Autonomous Driving Lane Change Timing Based on Road Shape Analysis
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Solution Overview
Problem
Current autonomous driving systems lack the ability to effectively determine when and how to perform lane changes based on road shapes, link relationships, speed limits, and road characteristics, which limits their assistance to drivers in achieving convenient and stable navigation.
Innovation Solution
An autonomous driving control apparatus and method that generates a path plan using a detailed map to determine whether a lane change is required and calculates the optimal timing for the lane change by considering road shapes, link relationships, speed limits, and road characteristics, using modules such as a path generator, segment/local goal point determiner, lane change/direction determiner, and lane change timing determiner to execute the lane change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous driving systems use basic navigation without detailed road analysis, then the system complexity is low, but the ability to determine optimal lane change timing and necessity is insufficient
Solution Approach 1:
The patent segments the road ahead into multiple segments and analyzes each segment's characteristics (curvature, speed limit, road type) independently. This segmentation allows the system to determine lane change necessity and timing by evaluating specific road conditions in each segment, enhancing adaptability without requiring complete reanalysis of the entire route.
Solution Approach 2:
The system performs preliminary analysis of road shapes, link relationships, and characteristics before making lane change decisions. By pre-processing and storing detailed map information about upcoming segments, the system prepares lane change determination data in advance, enabling timely and accurate decisions when needed without increasing real-time computational complexity.
2Reliability
If the system determines lane change timing based on detailed road analysis, then the arrival convenience and stability improve, but the computational time and processing requirements increase
Solution Approach 1:
By dividing the road into discrete segments with specific characteristics, the system can efficiently process and compare segment data to determine lane change timing. This segmentation reduces computational complexity by allowing localized analysis rather than evaluating the entire route continuously, thus maintaining driving stability without excessive computational time.
Solution Approach 2:
The system applies different analysis criteria to different road segments based on their specific characteristics (e.g., curvature, speed limit, road type). This local quality approach allows the system to focus computational resources on segments where lane changes are more likely or more critical, improving reliability while reducing overall processing time.
3Measurement precision
If the autonomous driving apparatus considers multiple road characteristics for lane change determination, then the navigation accuracy improves, but the device complexity increases
Solution Approach 1:
The patent segments road analysis into distinct characteristic evaluations (curvature, speed limit, road type, link relationships) for each segment. This segmentation allows the system to precisely determine lane change timing by considering multiple factors independently and systematically, improving measurement precision while managing analysis complexity through structured processing.
Solution Approach 2:
The system employs a universal analysis framework that handles multiple road characteristics using consistent methods and data structures. This multi-functional approach allows the same computational infrastructure to evaluate various road features (curvature, speed limits, road types) uniformly, improving timing precision without proportionally increasing device complexity.
Data Source
AI summary
An autonomous driving control apparatus and method are provided to automatically determine whether a lane change is required by considering shapes of forward roads, a link relationship between the roads, a speed limit, the number of lanes, road characteristics (e.g., a crossroad, a crosswalk, an interchange, a junction, a speed bump, a dead-end, etc.), and the like which are recognized from a detailed map. The method also effectively determines a timing of the lane change when the lane change is required for a driver to more conveniently, stably, and efficiently arrive at a destination using an autonomous driving.