Lane Change Gap Search Using Far-Mid-Near Field Sequencing
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Solution Overview
Problem
Existing driver assistance systems for lane-changing maneuvers are inefficient in searching and selecting gaps between road users, particularly when the detection range of environmental sensors is limited.
Innovation Solution
A method and system that define a far field, midfield, and near field in a vehicle's environment for gap searching and selection, utilizing a predetermined sequence to check these areas for available gaps, combining empirical data and sensor data to optimize gap detection and selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle system searches for gaps in the entire detection range without prioritizing areas, then all possible gaps can be detected, but the computing power and processing time are excessively high
Solution Approach 1:
The detection range is segmented into three distinct fields: far field (outside sensor detection range), midfield (within sensor detection range), and near field (immediate vehicle surroundings). This segmentation allows the system to apply different search strategies and resource allocation to each field, improving overall processing efficiency while maintaining comprehensive gap detection.
Solution Approach 2:
The system performs preliminary assessment of traffic density in the far field using empirical data before committing significant computational resources to detailed gap searching. By predicting traffic conditions in advance and prioritizing searches in areas with lower predicted density, the system reduces unnecessary processing while ensuring reliable gap detection.
2Productivity
If the system prioritizes gaps in front of the vehicle over gaps in rear, then the lane change selection is optimized for forward motion, but gaps behind the vehicle may be missed
Solution Approach 1:
Different search priorities and strategies are applied to different spatial regions. The far field search uses empirical traffic density data to identify promising areas, the midfield search within detection range applies sensor-based gap detection, and the near field search focuses on immediate safety. This localized quality approach ensures efficient processing in each region while maintaining overall reliability.
Data Source
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AI summary
The invention relates to a method for assisting a vehicle user during a lane change manoeuvre, said method comprising the steps of: receiving a navigation command for performing the lane change manoeuvre 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, the near field being adjacent to the vehicle, the mid field being adjacent to the near field, and the far field being adjacent to the mid field, the far field being situated outside a detection range of surroundings sensors of the vehicle, and the near field and the mid field being situated within the detection range; 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 which is situated in front of the vehicle in the direction of travel, then a region of the mid field which is situated in front of the vehicle in the direction of travel, and then the near field are checked.