Virtual Vehicle Route Simulation for Problematic Maneuver Detection
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles face challenges in navigating complex maneuvers due to their inability to rely on human driver skills, particularly in situations requiring tight turns, low bridges, construction zones, and congested roadways, which smaller vehicles can easily navigate but larger vehicles like semi-trucks struggle with.
Innovation Solution
A computing device performs simulations using vehicle parameters and real-world conditions to identify optimal routes, adjusting for potential obstacles and constraints, and provides route instructions for the vehicle to autonomously navigate around difficult maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles use standard routing systems designed for human drivers, then they can follow general navigation instructions, but they cannot account for vehicle-specific physical constraints and maneuverability limitations
Solution Approach 1:
The patent creates a virtual vehicle that is a digital copy of the physical vehicle, replicating its specific physical parameters and characteristics. This virtual replica is then used in simulated routing scenarios to determine feasible routes without risking the actual vehicle or requiring complex real-world testing. The copying approach allows the system to adapt routes to vehicle capabilities while managing complexity through virtual experimentation.
Solution Approach 2:
The system performs preliminary simulations of potential routes using the virtual vehicle before the physical vehicle actually travels them. By pre-testing routes in a virtual environment and identifying problematic maneuvers in advance, the system can select optimized routes that account for vehicle-specific constraints without requiring complex real-time adjustments during actual travel.
2Productivity
If autonomous vehicles attempt to navigate complex maneuvers like tight turns or low bridges, then they can access more direct routes, but they risk collision or failure due to inability to perform human-level manual control
Solution Approach 1:
The system performs preliminary simulations of complex maneuvers using the virtual vehicle to assess feasibility and identify potential collision risks before the physical vehicle attempts them. By pre-evaluating tight turns, low bridges, and other challenging maneuvers in a risk-free virtual environment, the system can determine which direct routes are safe for the specific vehicle to attempt, balancing navigation efficiency with maneuver execution safety.
Solution Approach 2:
The system uses simulations to identify problematic maneuvers that could lead to collisions or failures, and proactively selects alternative routes that avoid these hazardous situations. By anticipating potential safety issues before they occur and taking preventive action through route selection, the system maintains reliability while still pursuing efficient navigation where safe maneuvers are available.
3Loss of time
If autonomous vehicles rely on generic routing algorithms, then they can process routes quickly, but they cannot optimize for vehicle-specific physical attributes like length, width, height, and weight
Solution Approach 1:
The patent creates a virtual vehicle that replicates the physical vehicle's specific attributes including length, width, height, and weight. This virtual copy enables customized route simulations that account for vehicle-specific constraints without requiring time-consuming real-world testing. The copying approach allows the system to efficiently evaluate multiple potential routes while adapting to the specific physical characteristics of each vehicle.
Solution Approach 2:
The system uses the virtual vehicle to autonomously test and evaluate routes in simulation, allowing the routing system to self-assess feasibility based on vehicle parameters without requiring external validation or manual intervention. This self-service approach enables the system to efficiently process customized route planning for each vehicle's specific attributes while maintaining adaptability across different vehicle types.
Data Source
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AI summary
Example embodiments relate to methods and systems for automatic problematic maneuver detection and adapted motion planning. A computing device may obtain a route for navigation by a vehicle and a set of vehicle parameters corresponding to the vehicle. Each vehicle parameter can represent a physical attribute of the vehicle. The computing device may generate a virtual vehicle that represents the vehicle based on the set of vehicle parameters and perform a simulation that involves the virtual vehicle navigating the route. Based on the results of the simulation, the computing device may provide the original route or a modified route to the vehicle for the vehicle to subsequently navigate to its destination. In some cases, the simulation may further factor additional conditions, such as potential weather and traffic conditions that are likely to occur during the time when the vehicle plans on navigating the route.