Intersection Deadlock Identification for Mixed Autonomous Vehicle Flow
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Solution Overview
Problem
Current technologies lack an effective method to detect and resolve traffic deadlocks in intersections where both human-driven and autonomous vehicles are present, leading to inefficient resource utilization and potential indefinite waiting for autonomous vehicles.
Innovation Solution
A method to identify traffic deadlocks in mixed autonomous vehicle environments by categorizing them into weak and strong types, using vehicle information such as coordinates and steering angles, and employing extended Kalman filters to estimate human-driven vehicle steering angles, enabling the construction of blockage graphs to determine deadlock conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traffic deadlock identification method is implemented for mixed autonomous vehicle environments, then autonomous vehicles can detect and escape deadlocks, but system complexity increases due to need for blockage graph construction and steering angle estimation
Solution Approach 1:
The patent segments the deadlock detection process into distinct modules: blockage graph construction, steering angle estimation using extended Kalman filter, and deadlock judgment based on graph properties. This segmentation allows each module to be optimized independently while working together to solve the overall complex problem of mixed traffic deadlock detection.
Solution Approach 2:
The patent introduces an intermediary blockage graph structure that mediates between raw vehicle data and deadlock detection. The blockage graph translates complex vehicle interactions into a simplified graph representation where nodes are vehicles and edges represent blockage relationships, making the detection process more manageable and systematic.
2Reliability
If manual intervention is used to resolve traffic deadlocks, then deadlocks can be unlocked, but time and human resources are consumed
Solution Approach 1:
The patent implements preliminary action by detecting deadlocks before they fully develop into unresolved situations. The blockage graph construction and steering angle estimation occur in advance, allowing the system to identify deadlock conditions and trigger escape maneuvers before vehicles are completely trapped, thereby reducing resolution time.
Solution Approach 2:
The patent enables self-service by allowing autonomous vehicles to detect and resolve deadlocks independently using their own sensors and the blockage graph analysis. The system uses the vehicle's own state information (position, speed, steering angle) and surrounding vehicle data to autonomously determine deadlock conditions and execute escape maneuvers without requiring external human intervention.
3Loss of information
If steering angle of human driven vehicles is estimated using extended Kalman filter, then complete vehicle information can be obtained, but computational complexity increases
Solution Approach 1:
The patent replaces direct mechanical measurement of human-driven vehicle steering angles with an estimation system using the extended Kalman filter. Instead of directly capturing steering angle data, the system uses observable vehicle dynamics (position, speed) and mathematical models to infer the steering angles, reducing the need for direct sensor installation while obtaining complete vehicle state information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the detection of traffic deadlocks, allowing autonomous vehicles to potentially escape weak deadlocks by changing direction and providing a framework for understanding when manual intervention may be necessary for strong deadlocks, thus optimizing traffic flow and resource utilization.
Implementation Method 1
the front wheel steering angle of a human driven vehicle is estimated by an extended Kalman filter
Data Source
AI summary
Provided is an intersection deadlock identification method for a mixed flow of autonomous vehicles. This method considers the reality that the intersection traffic flow is composed of human driven vehicles and connected autonomous vehicles. Firstly, the two-dimensional coordinates, speed and front wheel steering angle information of all vehicles in the intersection are obtained, and the blockage graph of vehicles is constructed on the assumption that the front wheel steering angles of all vehicles are fixed. If there is no ring structure in the blockage graph, there is no deadlock; if there is a ring structure, the evasion distance propagation algorithm is used to calculate the evasion requirement distance of a vehicle in the ring. When the evasion requirement distance is greater than the permitted travelling distance of the vehicle itself, a weak traffic deadlock exists.