Autonomous Vehicle Motion Planning with Human-Corrected Trajectory Labels
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Solution Overview
Problem
Existing motion planning systems for autonomous vehicles rely on manually set parameter value thresholds that fail to capture nuanced driving behaviors, leading to suboptimal trajectories that can be corrected by human operators, lacking the ability to learn context-dependent driving envelopes effectively.
Innovation Solution
A training pipeline that utilizes log data from autonomous vehicles, incorporating human corrective actions to label suboptimal trajectories with granular feedback, characterizing deviations along constraint dimensions, enabling machine-learned models to learn context-specific driving envelopes and improve trajectory generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manually set parameter value thresholds are used to constrain motion planning, then the system is simple to implement, but it fails to capture nuanced driving behaviors and produces suboptimal trajectories
Solution Approach 1:
The system performs preliminary actions by collecting log data from human operators and preparing correction data before actual training. Human operators review logged trajectories and provide corrective actions in advance, creating a prepared dataset that captures nuanced driving behaviors. This preliminary preparation enables the motion planning system to learn from real human decision-making patterns without requiring complex real-time processing during actual operation.
Solution Approach 2:
The system implements feedback mechanisms where human operators review trajectories generated by the motion planning system and provide corrections when suboptimal behavior is detected. These corrections are fed back into the training pipeline, creating a closed-loop system that continuously improves the model's ability to capture nuanced driving behaviors. The feedback includes both the original trajectory and the human-corrected version, allowing the system to learn from the difference.
2Measurement precision
If human corrective actions are incorporated to label suboptimal trajectories, then training precision is improved, but data processing time increases
Solution Approach 1:
The system segments the trajectory evaluation process into distinct components: automatic detection of suboptimal conditions using predefined criteria, human review of flagged segments, and selective correction only where needed. This segmentation allows the system to process large volumes of log data efficiently by automating routine evaluations while reserving human expertise for complex or ambiguous cases, thereby reducing overall processing time while maintaining high precision.
Solution Approach 2:
The system introduces an intermediary automated analysis layer that pre-processes log data and identifies candidate suboptimal trajectories before human review. This intermediary system filters out clearly optimal trajectories and only presents ambiguous or potentially suboptimal cases to human operators, significantly reducing the time humans need to spend on data processing while maintaining high labeling precision.
3Manufacturing precision
If machine-learned models are trained with granular feedback on constraint dimensions, then motion planning accuracy is improved, but computational resources required increase
Solution Approach 1:
The system applies local quality by providing granular feedback specifically on constraint dimensions where the motion planning system demonstrates weakness or makes errors. Instead of uniformly processing all trajectory parameters with equal detail, the system focuses computational resources on locally identifying and correcting suboptimal behavior in specific dimensions such as spacing, speed, or lane positioning, thereby improving accuracy where needed while conserving computational energy elsewhere.
Solution Approach 2:
The system dynamically adjusts training parameters based on the specific characteristics of each trajectory and the identified suboptimal conditions. When granular feedback indicates specific constraint violations, the training process adapts by emphasizing those particular parameter ranges and constraint dimensions. This parameter adaptation allows the model to learn more efficiently from limited computational resources, focusing on the most critical aspects of motion planning accuracy.
Data Source
AI summary
The present disclosure provides an example method for obtaining labeled trajectories. The example method can include obtaining log data describing a trajectory of a vehicle traveling through an environment. The example method can include determining a suboptimal condition associated with the trajectory. The example method can include generating label data that characterizes the suboptimal condition along one or more constraint dimensions of a motion planner of the autonomous vehicle control system. The example method can include generating a training example for training the one or more machine-learned models of the autonomous vehicle control system to decrease a probability of the autonomous vehicle control system inducing the suboptimal condition.