Autonomous Lane Change Routing With Node Cost Feedback
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Solution Overview
Problem
Autonomous vehicles face challenges in determining the optimal location for lane changes while navigating, often getting stuck in infinite loops due to inability to execute lane changes, which affects route efficiency and safety.
Innovation Solution
The method involves using a cost analysis based on factors like duration, traffic conditions, previous lane change history, solid white line crossings, intersection involvement, and time of day to assess the best node pairs for lane changes, with processors iterating through possible transitions to select the lowest-cost option and adjust costs dynamically to avoid infinite loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the vehicle uses a cost function to determine lane change timing, then route efficiency is improved, but the system complexity increases due to multiple cost factors
Solution Approach 1:
The patent applies parameter changes by using a cost function that evaluates multiple parameters (duration, traffic conditions, previous lane change history, solid white line crossings, intersection involvement, time of day) to determine lane change timing. This transforms the lane change decision from a simple binary choice into a multi-parameter optimization problem, improving route efficiency while systematically managing complexity through defined cost factors.
2Measurement precision
If the vehicle iterates through all node pairs to find optimal lane change location, then lane change accuracy is improved, but computational time increases
Solution Approach 1:
The patent segments the road network into discrete nodes representing specific locations where lane changes can occur. By dividing the continuous road into finite node pairs, the system can systematically evaluate each segment independently using the cost function, achieving precise lane change location selection while managing computational complexity through structured segmentation of the problem space.
3Reliability
If the vehicle increases cost for missed lane changes, then infinite loops are prevented, but the cost function complexity increases
Solution Approach 1:
The patent implements feedback by incorporating previous lane change history into the cost function. When a lane change is missed or not executed, the system increases the cost for that particular node pair in subsequent evaluations. This feedback mechanism prevents infinite loops by ensuring that repeatedly failed lane change attempts become progressively less favorable, guiding the vehicle to alternative locations while systematically managing cost function complexity through structured cost adjustment.
Data Source
AI summary
Aspects of the disclosure relate to routing an autonomous vehicle. For instance, the vehicle may be maneuvered along a route in a first lane using map information identifying a first plurality of nodes representing locations within the first lane and a second plurality of nodes representing locations within a second lane different from the first lane. While maneuvering, when the vehicle should make a lane change may be determined by assessing a cost of connecting a first node of the first plurality of nodes with a second node of a second plurality of nodes. The assessment may be used to make the lane change from the first lane to the second lane.