Autonomous Vehicle Reactive Prediction for Multi-Plan Motion Evaluation
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Solution Overview
Problem
Autonomous vehicles face computational challenges in efficiently predicting the behavior of actors in their environment, particularly when considering multiple potential movements, leading to suboptimal driving maneuvers due to the complexity of behavioral predictions.
Innovation Solution
A computer-implemented method using a machine-learned multi-model system that extracts actor prediction parameters with a heavyweight model and generates reactive predictions using a lightweight model, allowing for efficient evaluation of multiple candidate motion plans and accurate prediction of actor behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computationally complex behavioral predictions are performed to accurately predict actor behavior in response to autonomous vehicle maneuvers, then prediction accuracy is improved, but computational processing time increases
Solution Approach 1:
The system pre-computes and stores actor behavior response data in a lookup table during an offline training phase, where multiple hypothetical actor responses to various autonomous vehicle maneuvers are calculated and saved. During real-time operation, the system simply queries this pre-computed table rather than performing complex calculations, thereby achieving accurate predictions without real-time computational delays
Solution Approach 2:
The prediction system is divided into two distinct components: an offline training phase that performs computationally intensive behavior modeling and data storage, and an online inference phase that performs simple table lookups. This segmentation allows the complex computational work to be done in advance when time is not constrained, while real-time operation uses only lightweight queries
2Measurement precision
If computationally complex behavioral predictions are performed for multiple actors and maneuvers, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs complex behavior modeling, actor response calculation, and data organization in an offline training phase, storing all results in a structured lookup table. This preliminary computation captures the complexity upfront, allowing the runtime system to use only simple table queries, thereby reducing operational system complexity while maintaining high prediction accuracy
Solution Approach 2:
Instead of implementing complex real-time calculation algorithms, the system creates a static copy of all possible actor responses in a lookup table during offline training. The runtime system then uses this copied data structure for predictions, replacing complex computational logic with simple data retrieval operations
Data Source
AI summary
Systems and methods are directed to generating behavioral predictions in reaction to autonomous vehicle movement. In one example, a computer-implemented method includes obtaining, by a computing system, local scene data associated with an environment external to an autonomous vehicle, the local scene data including actor data for an actor in the environment external to the autonomous vehicle. The method includes extracting, by the computing system and from the local scene data, one or more actor prediction parameters for the actor using a machine-learned parameter extraction model. The method includes determining, by the computing system, a candidate motion plan for the autonomous vehicle. The method includes generating, by the computing system and using a machine-learned prediction model, a reactive prediction for the actor based at least in part on the one or more actor prediction parameters and the candidate motion plan.