Autonomous Vehicle Motion Planning Using Object-Specific Cost Data
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Solution Overview
Problem
Autonomous vehicles face challenges in effectively navigating and controlling their motion around proximate objects, particularly in predicting and adjusting to the paths of nearby vehicles, pedestrians, and other obstacles to prevent collisions and optimize travel routes.
Innovation Solution
An autonomous vehicle system that processes sensor data to identify objects of interest, generates cost data based on potential motion adjustments, and determines a motion plan to travel behind or in front of these objects, optimizing speed and trajectory to maintain safe distances and follow traffic laws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle continuously monitors and adjusts its motion based on proximate objects, then collision avoidance and safety are improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the environment into discrete objects of interest and generates separate cost data for each object. The motion planning process is divided into distinct stages: obtaining object data, identifying objects of interest, generating cost data, and determining motion plans. This segmentation reduces computational complexity by breaking down the complex task of navigating around multiple objects into manageable, modular steps that can be processed independently and efficiently.
2Measurement precision
If the autonomous vehicle generates cost data for multiple proximate objects, then navigation accuracy and route optimization are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary identification of objects of interest before generating detailed cost data for each object. By pre-filtering and categorizing proximate objects based on their relevance to the vehicle's travel route, the system prepares the data structure in advance, enabling faster and more efficient cost data generation. This preliminary action reduces processing time while maintaining high navigation accuracy by focusing computational resources on the most relevant objects.
3Reliability
If the autonomous vehicle adjusts its motion to travel behind or in front of objects of interest, then safety and route optimization are improved, but motion flexibility and response time to unexpected events may be reduced
Solution Approach 1:
The system dynamically adjusts the vehicle's motion plan based on real-time cost data and object movements. The motion plan is not fixed but continuously updated as new information about proximate objects becomes available. This dynamic approach allows the vehicle to maintain safe distances from objects of interest while preserving the flexibility to respond to unexpected events, as the system can rapidly recalculate and adjust the motion plan when new conditions arise.
Data Source
AI summary
Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.