Probabilistic State Simulation for Autonomous Drive Stack Learning
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Solution Overview
Problem
Autonomous machines face challenges in navigating real-world scenarios due to covariate shift, where the distribution of states during training differs from deployment, leading to suboptimal or unsafe behaviors, and current perception techniques are limited in accurately encoding real-world environments.
Innovation Solution
A probabilistic state simulation stack using a perception encoder and generative DNNs to predict and sample state distributions, co-training a navigation policy to handle unseen scenarios, improving accuracy and performance by generating simulated ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If simulated training data is manually hand-crafted to represent edge cases and long tail scenarios, then the neural planner can handle a broader spectrum of driving contexts, but the process is error-prone and leads to unrealistic driving behaviors
Solution Approach 1:
The patent uses real-world sensor data and expert demonstrations as the ground truth to copy authentic driving scenarios, rather than manually crafting simulated data. This ensures the training data accurately represents real-world conditions while still capturing edge cases and long tail scenarios through the diversity of collected real-world data.
Solution Approach 2:
The patent replaces the manual mechanical process of hand-crafting simulation data with an automated machine learning pipeline that collects, processes, and synthesizes training data from real-world sensors and expert demonstrations, eliminating human error in data creation while maintaining realism.
2Measurement precision
If the neural planner is trained on expert demonstrations under typical driving conditions, then it performs well in represented situations, but it generates random or incorrect actions in response to previously unseen situations during deployment
Solution Approach 1:
The patent prepares the neural planner for unseen situations by pre-training it on a diverse dataset that includes edge cases and long tail scenarios collected from real-world sensors and expert demonstrations. This preliminary exposure to varied conditions before deployment enables the planner to generalize better to previously unseen situations.
Solution Approach 2:
The patent employs a dynamic training approach where the training dataset is continuously expanded and updated with new real-world data and expert demonstrations, allowing the neural planner to adapt and improve its performance on unseen situations over time rather than being static.
3Device complexity
If conventional perception techniques are used to encode the environment, then the system is simpler, but the accuracy and richness of encoded inputs are limited, leading to incomplete environmental understanding
Solution Approach 1:
The patent combines multiple perception inputs (sensor data, map data, expert demonstrations) into a composite encoded representation of the environment. This composite encoding integrates diverse information sources to create a more accurate and rich environmental understanding while maintaining system manageability through unified processing.
Data Source
AI summary
In various examples, perception encoder uses one or more neural networks implemented using a transformer architecture, a sensor perspective encoding, a planned navigation route, and/or detected ego-motion to extract a scene embedding representing one or more aspects of an observed scene, such as visual information, motion information, ego-state of an ego-machine, a planned navigation route, and/or other types of information. The perception encoder may be used in a probabilistic state simulation stack, and/or may be used to extract and apply a scene embedding as an input for 3D perception or reconstruction tasks such as object detection and classification, semantic segmentation, depth map extraction, trajectory prediction, path planning, navigation control, and/or localization or mapping, to name a few example tasks.