Traffic Scene Generation Using Learned Probabilistic Sampling
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Solution Overview
Problem
Existing methods for simulating traffic scenes lack the fidelity to train and test self-driving vehicles due to limited ability to model the true complexity and diversity of real-world traffic scenarios, resulting in a content gap that inhibits the development of robust machine-learned models.
Innovation Solution
A machine-learned traffic scene generation model that samples scenes from probabilistic distributions of traffic configurations, learned from real-world data, to generate complex and diverse simulated scenes, which are used to train perception systems for autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If hand-crafted heuristics are used to generate simulated traffic scenes, then the generation process is simple and controllable, but the complexity and diversity of the generated scenes are insufficient
Solution Approach 1:
The patent transforms fixed hand-crafted heuristic parameters into learnable probabilistic distributions. By representing scene parameters (object positions, velocities, types) as probability distributions learned from real-world data, the system achieves both controllability through sampling and diversity through the inherent variability in the learned distributions.
Solution Approach 2:
The patent introduces dynamic adaptability by using machine-learned probabilistic models that can adapt to different traffic conditions. The scene generation process dynamically samples from these distributions, allowing the system to generate diverse scenes while maintaining consistency with real-world traffic patterns observed during training.
2Reliability
If more complex and diverse simulated traffic scenes are generated, then the training quality of autonomous vehicle systems improves, but the computational resources and time required increase
Solution Approach 1:
The patent creates synthetic copies of real-world traffic scenes through probabilistic simulation. By learning from a relatively small set of real-world data and generating numerous synthetic variations, the system efficiently produces large-scale training datasets without the time and resource costs of collecting equivalent real-world data.
Solution Approach 2:
The patent performs preliminary learning from real-world traffic data to establish probabilistic distributions before generating synthetic scenes. This pre-training phase captures essential traffic patterns, enabling rapid generation of diverse training scenes without requiring extensive computational resources during the actual scene generation process.
3Adaptability or versatility
If real-world traffic data is collected to train autonomous vehicle systems, then the training data is authentic and diverse, but the data collection process is time-consuming and expensive
Solution Approach 1:
The patent creates synthetic copies of real-world traffic scenes through probabilistic simulation. By learning from a relatively small set of real-world data and generating numerous synthetic variations, the system efficiently produces large-scale training datasets without the time and resource costs of collecting equivalent real-world data.
Data Source
AI summary
Example aspects of the present disclosure describe a scene generator for simulating scenes in an environment. For example, snapshots of simulated traffic scenes can be generated by sampling a joint probability distribution trained on real-world traffic scenes. In some implementations, samples of the joint probability distribution can be obtained by sampling a plurality of factorized probability distributions for a plurality of objects for sequential insertion into the scene.