End-to-End Deep Neural Network for Danger Ranking
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Solution Overview
Problem
Current machine learning techniques for detecting danger in driving scenarios, such as those used in autonomous vehicles, face challenges in attributing different danger levels to multiple objects, handling rare and unseen situations, and predicting future states of static or non-visible objects, often requiring extensive human supervision and annotation.
Innovation Solution
A danger ranking training method utilizing a sequence of deep neural networks for generic and specific object recognition, scene flow prediction, and potential danger area localization, combined through end-to-end transfer learning and human-tagged data, enables the detection of danger pixels and levels in real-time images or sequences, even in rare scenarios, by leveraging human reaction analysis and multi-expert label combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based systems with atomic scenes are used for danger detection, then accuracy is sufficient for 90% of daily driving scenarios, but the system cannot cope with rare and previously unseen situations
Solution Approach 1:
The system transitions from static rule-based atomic scenes to dynamic deep neural network models that can adapt to new situations. The DNNs learn from training data and can generalize to rare and unseen driving scenarios, making the danger detection system dynamic and adaptable rather than fixed and rigid.
Solution Approach 2:
The system changes from discrete rule-based parameters to continuous probability distributions generated by deep neural networks. The danger level is represented as a continuous value between 0 and 1, allowing for nuanced detection of rare events that don't fit predefined rules.
2Extent of automation
If convolutional neural networks map raw pixels to steering commands, then automatic learning of road features is achieved, but the system cannot learn generic danger-related features without human supervision
Solution Approach 1:
The system performs preliminary action by pre-training deep neural networks on large datasets of driving scenes with annotated danger levels. This pre-training enables the network to learn generic danger-related features automatically before deployment, reducing the need for continuous human supervision while maintaining high accuracy in detecting rare events.
3Reliability
If grid-based approaches are used for collision risk prediction, then risk prediction is provided, but the system cannot estimate danger level for static objects or non-visible objects
Solution Approach 1:
The system adds temporal and contextual dimensions to the danger detection process. By processing sequences of images and using recurrent neural networks, the system can infer the state and potential danger of static objects and predict the appearance of non-visible objects, extending beyond the limitations of single-frame grid-based approaches.
4Reliability
If agent-centric approaches are used for accident anticipation, then risk assessment is provided, but the system requires annotation of single objects and cannot evaluate multiple potentially dangerous objects simultaneously
Solution Approach 1:
The deep neural network system is designed to be universal and multi-functional, capable of detecting and evaluating multiple potentially dangerous objects simultaneously in a single processing pass. The network outputs danger levels for all objects in the scene, eliminating the need for separate annotation and evaluation processes for each object.
Data Source
AI summary
A danger ranking training method comprising training a first deep neural network for generic object recognition within generic images, training a second deep neural network for specific object recognition within images of a specific application, training a third deep neural network for specific scene flow prediction within image sequences of the application, training a fourth deep neural network for potential danger areas localization within images or image sequences of the application using at least one human trained danger tagging method, training a fifth deep neural network for non-visible specific object anticipation and/or visible specific object prediction within image or image sequences of the application, and determining at least one danger pixel within an image or an image sequence of the application using an end-to-end deep neural network as a sequence of transfer learning of the five deep neural networks followed by one or several end-to-end top layers.