Prediction Loss Weighting for Lane-Change Intent Evaluation
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Solution Overview
Problem
The performance evaluation of autonomous driving vehicle (ADV) prediction modules is limited by relying solely on mean waypoint distance error and final point distance error, which does not accurately reflect the vehicle's intent to change lanes or speed up, hindering the improvement of overall ADV performance.
Innovation Solution
A decoupled or weighted loss function is introduced to evaluate the prediction module's performance, decomposing mean waypoint distance error into lateral and longitudinal components with different weightings, prioritizing lateral displacement to improve the planning and decision-making processes, thereby enhancing safety and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mean waypoint distance error and final point distance error are used to evaluate prediction module performance, then the evaluation is simple, but the accuracy in predicting vehicle intent (lane change, speed up) cannot be reflected
Solution Approach 1:
The patent segments the evaluation metric into multiple components: lateral displacement error and longitudinal displacement error. By decomposing the overall distance error into these two orthogonal components, the system can independently evaluate and weight each dimension according to its importance for predicting vehicle intent, thereby improving measurement precision without excessive complexity
Solution Approach 2:
The patent applies different weightings to different components of the error metric. Specifically, lateral displacement error is given higher weighting than longitudinal displacement error, reflecting the local quality that lateral movements are more indicative of lane change intent. This differential weighting allows the evaluation to focus on the most critical aspects of prediction accuracy
2Reliability
If lateral displacement is prioritized in the loss function, then lane change prediction improves, but the overall evaluation becomes more complex
Solution Approach 1:
The patent changes the parameters of the loss function by introducing differential weightings for lateral and longitudinal displacement errors. By adjusting these weightings (giving higher weight to lateral displacement), the system improves lane change prediction reliability while maintaining a relatively simple quadratic loss function structure that is computationally efficient
Data Source
AI summary
A trajectory of an obstacle is predicted by a prediction module of the ADV. A trajectory of the ADV is determined based on the trajectory of the obstacle by a planning module of the ADV. A loss function of an analysis model of the prediction module is decomposed to multiple components with multiple weightings to generate a weighted loss function based on the trajectory of the ADV. A performance of the prediction module is evaluated based on the weighted loss function to improve the performance of the prediction module to increase a safety and comfort of the ADV.