Virtual Perception Fields for Robust Autonomous Driving Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous vehicle (AV) technologies face scalability issues due to limited field of view, lighting and weather challenges, and occlusions, leading to detection errors and noisy localization/kinematics, which are costly to address with expensive sensors and infrastructure, and require vast data and computational resources for machine learning-based solutions that struggle with edge cases.
Innovation Solution
The concept of perception fields, where road objects are represented as virtual force fields learned through ADAS and AV software, allowing for a holistic driving policy that decomposes actions into fundamental components, providing explainability, generalizability, and robustness to noisy inputs, and can be trained using behavioral cloning and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive sensors and specialized infrastructure are used to improve detection accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical sensor systems with a virtual field-based computational model. Instead of using expensive physical sensors to detect objects, the system creates virtual force fields that represent the spatial distribution of road objects and their impacts on vehicle behavior. This substitution of physical detection mechanisms with computational field models reduces hardware complexity while maintaining detection accuracy.
Solution Approach 2:
The patent introduces virtual force fields as intermediary representations between the physical environment and the vehicle control system. These virtual fields act as mediators that encode spatial information about road objects and their potential impacts, allowing the system to process environmental data without relying on complex sensor arrays or specialized infrastructure.
2Productivity
If vast amounts of data and computational resources are used for machine learning-based AV solutions, then productivity is improved, but loss of energy and computational cost increase
Solution Approach 1:
The patent transforms the problem from learning complex driving policies through massive data processing to learning virtual field representations through behavioral cloning. By changing the fundamental parameters of the learning task—from raw sensor data to virtual force field parameters—the system achieves comparable productivity with reduced computational resource consumption.
Solution Approach 2:
The patent uses behavioral cloning to copy human driving behavior patterns and represent them as virtual field models. Instead of requiring vast amounts of data to train complex neural networks, the system copies simplified virtual field representations from human driving behavior, significantly reducing the computational resources and data requirements while maintaining effective driving policy capabilities.
3Adaptability or versatility
If black-box deep neural networks are used to handle complex driving situations, then adaptability is improved, but difficulty of detecting and measuring faulty behavior increases
Solution Approach 1:
The patent segments the complex driving problem into distinct virtual force field components, each representing specific spatial relationships and object impacts. This segmentation provides interpretability by breaking down the black-box neural network into understandable virtual field elements, allowing analysts to detect and measure faulty behavior in individual field components rather than in an opaque overall system.
Solution Approach 2:
The patent introduces virtual force fields as intermediary representations that bridge the gap between complex neural network processing and interpretable driving decisions. These virtual fields serve as measurable intermediaries that maintain the adaptability of deep learning while providing a transparent framework for detecting and analyzing faulty behavior through field parameter inspection.
Data Source
AI summary
A method for augmented driving related virtual fields, the method includes obtaining object information regarding one or more objects located within an environment of a vehicle; and determining, by a processing circuit, and based on the object information, a desired virtual acceleration of the vehicle, wherein the determining of the desired virtual acceleration of the vehicle is based on a virtual physical model that represents an impact of the one or more objects on a behavior of the vehicle, wherein the virtual physical model is built based on one or more physical laws and at least one additional driving related parameter.