Autonomous Traffic Simulation Using Goal-Conditioned Trajectories
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Solution Overview
Problem
Current simulation systems for autonomous vehicles lack the ability to generate realistic and diverse traffic behaviors, particularly in simulating human-like behaviors of road users, and struggle with accurately predicting trajectories in new environments and over long periods, due to limitations in complexity and expressiveness.
Innovation Solution
The system decouples the simulation process into high-level intent inferences and low-level goal-conditioned control, using multiple models to generate navigational goals, actions, and trajectories, allowing for more realistic and stable simulation of traffic behaviors, including in new environments and over extended time frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning-based approaches are used to predict trajectories from driving logs, then prediction accuracy in familiar scenarios is improved, but performance deteriorates under domain shifts to new environments with unseen driving behavior
Solution Approach 1:
The system segments trajectory prediction into two distinct components: (1) a learning-based module that predicts trajectories for familiar scenarios using driving logs, and (2) a planning module that generates behaviors for unseen scenarios using hand-crafted rules and world models. This segmentation allows each component to specialize, resolving the contradiction between accuracy in familiar scenarios and adaptability to new environments.
Solution Approach 2:
The system introduces an intermediary planning module that acts as a bridge between the learning-based predictor and the simulation system. This intermediary uses hand-crafted rules and world models to handle unseen scenarios, preventing domain shift issues while maintaining realistic behavior generation for both familiar and novel environments.
2Measurement precision
If learning-based approaches are used for trajectory prediction, then short-term prediction accuracy is improved, but performance deteriorates over long periods due to compounding prediction errors
Solution Approach 1:
The system performs preliminary action by generating complete trajectories upfront using the learning-based predictor for the entire simulation horizon, rather than step-by-step prediction. This allows the system to plan the entire path in advance and execute it without compounding errors, resolving the contradiction between short-term accuracy and long-term stability.
Solution Approach 2:
The system replaces the mechanical step-by-step prediction approach with a holistic trajectory generation approach. Instead of making sequential predictions that compound errors over time, the system generates complete trajectories using learning-based methods followed by world model-based execution, eliminating the compounding error problem while maintaining long simulation horizons.
3Device complexity
If conventional microscopic simulation with fixed trajectories is used, then system complexity is reduced, but the ability to generate diverse and realistic traffic behaviors is lost
Solution Approach 1:
The system segments behavior generation into two segments: (1) a learning-based trajectory predictor that captures diverse realistic behaviors from driving logs, and (2) a world model-based execution engine that handles physics and environmental constraints. This segmentation maintains manageable system complexity while achieving high behavioral diversity and realism.
Solution Approach 2:
The system changes the fundamental parameters of simulation from fixed analytical trajectories to learned probabilistic trajectories. By using learning-based models trained on diverse driving logs, the system generates variable and realistic traffic behaviors while maintaining system tractability through modular architecture and efficient sampling strategies.
Data Source
AI summary
In various examples, techniques for generating simulations for autonomous machines and applications are described herein. Systems and methods are disclosed that use various models to generate simulations. For instance, a first model(s) may process input data, such as input data representing maps indicating the locations of objects and state history of the objects within the environment, to determine navigation goals for the objects. Additionally, a second model(s) may then process the input data and data representing the navigation goals in order to determine possible trajectories (e.g., action samples) for the objects within the environment. Furthermore, a third model(s) may process the input data to predict trajectories of the objects within the environment. The systems and methods may then use at least the possible trajectories and the predicted trajectories to simulate the motion (e.g., one or more trajectories) of one or more of the objects.