Autonomous Vehicle Coasting Control for Variable Traffic Flow
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Solution Overview
Problem
Conventional approaches for determining when an autonomous vehicle should coast in traffic fail to account for variability among leading vehicles and combine vehicle tracking and coasting decisions into a single function, lacking independence and flexibility.
Innovation Solution
The system determines the speeds and stations of leading vehicles, calculates a coasting viability factor based on these data, and compares it to a threshold to decide whether the ego vehicle should coast, using a combination of time-station and station-speed profiles and coasting functions to assess coasting viability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional approaches combine vehicle tracking and coasting decisions into a single function, then the system structure is simpler, but the determination accuracy and flexibility are reduced
Solution Approach 1:
The patent divides the combined tracking and coasting decision system into separate independent modules: a tracking function that monitors leading vehicles and a coasting decision function that determines when to coast. This segmentation allows each module to specialize in its specific task, improving overall determination accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a coasting viability factor as an intermediary metric that bridges vehicle tracking data and coasting decisions. This intermediary enables independent optimization of tracking accuracy and coasting decision logic, as the viability factor serves as a standardized interface between the two previously combined functions.
2Ease of manufacture
If conventional approaches use a single function for both tracking and coasting decisions, then the system is easier to implement, but the adaptability to different traffic conditions is reduced
Solution Approach 1:
By separating tracking and coasting decision functions into independent modules, the system gains adaptability to different traffic conditions. Each module can be independently tuned and optimized for specific scenarios without affecting the other, allowing flexible adaptation while maintaining relatively simple implementation through modular design.
Solution Approach 2:
The patent implements dynamic adaptability by allowing the coasting decision function to independently adjust its parameters and thresholds based on real-time traffic conditions detected by the tracking function. This dynamic behavior enables the system to adapt to varying traffic scenarios without requiring complete system redesign.
3Measurement precision
If the system considers variability among multiple leading vehicles, then the coasting determination becomes more accurate, but the computational complexity increases
Solution Approach 1:
The patent extracts the essential variability information from multiple leading vehicles by focusing on key parameters such as their speeds and positions relative to the ego vehicle. By extracting only the relevant variability metrics needed for coasting decisions rather than processing all possible vehicle attributes, the system achieves accurate determination with reduced computational complexity.
Solution Approach 2:
The system manages computational complexity by dynamically adjusting which vehicle variability parameters are considered based on the coasting viability assessment. Not all leading vehicle parameters are processed with equal depth - the system adapts its parameter analysis scope to balance accuracy requirements with computational resources.
Data Source
AI summary
Methods, systems, and non-transitory computer readable media configured to perform operations comprise determining, by a computing system, speeds and stations of a plurality of leading vehicles traveling ahead of an ego vehicle; calculating, by the computing system, a coasting viability factor based on the speeds and stations of the plurality of leading vehicles and a speed and station of the ego vehicle; comparing, by the computing system, the coasting viability factor to a coasting viability threshold; and determining, by the computing system, whether the ego vehicle should coast based on the comparing.