Autonomous Vehicle Simulation Augmentation for Relevant Motion Planning
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Solution Overview
Problem
Current motion planning systems for autonomous vehicles face inefficiencies in training due to the need for extensive and diverse simulation scenarios, leading to wasteful use of computing resources and time, as purely random simulation generates numerous irrelevant scenarios.
Innovation Solution
A method and system for generating augmented simulation scenarios by defining interaction zones and adding augmentation elements with specific behaviors to base scenarios, allowing the vehicle's motion planning model to simulate and react to relevant scenarios, thereby focusing training on critical events like lane changes and intersections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If purely random simulation scenarios are generated, then the motion planning model is exposed to a wide variety of events, but the majority of scenarios are irrelevant or extremely unlikely in the real world, causing significant waste of computing resources and time
Solution Approach 1:
The scenario generation process is segmented into two distinct phases: (1) generating a base scenario with realistic environmental elements using a simulation engine, and (2) selectively augmenting specific portions of the base scenario with augmentation elements. This segmentation allows the system to maintain scenario diversity while concentrating computational resources on relevant training events rather than wasting resources on entirely random unrealistic scenarios.
Solution Approach 2:
Instead of making the entire scenario random, the patent applies randomness and variation only to specific local regions (augmentation zones) where trigger events occur. The base scenario maintains realistic global structure, while local augmentation zones introduce controlled variations. This local quality approach ensures computational resources are focused on training the model for specific critical events rather than wasting resources on unrealistic global scenarios.
2Reliability
If manual development of simulation scenarios is performed to ensure relevance, then the training scenarios are highly relevant to real-world conditions, but a significant investment in time and manpower is required
Solution Approach 1:
The system performs preliminary action by pre-defining augmentation zones and trigger events in the base scenario before the actual training process. These pre-configured elements guide the automated augmentation process, ensuring that relevant scenarios are generated without requiring manual development of each individual scenario. This preliminary setup reduces both time and manpower investment while maintaining scenario relevance.
Solution Approach 2:
The system enables self-service by allowing the simulation engine to automatically generate base scenarios and the augmentation system to automatically add relevant elements based on predefined rules and trigger events. This automated self-service process eliminates the need for manual scenario development while maintaining high relevance to real-world conditions, significantly reducing time and manpower investment.
3Adaptability or versatility
If the motion planning model is trained on a large number of less relevant scenarios, then the model is exposed to more events, but training efficiency decreases and the vehicle must be trained on irrelevant scenarios well before relevant scenarios are generated
Solution Approach 1:
The patent introduces an intermediary mechanism - the augmentation system with trigger events and augmentation zones - that acts as a bridge between the base scenario generation and the final training scenario. This intermediary selectively adds relevant elements to base scenarios, ensuring that the model is exposed to diverse events while maintaining training efficiency. The intermediary filters out irrelevant scenarios and concentrates training on meaningful events.
Data Source
AI summary
Method and systems for generating vehicle motion planning model simulation scenarios are disclosed. The system receives a base simulation scenario with features of a scene through which a vehicle may travel. The system then generates an augmentation element with a simulated behavior for an object in the scene by: (i) accessing a data store in which behavior probabilities are mapped to object types to retrieve a set of behavior probabilities for the object; and (ii) applying a randomization function to the behavior probabilities to select the simulated behavior for the object. The system will add the augmentation element to the base simulation scenario at the interaction zone to yield an augmented simulation scenario. The system will then apply the augmented simulation scenario to an autonomous vehicle motion planning model to train the motion planning model.