Autonomous Vehicle Intersection Constraints for Gridlock Prevention
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Solution Overview
Problem
Autonomous vehicles struggle to properly react at intersections due to the lack of formalized rules for complex scenarios, such as turning pockets, inactive crosswalks, and inferred 'keep clear' areas, making it difficult to prevent gridlock situations.
Innovation Solution
A method for autonomous vehicles to obtain sensor data, determine intersection parameters, and generate trajectories that minimize gridlock by using processors to analyze constraints and select optimal paths, incorporating vehicle-to-infrastructure communication and advanced sensors like cameras, LiDAR, and radar to navigate marked and unmarked intersections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous vehicles use basic traffic rules for navigation, then operation is simple and ease of operation is improved, but gridlock prevention capability deteriorates due to inability to handle complex intersection scenarios
Solution Approach 1:
The system changes the parameter of rule representation from basic binary compliance to a multi-level constraint system with different priorities. Constraints are assigned priority levels (e.g., safety constraints have higher priority than efficiency constraints), allowing the vehicle to handle complex scenarios by selectively applying different constraint levels based on the situation.
Solution Approach 2:
The navigation decision-making process is segmented into multiple independent constraint modules. Each module handles a specific aspect (e.g., intersection entry constraints, keep-clear constraints, turning pocket constraints), allowing the system to manage complexity through modular processing while maintaining overall simplicity.
2Reliability
If comprehensive constraints are applied to prevent gridlock, then gridlock prevention capability is improved, but computational complexity and device complexity increase
Solution Approach 1:
Constraints are pre-processed and stored in a structured format before runtime. The system pre-defines constraint templates for common intersection scenarios (marked intersections, unmarked intersections, turning pockets, crosswalks), reducing the computational burden during real-time operation by avoiding complex on-the-fly analysis.
Solution Approach 2:
A single constraint processing module handles multiple types of constraints through a unified framework. The same basic constraint evaluation logic is applied universally across different constraint types by varying only the specific parameters, reducing the need for multiple specialized processing paths.
3Measurement precision
If detailed sensor data processing is used to identify all intersection types, then measurement precision is improved, but processing time and loss of time increase
Solution Approach 1:
The system dynamically adjusts the level of analysis based on the situation. For clearly marked intersections, basic detection suffices. For ambiguous cases, the system dynamically increases analysis depth by examining additional sensor data and applying more sophisticated classification algorithms only when necessary.
Solution Approach 2:
The system applies a two-stage detection approach: first using simple, fast features for initial classification, then applying more detailed analysis only to cases that are ambiguous or critical. This partial application of excessive analysis ensures high precision for uncertain cases while maintaining speed for clear cases.
Data Source
AI summary
Provided are methods for gridlock prevention, which can include obtaining sensor data, obtaining an intersection parameter, and determining a constraint. Some methods described also include generating trajectories and providing data associated with a selected trajectory, such as for operation of an autonomous vehicle along the trajectory. Systems and computer program products are also provided.