Lane Change Gap Search Using Far, Mid, and Near Field Zones
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Solution Overview
Problem
Existing driver assistance systems for lane change maneuvers are inefficient in searching for and selecting gaps between road users, particularly when the capture range of environmental sensors is limited, and they do not effectively utilize traffic density and gap size predictions to optimize lane change operations.
Innovation Solution
A method and system that define a far field, mid field, and near field around the vehicle to search and select gaps for lane changes, using environmental sensors for the near and mid fields and predictive traffic analysis for the far field, optimizing the search process with empirical values and heuristics to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If environmental sensors are used to search for gaps in all areas around the vehicle, then comprehensive gap detection is achieved, but computing power and time requirements increase significantly
Solution Approach 1:
The patent divides the surrounding area into three distinct fields: near field (within sensor capture range), mid field (beyond sensor capture range but within prediction range), and far field (distant areas). Each field is searched using different methods - sensors for near field, predictive models for mid and far fields - thereby segmenting the computational task to improve efficiency while maintaining comprehensive detection.
Solution Approach 2:
The system performs preliminary gap searching in the far field using predictive traffic analysis before the vehicle actually reaches those areas. By predicting future traffic density and gap sizes in advance, the system prepares potential lane change targets ahead of time, reducing real-time computational burden when the vehicle is closer to the target lane.
2Device complexity
If environmental sensors with limited capture range are used, then device complexity is reduced, but the ability to detect gaps in distant areas is compromised
Solution Approach 1:
The patent introduces predictive traffic analysis as an intermediary between the limited-range environmental sensors and the far-field gap detection requirement. The predictive model acts as a mediator that extends the effective detection range by estimating traffic conditions beyond sensor reach, allowing the simple sensor system to achieve comprehensive gap awareness through computational supplementation.
3Speed
If gap searching is performed without considering traffic density predictions, then real-time responsiveness is improved, but lane change optimization is reduced
Solution Approach 1:
The system performs preliminary analysis of traffic density patterns and gap size predictions in advance, before actual lane change execution. By pre-processing traffic data and identifying promising gap candidates ahead of time, the system reduces the need for exhaustive real-time searching, thereby maintaining fast response while improving lane change optimization through informed decision-making.
Data Source
AI summary
A method for assisting a vehicle user during a lane change maneuver includes receiving a navigation command for performing the lane change maneuver from a second lane to a first lane of a road; searching for gaps for the vehicle in the first lane and selecting one of the gaps; defining a far field, a mid field and a near field; and performing the search and/or the selection of the gap according to a predefined sequence in which first a region of the far field, then a region of the mid field, and then the near field are checked.