.
Innovation Solution
A method and system that estimate the lane course by detecting and analyzing the trajectories of other vehicles moving in the same direction, assigning these trajectories to possible lanes, and selecting the most accurate lane based on trajectory data, while filtering out outliers and lane changes to provide a reliable and accurate lane estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lane detection is based on camera or LIDAR systems detecting lane markings, then lane course can be recognized under optimal conditions, but detection range is limited and reliability deteriorates in poor visibility conditions or when markings are absent
Solution Approach 1:
The patent uses other vehicles as intermediary objects to infer lane course information. Instead of directly detecting lane markings, the system tracks trajectories of surrounding vehicles and uses their collective movement patterns as a mediator to determine the actual lane course, especially when markings are invisible or unreliable
Solution Approach 2:
The patent inverts the traditional approach by not detecting lane markings directly, but rather detecting vehicle trajectories and inferring lane markings from them. This inversion allows the system to determine lane course indirectly through vehicle behavior patterns rather than direct visual detection
2Adaptability or versatility
If lane course is determined solely by trajectory of a single vehicle driving ahead, then lane course can be estimated when a vehicle is present, but accuracy deteriorates when the vehicle changes lanes or turns
Solution Approach 1:
The patent merges trajectory data from multiple vehicles into a collective evaluation. By combining trajectory information from several vehicles and analyzing their collective movement patterns, the system achieves more robust lane course determination that is not easily affected by individual vehicle maneuvers such as lane changes or turns
3Reliability
If trajectories of multiple vehicles are recorded and evaluated collectively, then lane course estimation becomes more robust, but complexity of trajectory assignment and lane change detection increases
Solution Approach 1:
The patent segments the evaluation process into distinct stages: trajectory recording, trajectory assignment to reference vehicles, collective evaluation, and lane change detection. This segmentation allows complex multi-vehicle trajectory analysis to be broken down into manageable processing steps, reducing overall system complexity while maintaining reliability
Data Source
Figure 1~5
Figure 2
Figure 3A~3C
AI summary
In a method for estimating a course of a traffic lane for an ego-vehicle which travels on a road with multiple lanes, the following steps are repeatedly executed: identification of one or more other vehicles moving in the same direction on the road as the ego-vehicle; determination of vehicle positions for each of the other vehicles identified; formation of a trajectory for each of the other vehicles identified based on the respectively determined vehicle positions; assignment of the trajectories to possible traffic lanes based on a course of the trajectories formed in the environment of the ego-vehicle; selection of one of the possible traffic lanes to which at least one trajectory is assigned; and estimation of the course of the traffic lane based on the at least one trajectory assigned to the selected traffic lane.