Adaptive Motion Forecasting Ensemble for OOD Traffic Scenarios
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Solution Overview
Problem
Existing motion forecasting algorithms in autonomous vehicles perform poorly in out-of-distribution (OOD) scenarios without prior exposure, leading to unreliable predictions and potential safety issues.
Innovation Solution
An adaptive prediction ensemble system that combines a motion prediction neural network with a rule-based model, utilizing a routing function network to switch between models based on reliability, trained on both familiar and OOD datasets, enhancing generalization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning-based prediction algorithms are applied to out-of-distribution scenarios without prior exposure, then the system can handle diverse traffic conditions, but the prediction accuracy drops significantly
Solution Approach 1:
The system dynamically adapts its prediction strategy by using a routing function network that selects between deep learning-based predictions and rule-based predictions based on scene familiarity. This dynamic switching mechanism allows the system to maintain high reliability in both familiar and out-of-distribution scenarios, resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The routing function network acts as an intermediary between the deep learning prediction model and rule-based prediction model. It evaluates scene familiarity and directs the appropriate prediction method, enabling the system to leverage the adaptability of deep learning while maintaining the reliability of rule-based approaches in unfamiliar scenarios.
2Measurement precision
If deep learning-based prediction algorithms are used, then high accuracy is achieved in familiar scenarios, but performance deteriorates in zero-shot out-of-distribution scenarios
Solution Approach 1:
The prediction system is segmented into multiple specialized components: a deep learning-based motion prediction neural network for familiar scenarios, a rule-based prediction model for out-of-distribution scenarios, and a routing function network that segments the decision-making process based on scene familiarity evaluation. This segmentation allows each component to excel in its designated domain.
Solution Approach 2:
The system combines different prediction approaches (deep learning and rule-based methods) into a composite prediction framework. This composite structure integrates the strengths of both approaches, using the routing function network to determine which component to apply in each scenario, thereby achieving both high accuracy in familiar scenarios and adaptability in zero-shot scenarios.
3Reliability
If rule-based models are used, then reliable predictions are made in out-of-distribution scenarios, but accuracy is lower compared to deep learning models in familiar scenarios
Solution Approach 1:
The system dynamically selects between rule-based and deep learning-based prediction methods based on scene familiarity. The routing function network evaluates whether a scenario is familiar or out-of-distribution and dynamically routes to the appropriate prediction model, allowing rule-based models to provide reliable predictions in OOD scenarios while deep learning models handle familiar scenarios with higher accuracy.
Data Source
AI summary
A system and a method for motion forecasting are provided. The system acquires input data including road map images and historical trajectory information of a set of agents and transforms the input data into a vectorized representation. The system generates a first candidate trajectory prediction for the set of agents by application of a motion prediction neural network on the vectorized representation. The system further generates a second candidate trajectory prediction for the set of agents by application of a rule-based prediction model on the acquired input data. The system trains the motion prediction neural network based on the first candidate trajectory prediction and a set of ground truth trajectories of the set of agents. The system generates ranking results for the first candidate trajectory prediction and the second candidate trajectory prediction based on a routing function network and trains the routing function network based on the ranking results.