Trajectory Determination for Unmanned Vehicles
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Solution Overview
Problem
Unmanned vehicles face challenges in safely navigating to destinations without human intervention, as existing systems lack efficient methods for determining optimal trajectories that avoid obstacles and ensure safe navigation.
Innovation Solution
A system and method that utilize a processor to obtain vehicle status information, determine original trajectories, perform collision detection, and evaluate candidate trajectories using a trajectory evaluation model to select a target trajectory, incorporating machine learning techniques and sensor data for obstacle avoidance and speed planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple original trajectories are generated and evaluated using a trajectory evaluation model, then the safety and optimality of the selected trajectory is improved, but the computational complexity and time consumption increase
Solution Approach 1:
The trajectory determination process is segmented into distinct modules: original trajectory generation module, collision detection module, and trajectory evaluation module. Each module handles a specific aspect of the problem, making the overall complex system manageable and efficient. The segmentation allows parallel processing of multiple trajectories while maintaining systematic evaluation.
Solution Approach 2:
Collision detection is performed as a preliminary action before full trajectory evaluation. By first filtering out trajectories that collide with obstacles using the collision detection module, the system avoids wasting computational resources on evaluating obviously unsafe trajectories, thus reducing overall computational complexity while maintaining safety.
2Reliability
If collision detection is performed on multiple original trajectories, then the safety of the selected trajectory is improved, but the computational time increases
Solution Approach 1:
The system performs partial evaluation by conducting collision detection on multiple original trajectories without completing full trajectory evaluation for all of them. Trajectories that fail collision detection are discarded early, while only the surviving candidates undergo complete evaluation. This partial action approach maintains collision avoidance safety while reducing total computational time.
3Manufacturing precision
If a trajectory evaluation model based on machine learning is used, then the quality of trajectory selection is improved, but the computational resources required increase
Solution Approach 1:
The trajectory evaluation model applies machine learning algorithms locally to specific candidate trajectories rather than evaluating all possible trajectories. By focusing computational resources on evaluating only the quality of trajectories that have passed collision detection, the system achieves high trajectory optimization quality while consuming fewer computational energy resources.
Data Source
AI summary
Systems and methods for trajectory determination are provided. The systems perform the operations including obtaining status information of a vehicle; determining one or more original trajectories from a current location of the vehicle to a destination based on the status information of the vehicle; determining one or more candidate trajectories from the original trajectories; and determining a target trajectory from the candidate trajectories based on a trajectory evaluation model.