Vehicle ETA Estimation Using Dynamic Adjacency Graph Routing
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Solution Overview
Problem
Existing systems lack an efficient method to estimate the time of arrival for autonomous vehicles at their destinations, especially in dynamic environments with varying traffic conditions.
Innovation Solution
The system utilizes an adjacency graph, which is a graph structure where each node represents a unique location and each edge represents the estimated travel time between those locations. By accessing this graph, the system calculates the shortest path from the vehicle's current location to its destination and estimates the time of arrival based on the edge travel times, optionally adjusting for traffic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional routing methods are used to estimate arrival time, then the system is simpler to implement, but the estimation precision deteriorates in dynamic traffic conditions
Solution Approach 1:
The geographic area is segmented into multiple nodes representing different locations, and edges represent travel paths between nodes. This segmentation allows the system to break down complex routing into manageable segments, improving estimation precision while keeping computational complexity controlled through localized processing.
Solution Approach 2:
The system dynamically updates the adjacency graph with real-time traffic data, adjusting travel times between nodes based on current conditions. This dynamic adaptation enables precise arrival time estimation in changing traffic environments without requiring complete system redesign.
2Adaptability or versatility
If real-time traffic data is incorporated into the estimation, then the adaptability to dynamic conditions improves, but the computational time increases
Solution Approach 1:
The system pre-calculates and stores the adjacency graph structure, including all possible paths and baseline travel times between nodes. When real-time traffic data arrives, only the travel time values need updating, not the entire routing structure, thus maintaining high adaptability while minimizing computational overhead.
Solution Approach 2:
The system continuously receives feedback from traffic data sources and updates the adjacency graph accordingly. This feedback mechanism allows the system to adapt to changing conditions efficiently by only recalculating affected portions of the route, balancing adaptability with computational efficiency.
3Measurement precision
If the adjacency graph covers the entire geographic area, then the measurement precision improves, but the data processing load increases
Solution Approach 1:
The geographic area is divided into discrete nodes and edges, allowing the system to process only relevant portions of the graph for each routing query. This segmentation reduces the effective data volume needed for each calculation while maintaining comprehensive coverage for precision.
Solution Approach 2:
The system extracts and processes only the necessary subset of the adjacency graph relevant to the current vehicle route, rather than processing the entire graph. This extraction approach maintains accurate estimation by focusing on relevant data while reducing overall processing load.
Data Source
AI summary
Methods and systems for estimating a time of arrival for a vehicle at a destination are disclosed. The system will access an adjacency graph comprising nodes and edges. Each node is associated with a unique location in a geographic area in which the vehicle is traveling. Each edge connects two of the nodes and is associated with an estimated travel time between the two connected nodes. The system will select, from the locations in adjacency graph, a first location that is near the vehicle and a second location that is near the destination. The location and destination are each associated with nodes in adjacency graph. The system will calculate a shortest path along the edges in the adjacency graph from the location and destination nodes, and it will calculate an estimated time of arrival for the vehicle as a function of the estimated travel times along the shortest path.