Autonomous Vehicle Coordination Using Shared Location Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the movements of objects, such as pedestrians and other vehicles, especially in complex environments like intersections, leading to increased collision risks and inefficient traffic flow due to incomplete or inaccurate location data.
Innovation Solution
The system allows autonomous vehicles to exchange information with each other and with fixed sensors to generate a comprehensive real-time view of their surroundings, using predictive models based on historical data and physics-based principles to forecast the future locations of objects, thereby coordinating movements to reduce collisions and improve traffic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicles use only on-board sensors and GPS for location determination, then the vehicle can operate independently without external communication, but the location information becomes incomplete or inaccurate especially for obscured mobile objects
Solution Approach 1:
The patent merges data from multiple sources including on-board sensors, GPS, and remote systems to create a comprehensive location determination system. This combination allows the vehicle to overcome the limitations of individual sensors by integrating their strengths, thereby improving location accuracy without relying on a single complex system
Solution Approach 2:
The patent introduces remote systems as intermediary components that facilitate communication between autonomous vehicles and external infrastructure. These remote systems act as mediators that provide additional location information about mobile objects, enabling vehicles to detect obscured objects without directly increasing onboard sensor complexity
2Reliability
If autonomous vehicles cannot predict movements of other objects, then the vehicle operation remains simple without complex prediction algorithms, but collision risk increases and traffic flow becomes inefficient
Solution Approach 1:
The patent implements prediction algorithms that perform preliminary actions by forecasting the future movements of pedestrians, vehicles, and other objects before actual collisions or traffic conflicts occur. This advance prediction enables the vehicle to plan safer trajectories and coordinate movements proactively, improving reliability while managing complexity through structured prediction models
Solution Approach 2:
The patent incorporates feedback mechanisms where prediction algorithms continuously update their models based on actual observed movements of other objects. This feedback loop refines prediction accuracy over time, allowing the system to improve collision avoidance capability while adapting to real-world patterns without requiring overly complex static algorithms
3Productivity
If autonomous vehicles operate without exchanging information with other vehicles and sensors, then the system architecture remains simple, but traffic flow efficiency decreases and energy consumption increases
Solution Approach 1:
The patent creates a universal communication framework where vehicles, pedestrians, and infrastructure sensors can all exchange information through standardized protocols. This multi-functional communication system allows different types of actors to coordinate their movements efficiently, improving traffic flow productivity while managing complexity through universal interfaces rather than specialized systems for each participant type
Data Source
AI summary
A method in accordance with the invention controls movements of an actor, such as an autonomous vehicle. The method includes collecting one or more data sets of location information from sources that include a first actor, a second actor, and a fixed agent. Each data set indicates a current location of an object from one or more objects. The method also includes merging the data sets to generate a second set of current location information of the one or more objects, filtering the second set of current location information to remove location information of static objects to generate a subset of the current locations corresponding to a subset of the objects, using the subset of current locations to predict future locations of the subset of objects, and coordinating the movement of the first actor based on the predicted future locations to optimize one or more pre-determined criteria.