Vehicle Movement Control via Static Node Topology
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Solution Overview
Problem
Controlling autonomous vehicles in confined geographical areas with narrow passages and dynamic conditions poses challenges, including increased risk of collision and inefficient traffic planning.
Innovation Solution
A computer system that uses processing circuitry to define vehicle paths with static nodes, obtain real-time vehicle profiles, and apply a cost function to identify alternative movement controls that reduce fuel consumption, fulfill transport missions, and minimize vehicle wear.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles are controlled in confined geographical areas with narrow passages, then the risk of collision increases, but the traffic planning efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-defining vehicle paths through static nodes and pre-calculating optimal routes before vehicles enter confined areas. The computer system proactively plans movements and identifies alternative paths in advance, allowing vehicles to navigate narrow passages efficiently without collisions while maintaining traffic planning effectiveness.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring vehicle locations in real-time and adjusting traffic planning data structures based on actual vehicle positions and threshold condition violations. This closed-loop approach enables dynamic optimization of vehicle movements through confined areas, reducing collision risks while maintaining planning efficiency.
2Reliability
If vehicles are slowed down well-ahead of narrow passages to reduce collision risk, then fuel consumption increases, but safety improves
Solution Approach 1:
The system applies dynamic speed adjustments by calculating optimal velocity profiles that adapt to vehicle positions, passage locations, and traffic conditions. Instead of uniform deceleration, the system dynamically modulates speeds to maintain safety margins while minimizing energy consumption, allowing vehicles to accelerate safely through narrow passages when conditions permit.
Solution Approach 2:
The system changes operational parameters by optimizing speed, acceleration, and position parameters to balance safety and fuel efficiency. The cost function evaluates different parameter combinations and selects movement controls that achieve safety objectives with minimal energy consumption, enabling intelligent trade-off between these competing requirements.
3Loss of energy
If alternative movement controls are applied to reduce fuel consumption and vehicle wear, then traffic planning complexity increases, but sustainability improves
Solution Approach 1:
The system uses simplified representations by creating a topological model of the geographical area using static nodes and vehicle paths. This abstracted copy of the environment allows complex traffic planning to be performed on a simplified data structure rather than raw geographical data, reducing computational complexity while enabling sophisticated optimization for fuel efficiency and vehicle wear reduction.
Solution Approach 2:
The system replaces complex real-time mechanical traffic planning with a computational approach using cost functions and optimized data structures. By substituting traditional reactive traffic control with proactive computational optimization, the system achieves sophisticated movement control for sustainability goals while managing complexity through algorithmic approaches rather than mechanical complexity.
Data Source
AI summary
A computer system controls movements of a plurality of vehicles in a confined geographical area. A subset of static nodes defines a topological representation of the at least one vehicle path, each static node further having a set of vehicle-related threshold conditions. The system obtains real-time vehicle travelling profiles of the plurality of vehicles; determines, for each vehicle of the plurality of vehicles, a vehicle location at a given point of time based on data from the static nodes and the obtained real-time vehicle travelling profiles; generates a traffic planning data structure containing data indicative of the determined vehicle location at the given point in time and the positional order of the static node occupied by the vehicle; determines that at least one vehicle of the plurality of vehicles exceeds at least one vehicle-related threshold condition of the set of vehicle-related threshold conditions; applies a cost function on the data contained in the generated traffic planning data structure to identify an alternative movement control of the at least one vehicle through the at least one vehicle path; updates the traffic planning data structure to an updated traffic planning data structure; and feeds motion commands to the plurality of vehicles.