Risk Level Sets for Autonomous Vehicle Path Planning
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating dynamic, congested, and cluttered environments, where existing path planning technologies struggle to effectively quantify congestion and ensure collision avoidance in changing scenarios.
Innovation Solution
The use of risk level sets to quantify the level of risk at different locations in the environment, allowing agents to choose a risk threshold that guarantees collision avoidance by reducing the control space and adapting to the density of obstacles and agents, employing a cost function that maps occupancy to risk and using Djikstra's Algorithm for optimal route planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional path planning methods are used in congested environments, then the vehicle can navigate through the environment, but the collision avoidance capability is insufficient due to inability to effectively quantify congestion levels
Solution Approach 1:
The patent transforms the qualitative concept of congestion into a quantitative metric by introducing a cost function that assigns numerical values to different environmental states. This cost function evaluates occupancy, velocity, and acceleration of surrounding agents to generate a scalar congestion metric, enabling the planner to make informed decisions about safe navigation paths without requiring complex explicit congestion models.
2Reliability
If the control space is reduced to ensure collision avoidance, then safety is improved, but the navigation flexibility and ability to adapt to changing environments is limited
Solution Approach 1:
The risk level set is constructed dynamically based on current environmental conditions, including the positions, velocities, and accelerations of surrounding agents. As the environment changes, the cost function continuously updates the risk metrics, and the level set adapts to provide collision-free control inputs that are optimized for the current state, enabling both safety guarantees and environmental adaptability.
3Reliability
If a conservative risk threshold is chosen to ensure safety, then collision avoidance is improved, but the travel efficiency and speed are reduced
Solution Approach 1:
The patent allows the autonomous vehicle to operate with a chosen risk threshold that balances safety and efficiency. By adjusting the threshold, the system can adopt a conservative approach (lower threshold for higher safety) or a more aggressive approach (higher threshold for better efficiency). This partial action principle enables flexible risk management where the vehicle can tolerate some level of risk in exchange for improved travel efficiency when appropriate.
Data Source
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AI summary
A method is disclosed for use in a planning agent, the method including: identifying a first agent in a vicinity of the planning agent; identifying a location of the first agent and a velocity of the first agent; calculating a set of occupancy costs for the first agent, each occupancy cost in the set of occupancy costs being associated with a different respective location in the vicinity of the planning agent, each occupancy cost in the set of occupancy costs being calculated at least in part based on a cost function that depends on the location of the first agent and the velocity of the first agent; and changing at least one of speed or direction of travel of the planning agent based on the set of occupancy costs.