Autonomous Vehicle Trajectory Segmentation for Data Efficiency
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Solution Overview
Problem
Current autonomous vehicle systems face inefficiencies in trajectory planning and execution, requiring extensive data processing and storage, and lack robustness in handling faults, which affects their navigation and control precision.
Innovation Solution
The system determines a simplified trajectory based on a planned trajectory, generating vehicle commands and control signals that reduce data requirements while maintaining compatibility with existing systems, and includes fault detection mechanisms to implement emergency commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a detailed planned trajectory with multiple states is used for autonomous vehicle control, then navigation precision is improved, but data processing requirements and storage needs increase
Solution Approach 1:
The trajectory is segmented into a first trajectory (planned trajectory) and a second trajectory (simplified trajectory). The first trajectory contains detailed state information for precise navigation, while the second trajectory contains only essential states (first state and secondary states) for efficient processing. This segmentation allows the system to maintain high navigation precision using the detailed first trajectory while reducing data processing requirements by utilizing the simplified second trajectory for control signal generation.
2Manufacturing precision
If extensive trajectory data is stored and processed, then control accuracy is improved, but system resource allocation efficiency decreases
Solution Approach 1:
The trajectory data is divided into two segments: the first trajectory storing complete detailed state information for accuracy, and the second trajectory storing only critical states (first state representing current motion path and secondary states representing relative states) for efficient processing. This allows the system to maintain control accuracy by referencing the detailed first trajectory while improving resource allocation efficiency by processing the compact second trajectory for real-time control signal generation.
Solution Approach 2:
The system extracts only the essential states from the detailed planned trajectory to create the simplified second trajectory. Specifically, it extracts the first state (current motion path) and secondary states (relative states indicating vehicle state relative to first state), removing redundant detailed state information. This extraction maintains control accuracy for essential parameters while significantly reducing data processing and storage requirements, thereby improving resource allocation efficiency.
3Productivity
If a simplified trajectory is used to reduce data processing, then resource efficiency is improved, but robustness in handling faults decreases
Solution Approach 1:
The system maintains both the detailed first trajectory and the simplified second trajectory in parallel. The segmented architecture allows the system to use the simplified second trajectory for efficient real-time control while keeping the detailed first trajectory available as a backup for fault recovery, thus improving resource efficiency without compromising robustness.
Solution Approach 2:
The system prepares both the detailed first trajectory and simplified second trajectory in advance. By having the detailed first trajectory pre-computed and stored, the system creates a safety buffer that can be activated if faults are detected during simplified trajectory execution. This beforehand cushioning ensures that resource efficiency gains from using the simplified trajectory do not come at the cost of reduced fault handling robustness.
Data Source
AI summary
Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining data representing a first trajectory including one or more states corresponding to a motion path. The method includes determining a second trajectory based at least in part on the first trajectory, the second trajectory including a first state corresponding to the motion path, and one or more secondary states corresponding to the motion path, the one or more secondary states indicating a state of the autonomous vehicle relative to the first state. The method includes determining one or more control signals based at least in part on the second trajectory. The method includes controlling a motion of the autonomous vehicle according to the motion path, based at least in part on the one or more control signals.