Lane Change Detection Filtering Contextual Vehicle Groups
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing lane change detection systems face challenges in accurately determining lane changes, especially in areas with complex road structures and inadequate lane markings, leading to false detections due to collective movements of vehicles on on-ramps and off-ramps.
Innovation Solution
The system filters vehicles based on contextual analysis, assigning weights to surrounding vehicles depending on their location, such as on-ramps or merge lanes, to prevent false lane change detections by grouping vehicles into change groups and analyzing their relative movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses collective movements of surrounding vehicles to identify lane changes, then lane change detection capability is improved, but false detections increase in areas with complex road structures
Solution Approach 1:
The system applies different processing rules to vehicles in different spatial contexts. Vehicles in complex road structure areas (on-ramps, off-ramps, parallel roadways) are filtered out from collective movement analysis, while vehicles in normal areas are included. This local differentiation resolves the contradiction by maintaining detection accuracy in suitable areas while avoiding false detections in problematic areas.
Solution Approach 2:
The system dynamically changes the parameter of vehicle inclusion in collective movement analysis based on the detected road structure context. When complex road structures are detected, the system changes the parameter to exclude vehicles in those areas from the analysis, thereby preventing false detections while maintaining accurate lane change detection in normal areas.
2Reliability
If the system filters vehicles in areas of collective movement, then false detections are reduced, but lane change detection capability deteriorates in areas without lane markings
Solution Approach 1:
The system applies selective filtering based on local road characteristics. Only vehicles in specific problematic areas (on-ramps, off-ramps, parallel roadways) are filtered out, while vehicles in other areas continue to contribute to lane change detection. This localized approach maintains detection reliability where needed without sacrificing accuracy in areas where collective movement analysis remains valid.
Solution Approach 2:
The system performs partial filtering rather than complete exclusion of vehicles in complex areas. By selectively filtering only those vehicles in identified problematic zones while retaining others, the system achieves sufficient reliability improvement without过度 reducing the data available for lane change detection, thus maintaining adequate detection capability.
3Adaptability or versatility
If the system relies on sensor data and collective vehicle movements, then lane change detection can be performed without lane markings, but false detections increase due to inadequate data quality
Solution Approach 1:
The system changes the parameter of data inclusion based on contextual quality assessment. When operating in areas without lane markings, the system dynamically adjusts which vehicle movement data is included in analysis, excluding vehicles in complex road structure areas while incorporating data from other vehicles. This parameter adjustment maintains adaptability to operate without lane markings while improving measurement precision through selective data quality control.
Data Source
AI summary
System, methods, and other embodiments described herein relate to improving detection of lane changes for an ego vehicle. In one embodiment, a method includes, in response to detecting a surrounding vehicle from sensor data acquired about the surrounding environment by the ego vehicle, estimating a relative position of the surrounding vehicle in relation to the ego vehicle. The method includes determining a context of the surrounding vehicle in relation to a present roadway on which the ego vehicle is traveling. The method includes selectively grouping the surrounding vehicle into a change group according to the context. The change group including one or more vehicles for assessing movements of the ego vehicle. The method includes analyzing relative movements of vehicles in the change group to generate an indicator of whether the ego vehicle has performed a lane change.