Autonomous Driving Prediction Using Generative Image Token Learning
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Solution Overview
Problem
Existing autonomous driving systems rely heavily on expensive and labor-intensive accurate perception annotations, limiting their deployment cost, scalability, and practical applicability due to the need for time-consuming and costly supervision signals.
Innovation Solution
An information prediction method that uses a generative model to predict control information for vehicles by encoding perception data, including image and driving data, without relying on precise annotations, by incorporating an image generation task as an auxiliary task to improve environmental perception and representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If accurate perception annotations are used for training autonomous driving systems, then the prediction accuracy and reliability are improved, but the cost and time consumption increase significantly
Solution Approach 1:
The system performs self-supervised learning by automatically generating supervision signals from raw perception data without external annotations. The generative model creates synthetic training data and supervision signals autonomously, allowing the system to train itself using unannotated data, thereby eliminating the need for time-consuming manual annotations while maintaining training effectiveness
Solution Approach 2:
The generative model creates synthetic copies of real-world perception data through image generation tasks. By generating synthetic images and their corresponding annotations, the system obtains virtual supervision signals that mimic real annotated data, enabling training without accessing costly real-world annotated datasets
2Manufacturing precision
If expensive accurate perception annotations are used, then the model training quality is improved, but the deployment cost and scalability are limited
Solution Approach 1:
The system autonomously generates its own training data and supervision signals through self-supervised learning mechanisms. The generative model creates synthetic annotated data from unannotated perception inputs, enabling the system to achieve high training quality without incurring costs associated with manual annotation services, thereby reducing deployment costs and improving scalability
Solution Approach 2:
The system replaces expensive, hard-to-obtain accurate perception annotations with cheap, easily generated synthetic annotations from the generative model. These synthetic annotations serve as disposable training data that can be generated on-demand without financial cost, making the system scalable and economically viable for widespread deployment
3Measurement precision
If precise perception annotations are required, then the supervision signal quality is improved, but the scalability and practical applicability are reduced
Solution Approach 1:
The system achieves self-supervised learning by automatically generating supervision signals from raw perception data through image generation tasks. This self-service mechanism allows the system to maintain high supervision signal quality while processing unlimited amounts of unannotated data, thereby achieving both precision and scalability simultaneously without being constrained by annotation availability
Data Source
AI summary
An information prediction method, a method of training an autonomous driving model, a device, a medium, and an autonomous driving vehicle, which relate to a field of artificial intelligence technology, and in particular, to fields of computer vision technology and deep learning technology, which may be applied to scenarios such as autonomous driving. Specific implementation scheme of the information prediction method is: acquiring perception data including image data acquired by a sensor in a vehicle and driving data of the vehicle; encoding the image data to obtain an image token sequence corresponding to the image data; encoding the driving data to obtain a driving feature corresponding to the driving data; and generating, using a generative model, a predicted token sequence corresponding to the image token sequence and a control information for the vehicle based on the driving feature and the image token sequence.